system

Abstract

The system according to this embodiment aims to monitor the usage of music on the internet, detect copyright infringement early, and take appropriate action. [Solution] The system according to the embodiment comprises a monitoring unit, a detection unit, a notification unit, a management unit, and a reporting unit. The monitoring unit monitors the usage status of music on the internet. The detection unit detects copyright infringement based on the music usage status monitored by the monitoring unit. The notification unit makes a notification based on the copyright infringement detected by the detection unit. The management unit manages the licensing status based on the information notified by the notification unit. The reporting unit generates a report based on the licensing status managed by the management unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it is difficult to individually monitor the usage status of music on the Internet and detect copyright infringement, and there is a risk that appropriate responses may be delayed.

[0005] The system according to the embodiment aims to monitor the usage status of music on the Internet, detect copyright infringement at an early stage, and take appropriate actions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a monitoring unit, a detection unit, a notification unit, a management unit, and a reporting unit. The monitoring unit monitors the usage status of music on the internet. The detection unit detects copyright infringement based on the music usage status monitored by the monitoring unit. The notification unit issues notifications based on the copyright infringement detected by the detection unit. The management unit manages the licensing status based on the information notified by the notification unit. The reporting unit generates reports based on the licensing status managed by the management unit. [Effects of the Invention]

[0007] The system according to this embodiment can monitor the usage of music on the internet, detect copyright infringement early, and take appropriate action. [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, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 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 AI ​​agent system according to an embodiment of the present invention is a system for musicians and music publishers. This AI agent system monitors the usage of music on the internet and detects copyright infringement. This system aims to protect artists' revenue and rights and contribute to the healthy development of the entire music industry. For example, the AI ​​agent system monitors the usage of music on the internet. Specifically, it monitors music upload sites and streaming services 24 hours a day to detect unauthorized use of music. For example, it uses speech recognition and similarity detection algorithms to detect unauthorized use of music. This automates monitoring tasks that would be difficult for individuals to perform. Next, when copyright infringement is detected, the AI ​​agent system immediately notifies and proposes countermeasures. For example, a generative AI creates legal documents and notices, and natural language processing assists in communication with relevant parties. This also streamlines legal procedures. Furthermore, the AI ​​agent system manages legitimate licensing status and maximizes licensing revenue. For example, it automates licensing management to maximize licensing revenue. Finally, the AI ​​agent system generates reports that visualize the infringement status and the results of countermeasures in data. This allows rights holders to concentrate on their creative activities with peace of mind, maximizing revenue and promoting the health of the industry. This allows the AI ​​agent system to protect artists' earnings and rights, and contribute to the healthy development of the entire music industry.

[0029] The AI ​​agent system according to this embodiment comprises a monitoring unit, a detection unit, a notification unit, a management unit, and a reporting unit. The monitoring unit monitors the usage status of music on the internet. The monitoring unit monitors, for example, music upload sites and streaming services 24 hours a day to detect unauthorized use of music. The monitoring unit can detect unauthorized use of music using speech recognition and similarity detection algorithms. The detection unit detects copyright infringement based on the music usage status monitored by the monitoring unit. The detection unit detects unauthorized use of music using, for example, speech recognition and similarity detection algorithms. The notification unit makes notifications based on copyright infringement detected by the detection unit. The notification unit creates legal documents and notification letters using generative AI. The notification unit, for example, uses generative AI to create legal documents and notification letters and supports communication with relevant parties using natural language processing. The management unit manages the licensing status based on the information notified by the notification unit. The management unit automates the management of licenses and maximizes licensing revenue. The management unit automates the management of licenses and aims to maximize licensing revenue. The reporting unit generates reports based on the licensing status managed by the management unit. The reporting unit generates reports that visualize infringement status and response results in data. For example, the reporting unit generates reports that visualize infringement status and response results in data. This enables the AI ​​agent system according to the embodiment to efficiently monitor, detect, notify, manage, and generate reports on music usage on the internet.

[0030] The monitoring unit monitors the usage of music on the internet. For example, the monitoring unit monitors music upload sites and streaming services 24 hours a day to detect unauthorized use of music. Specifically, the monitoring unit uses APIs of music upload sites and streaming services to acquire newly uploaded music and music currently being streamed in real time. This allows the monitoring unit to efficiently collect a vast amount of music data and quickly identify music that may be being used without permission. Furthermore, the monitoring unit can detect unauthorized use of music using speech recognition and similarity detection algorithms. Speech recognition technology analyzes features such as the melody, rhythm, and lyrics of a song and compares them with an existing music database. Similarity detection algorithms perform waveform and spectral analysis of the song and evaluate how similar it is to existing music. This allows the monitoring unit to detect unauthorized use of music with high accuracy. In addition, the monitoring unit centrally manages the collected data and can cooperate with other systems and departments as needed. For example, the monitoring unit stores data on a cloud server, making it accessible to the detection and notification units. Furthermore, the monitoring unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the monitoring unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The detection unit detects copyright infringement based on music usage monitored by the monitoring unit. Specifically, the detection unit uses speech recognition and similarity detection algorithms to detect unauthorized use of music. Speech recognition technology analyzes features such as melody, rhythm, and lyrics of music and compares them with an existing music database. The similarity detection algorithm performs waveform and spectral analysis of music to evaluate how similar it is to existing music. This allows the detection unit to detect unauthorized use of music with high accuracy. Furthermore, the detection unit uses AI to analyze the collected data and identify music that may be infringing on copyright. The AI ​​can also utilize historical data and statistical information to perform long-term risk assessment and trend analysis. For example, based on past copyright infringement data, it can predict trends in unauthorized use by specific sites or users and issue warnings early. In addition, the detection unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and respond quickly. This allows the detection unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0032] The notification unit issues notifications based on copyright infringement detected by the detection unit. Specifically, the notification unit uses generative AI to create legal documents and notification letters. The generative AI automatically generates appropriate legal documents and notification letters based on detailed information about copyright infringement. For example, the generative AI creates a notification letter that includes information about the infringed song, the specific details of the infringement, and an outline of the legal action to be taken. Furthermore, the notification unit uses natural language processing technology to support communication with relevant parties. The natural language processing technology automatically analyzes the content of the notification letter and generates messages that encourage appropriate action from the relevant parties. This allows the notification unit to issue notifications quickly and accurately, and to support appropriate responses to copyright infringement. In addition, the notification unit centrally manages the notification transmission history and response status, and can collaborate with other systems and departments as needed. For example, the notification unit stores data on a cloud server, making it accessible to the management and reporting units. The notification unit can also adjust the frequency and content of notifications, enabling flexible responses to specific situations and conditions. This allows the notification unit to issue notifications efficiently and effectively, improving the overall system performance.

[0033] The Management Department manages the licensing status based on information notified by the Notification Department. Specifically, the Management Department automates licensing management to maximize license revenue. For example, the Management Department builds a system that automates processes such as licensing application, approval, renewal, and cancellation. This allows the Management Department to significantly reduce the effort and time required for licensing management and efficiently maximize license revenue. Furthermore, the Management Department can grasp the licensing status in real time and take appropriate action. For example, the Management Department automatically notifies users when licensing is nearing its expiration date and prompts them to renew. In addition, the Management Department can centrally manage licensing status and collaborate with other systems and departments as needed. For example, the Management Department stores data on a cloud server and makes it accessible to the Reporting Department. This allows the Management Department to manage licensing efficiently and effectively and improve the overall system performance.

[0034] The reporting department generates reports based on the licensing status managed by the management department. Specifically, the reporting department generates reports that visualize infringement situations and response results using data. For example, the reporting department visually represents infringement situations and response results using graphs and charts, making them easy to understand for stakeholders. Furthermore, the reporting department automatically generates report content using generation AI. The generation AI analyzes the collected data and creates appropriate reports. For example, the generation AI analyzes the frequency of infringements, the speed of response, and the licensing status to generate detailed reports. This allows the reporting department to generate reports quickly and accurately, providing relevant information to stakeholders. In addition, the reporting department centrally manages the report generation history and distribution status, and can collaborate with other systems and departments as needed. For example, the reporting department stores data on a cloud server, making it accessible to the management and notification departments. The reporting department can also adjust the frequency and content of report generation, enabling flexible responses to specific situations and conditions. This allows the reporting department to generate reports efficiently and effectively, improving the overall system performance.

[0035] The monitoring unit can detect unauthorized use of music using speech recognition and similarity detection algorithms. The monitoring unit can detect unauthorized use of music using, for example, speech recognition technology. The monitoring unit can detect unauthorized use of music using, for example, similarity detection algorithms. The monitoring unit can detect unauthorized use of music with high accuracy by, for example, combining speech recognition and similarity detection algorithms. This makes it possible to detect unauthorized use of music with high accuracy by using speech recognition and similarity detection algorithms. The speech recognition technology can, for example, use a speech recognition model using deep learning. The similarity detection algorithm can, for example, use cosine similarity or Jaccard coefficients. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or without AI. For example, the monitoring unit can input speech data acquired using speech recognition technology into a generating AI, and the generating AI can detect unauthorized use using a similarity detection algorithm.

[0036] The notification unit can create legal documents and notices using generative AI. For example, the notification unit can create legal documents concerning copyright infringement using generative AI. For example, the notification unit can create notices concerning copyright infringement using generative AI. For example, the notification unit can create legal documents and notices quickly and accurately using generative AI. This allows for the creation of legal documents and notices quickly and accurately by using generative AI. The generative AI may, for example, use text generation AI (e.g., LLM). The generative AI may, for example, use natural language processing technology to generate legal documents and notices. Some or all of the above-described processes in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can send legal documents and notices created using generative AI to the relevant parties.

[0037] The management department can automate license management and maximize license revenue. The management department can, for example, automate license management. The management department can, for example, aim to maximize license revenue. The management department can, for example, automate license management and aim to maximize license revenue. By automating license management, it is possible to maximize license revenue. License management includes, for example, issuing, renewing, and revoking licenses. Maximizing license revenue includes, for example, proper management of licenses and optimization of revenue. Some or all of the above processes in the management department may be performed using, for example, AI, or not using AI. For example, the management department can use AI to issue, renew, and revoke licenses in order to automate license management.

[0038] The reporting unit can generate reports that visualize infringement situations and response results using data. For example, the reporting unit generates reports that visualize infringement situations using data. For example, the reporting unit generates reports that visualize response results using data. For example, the reporting unit generates reports that visualize infringement situations and response results using data. This makes it easier for rights holders to understand the situation by visualizing infringement situations and response results using data. Methods of data visualization include using graphs and charts. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can generate reports that visualize infringement situations and response results based on data generated using AI.

[0039] The notification unit can support communication with stakeholders using natural language processing. The notification unit supports communication with stakeholders using, for example, natural language processing technology. The notification unit supports communication with stakeholders using, for example, generative AI. The notification unit facilitates communication with stakeholders using, for example, natural language processing technology. As a result, communication with stakeholders can be facilitated by using natural language processing. The natural language processing technology uses, for example, text generation AI (e.g., LLM). Some or all of the above processing in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can support communication by sending a notification text created using generative AI to stakeholders.

[0040] The monitoring unit can enhance monitoring for specific music genres or artists. For example, the monitoring unit can enhance monitoring for specific music genres. For example, the monitoring unit can enhance monitoring for specific artists. For example, the monitoring unit can enhance monitoring for specific music genres or artists. This makes it possible to detect infringements early by enhancing monitoring for specific music genres or artists. Enhanced monitoring includes, for example, increasing the scope of monitoring and the frequency of monitoring. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can use AI to adjust the scope of monitoring and the frequency of monitoring in order to enhance monitoring for specific music genres or artists.

[0041] The monitoring unit can automatically detect new audio upload platforms on the internet and add them to its monitoring scope. For example, the monitoring unit can automatically detect newly appearing audio upload sites and add them to its monitoring scope. For example, the monitoring unit can include audio sharing platforms on social media in its monitoring scope. For example, the monitoring unit can automatically add new platforms specified by users to its monitoring scope. This expands the monitoring scope by automatically detecting and adding new audio upload platforms to the monitoring scope. The detection of new audio upload platforms is performed, for example, using AI. Some or all of the above processing in the monitoring unit may be performed, for example, using AI, or without using AI. For example, the monitoring unit can use AI to detect new audio upload platforms and add them to its monitoring scope.

[0042] The monitoring unit can focus its monitoring on music usage in specific regions or countries. For example, the monitoring unit will focus its monitoring on music usage in a particular region. For example, the monitoring unit will strengthen its monitoring in countries where copyright infringement is frequent. For example, the monitoring unit will focus its monitoring on music usage in regions or countries specified by the user. This allows for an understanding of infringement situations in each region or country by focusing its monitoring on music usage in specific regions or countries. Monitoring specific regions or countries may include, for example, increasing the scope of monitoring and the frequency of monitoring. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can use AI to adjust the scope of monitoring and the frequency of monitoring in order to focus its monitoring on music usage in specific regions or countries.

[0043] The monitoring unit can also include music usage on social media as part of its monitoring scope. For example, the monitoring unit may include music usage on social media as part of its monitoring scope. For example, the monitoring unit may add social media platforms specified by the user to its monitoring scope. For example, the monitoring unit may monitor trends related to music usage on social media. This allows for broader monitoring by including music usage on social media as part of the monitoring scope. Monitoring on social media may include, for example, increasing the scope of monitoring and the frequency of monitoring. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit may use AI to adjust the scope of monitoring and the frequency of monitoring in order to include music usage on social media as part of its monitoring scope.

[0044] The detection unit can optimize its detection algorithm by referring to past copyright infringement data. For example, the detection unit optimizes its detection algorithm based on past copyright infringement data. For example, the detection unit analyzes past infringement patterns to improve detection accuracy. For example, the detection unit predicts new infringement patterns by referring to past data. In this way, the accuracy of the detection algorithm can be improved by referring to past copyright infringement data. Referencing past copyright infringement data can be done, for example, by using a database. Some or all of the above processes in the detection unit may be performed using, for example, AI, or not using AI. For example, the detection unit can input past copyright infringement data into AI, and the AI ​​can analyze the data to optimize the detection algorithm.

[0045] The detection unit can also include partial use of a song or remixes as detection targets. For example, the detection unit may include partial use of a song as detection targets. For example, the detection unit may include remixed songs as detection targets. For example, the detection unit may include sampling use of a song as detection targets. By including partial use of a song or remixes as detection targets, a broader range of copyright infringement can be detected. Detection of partial use of a song or remixes may be performed using, for example, speech recognition technology. Some or all of the above processing in the detection unit may be performed using, for example, AI, or not using AI. For example, the detection unit can input partial use of a song or remixes into the AI, and the AI ​​can detect them.

[0046] The detection unit can enhance detection for specific music genres or artists. For example, the detection unit can enhance detection for specific music genres. For example, the detection unit can enhance detection for specific artists. For example, the detection unit can enhance detection for specific music genres or artists. This makes it possible to detect infringements earlier by enhancing detection for specific music genres or artists. Enhanced detection includes, for example, increasing the scope of detection targets or the frequency of detection. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can use AI to adjust the scope of detection targets or the frequency of detection in order to enhance detection for specific music genres or artists.

[0047] The detection unit can also include the unauthorized use of music videos and live performances in its detection targets. For example, the detection unit may include the unauthorized use of music videos in its detection targets. For example, the detection unit may include the unauthorized use of live performances in its detection targets. For example, the detection unit may add a specific video platform designated by the user to its monitoring targets. This allows for the detection of a broader range of copyright infringements by including the unauthorized use of music videos and live performances in its detection targets. For example, voice recognition technology may be used to detect music videos and live performances. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit may input the unauthorized use of music videos and live performances into an AI, which can then detect them.

[0048] The notification unit can select the optimal notification method by referring to the past interaction history of the relevant parties. For example, the notification unit selects the optimal notification method based on the past interaction history of the relevant parties. For example, the notification unit selects a notification method suitable for situations requiring a rapid response based on past interaction history. For example, the notification unit analyzes the past interaction history of the relevant parties and selects the most effective notification method. This allows the optimal notification method to be selected by referring to the past interaction history of the relevant parties. Referencing past interaction history can be done, for example, by using a database. Some or all of the above processing in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can input the past interaction history of the relevant parties into AI, and the AI ​​can analyze the data to select the optimal notification method.

[0049] The notification unit can use a combination of multiple notification methods (email, SMS, app notifications, etc.). For example, the notification unit can send notifications using a combination of email and SMS. For example, the notification unit can send notifications using a combination of app notifications and email. For example, the notification unit can use a combination of multiple notification methods specified by the user. By using a combination of multiple notification methods, the reliability of notifications is improved. The use of multiple notification methods is done, for example, to increase the reliability of notifications. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can improve the reliability of notifications by using AI to combine multiple notification methods.

[0050] The notification unit can select the optimal notification method by considering the geographical location information of the relevant parties. For example, the notification unit selects the optimal notification method based on the geographical location information of the relevant parties. For example, the notification unit selects a notification method suitable for situations requiring a rapid response based on geographical location information. For example, the notification unit analyzes the geographical location information of the relevant parties and selects the most effective notification method. In this way, the optimal notification method can be selected by considering the geographical location information of the relevant parties. For example, GPS data can be used to reference geographical location information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the geographical location information of the relevant parties into AI, and the AI ​​can analyze the data and select the optimal notification method.

[0051] The notification unit can send notifications through the social media accounts of relevant parties. For example, the notification unit sends notifications through the social media accounts of relevant parties. For example, the notification unit uses social media accounts to send rapid notifications. For example, the notification unit sends notifications through social media accounts designated by relevant parties. This enables rapid notification by sending notifications through the relevant parties' social media accounts. The use of social media accounts is done, for example, to increase the reliability of the notifications. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can achieve rapid notification by using AI to send notifications through the relevant parties' social media accounts.

[0052] The management department can optimize the management algorithm by referring to past license data. For example, the management department optimizes the management algorithm based on past license data. For example, the management department provides the optimal management method by referring to past data. For example, the management department analyzes past license data to improve management accuracy. This allows the accuracy of the management algorithm to be improved by referring to past license data. Referencing past license data can be done, for example, by using a database. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input past license data into AI, and the AI ​​can analyze the data to optimize the management algorithm.

[0053] The management department can add a function to automatically notify users of license renewals and changes. For example, the management department can automatically notify users when a license is renewed. For example, the management department can automatically notify users when a license is changed. For example, the management department can automatically notify users when a license is about to expire. This improves management efficiency by automatically notifying users of license renewals and changes. Notifications of license renewals and changes can be sent using methods such as email or app notifications. 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 use AI to detect license renewals and changes and automatically send notifications.

[0054] The management department can focus its management on licensing status in specific regions or countries. For example, the management department can focus its management on licensing status in a particular region. For example, the management department can strengthen its management in countries where licensing is frequent. For example, the management department can focus its management on licensing status in regions or countries specified by the user. This allows for an understanding of licensing status by region or country by focusing management on specific regions or countries. Managing specific regions or countries includes, for example, increasing the scope of management and the frequency of management. Some or all of the above processes by the management department may be performed using AI, for example, or not using AI. For example, the management department can use AI to adjust the scope of management and the frequency of management in order to focus its management on licensing status in specific regions or countries.

[0055] The management department can add a function to visualize the license history and provide it to stakeholders. For example, the management department can visualize the license history and provide it to stakeholders. For example, the management department can display the license history in graphs or charts. For example, the management department can make it easy for stakeholders to check the license history. By visualizing the license history and providing it to stakeholders, it becomes easier to understand the licensing status. Visualization of the license history can be done using a database, for example. 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 use AI to visualize the license history and provide it to stakeholders.

[0056] The reporting unit can optimize the report generation algorithm by referring to past report data. For example, the reporting unit optimizes the report generation algorithm based on past report data. For example, the reporting unit provides the optimal report generation method by referring to past data. For example, the reporting unit analyzes past report data to improve report generation accuracy. This allows the accuracy of the report generation algorithm to be improved by referring to past report data. Referencing past report data can be done, for example, by using a database. Some or all of the above processes in the reporting unit may be performed using, for example, AI, or not using AI. For example, the reporting unit can input past report data into AI, and the AI ​​can analyze the data to optimize the report generation algorithm.

[0057] The reporting unit can enhance its reporting for specific music genres or artists. For example, the reporting unit can enhance its reporting for specific music genres. The reporting unit can enhance its reporting for specific artists. This enhances reporting for specific music genres or artists, enabling earlier detection of infringements. Report enhancement includes, for example, increasing the scope of reporting or the frequency of reporting. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not. For example, the reporting unit can use AI to adjust the scope of reporting or the frequency of reporting in order to enhance reporting for specific music genres or artists.

[0058] The reporting unit can focus its reporting on infringement in specific regions or countries. For example, the reporting unit can focus its reporting on infringement in a particular region. For example, the reporting unit can enhance reporting for countries where infringement is frequent. For example, the reporting unit can focus its reporting on infringement in regions or countries specified by the user. This allows for an understanding of infringement in each region or country by focusing reporting on infringement in specific regions or countries. Reporting for specific regions or countries may include, for example, increasing the scope of reporting or the frequency of reporting. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can use AI to adjust the scope of reporting or the frequency of reporting in order to focus its reporting on infringement in specific regions or countries.

[0059] The reporting unit can also include reactions and comments from social media in its reports. For example, the reporting unit can include reactions from social media in its reports. For example, the reporting unit can include comments from social media in its reports. For example, the reporting unit can include reactions and comments from social media platforms specified by the user in its reports. This allows for a more detailed understanding of the situation by including reactions and comments from social media in the reports. For example, a database can be used to collect reactions and comments from social media. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can use AI to collect reactions and comments from social media and include them in its reports.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The monitoring unit can focus its monitoring on music usage in specific regions or countries. For example, it can strengthen monitoring in regions or countries where copyright infringement is frequent. It can also focus its monitoring on music usage in regions or countries specified by the user. This makes it easier to understand the infringement situation in each region or country by focusing monitoring on music usage in specific regions or countries. Furthermore, the monitoring unit can accumulate data for each region and country and improve the accuracy of monitoring based on past infringement patterns.

[0062] The notification unit can select the most appropriate notification method by referring to the past interaction history of the relevant parties. For example, it can select a notification method suitable for situations requiring a rapid response based on past interaction history. It can also analyze the past interaction history of the relevant parties and select the most effective notification method. In this way, the optimal notification method can be selected by referring to the past interaction history of the relevant parties. Furthermore, the notification unit can accumulate past interaction history data and use it to optimize future notification methods.

[0063] The management department can add a function to automatically notify users of license renewals and changes. For example, it can automatically notify users when a license is renewed. It can also automatically notify users when a license is changed. This improves management efficiency by automatically notifying users of license renewals and changes. Furthermore, the management department can accumulate data on license renewals and changes, which can be used to optimize future management methods.

[0064] The reporting unit can optimize its report generation algorithm by referring to past report data. For example, it can optimize the report generation algorithm based on past report data. It can also provide the optimal report generation method by referring to past data. This allows for improved accuracy of the report generation algorithm by referring to past report data. Furthermore, the reporting unit can accumulate past report data and use it to optimize future report generation methods.

[0065] The detection unit can enhance detection for specific music genres or artists. For example, it can enhance detection for specific music genres. It can also enhance detection for specific artists. This allows for early detection of infringement by enhancing detection for specific music genres or artists. Furthermore, the detection unit can accumulate data on specific music genres and artists, which can be used to improve detection accuracy in the future.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The monitoring unit monitors the usage of music on the internet. For example, the monitoring unit monitors music upload sites and streaming services 24 hours a day to detect unauthorized use of music. The monitoring unit can detect unauthorized use of music using speech recognition and similarity detection algorithms. Step 2: The detection unit detects copyright infringement based on the music usage status monitored by the monitoring unit. The detection unit detects unauthorized use of music, for example, using speech recognition and similarity detection algorithms. Step 3: The notification unit issues a notification based on the copyright infringement detected by the detection unit. The notification unit uses generative AI to create legal documents and notification letters. For example, the notification unit uses generative AI to create legal documents and notification letters and supports communication with stakeholders using natural language processing. Step 4: The Management Department manages the licensing status based on the information notified by the Notification Department. The Management Department automates licensing management and maximizes license revenue. For example, the Management Department automates licensing management and maximizes license revenue. Step 5: The reporting unit generates reports based on the licensing status managed by the management unit. The reporting unit generates reports that visualize infringement situations and response results in data. For example, the reporting unit generates reports that visualize infringement situations and response results in data.

[0068] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system for musicians and music publishers. This AI agent system monitors the usage of music on the internet and detects copyright infringement. This system aims to protect artists' revenue and rights and contribute to the healthy development of the entire music industry. For example, the AI ​​agent system monitors the usage of music on the internet. Specifically, it monitors music upload sites and streaming services 24 hours a day to detect unauthorized use of music. For example, it uses speech recognition and similarity detection algorithms to detect unauthorized use of music. This automates monitoring tasks that would be difficult for individuals to perform. Next, when copyright infringement is detected, the AI ​​agent system immediately notifies and proposes countermeasures. For example, a generative AI creates legal documents and notices, and natural language processing assists in communication with relevant parties. This also streamlines legal procedures. Furthermore, the AI ​​agent system manages legitimate licensing status and maximizes licensing revenue. For example, it automates licensing management to maximize licensing revenue. Finally, the AI ​​agent system generates reports that visualize the infringement status and the results of countermeasures in data. This allows rights holders to concentrate on their creative activities with peace of mind, maximizing revenue and promoting the health of the industry. This allows the AI ​​agent system to protect artists' earnings and rights, and contribute to the healthy development of the entire music industry.

[0069] The AI ​​agent system according to this embodiment comprises a monitoring unit, a detection unit, a notification unit, a management unit, and a reporting unit. The monitoring unit monitors the usage status of music on the internet. The monitoring unit monitors, for example, music upload sites and streaming services 24 hours a day to detect unauthorized use of music. The monitoring unit can detect unauthorized use of music using speech recognition and similarity detection algorithms. The detection unit detects copyright infringement based on the music usage status monitored by the monitoring unit. The detection unit detects unauthorized use of music using, for example, speech recognition and similarity detection algorithms. The notification unit makes notifications based on copyright infringement detected by the detection unit. The notification unit creates legal documents and notification letters using generative AI. The notification unit, for example, uses generative AI to create legal documents and notification letters and supports communication with relevant parties using natural language processing. The management unit manages the licensing status based on the information notified by the notification unit. The management unit automates the management of licenses and maximizes licensing revenue. The management unit automates the management of licenses and aims to maximize licensing revenue. The reporting unit generates reports based on the licensing status managed by the management unit. The reporting unit generates reports that visualize infringement status and response results in data. For example, the reporting unit generates reports that visualize infringement status and response results in data. This enables the AI ​​agent system according to the embodiment to efficiently monitor, detect, notify, manage, and generate reports on music usage on the internet.

[0070] The monitoring unit monitors the usage of music on the internet. For example, the monitoring unit monitors music upload sites and streaming services 24 hours a day to detect unauthorized use of music. Specifically, the monitoring unit uses APIs of music upload sites and streaming services to acquire newly uploaded music and music currently being streamed in real time. This allows the monitoring unit to efficiently collect a vast amount of music data and quickly identify music that may be being used without permission. Furthermore, the monitoring unit can detect unauthorized use of music using speech recognition and similarity detection algorithms. Speech recognition technology analyzes features such as the melody, rhythm, and lyrics of a song and compares them with an existing music database. Similarity detection algorithms perform waveform and spectral analysis of the song and evaluate how similar it is to existing music. This allows the monitoring unit to detect unauthorized use of music with high accuracy. In addition, the monitoring unit centrally manages the collected data and can cooperate with other systems and departments as needed. For example, the monitoring unit stores data on a cloud server, making it accessible to the detection and notification units. Furthermore, the monitoring unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the monitoring unit to collect data efficiently and effectively, improving the overall system performance.

[0071] The detection unit detects copyright infringement based on music usage monitored by the monitoring unit. Specifically, the detection unit uses speech recognition and similarity detection algorithms to detect unauthorized use of music. Speech recognition technology analyzes features such as melody, rhythm, and lyrics of music and compares them with an existing music database. The similarity detection algorithm performs waveform and spectral analysis of music to evaluate how similar it is to existing music. This allows the detection unit to detect unauthorized use of music with high accuracy. Furthermore, the detection unit uses AI to analyze the collected data and identify music that may be infringing on copyright. The AI ​​can also utilize historical data and statistical information to perform long-term risk assessment and trend analysis. For example, based on past copyright infringement data, it can predict trends in unauthorized use by specific sites or users and issue warnings early. In addition, the detection unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and respond quickly. This allows the detection unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0072] The notification unit issues notifications based on copyright infringement detected by the detection unit. Specifically, the notification unit uses generative AI to create legal documents and notification letters. The generative AI automatically generates appropriate legal documents and notification letters based on detailed information about copyright infringement. For example, the generative AI creates a notification letter that includes information about the infringed song, the specific details of the infringement, and an outline of the legal action to be taken. Furthermore, the notification unit uses natural language processing technology to support communication with relevant parties. The natural language processing technology automatically analyzes the content of the notification letter and generates messages that encourage appropriate action from the relevant parties. This allows the notification unit to issue notifications quickly and accurately, and to support appropriate responses to copyright infringement. In addition, the notification unit centrally manages the notification transmission history and response status, and can collaborate with other systems and departments as needed. For example, the notification unit stores data on a cloud server, making it accessible to the management and reporting units. The notification unit can also adjust the frequency and content of notifications, enabling flexible responses to specific situations and conditions. This allows the notification unit to issue notifications efficiently and effectively, improving the overall system performance.

[0073] The Management Department manages the licensing status based on information notified by the Notification Department. Specifically, the Management Department automates licensing management to maximize license revenue. For example, the Management Department builds a system that automates processes such as licensing application, approval, renewal, and cancellation. This allows the Management Department to significantly reduce the effort and time required for licensing management and efficiently maximize license revenue. Furthermore, the Management Department can grasp the licensing status in real time and take appropriate action. For example, the Management Department automatically notifies users when licensing is nearing its expiration date and prompts them to renew. In addition, the Management Department can centrally manage licensing status and collaborate with other systems and departments as needed. For example, the Management Department stores data on a cloud server and makes it accessible to the Reporting Department. This allows the Management Department to manage licensing efficiently and effectively and improve the overall system performance.

[0074] The reporting department generates reports based on the licensing status managed by the management department. Specifically, the reporting department generates reports that visualize infringement situations and response results using data. For example, the reporting department visually represents infringement situations and response results using graphs and charts, making them easy to understand for stakeholders. Furthermore, the reporting department automatically generates report content using generation AI. The generation AI analyzes the collected data and creates appropriate reports. For example, the generation AI analyzes the frequency of infringements, the speed of response, and the licensing status to generate detailed reports. This allows the reporting department to generate reports quickly and accurately, providing relevant information to stakeholders. In addition, the reporting department centrally manages the report generation history and distribution status, and can collaborate with other systems and departments as needed. For example, the reporting department stores data on a cloud server, making it accessible to the management and notification departments. The reporting department can also adjust the frequency and content of report generation, enabling flexible responses to specific situations and conditions. This allows the reporting department to generate reports efficiently and effectively, improving the overall system performance.

[0075] The monitoring unit can detect unauthorized use of music using speech recognition and similarity detection algorithms. The monitoring unit can detect unauthorized use of music using, for example, speech recognition technology. The monitoring unit can detect unauthorized use of music using, for example, similarity detection algorithms. The monitoring unit can detect unauthorized use of music with high accuracy by, for example, combining speech recognition and similarity detection algorithms. This makes it possible to detect unauthorized use of music with high accuracy by using speech recognition and similarity detection algorithms. The speech recognition technology can, for example, use a speech recognition model using deep learning. The similarity detection algorithm can, for example, use cosine similarity or Jaccard coefficients. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or without AI. For example, the monitoring unit can input speech data acquired using speech recognition technology into a generating AI, and the generating AI can detect unauthorized use using a similarity detection algorithm.

[0076] The notification unit can create legal documents and notices using generative AI. For example, the notification unit can create legal documents concerning copyright infringement using generative AI. For example, the notification unit can create notices concerning copyright infringement using generative AI. For example, the notification unit can create legal documents and notices quickly and accurately using generative AI. This allows for the creation of legal documents and notices quickly and accurately by using generative AI. The generative AI may, for example, use text generation AI (e.g., LLM). The generative AI may, for example, use natural language processing technology to generate legal documents and notices. Some or all of the above-described processes in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can send legal documents and notices created using generative AI to the relevant parties.

[0077] The management department can automate license management and maximize license revenue. The management department can, for example, automate license management. The management department can, for example, aim to maximize license revenue. The management department can, for example, automate license management and aim to maximize license revenue. By automating license management, it is possible to maximize license revenue. License management includes, for example, issuing, renewing, and revoking licenses. Maximizing license revenue includes, for example, proper management of licenses and optimization of revenue. Some or all of the above processes in the management department may be performed using, for example, AI, or not using AI. For example, the management department can use AI to issue, renew, and revoke licenses in order to automate license management.

[0078] The reporting unit can generate reports that visualize infringement situations and response results using data. For example, the reporting unit generates reports that visualize infringement situations using data. For example, the reporting unit generates reports that visualize response results using data. For example, the reporting unit generates reports that visualize infringement situations and response results using data. This makes it easier for rights holders to understand the situation by visualizing infringement situations and response results using data. Methods of data visualization include using graphs and charts. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can generate reports that visualize infringement situations and response results based on data generated using AI.

[0079] The notification unit can support communication with stakeholders using natural language processing. The notification unit supports communication with stakeholders using, for example, natural language processing technology. The notification unit supports communication with stakeholders using, for example, generative AI. The notification unit facilitates communication with stakeholders using, for example, natural language processing technology. As a result, communication with stakeholders can be facilitated by using natural language processing. The natural language processing technology uses, for example, text generation AI (e.g., LLM). Some or all of the above processing in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can support communication by sending a notification text created using generative AI to stakeholders.

[0080] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated user emotions. The monitoring unit, for example, estimates the user's emotions. The monitoring unit, for example, adjusts the monitoring frequency based on the estimated user emotions. The monitoring unit, for example, estimates the user's emotions and adjusts the monitoring frequency based on the estimated emotions. This allows for an increase in the user's sense of security by adjusting the monitoring frequency according to the user's emotions. The estimation of the user's emotions is performed using, for example, an emotion engine or a generative AI. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the monitoring frequency can be adjusted based on the result.

[0081] The monitoring unit can enhance monitoring for specific music genres or artists. For example, the monitoring unit can enhance monitoring for specific music genres. For example, the monitoring unit can enhance monitoring for specific artists. For example, the monitoring unit can enhance monitoring for specific music genres or artists. This makes it possible to detect infringements early by enhancing monitoring for specific music genres or artists. Enhanced monitoring includes, for example, increasing the scope of monitoring and the frequency of monitoring. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can use AI to adjust the scope of monitoring and the frequency of monitoring in order to enhance monitoring for specific music genres or artists.

[0082] The monitoring unit can automatically detect new audio upload platforms on the internet and add them to its monitoring scope. For example, the monitoring unit can automatically detect newly appearing audio upload sites and add them to its monitoring scope. For example, the monitoring unit can include audio sharing platforms on social media in its monitoring scope. For example, the monitoring unit can automatically add new platforms specified by users to its monitoring scope. This expands the monitoring scope by automatically detecting and adding new audio upload platforms to the monitoring scope. The detection of new audio upload platforms is performed, for example, using AI. Some or all of the above processing in the monitoring unit may be performed, for example, using AI, or without using AI. For example, the monitoring unit can use AI to detect new audio upload platforms and add them to its monitoring scope.

[0083] The monitoring unit can estimate the user's emotions and determine the priority of monitored targets based on the estimated user emotions. The monitoring unit, for example, estimates the user's emotions. The monitoring unit, for example, determines the priority of monitored targets based on the estimated user emotions. The monitoring unit, for example, estimates the user's emotions and determines the priority of monitored targets based on the estimated emotions. This allows important monitored targets to be monitored preferentially by determining the priority of monitored targets according to the user's emotions. The estimation of user emotions is performed using, for example, an emotion engine or a generative AI. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the priority of monitored targets can be determined based on the results.

[0084] The monitoring unit can focus its monitoring on music usage in specific regions or countries. For example, the monitoring unit will focus its monitoring on music usage in a particular region. For example, the monitoring unit will strengthen its monitoring in countries where copyright infringement is frequent. For example, the monitoring unit will focus its monitoring on music usage in regions or countries specified by the user. This allows for an understanding of infringement situations in each region or country by focusing its monitoring on music usage in specific regions or countries. Monitoring specific regions or countries may include, for example, increasing the scope of monitoring and the frequency of monitoring. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can use AI to adjust the scope of monitoring and the frequency of monitoring in order to focus its monitoring on music usage in specific regions or countries.

[0085] The monitoring unit can also include music usage on social media as part of its monitoring scope. For example, the monitoring unit may include music usage on social media as part of its monitoring scope. For example, the monitoring unit may add social media platforms specified by the user to its monitoring scope. For example, the monitoring unit may monitor trends related to music usage on social media. This allows for broader monitoring by including music usage on social media as part of the monitoring scope. Monitoring on social media may include, for example, increasing the scope of monitoring and the frequency of monitoring. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit may use AI to adjust the scope of monitoring and the frequency of monitoring in order to include music usage on social media as part of its monitoring scope.

[0086] The detection unit can estimate the user's emotions and adjust the detection accuracy based on the estimated user emotions. The detection unit, for example, estimates the user's emotions. The detection unit, for example, adjusts the detection accuracy based on the estimated user emotions. The detection unit, for example, estimates the user's emotions and adjusts the detection accuracy based on the estimated emotions. By adjusting the detection accuracy according to the user's emotions, the user's sense of security can be enhanced. The estimation of the user's emotions is performed using, for example, an emotion engine or a generative AI. Some or all of the above processing in the detection unit may be performed using, for example, AI, or not using AI. For example, the detection unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the detection accuracy can be adjusted based on the result.

[0087] The detection unit can optimize its detection algorithm by referring to past copyright infringement data. For example, the detection unit optimizes its detection algorithm based on past copyright infringement data. For example, the detection unit analyzes past infringement patterns to improve detection accuracy. For example, the detection unit predicts new infringement patterns by referring to past data. In this way, the accuracy of the detection algorithm can be improved by referring to past copyright infringement data. Referencing past copyright infringement data can be done, for example, by using a database. Some or all of the above processes in the detection unit may be performed using, for example, AI, or not using AI. For example, the detection unit can input past copyright infringement data into AI, and the AI ​​can analyze the data to optimize the detection algorithm.

[0088] The detection unit can also include partial use of a song or remixes as detection targets. For example, the detection unit may include partial use of a song as detection targets. For example, the detection unit may include remixed songs as detection targets. For example, the detection unit may include sampling use of a song as detection targets. By including partial use of a song or remixes as detection targets, a broader range of copyright infringement can be detected. Detection of partial use of a song or remixes may be performed using, for example, speech recognition technology. Some or all of the above processing in the detection unit may be performed using, for example, AI, or not using AI. For example, the detection unit can input partial use of a song or remixes into the AI, and the AI ​​can detect them.

[0089] The detection unit can estimate the user's emotions and adjust the display method of the detection results based on the estimated user emotions. The detection unit, for example, estimates the user's emotions. The detection unit, for example, adjusts the display method of the detection results based on the estimated user emotions. The detection unit, for example, estimates the user's emotions and adjusts the display method of the detection results based on the estimated emotions. This allows for a deeper understanding of the user by adjusting the display method of the detection results according to the user's emotions. The estimation of the user's emotions is performed, for example, using an emotion engine or a generative AI. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the display method of the detection results can be adjusted based on the result.

[0090] The detection unit can enhance detection for specific music genres or artists. For example, the detection unit can enhance detection for specific music genres. For example, the detection unit can enhance detection for specific artists. For example, the detection unit can enhance detection for specific music genres or artists. This makes it possible to detect infringements earlier by enhancing detection for specific music genres or artists. Enhanced detection includes, for example, increasing the scope of detection targets or the frequency of detection. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can use AI to adjust the scope of detection targets or the frequency of detection in order to enhance detection for specific music genres or artists.

[0091] The detection unit can also include the unauthorized use of music videos and live performances in its detection targets. For example, the detection unit may include the unauthorized use of music videos in its detection targets. For example, the detection unit may include the unauthorized use of live performances in its detection targets. For example, the detection unit may add a specific video platform designated by the user to its monitoring targets. This allows for the detection of a broader range of copyright infringements by including the unauthorized use of music videos and live performances in its detection targets. For example, voice recognition technology may be used to detect music videos and live performances. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit may input the unauthorized use of music videos and live performances into an AI, which can then detect them.

[0092] The notification unit can estimate the user's emotions and adjust the urgency of the notification based on the estimated emotions. The notification unit, for example, estimates the user's emotions. The notification unit, for example, adjusts the urgency of the notification based on the estimated emotions. The notification unit, for example, estimates the user's emotions and adjusts the urgency of the notification based on the estimated emotions. This can increase the user's sense of security by adjusting the urgency of the notification according to the user's emotions. The estimation of the user's emotions is performed using, for example, an emotion engine or a generative AI. Some or all of the above processing in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the urgency of the notification can be adjusted based on the result.

[0093] The notification unit can select the optimal notification method by referring to the past interaction history of the relevant parties. For example, the notification unit selects the optimal notification method based on the past interaction history of the relevant parties. For example, the notification unit selects a notification method suitable for situations requiring a rapid response based on past interaction history. For example, the notification unit analyzes the past interaction history of the relevant parties and selects the most effective notification method. This allows the optimal notification method to be selected by referring to the past interaction history of the relevant parties. Referencing past interaction history can be done, for example, by using a database. Some or all of the above processing in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can input the past interaction history of the relevant parties into AI, and the AI ​​can analyze the data to select the optimal notification method.

[0094] The notification unit can use a combination of multiple notification methods (email, SMS, app notifications, etc.). For example, the notification unit can send notifications using a combination of email and SMS. For example, the notification unit can send notifications using a combination of app notifications and email. For example, the notification unit can use a combination of multiple notification methods specified by the user. By using a combination of multiple notification methods, the reliability of notifications is improved. The use of multiple notification methods is done, for example, to increase the reliability of notifications. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can improve the reliability of notifications by using AI to combine multiple notification methods.

[0095] The notification unit can estimate the user's emotions and adjust the way the notification content is expressed based on the estimated emotions. The notification unit, for example, estimates the user's emotions. The notification unit, for example, adjusts the way the notification content is expressed based on the estimated emotions. The notification unit, for example, estimates the user's emotions and adjusts the way the notification content is expressed based on the estimated emotions. This allows for a deeper understanding of the user by adjusting the way the notification content is expressed according to the user's emotions. The estimation of the user's emotions is performed, for example, using an emotion engine or a generative AI. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the way the notification content is expressed based on the result.

[0096] The notification unit can select the optimal notification method by considering the geographical location information of the relevant parties. For example, the notification unit selects the optimal notification method based on the geographical location information of the relevant parties. For example, the notification unit selects a notification method suitable for situations requiring a rapid response based on geographical location information. For example, the notification unit analyzes the geographical location information of the relevant parties and selects the most effective notification method. In this way, the optimal notification method can be selected by considering the geographical location information of the relevant parties. For example, GPS data can be used to reference geographical location information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the geographical location information of the relevant parties into AI, and the AI ​​can analyze the data and select the optimal notification method.

[0097] The notification unit can send notifications through the social media accounts of relevant parties. For example, the notification unit sends notifications through the social media accounts of relevant parties. For example, the notification unit uses social media accounts to send rapid notifications. For example, the notification unit sends notifications through social media accounts designated by relevant parties. This enables rapid notification by sending notifications through the relevant parties' social media accounts. The use of social media accounts is done, for example, to increase the reliability of the notifications. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can achieve rapid notification by using AI to send notifications through the relevant parties' social media accounts.

[0098] The management department can estimate the user's emotions and adjust the license management method based on the estimated user emotions. The management department can, for example, estimate the user's emotions. The management department can, for example, adjust the license management method based on the estimated user emotions. The management department can, for example, estimate the user's emotions and adjust the license management method based on the estimated emotions. This can enhance the user's sense of security by adjusting the license management method according to the user's emotions. The estimation of the user's emotions is performed, for example, using an emotion engine or a generative AI. Some or all of the above processing in the management department may be performed, for example, using AI or not using AI. For example, the management department can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the license management method can be adjusted based on the result.

[0099] The management department can optimize the management algorithm by referring to past license data. For example, the management department optimizes the management algorithm based on past license data. For example, the management department provides the optimal management method by referring to past data. For example, the management department analyzes past license data to improve management accuracy. This allows the accuracy of the management algorithm to be improved by referring to past license data. Referencing past license data can be done, for example, by using a database. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input past license data into AI, and the AI ​​can analyze the data to optimize the management algorithm.

[0100] The management department can add a function to automatically notify users of license renewals and changes. For example, the management department can automatically notify users when a license is renewed. For example, the management department can automatically notify users when a license is changed. For example, the management department can automatically notify users when a license is about to expire. This improves management efficiency by automatically notifying users of license renewals and changes. Notifications of license renewals and changes can be sent using methods such as email or app notifications. 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 use AI to detect license renewals and changes and automatically send notifications.

[0101] The management department can estimate the user's emotions and determine the priority of licenses based on the estimated user emotions. The management department, for example, estimates the user's emotions. The management department, for example, determines the priority of licenses based on the estimated user emotions. The management department, for example, estimates the user's emotions and determines the priority of licenses based on the estimated emotions. This allows important licenses to be managed preferentially by determining the priority of licenses according to the user's emotions. The estimation of user emotions is performed using, for example, an emotion engine or a generative AI. Some or all of the above processing in the management department may be performed using, for example, AI, or not using AI. For example, the management department can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the priority of licenses can be determined based on the results.

[0102] The management department can focus its management on licensing status in specific regions or countries. For example, the management department can focus its management on licensing status in a particular region. For example, the management department can strengthen its management in countries where licensing is frequent. For example, the management department can focus its management on licensing status in regions or countries specified by the user. This allows for an understanding of licensing status by region or country by focusing management on specific regions or countries. Managing specific regions or countries includes, for example, increasing the scope of management and the frequency of management. Some or all of the above processes by the management department may be performed using AI, for example, or not using AI. For example, the management department can use AI to adjust the scope of management and the frequency of management in order to focus its management on licensing status in specific regions or countries.

[0103] The management department can add a function to visualize the license history and provide it to stakeholders. For example, the management department can visualize the license history and provide it to stakeholders. For example, the management department can display the license history in graphs or charts. For example, the management department can make it easy for stakeholders to check the license history. By visualizing the license history and providing it to stakeholders, it becomes easier to understand the licensing status. Visualization of the license history can be done using a database, for example. 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 use AI to visualize the license history and provide it to stakeholders.

[0104] The reporting unit can estimate the user's emotions and adjust the level of detail in the report based on the estimated emotions. The reporting unit, for example, estimates the user's emotions. The reporting unit, for example, adjusts the level of detail in the report based on the estimated emotions. The reporting unit, for example, estimates the user's emotions and adjusts the level of detail in the report based on the estimated emotions. This allows for a deeper understanding of the user by adjusting the level of detail in the report according to the user's emotions. The estimation of the user's emotions is performed using, for example, an emotion engine or a generative AI. Some or all of the above processing in the reporting unit may be performed using, for example, AI, or not using AI. For example, the reporting unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the level of detail in the report can be adjusted based on the result.

[0105] The reporting unit can optimize the report generation algorithm by referring to past report data. For example, the reporting unit optimizes the report generation algorithm based on past report data. For example, the reporting unit provides the optimal report generation method by referring to past data. For example, the reporting unit analyzes past report data to improve report generation accuracy. This allows the accuracy of the report generation algorithm to be improved by referring to past report data. Referencing past report data can be done, for example, by using a database. Some or all of the above processes in the reporting unit may be performed using, for example, AI, or not using AI. For example, the reporting unit can input past report data into AI, and the AI ​​can analyze the data to optimize the report generation algorithm.

[0106] The reporting unit can enhance its reporting for specific music genres or artists. For example, the reporting unit can enhance its reporting for specific music genres. The reporting unit can enhance its reporting for specific artists. This enhances reporting for specific music genres or artists, enabling earlier detection of infringements. Report enhancement includes, for example, increasing the scope of reporting or the frequency of reporting. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not. For example, the reporting unit can use AI to adjust the scope of reporting or the frequency of reporting in order to enhance reporting for specific music genres or artists.

[0107] The reporting unit can estimate the user's emotions and adjust the way the report is displayed based on the estimated emotions. The reporting unit, for example, estimates the user's emotions. The reporting unit, for example, adjusts the way the report is displayed based on the estimated emotions. The reporting unit, for example, estimates the user's emotions and adjusts the way the report is displayed based on the estimated emotions. This allows for a deeper understanding of the user by adjusting the way the report is displayed according to the user's emotions. The estimation of the user's emotions is performed using, for example, an emotion engine or a generative AI. Some or all of the above processing in the reporting unit may be performed using, for example, AI, or not using AI. For example, the reporting unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the way the report is displayed can be adjusted based on the result.

[0108] The reporting unit can focus its reporting on infringement in specific regions or countries. For example, the reporting unit can focus its reporting on infringement in a particular region. For example, the reporting unit can enhance reporting for countries where infringement is frequent. For example, the reporting unit can focus its reporting on infringement in regions or countries specified by the user. This allows for an understanding of infringement in each region or country by focusing reporting on infringement in specific regions or countries. Reporting for specific regions or countries may include, for example, increasing the scope of reporting or the frequency of reporting. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can use AI to adjust the scope of reporting or the frequency of reporting in order to focus its reporting on infringement in specific regions or countries.

[0109] The reporting unit can also include reactions and comments from social media in its reports. For example, the reporting unit can include reactions from social media in its reports. For example, the reporting unit can include comments from social media in its reports. For example, the reporting unit can include reactions and comments from social media platforms specified by the user in its reports. This allows for a more detailed understanding of the situation by including reactions and comments from social media in the reports. For example, a database can be used to collect reactions and comments from social media. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can use AI to collect reactions and comments from social media and include them in its reports.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on those estimates. For example, if the user is feeling anxious, the monitoring unit can increase the monitoring frequency. Conversely, if the user feels secure, the monitoring frequency can be decreased. This enables flexible monitoring that responds to the user's emotions, thereby increasing the user's sense of security. Furthermore, by accumulating user emotion data and analyzing long-term emotional changes, the monitoring unit can adjust the monitoring frequency with greater accuracy.

[0112] The notification unit can estimate the user's emotions and adjust the way the notification is expressed based on those emotions. For example, if the user is angry, the notification unit can use calm and polite language. If the user is sad, the notification unit can include words of encouragement. This enables notifications that are sensitive to the user's emotions, making them easier to understand and accept. Furthermore, the notification unit can accumulate user emotion data and select the most appropriate notification expression based on past emotional patterns.

[0113] The management department can estimate user emotions and adjust license management methods based on those estimates. For example, if a user is feeling anxious, the management department can provide detailed explanations regarding license renewals or changes. Conversely, if a user is feeling reassured, a concise notification may suffice. This enables flexible management tailored to user emotions, thereby increasing user confidence. Furthermore, by accumulating user emotion data and analyzing long-term emotional changes, the management department can adjust management methods with greater precision.

[0114] The reporting unit can estimate the user's emotions and adjust the level of detail in the report based on those estimates. For example, if the user is feeling anxious, the reporting unit can include detailed data and explanations. Conversely, if the user is feeling reassured, a concise report can suffice. This enables flexible report creation tailored to the user's emotions, leading to a deeper understanding of the user. Furthermore, the reporting unit can accumulate user emotion data and select the optimal level of report detail based on past emotion patterns.

[0115] The detection unit can estimate the user's emotions and adjust the display method of the detection results based on the estimated emotions. For example, if the user is feeling anxious, the detection unit can display the detection results with a detailed explanation. Conversely, if the user is feeling at ease, a concise display can suffice. This enables flexible display methods that respond to the user's emotions, deepening the user's understanding. Furthermore, the detection unit can accumulate user emotion data and select the optimal display method based on past emotion patterns.

[0116] The monitoring unit can focus its monitoring on music usage in specific regions or countries. For example, it can strengthen monitoring in regions or countries where copyright infringement is frequent. It can also focus its monitoring on music usage in regions or countries specified by the user. This makes it easier to understand the infringement situation in each region or country by focusing monitoring on music usage in specific regions or countries. Furthermore, the monitoring unit can accumulate data for each region and country and improve the accuracy of monitoring based on past infringement patterns.

[0117] The notification unit can select the most appropriate notification method by referring to the past interaction history of the relevant parties. For example, it can select a notification method suitable for situations requiring a rapid response based on past interaction history. It can also analyze the past interaction history of the relevant parties and select the most effective notification method. In this way, the optimal notification method can be selected by referring to the past interaction history of the relevant parties. Furthermore, the notification unit can accumulate past interaction history data and use it to optimize future notification methods.

[0118] The management department can add a function to automatically notify users of license renewals and changes. For example, it can automatically notify users when a license is renewed. It can also automatically notify users when a license is changed. This improves management efficiency by automatically notifying users of license renewals and changes. Furthermore, the management department can accumulate data on license renewals and changes, which can be used to optimize future management methods.

[0119] The reporting unit can optimize its report generation algorithm by referring to past report data. For example, it can optimize the report generation algorithm based on past report data. It can also provide the optimal report generation method by referring to past data. This allows for improved accuracy of the report generation algorithm by referring to past report data. Furthermore, the reporting unit can accumulate past report data and use it to optimize future report generation methods.

[0120] The detection unit can enhance detection for specific music genres or artists. For example, it can enhance detection for specific music genres. It can also enhance detection for specific artists. This allows for early detection of infringement by enhancing detection for specific music genres or artists. Furthermore, the detection unit can accumulate data on specific music genres and artists, which can be used to improve detection accuracy in the future.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The monitoring unit monitors the usage of music on the internet. For example, the monitoring unit monitors music upload sites and streaming services 24 hours a day to detect unauthorized use of music. The monitoring unit can detect unauthorized use of music using speech recognition and similarity detection algorithms. Step 2: The detection unit detects copyright infringement based on the music usage status monitored by the monitoring unit. The detection unit detects unauthorized use of music, for example, using speech recognition and similarity detection algorithms. Step 3: The notification unit issues a notification based on the copyright infringement detected by the detection unit. The notification unit uses generative AI to create legal documents and notification letters. For example, the notification unit uses generative AI to create legal documents and notification letters and supports communication with stakeholders using natural language processing. Step 4: The Management Department manages the licensing status based on the information notified by the Notification Department. The Management Department automates licensing management and maximizes license revenue. For example, the Management Department automates licensing management and maximizes license revenue. Step 5: The reporting unit generates reports based on the licensing status managed by the management unit. The reporting unit generates reports that visualize infringement situations and response results in data. For example, the reporting unit generates reports that visualize infringement situations and response results in data.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the monitoring unit, detection unit, notification unit, management unit, and reporting unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit monitors the unauthorized use of music using the camera 42 and microphone 38B of the smart device 14 and processes the monitoring data with the control unit 46A. The detection unit is implemented in the identification processing unit 290 of the data processing unit 12 and detects copyright infringement using speech recognition and similarity detection algorithms. The notification unit is implemented in the identification processing unit 290 of the data processing unit 12 and creates legal documents and notices using generation AI and supports communication with relevant parties using natural language processing. The management unit is implemented in the identification processing unit 290 of the data processing unit 12 and automates the management of licenses and aims to maximize license revenue. The reporting unit is implemented in the identification processing unit 290 of the data processing unit 12 and generates reports that visualize the infringement situation and the results of the response in data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the monitoring unit, detection unit, notification unit, management unit, and reporting unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit monitors the unauthorized use of music using the camera 42 and microphone 238 of the smart glasses 214 and processes the monitoring data with the control unit 46A. The detection unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and detects copyright infringement using speech recognition and similarity detection algorithms. The notification unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and creates legal documents and notices using generation AI and supports communication with relevant parties using natural language processing. The management unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and automates the management of licenses and aims to maximize license revenue. The reporting unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and generates reports that visualize the infringement situation and the results of the response in data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the monitoring unit, detection unit, notification unit, management unit, and reporting unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit monitors the unauthorized use of music using the camera 42 and microphone 238 of the headset terminal 314 and processes the monitoring data with the control unit 46A. The detection unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and detects copyright infringement using speech recognition and similarity detection algorithms. The notification unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and creates legal documents and notices using generation AI and supports communication with relevant parties using natural language processing. The management unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and automates the management of licenses and aims to maximize license revenue. The reporting unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and generates reports that visualize the infringement situation and the results of the response using data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the monitoring unit, detection unit, notification unit, management unit, and reporting unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit monitors the unauthorized use of music using the camera 42 and microphone 238 of the robot 414 and processes the monitoring data with the control unit 46A. The detection unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and detects copyright infringement using speech recognition and similarity detection algorithms. The notification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and creates legal documents and notices using generation AI and supports communication with relevant parties using natural language processing. The management unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and automates the management of licenses and aims to maximize license revenue. The reporting unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates reports that visualize the infringement situation and the results of the response using data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A monitoring department that monitors the usage of music on the internet, A detection unit that detects copyright infringement based on the music usage status monitored by the aforementioned monitoring unit, A notification unit that issues a notification based on the copyright infringement detected by the aforementioned detection unit, A management unit manages the licensing status based on the information notified by the aforementioned notification unit, The system includes a reporting unit that generates a report based on the licensing status managed by the aforementioned management unit. A system characterized by the following features. (Note 2) The aforementioned monitoring unit, Unauthorized use of music is detected using speech recognition and similarity detection algorithms. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned notification unit, Create legal documents and notices using generative AI. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned management department, Automate license management and maximize license revenue. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned report section is, Generate reports that visualize the infringement situation and the results of the response using data. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned notification unit, Supporting communication with stakeholders through natural language processing. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned monitoring unit, It estimates the user's emotions and adjusts the monitoring frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned monitoring unit, Increased surveillance on specific music genres and artists. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned monitoring unit, Automatically detects and adds new online music upload platforms to the monitoring list. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned monitoring unit, It estimates user sentiment and determines the priority of monitoring targets based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned monitoring unit, Focus on monitoring music usage in specific regions or countries. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned monitoring unit, The monitoring will also include the use of music on social media. The system described in Appendix 1, characterized by the features described herein. (Note 13) The detection unit is It estimates the user's emotions and adjusts the accuracy of the detection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The detection unit is Optimize the detection algorithm by referring to past copyright infringement data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The detection unit is The detection will also include partial use of songs and remixes. The system described in Appendix 1, characterized by the features described herein. (Note 16) The detection unit is It estimates the user's emotions and adjusts how the detection results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The detection unit is Enhance detection for specific music genres and artists. The system described in Appendix 1, characterized by the features described herein. (Note 18) The detection unit is Unauthorized use of music videos and live performances will also be included in the detection criteria. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned notification unit, It estimates the user's emotions and adjusts the urgency of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned notification unit, We will select the most appropriate notification method by referring to the past response history of the parties involved. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned notification unit, Use multiple notification methods in combination. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned notification unit, It estimates the user's emotions and adjusts the way notifications are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned notification unit, The most suitable notification method will be selected, taking into account the geographical location information of the relevant parties. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned notification unit, Notifications will be sent through the social media accounts of those involved. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned management department, We estimate user sentiment and adjust the licensing management method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned management department, Optimize the management algorithm by referring to past license data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned management department, Add a feature to automatically notify users of license agreement updates and changes. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned management department, It estimates the user's emotions and determines the priority of licensing based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned management department, Focus on managing licensing status in specific regions or countries. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned management department, Add a feature to visualize the license history and provide it to relevant parties. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned report section is, It estimates the user's sentiment and adjusts the level of detail in the report based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned report section is, Optimizing the report generation algorithm by referencing past report data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned report section is, Strengthen reporting on specific music genres and artists. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned report section is, It estimates user sentiment and adjusts how reports are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned report section is, Focus on reporting the extent of infringement in specific regions or countries. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned report section is, The report will also include reactions and comments from social media. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 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. A monitoring department that monitors the usage of music on the internet, A detection unit that detects copyright infringement based on the music usage status monitored by the aforementioned monitoring unit, A notification unit that issues a notification based on the copyright infringement detected by the aforementioned detection unit, A management unit manages the licensing status based on the information notified by the aforementioned notification unit, The system includes a reporting unit that generates a report based on the licensing status managed by the aforementioned management unit. A system characterized by the following features.

2. The aforementioned monitoring unit, Unauthorized use of music is detected using speech recognition and similarity detection algorithms. The system according to feature 1.

3. The aforementioned notification unit, Create legal documents and notices using generation AI. The system according to feature 1.

4. The aforementioned management department, Automate license management and maximize license revenue. The system according to feature 1.

5. The aforementioned report section is, Generate reports that visualize the infringement situation and the results of the response using data. The system according to feature 1.

6. The aforementioned notification unit, Supporting communication with stakeholders through natural language processing. The system according to feature 1.

7. The aforementioned monitoring unit, It estimates the user's emotions and adjusts the monitoring frequency based on the estimated emotions. The system according to feature 1.

8. The aforementioned monitoring unit, Increased surveillance on specific music genres and artists. The system according to feature 1.

Images