system

Abstract

The system according to this embodiment aims to simplify and streamline the process of obtaining licenses for musical works. [Solution] The system according to the embodiment comprises a search unit, a procedure unit, a negotiation unit, a matching unit, and a tracking unit. The search unit searches for music license information. The procedure unit applies for and makes payments for license agreements based on the license conditions found by the search unit. The negotiation unit assists in price negotiations with rights holders based on the license agreements made by the procedure unit. The matching unit matches creators with rights holders based on the price negotiations supported by the negotiation unit. The tracking unit tracks the usage status of the licenses matched by the matching unit.

Description

Technical Field

[0006] , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the process of obtaining a license for a music piece is complicated and requires a great deal of time and labor for procedures and negotiations.

[0005] The system according to the embodiment aims to simplify and efficiently perform the process of obtaining a license for a music piece.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a search unit, a procedure unit, a negotiation unit, a matching unit, and a tracking unit. The search unit searches for music license information. The procedure unit applies for and makes payments for license agreements based on the license conditions found by the search unit. The negotiation unit assists in price negotiations with rights holders based on the license agreements made by the procedure unit. The matching unit matches creators with rights holders based on the price negotiations supported by the negotiation unit. The tracking unit tracks the usage status of the licenses matched by the matching unit. [Effects of the Invention]

[0007] The system according to this embodiment can simplify and efficiently carry out the music licensing process. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. 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 music license acquisition support system according to an embodiment of the present invention is an AI agent that simplifies the acquisition of licenses for the commercial use of music. This music license acquisition support system centrally manages license information for songs requested by advertising agencies and video producers, and supports appropriate procedures. For example, the music license acquisition support system uses a license information search function to present songs and license conditions that match the purpose from a vast music database. For example, if an advertising agency is looking for songs that fit a specific theme, the AI ​​will search for songs that match those conditions and present the license conditions. Next, the music license acquisition support system uses a procedure automation function to complete the application and payment of license agreements online. This prevents delays and errors in the procedure, and allows for quick and reliable license acquisition. Furthermore, the music license acquisition support system uses a price negotiation support function, where the AI ​​proposes a fair price based on past transaction data and supports price negotiations with rights holders. For example, if an advertising agency wants to license a song within a budget, the AI ​​will propose a fair price and support negotiations. In addition, the music license acquisition support system uses a rights holder matching function, where the AI ​​compares the creator's requests with the conditions offered by the rights holder and performs matching. For example, if a video producer wants to use a specific song, the AI ​​identifies the rights holder of that song and performs a matching process. Finally, the music licensing support system uses a usage tracking function, allowing the AI ​​to manage license periods and conditions and ensure compliance. For example, it notifies users before the license period expires and supports the renewal process. In this way, the music licensing support system simplifies the process of obtaining licenses for commercial use of music, enabling creative projects to proceed smoothly.

[0029] The music license acquisition support system according to this embodiment comprises a search unit, a procedure unit, a negotiation unit, a matching unit, and a tracking unit. The search unit searches for music license information. The search unit presents suitable music and license conditions from a vast music database, for example. For example, if an advertising agency is looking for music that fits a specific theme, the AI ​​in the search unit searches for music that matches the conditions and presents the license conditions. The procedure unit applies for and makes payments for license agreements based on the license conditions found by the search unit. For example, the procedure unit completes the application and payment for license agreements online. This prevents delays and errors in the procedure, enabling quick and reliable license acquisition. The negotiation unit supports price negotiations with rights holders based on the license agreements made by the procedure unit. For example, the AI ​​in the negotiation unit proposes a fair price based on past transaction data and supports price negotiations with rights holders. For example, if an advertising agency wants to license music within a budget, the AI ​​in the negotiation unit proposes a fair price and supports negotiations. The matching unit matches creators with rights holders based on the price negotiations supported by the negotiation unit. The matching unit, for example, uses AI to compare the creator's requests with the rights holder's terms and conditions to perform matching. If a video producer wants to use a specific song, the matching unit's AI identifies the rights holder of that song and performs matching. The tracking unit tracks the usage status of licenses matched by the matching unit. The tracking unit, for example, uses AI to manage license periods and conditions and ensure compliance. The tracking unit, for example, provides notification before the license period expires and supports the renewal process. As a result, the music license acquisition support system according to this embodiment can efficiently perform searching, processing, negotiation, matching, and tracking of song license information.

[0030] The search unit searches for music licensing information. For example, it presents suitable music and licensing conditions from a vast music database. Specifically, the search unit filters the music in the database based on keywords and conditions entered by the user and lists the best candidates. For example, if an advertising agency is looking for music that fits a specific theme, the AI ​​will search for music that matches those conditions and present the licensing conditions. The AI ​​uses natural language processing technology to analyze the user's input and compare it with the music's metadata and tag information. Furthermore, the AI ​​can learn from past search history and user preferences to provide more accurate search results. When presenting search results to the user, the search unit also displays a preview of the music and detailed licensing conditions. This allows the user to compare and consider licensing conditions while reviewing the music content. The search unit also has a function to save search results for later review. This allows the user to compare multiple songs and select the best one. To enhance user convenience, the search unit also provides functions to refine and sort search results. For example, search results can be narrowed down by criteria such as song genre, release year, and artist name. It's also possible to sort search results by popularity or newest release. This allows the search engine to help users efficiently find songs and discover the most suitable licensing conditions.

[0031] The Procedures Department handles license agreement applications and payments based on the license conditions retrieved by the Search Department. For example, the Procedures Department allows users to complete license agreement applications and payments online. Specifically, the Procedures Department verifies the license conditions of the music selected by the user and provides a form for entering the necessary information. The user enters the required information into the form and submits the license agreement application. The Procedures Department automatically verifies the entered information to ensure there are no errors. Once the license agreement application is complete, the Procedures Department provides an interface for payment. Users can pay the license fee using a credit card or electronic payment service. Upon completion of payment, the Procedures Department issues a license agreement confirmation and notifies the user. This prevents delays and errors in the process, ensuring quick and reliable license acquisition. Furthermore, the Procedures Department also has a function to track the progress of the license agreement in real time and notify the user. For example, it sends notifications to the user when the license agreement application is approved or when payment is completed. This allows users to understand the progress of the process and proceed with license acquisition with confidence. Furthermore, the procedures section also provides a function to save a history of license agreements for later reference. This allows users to review past license agreements and reuse them as needed. To enhance user convenience, the procedures section also supports multiple languages ​​and currencies. This allows it to accommodate international users and assist in obtaining global licenses.

[0032] The Negotiation Department assists in price negotiations with rights holders based on license agreements made by the Procedures Department. For example, the Negotiation Department uses AI to propose a fair price based on past transaction data and support price negotiations with rights holders. Specifically, the Negotiation Department uses algorithms to calculate a fair price by analyzing past transaction data and market trends. The AI ​​learns from past transaction data and proposes a fair price by referring to transaction prices under similar conditions. Based on the proposed fair price, the Negotiation Department conducts price negotiations between the user and the rights holder. If a user wants to license music within their budget, the Negotiation Department's AI proposes a fair price and supports the negotiation. For example, if an advertising agency wants to license music within its budget, the AI ​​proposes a fair price based on past transaction data and supports the negotiation with the rights holder. If an agreement is reached between the user and the rights holder, the Negotiation Department finalizes the terms of the license agreement and notifies the Procedures Department. This allows the Negotiation Department to help users license music at a fair price and minimize costs. Furthermore, the Negotiation Department also has a function to track the progress of negotiations in real time and notify the user. For example, the system sends notifications to users when negotiations progress or when an agreement is reached. This allows users to stay informed about the progress of negotiations and proceed with license acquisition with confidence. The negotiation system also provides a function to save negotiation history for later reference. This allows users to review past negotiation history and reuse it as needed. To enhance user convenience, the negotiation system also supports multiple languages ​​and currencies. This allows it to accommodate international users and support global license acquisition.

[0033] The matching department matches creators with rights holders based on price negotiations supported by the negotiation department. For example, the matching department uses AI to compare creator requests with rights holders' terms and conditions. Specifically, the matching department analyzes the requests and conditions entered by creators and compares them with the rights holders' terms and conditions. The AI ​​uses an algorithm to compare creator requests and rights holders' terms and conditions to achieve the best possible match. For example, if a video producer wants to use a specific song, the AI ​​identifies the rights holder of that song and performs the matching. Once a match is made between the creator and the rights holder, the matching department finalizes the terms of the license agreement and notifies the procedures department. This allows the matching department to help creators quickly find songs that meet their needs and streamline the licensing process. Furthermore, the matching department also has a function to track the progress of the matching in real time and notify users. For example, it sends notifications to users when the matching process progresses or when a match is made. This allows users to understand the progress of the matching and proceed with licensing with confidence. The matching department also provides a function to save the matching history for later reference. This allows users to review their past matching history and reuse matches as needed. The matching system also supports multiple languages ​​and currencies to enhance user convenience. This enables it to cater to international users and support global license acquisition.

[0034] The tracking unit tracks the usage of licenses matched by the matching unit. For example, the tracking unit uses AI to manage license periods and conditions, ensuring compliance. Specifically, the tracking unit registers the terms and periods of license agreements in a database and checks them regularly. The AI ​​provides notifications before the license period expires and supports the renewal process. For example, it sends a notification to the user one month before the license expires, prompting them to renew. The tracking unit monitors license usage in real time to check for compliance violations. For example, if usage that violates license conditions is detected, it issues a warning to the user and prompts them to correct the issue. This allows the tracking unit to ensure proper license use and enforce compliance. Furthermore, the tracking unit also has the function of compiling license usage reports and providing them to users. For example, it outputs reports on license usage and renewal history and provides them to users. This allows users to understand their license usage and manage it appropriately. The tracking unit can also analyze license usage to inform future license acquisition decisions. For example, it can predict future license demand based on past usage and develop an appropriate license acquisition plan. The tracking unit also includes features to support multiple languages ​​and currencies to enhance user convenience. This allows it to accommodate international users and support global license management.

[0035] The search unit can present suitable songs and licensing conditions from a vast music database. For example, if an advertising agency is looking for music that fits a specific theme, the AI ​​will search for songs that match the criteria and present the licensing conditions. If a filmmaker is looking for music that fits a specific scene, the AI ​​can also search for songs that match the criteria and present the licensing conditions. If an individual user is looking for music that fits a specific event, the AI ​​can also search for songs that match the criteria and present the licensing conditions. This allows the search unit to efficiently present suitable songs and licensing conditions. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or not. For example, the search unit can input a vast music database into a generative AI and have the generative AI perform the task of presenting suitable songs and licensing conditions.

[0036] The procedures department can complete license agreement applications and payments online. For example, the procedures department can apply for a license agreement online and complete the agreement using an electronic signature. The procedures department can also make license agreement payments using an online payment system. For example, the procedures department can complete license agreement applications and payments online in a single process. This allows the procedures department to apply for and pay for license agreements quickly and reliably. Some or all of the above processes in the procedures department may be performed using, for example, a generative AI, or not using a generative AI. For example, the procedures department can input license agreement application data into a generative AI and have the generative AI automate the application process.

[0037] The negotiation department can propose a fair price based on past transaction data. For example, the negotiation department can use AI to analyze past transaction data and calculate a fair price. The negotiation department can also use AI to propose a fair price based on market prices and past transaction data. The negotiation department can also use AI to propose a fair price based on the creator's budget and conditions. This allows the negotiation department to conduct price negotiations efficiently by proposing a fair price. Some or all of the above processes in the negotiation department may be performed using, for example, a generative AI, or not using a generative AI. For example, the negotiation department can input past transaction data into a generative AI and have the generative AI calculate a fair price.

[0038] The matching unit can perform matching by comparing the creator's requests with the rights holder's terms of service. For example, the matching unit can use AI to compare the creator's requests with the rights holder's terms of service and perform the optimal match. The matching unit can also use AI to compare the rights holder's terms of service based on the creator's intended use and budget. For example, the matching unit can use AI to evaluate the degree of agreement between the creator's requests and the rights holder's terms of service and propose the optimal match. This allows the matching unit to efficiently match the requests and conditions of creators and rights holders. Some or all of the above-described processes in the matching unit may be performed using, for example, a generative AI, or without a generative AI. For example, the matching unit can input creator request data and rights holder's terms of service data into a generative AI and leave the matching process to the generative AI.

[0039] The tracking unit can manage license periods and terms and ensure compliance. For example, the tracking unit can use AI to monitor license periods and send notifications before they expire. The tracking unit can also use AI to manage license terms and check for any violations. The tracking unit can also use AI to reflect changes in license periods and terms in real time and provide the latest information. This allows the tracking unit to manage license periods and terms and ensure compliance. Some or all of the above processes in the tracking unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the tracking unit can input license period and term data into a generative AI and have the generative AI perform management and monitoring.

[0040] The search unit can optimize its search algorithm by referring to the user's past search history during a search. For example, the search unit can analyze trends in songs the user has searched for in the past and prioritize displaying similar songs. For example, the search unit can also suggest songs with the most suitable conditions based on the license conditions the user has searched for in the past. For example, the search unit can prioritize displaying specific genres or artists based on the user's past search history. In this way, the search unit can provide optimal search results based on the user's past search history. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input the user's past search history data into a generative AI and have the generative AI perform the optimization of the search algorithm.

[0041] The search unit can filter search results based on the user's current projects and areas of interest during a search. For example, the search unit may prioritize displaying music that matches the theme of the project the user is currently working on. The search unit can also filter and display relevant music based on the user's areas of interest. The search unit can also suggest the most suitable music based on the requirements of a project specified by the user. In this way, the search unit can provide optimal search results based on the user's current projects and areas of interest. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the search unit can input the user's project data and area of ​​interest data into a generative AI and have the generative AI perform the filtering of search results.

[0042] The search unit can prioritize displaying highly relevant songs by considering the user's geographical location during a search. For example, if the user is in a specific region, the search unit can prioritize displaying music from that region. For example, if the user is traveling, the search unit can also display music related to the culture of the travel destination. For example, if the user is attending a specific event, the search unit can also display music related to that event. This allows the search unit to provide optimal search results based on the user's geographical location. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input the user's geographical location data into a generative AI and have the generative AI display highly relevant songs.

[0043] The search unit can analyze the user's social media activity during a search and display relevant songs. For example, the search unit can analyze trends in songs shared by the user on social media and display similar songs. For example, the search unit can also prioritize displaying new songs by artists the user follows. For example, the search unit can display songs in genres of interest based on the user's social media activity. This allows the search unit to provide optimal search results based on the user's social media activity. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input the user's social media activity data into a generative AI and have the generative AI display relevant songs.

[0044] The procedure unit can optimize procedures by referring to past procedure history during the procedure. For example, the procedure unit can suggest the optimal procedure method based on the user's past procedure history. The procedure unit can also prioritize displaying frequently used procedures from the user's past procedure history. The procedure unit can also analyze the user's past procedure history to improve procedure efficiency. This allows the procedure unit to provide the optimal procedure based on past procedure history. Some or all of the above processing in the procedure unit may be performed using, for example, a generative AI, or without a generative AI. For example, the procedure unit can input past procedure history data into a generative AI and have the generative AI perform procedure optimization.

[0045] The procedure unit can adjust the priority of procedures based on the user's current situation during the procedure. For example, if the user is in a hurry, the procedure unit will prioritize the most important procedures. For example, if the user is relaxed, the procedure unit may provide detailed procedural explanations and then proceed with the procedures. The procedure unit can also adjust how procedures are carried out according to the user's current situation. This allows the procedure unit to provide procedural priorities that are appropriate to the user's current situation. Some or all of the above processing in the procedure unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the procedure unit can input the user's current situation data into a generative AI and have the generative AI adjust the procedural priorities.

[0046] The procedure unit can optimize the progress of a procedure by taking into account the user's geographical location information during the procedure. For example, if the user is in a specific region, the procedure unit can prioritize displaying the procedure for that region. For example, if the user is traveling, the procedure unit can also display the procedure for the travel destination. For example, if the user is participating in a specific event, the procedure unit can also display the procedure related to that event. In this way, the procedure unit can provide the most suitable procedure based on the user's geographical location information. Some or all of the above processing in the procedure unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the procedure unit can input the user's geographical location data into a generative AI to optimize the progress of the procedure.

[0047] The procedure unit can analyze the user's social media activity during a procedure and support the procedure's progress. For example, the procedure unit can analyze the trends of procedures shared by the user on social media and display similar procedures. For example, the procedure unit can also prioritize displaying procedures from accounts the user follows. For example, the procedure unit can display procedures of interest based on the user's social media activity. This allows the procedure unit to provide the most suitable procedures based on the user's social media activity. Some or all of the above processing in the procedure unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the procedure unit can input the user's social media activity data into a generative AI to support the procedure's progress.

[0048] The negotiation unit can optimize its negotiation algorithm by referring to past negotiation history during negotiations. For example, the negotiation unit can suggest the optimal negotiation method based on the user's past negotiation history. The negotiation unit can also prioritize frequently used negotiation methods based on the user's past negotiation history. The negotiation unit can also analyze the user's past negotiation history to improve negotiation efficiency. This allows the negotiation unit to provide the optimal negotiation method based on past negotiation history. Some or all of the above processes in the negotiation unit may be performed using, for example, generative AI, or without generative AI. For example, the negotiation unit can input past negotiation history data into a generative AI and have the generative AI perform the optimization of the negotiation algorithm.

[0049] The negotiation unit can adjust negotiation priorities based on the user's current situation during negotiations. For example, if the user is in a hurry, the negotiation unit will prioritize the most important negotiations. If the user is relaxed, the negotiation unit can also provide detailed negotiation explanations and proceed with the negotiations accordingly. The negotiation unit can also adjust the negotiation process according to the user's current situation. This allows the negotiation unit to provide negotiation priorities that are appropriate to the user's current situation. Some or all of the above processes in the negotiation unit may be performed using, for example, a generative AI, or not. For example, the negotiation unit can input the user's current situation data into a generative AI and have the generative AI adjust the negotiation priorities.

[0050] The negotiation unit can optimize the negotiation process by taking into account the user's geographical location during negotiations. For example, if the user is in a specific region, the negotiation unit can prioritize displaying negotiation methods for that region. For example, if the user is traveling, the negotiation unit can also display negotiation methods for the travel destination. For example, if the user is participating in a specific event, the negotiation unit can also display negotiations related to that event. This allows the negotiation unit to provide the most suitable negotiation method based on the user's geographical location. Some or all of the above processing in the negotiation unit may be performed using, for example, generative AI, or without generative AI. For example, the negotiation unit can input the user's geographical location data into generative AI to optimize the negotiation process.

[0051] The negotiation unit can analyze the user's social media activity during negotiations and support the progress of those negotiations. For example, the negotiation unit can analyze the trends of negotiations shared by the user on social media and display similar negotiations. The negotiation unit can also prioritize displaying negotiations from accounts that the user follows. The negotiation unit can also display negotiations of interest based on the user's social media activity. This allows the negotiation unit to provide the optimal negotiation method based on the user's social media activity. Some or all of the above processing in the negotiation unit may be performed using, for example, generative AI, or not using generative AI. For example, the negotiation unit can input the user's social media activity data into generative AI to support the progress of negotiations.

[0052] The matching unit can optimize its matching algorithm by referring to past matching history during the matching process. For example, the matching unit can suggest the optimal matching method based on the user's past matching history. The matching unit can also prioritize displaying frequently used matching methods based on the user's past matching history. The matching unit can also analyze the user's past matching history to improve matching efficiency. This allows the matching unit to provide the optimal matching method based on past matching history. Some or all of the above-described processes in the matching unit may be performed using, for example, a generative AI, or without a generative AI. For example, the matching unit can input past matching history data into a generative AI and have the generative AI perform the optimization of the matching algorithm.

[0053] The matching unit can adjust the matching priority based on the user's current situation during the matching process. For example, if the user is in a hurry, the matching unit will prioritize the most important matches. If the user is relaxed, the matching unit can also provide a detailed matching description and proceed with the matching. The matching unit can also adjust how the matching process proceeds according to the user's current situation. This allows the matching unit to provide matching priorities that are appropriate to the user's current situation. Some or all of the above-described processes in the matching unit may be performed using, for example, a generative AI, or without a generative AI. For example, the matching unit can input the user's current situation data into a generative AI and have the generative AI adjust the matching priority.

[0054] The matching unit can optimize the matching process by considering the user's geographical location information during the matching process. For example, if the user is in a specific region, the matching unit can prioritize displaying matching methods for that region. For example, if the user is traveling, the matching unit can also display matching methods for the travel destination. For example, if the user is participating in a specific event, the matching unit can also display matching methods related to that event. This allows the matching unit to provide the most suitable matching method based on the user's geographical location information. Some or all of the above processing in the matching unit may be performed using, for example, a generative AI, or without a generative AI. For example, the matching unit can input the user's geographical location data into a generative AI to optimize the matching process.

[0055] The matching unit can analyze the user's social media activity during the matching process and support the matching process. For example, the matching unit can analyze the trends of matches shared by the user on social media and display similar matches. The matching unit can also prioritize displaying matches from accounts that the user follows. For example, the matching unit can display matches of interest based on the user's social media activity. This allows the matching unit to provide the optimal matching method based on the user's social media activity. Some or all of the above processing in the matching unit may be performed using, for example, a generative AI, or without a generative AI. For example, the matching unit can input the user's social media activity data into a generative AI to support the matching process.

[0056] The tracking unit can optimize the tracking algorithm by referring to past tracking history during tracking. For example, the tracking unit can suggest the optimal tracking method based on the user's past tracking history. The tracking unit can also prioritize displaying frequently used tracking methods based on the user's past tracking history. The tracking unit can also analyze the user's past tracking history to improve tracking efficiency. This allows the tracking unit to provide the optimal tracking method based on past tracking history. Some or all of the above processing in the tracking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the tracking unit can input past tracking history data into a generative AI and have the generative AI perform the optimization of the tracking algorithm.

[0057] The tracking unit can adjust tracking priorities based on the user's current situation during tracking. For example, if the user is in a hurry, the tracking unit will prioritize displaying the most important tracking information. If the user is relaxed, for example, the tracking unit can provide detailed tracking information and proceed with tracking accordingly. The tracking unit can also adjust how tracking proceeds according to the user's current situation. This allows the tracking unit to provide tracking priorities that are appropriate to the user's current situation. Some or all of the above processing in the tracking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the tracking unit can input the user's current situation data into a generative AI and have the generative AI adjust the tracking priorities.

[0058] The tracking unit can optimize the tracking process by considering the user's geographical location information during tracking. For example, if the user is in a specific region, the tracking unit can prioritize displaying the tracking method for that region. For example, if the user is traveling, the tracking unit can also display the tracking method for the travel destination. For example, if the user is participating in a specific event, the tracking unit can also display tracking related to that event. In this way, the tracking unit can provide the optimal tracking method based on the user's geographical location information. Some or all of the above processing in the tracking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the tracking unit can input the user's geographical location data into a generative AI to optimize the tracking process.

[0059] The tracking unit can analyze the user's social media activity during tracking and support the progress of tracking. For example, the tracking unit can analyze the tracking trends of the user's social media shares and display similar tracking. For example, the tracking unit can also prioritize displaying tracking of accounts that the user follows. For example, the tracking unit can display tracking of interest based on the user's social media activity. This allows the tracking unit to provide the optimal tracking method based on the user's social media activity. Some or all of the above processing in the tracking unit may be performed using, for example, generative AI, or without generative AI. For example, the tracking unit can input the user's social media activity data into generative AI to support the progress of tracking.

[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 music license acquisition support system can also include a history reference unit that optimizes the license acquisition process by referring to the user's past license acquisition history. For example, it can suggest the most suitable procedure based on the user's past license acquisition history. It can also prioritize displaying frequently used procedures from the user's past license acquisition history. Furthermore, it can analyze the user's past license acquisition history to improve the efficiency of the procedure. As a result, the history reference unit can provide the most suitable procedure based on the user's past license acquisition history.

[0062] The music licensing support system can also include a project reference section that optimizes the licensing process based on the user's current projects and areas of interest. For example, it can prioritize displaying music that matches the theme of the user's current project. It can also filter and display relevant music based on the user's areas of interest. Furthermore, it can suggest the most suitable music based on the requirements of the project specified by the user. In this way, the project reference section can provide an optimal licensing process based on the user's current projects and areas of interest.

[0063] The music licensing support system can further include a location information reference unit that optimizes the licensing process by considering the user's geographical location. For example, if the user is in a specific region, music from that region can be displayed preferentially. If the user is traveling, music related to the culture of their travel destination can be displayed. Furthermore, if the user is attending a specific event, music related to that event can be displayed. This allows the location information reference unit to provide an optimal licensing process based on the user's geographical location.

[0064] The music licensing support system can also include a social media reference section that analyzes the user's social media activity and optimizes the licensing process. For example, it can analyze the trends of songs the user shares on social media and display similar songs. It can also prioritize displaying new songs from artists the user follows. Furthermore, it can display songs in genres of interest based on the user's social media activity. In this way, the social media reference section can provide an optimal licensing process based on the user's social media activity.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The search unit searches for music licensing information. For example, it presents suitable music and licensing conditions from a vast music database. If an advertising agency is looking for music that fits a specific theme, the AI ​​will search for music that matches those criteria and present the licensing conditions. Step 2: The Procedures Department applies for and pays for the license agreement based on the license terms retrieved by the Search Department. For example, the application and payment for the license agreement can be completed online. This prevents delays and errors in the process, ensuring that licenses are obtained quickly and reliably. Step 3: The Negotiation Department assists in price negotiations with rights holders based on the license agreements made by the Procedures Department. For example, AI proposes a fair price based on past transaction data and supports price negotiations with rights holders. If an advertising agency wants to license music within its budget, the AI ​​proposes a fair price and supports the negotiations. Step 4: The matching department matches creators with rights holders based on price negotiations supported by the negotiation department. For example, AI compares the creator's requests with the rights holder's terms to perform the matching. If a video producer wants to use a specific song, the AI ​​identifies the rights holder of that song and performs the matching. Step 5: The tracking unit tracks the usage of licenses matched by the matching unit. For example, AI manages license periods and conditions to ensure compliance. It also provides notifications before the license period expires and supports the renewal process.

[0067] (Example of form 2) The music license acquisition support system according to an embodiment of the present invention is an AI agent that simplifies the acquisition of licenses for the commercial use of music. This music license acquisition support system centrally manages license information for songs requested by advertising agencies and video producers, and supports appropriate procedures. For example, the music license acquisition support system uses a license information search function to present songs and license conditions that match the purpose from a vast music database. For example, if an advertising agency is looking for songs that fit a specific theme, the AI ​​will search for songs that match those conditions and present the license conditions. Next, the music license acquisition support system uses a procedure automation function to complete the application and payment of license agreements online. This prevents delays and errors in the procedure, and allows for quick and reliable license acquisition. Furthermore, the music license acquisition support system uses a price negotiation support function, where the AI ​​proposes a fair price based on past transaction data and supports price negotiations with rights holders. For example, if an advertising agency wants to license a song within a budget, the AI ​​will propose a fair price and support negotiations. In addition, the music license acquisition support system uses a rights holder matching function, where the AI ​​compares the creator's requests with the conditions offered by the rights holder and performs matching. For example, if a video producer wants to use a specific song, the AI ​​identifies the rights holder of that song and performs a matching process. Finally, the music licensing support system uses a usage tracking function, allowing the AI ​​to manage license periods and conditions and ensure compliance. For example, it notifies users before the license period expires and supports the renewal process. In this way, the music licensing support system simplifies the process of obtaining licenses for commercial use of music, enabling creative projects to proceed smoothly.

[0068] The music license acquisition support system according to this embodiment comprises a search unit, a procedure unit, a negotiation unit, a matching unit, and a tracking unit. The search unit searches for music license information. The search unit presents suitable music and license conditions from a vast music database, for example. For example, if an advertising agency is looking for music that fits a specific theme, the AI ​​in the search unit searches for music that matches the conditions and presents the license conditions. The procedure unit applies for and makes payments for license agreements based on the license conditions found by the search unit. For example, the procedure unit completes the application and payment for license agreements online. This prevents delays and errors in the procedure, enabling quick and reliable license acquisition. The negotiation unit supports price negotiations with rights holders based on the license agreements made by the procedure unit. For example, the AI ​​in the negotiation unit proposes a fair price based on past transaction data and supports price negotiations with rights holders. For example, if an advertising agency wants to license music within a budget, the AI ​​in the negotiation unit proposes a fair price and supports negotiations. The matching unit matches creators with rights holders based on the price negotiations supported by the negotiation unit. The matching unit, for example, uses AI to compare the creator's requests with the rights holder's terms and conditions to perform matching. If a video producer wants to use a specific song, the matching unit's AI identifies the rights holder of that song and performs matching. The tracking unit tracks the usage status of licenses matched by the matching unit. The tracking unit, for example, uses AI to manage license periods and conditions and ensure compliance. The tracking unit, for example, provides notification before the license period expires and supports the renewal process. As a result, the music license acquisition support system according to this embodiment can efficiently perform searching, processing, negotiation, matching, and tracking of song license information.

[0069] The search unit searches for music licensing information. For example, it presents suitable music and licensing conditions from a vast music database. Specifically, the search unit filters the music in the database based on keywords and conditions entered by the user and lists the best candidates. For example, if an advertising agency is looking for music that fits a specific theme, the AI ​​will search for music that matches those conditions and present the licensing conditions. The AI ​​uses natural language processing technology to analyze the user's input and compare it with the music's metadata and tag information. Furthermore, the AI ​​can learn from past search history and user preferences to provide more accurate search results. When presenting search results to the user, the search unit also displays a preview of the music and detailed licensing conditions. This allows the user to compare and consider licensing conditions while reviewing the music content. The search unit also has a function to save search results for later review. This allows the user to compare multiple songs and select the best one. To enhance user convenience, the search unit also provides functions to refine and sort search results. For example, search results can be narrowed down by criteria such as song genre, release year, and artist name. It's also possible to sort search results by popularity or newest release. This allows the search engine to help users efficiently find songs and discover the most suitable licensing conditions.

[0070] The Procedures Department handles license agreement applications and payments based on the license conditions retrieved by the Search Department. For example, the Procedures Department allows users to complete license agreement applications and payments online. Specifically, the Procedures Department verifies the license conditions of the music selected by the user and provides a form for entering the necessary information. The user enters the required information into the form and submits the license agreement application. The Procedures Department automatically verifies the entered information to ensure there are no errors. Once the license agreement application is complete, the Procedures Department provides an interface for payment. Users can pay the license fee using a credit card or electronic payment service. Upon completion of payment, the Procedures Department issues a license agreement confirmation and notifies the user. This prevents delays and errors in the process, ensuring quick and reliable license acquisition. Furthermore, the Procedures Department also has a function to track the progress of the license agreement in real time and notify the user. For example, it sends notifications to the user when the license agreement application is approved or when payment is completed. This allows users to understand the progress of the process and proceed with license acquisition with confidence. Furthermore, the procedures section also provides a function to save a history of license agreements for later reference. This allows users to review past license agreements and reuse them as needed. To enhance user convenience, the procedures section also supports multiple languages ​​and currencies. This allows it to accommodate international users and assist in obtaining global licenses.

[0071] The Negotiation Department assists in price negotiations with rights holders based on license agreements made by the Procedures Department. For example, the Negotiation Department uses AI to propose a fair price based on past transaction data and support price negotiations with rights holders. Specifically, the Negotiation Department uses algorithms to calculate a fair price by analyzing past transaction data and market trends. The AI ​​learns from past transaction data and proposes a fair price by referring to transaction prices under similar conditions. Based on the proposed fair price, the Negotiation Department conducts price negotiations between the user and the rights holder. If a user wants to license music within their budget, the Negotiation Department's AI proposes a fair price and supports the negotiation. For example, if an advertising agency wants to license music within its budget, the AI ​​proposes a fair price based on past transaction data and supports the negotiation with the rights holder. If an agreement is reached between the user and the rights holder, the Negotiation Department finalizes the terms of the license agreement and notifies the Procedures Department. This allows the Negotiation Department to help users license music at a fair price and minimize costs. Furthermore, the Negotiation Department also has a function to track the progress of negotiations in real time and notify the user. For example, the system sends notifications to users when negotiations progress or when an agreement is reached. This allows users to stay informed about the progress of negotiations and proceed with license acquisition with confidence. The negotiation system also provides a function to save negotiation history for later reference. This allows users to review past negotiation history and reuse it as needed. To enhance user convenience, the negotiation system also supports multiple languages ​​and currencies. This allows it to accommodate international users and support global license acquisition.

[0072] The matching department matches creators with rights holders based on price negotiations supported by the negotiation department. For example, the matching department uses AI to compare creator requests with rights holders' terms and conditions. Specifically, the matching department analyzes the requests and conditions entered by creators and compares them with the rights holders' terms and conditions. The AI ​​uses an algorithm to compare creator requests and rights holders' terms and conditions to achieve the best possible match. For example, if a video producer wants to use a specific song, the AI ​​identifies the rights holder of that song and performs the matching. Once a match is made between the creator and the rights holder, the matching department finalizes the terms of the license agreement and notifies the procedures department. This allows the matching department to help creators quickly find songs that meet their needs and streamline the licensing process. Furthermore, the matching department also has a function to track the progress of the matching in real time and notify users. For example, it sends notifications to users when the matching process progresses or when a match is made. This allows users to understand the progress of the matching and proceed with licensing with confidence. The matching department also provides a function to save the matching history for later reference. This allows users to review their past matching history and reuse matches as needed. The matching system also supports multiple languages ​​and currencies to enhance user convenience. This enables it to cater to international users and support global license acquisition.

[0073] The tracking unit tracks the usage of licenses matched by the matching unit. For example, the tracking unit uses AI to manage license periods and conditions, ensuring compliance. Specifically, the tracking unit registers the terms and periods of license agreements in a database and checks them regularly. The AI ​​provides notifications before the license period expires and supports the renewal process. For example, it sends a notification to the user one month before the license expires, prompting them to renew. The tracking unit monitors license usage in real time to check for compliance violations. For example, if usage that violates license conditions is detected, it issues a warning to the user and prompts them to correct the issue. This allows the tracking unit to ensure proper license use and enforce compliance. Furthermore, the tracking unit also has the function of compiling license usage reports and providing them to users. For example, it outputs reports on license usage and renewal history and provides them to users. This allows users to understand their license usage and manage it appropriately. The tracking unit can also analyze license usage to inform future license acquisition decisions. For example, it can predict future license demand based on past usage and develop an appropriate license acquisition plan. The tracking unit also includes features to support multiple languages ​​and currencies to enhance user convenience. This allows it to accommodate international users and support global license management.

[0074] The search unit can present suitable songs and licensing conditions from a vast music database. For example, if an advertising agency is looking for music that fits a specific theme, the AI ​​will search for songs that match the criteria and present the licensing conditions. If a filmmaker is looking for music that fits a specific scene, the AI ​​can also search for songs that match the criteria and present the licensing conditions. If an individual user is looking for music that fits a specific event, the AI ​​can also search for songs that match the criteria and present the licensing conditions. This allows the search unit to efficiently present suitable songs and licensing conditions. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or not. For example, the search unit can input a vast music database into a generative AI and have the generative AI perform the task of presenting suitable songs and licensing conditions.

[0075] The procedures department can complete license agreement applications and payments online. For example, the procedures department can apply for a license agreement online and complete the agreement using an electronic signature. The procedures department can also make license agreement payments using an online payment system. For example, the procedures department can complete license agreement applications and payments online in a single process. This allows the procedures department to apply for and pay for license agreements quickly and reliably. Some or all of the above processes in the procedures department may be performed using, for example, a generative AI, or not using a generative AI. For example, the procedures department can input license agreement application data into a generative AI and have the generative AI automate the application process.

[0076] The negotiation department can propose a fair price based on past transaction data. For example, the negotiation department can use AI to analyze past transaction data and calculate a fair price. The negotiation department can also use AI to propose a fair price based on market prices and past transaction data. The negotiation department can also use AI to propose a fair price based on the creator's budget and conditions. This allows the negotiation department to conduct price negotiations efficiently by proposing a fair price. Some or all of the above processes in the negotiation department may be performed using, for example, a generative AI, or not using a generative AI. For example, the negotiation department can input past transaction data into a generative AI and have the generative AI calculate a fair price.

[0077] The matching unit can perform matching by comparing the creator's requests with the rights holder's terms of service. For example, the matching unit can use AI to compare the creator's requests with the rights holder's terms of service and perform the optimal match. The matching unit can also use AI to compare the rights holder's terms of service based on the creator's intended use and budget. For example, the matching unit can use AI to evaluate the degree of agreement between the creator's requests and the rights holder's terms of service and propose the optimal match. This allows the matching unit to efficiently match the requests and conditions of creators and rights holders. Some or all of the above-described processes in the matching unit may be performed using, for example, a generative AI, or without a generative AI. For example, the matching unit can input creator request data and rights holder's terms of service data into a generative AI and leave the matching process to the generative AI.

[0078] The tracking unit can manage license periods and terms and ensure compliance. For example, the tracking unit can use AI to monitor license periods and send notifications before they expire. The tracking unit can also use AI to manage license terms and check for any violations. The tracking unit can also use AI to reflect changes in license periods and terms in real time and provide the latest information. This allows the tracking unit to manage license periods and terms and ensure compliance. Some or all of the above processes in the tracking unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the tracking unit can input license period and term data into a generative AI and have the generative AI perform management and monitoring.

[0079] The search unit can estimate the user's emotions and adjust how search results are displayed based on the estimated emotions. For example, if the user is stressed, the search unit can display search results with a simple interface to reduce visual burden. If the user is relaxed, the search unit can display search results with more detailed information to broaden the music selection. If the user is in a hurry, the search unit can prioritize displaying the most relevant music to support quick selection. In this way, the search unit can provide a way to display search results that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, for example, or not using AI. For example, the search unit can input user emotion data into a generative AI and have the generative AI adjust how search results are displayed based on emotions.

[0080] The search unit can optimize its search algorithm by referring to the user's past search history during a search. For example, the search unit can analyze trends in songs the user has searched for in the past and prioritize displaying similar songs. For example, the search unit can also suggest songs with the most suitable conditions based on the license conditions the user has searched for in the past. For example, the search unit can prioritize displaying specific genres or artists based on the user's past search history. In this way, the search unit can provide optimal search results based on the user's past search history. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input the user's past search history data into a generative AI and have the generative AI perform the optimization of the search algorithm.

[0081] The search unit can filter search results based on the user's current projects and areas of interest during a search. For example, the search unit may prioritize displaying music that matches the theme of the project the user is currently working on. The search unit can also filter and display relevant music based on the user's areas of interest. The search unit can also suggest the most suitable music based on the requirements of a project specified by the user. In this way, the search unit can provide optimal search results based on the user's current projects and areas of interest. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the search unit can input the user's project data and area of ​​interest data into a generative AI and have the generative AI perform the filtering of search results.

[0082] The search unit can estimate the user's emotions and determine the priority of search results based on the estimated emotions. For example, if the user is stressed, the search unit may prioritize displaying relaxing music. If the user is relaxed, the search unit may also prioritize displaying energetic music. If the user is in a hurry, the search unit may also prioritize displaying the most relevant music. In this way, the search unit can provide search result priorities that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, for example, or not using AI. For example, the search unit can input user emotion data into a generative AI and have the generative AI perform the determination of emotion-based search result priorities.

[0083] The search unit can prioritize displaying highly relevant songs by considering the user's geographical location during a search. For example, if the user is in a specific region, the search unit can prioritize displaying music from that region. For example, if the user is traveling, the search unit can also display music related to the culture of the travel destination. For example, if the user is attending a specific event, the search unit can also display music related to that event. This allows the search unit to provide optimal search results based on the user's geographical location. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input the user's geographical location data into a generative AI and have the generative AI display highly relevant songs.

[0084] The search unit can analyze the user's social media activity during a search and display relevant songs. For example, the search unit can analyze trends in songs shared by the user on social media and display similar songs. For example, the search unit can also prioritize displaying new songs by artists the user follows. For example, the search unit can display songs in genres of interest based on the user's social media activity. This allows the search unit to provide optimal search results based on the user's social media activity. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input the user's social media activity data into a generative AI and have the generative AI display relevant songs.

[0085] The procedure unit can estimate the user's emotions and adjust the procedure's progression based on the estimated emotions. For example, if the user is stressed, the procedure unit may simplify the procedure steps and expedite the process. If the user is relaxed, the procedure unit may provide detailed explanations and support the procedure's progression. If the user is in a hurry, the procedure unit may suggest the shortest procedure route and complete it quickly. In this way, the procedure unit can provide a procedure progression method that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the processing described above in the procedure unit may be performed using AI or not. For example, the procedure unit can input user emotion data into a generative AI and have the generative AI perform the emotion-based adjustment of the procedure's progression.

[0086] The procedure unit can optimize procedures by referring to past procedure history during the procedure. For example, the procedure unit can suggest the optimal procedure method based on the user's past procedure history. The procedure unit can also prioritize displaying frequently used procedures from the user's past procedure history. The procedure unit can also analyze the user's past procedure history to improve procedure efficiency. This allows the procedure unit to provide the optimal procedure based on past procedure history. Some or all of the above processing in the procedure unit may be performed using, for example, a generative AI, or without a generative AI. For example, the procedure unit can input past procedure history data into a generative AI and have the generative AI perform procedure optimization.

[0087] The procedure unit can adjust the priority of procedures based on the user's current situation during the procedure. For example, if the user is in a hurry, the procedure unit will prioritize the most important procedures. For example, if the user is relaxed, the procedure unit may provide detailed procedural explanations and then proceed with the procedures. The procedure unit can also adjust how procedures are carried out according to the user's current situation. This allows the procedure unit to provide procedural priorities that are appropriate to the user's current situation. Some or all of the above processing in the procedure unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the procedure unit can input the user's current situation data into a generative AI and have the generative AI adjust the procedural priorities.

[0088] The procedure unit can estimate the user's emotions and adjust the notification method for the procedure based on the estimated user emotions. For example, if the user is stressed, the procedure unit can provide a simple notification method to reduce visual burden. For example, if the user is relaxed, the procedure unit can provide a detailed notification method to support the progress of the procedure. For example, if the user is in a hurry, the procedure unit can provide a rapid notification method to expedite the progress of the procedure. In this way, the procedure unit can provide a notification method for the procedure that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the procedure unit may be performed using AI, for example, or not using AI. For example, the procedure unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the procedure notification method based on emotions.

[0089] The procedure unit can optimize the progress of a procedure by taking into account the user's geographical location information during the procedure. For example, if the user is in a specific region, the procedure unit can prioritize displaying the procedure for that region. For example, if the user is traveling, the procedure unit can also display the procedure for the travel destination. For example, if the user is participating in a specific event, the procedure unit can also display the procedure related to that event. In this way, the procedure unit can provide the most suitable procedure based on the user's geographical location information. Some or all of the above processing in the procedure unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the procedure unit can input the user's geographical location data into a generative AI to optimize the progress of the procedure.

[0090] The procedure unit can analyze the user's social media activity during a procedure and support the procedure's progress. For example, the procedure unit can analyze the trends of procedures shared by the user on social media and display similar procedures. For example, the procedure unit can also prioritize displaying procedures from accounts the user follows. For example, the procedure unit can display procedures of interest based on the user's social media activity. This allows the procedure unit to provide the most suitable procedures based on the user's social media activity. Some or all of the above processing in the procedure unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the procedure unit can input the user's social media activity data into a generative AI to support the procedure's progress.

[0091] The negotiation unit can estimate the user's emotions and adjust the negotiation process based on those emotions. For example, if the user is stressed, the negotiation unit can simplify the negotiation steps and proceed quickly. If the user is relaxed, the negotiation unit can provide detailed explanations and support the negotiation process. If the user is in a hurry, the negotiation unit can suggest the shortest negotiation route and complete it quickly. In this way, the negotiation unit can provide a negotiation process that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the negotiation unit may be performed using AI or not. For example, the negotiation unit can input user emotion data into a generative AI and have the generative AI adjust the negotiation process based on those emotions.

[0092] The negotiation unit can optimize its negotiation algorithm by referring to past negotiation history during negotiations. For example, the negotiation unit can suggest the optimal negotiation method based on the user's past negotiation history. The negotiation unit can also prioritize frequently used negotiation methods based on the user's past negotiation history. The negotiation unit can also analyze the user's past negotiation history to improve negotiation efficiency. This allows the negotiation unit to provide the optimal negotiation method based on past negotiation history. Some or all of the above processes in the negotiation unit may be performed using, for example, generative AI, or without generative AI. For example, the negotiation unit can input past negotiation history data into a generative AI and have the generative AI perform the optimization of the negotiation algorithm.

[0093] The negotiation unit can adjust negotiation priorities based on the user's current situation during negotiations. For example, if the user is in a hurry, the negotiation unit will prioritize the most important negotiations. If the user is relaxed, the negotiation unit can also provide detailed negotiation explanations and proceed with the negotiations accordingly. The negotiation unit can also adjust the negotiation process according to the user's current situation. This allows the negotiation unit to provide negotiation priorities that are appropriate to the user's current situation. Some or all of the above processes in the negotiation unit may be performed using, for example, a generative AI, or not. For example, the negotiation unit can input the user's current situation data into a generative AI and have the generative AI adjust the negotiation priorities.

[0094] The negotiation unit can estimate the user's emotions and adjust the negotiation notification method based on the estimated user emotions. For example, if the user is stressed, the negotiation unit can provide a simple notification method to reduce visual burden. For example, if the user is relaxed, the negotiation unit can provide a detailed notification method to support the progress of the negotiation. For example, if the user is in a hurry, the negotiation unit can provide a rapid notification method to expedite the negotiation. In this way, the negotiation unit can provide negotiation notification methods that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the negotiation unit may be performed using AI or not using AI. For example, the negotiation unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of negotiation notification methods based on emotions.

[0095] The negotiation unit can optimize the negotiation process by taking into account the user's geographical location during negotiations. For example, if the user is in a specific region, the negotiation unit can prioritize displaying negotiation methods for that region. For example, if the user is traveling, the negotiation unit can also display negotiation methods for the travel destination. For example, if the user is participating in a specific event, the negotiation unit can also display negotiations related to that event. This allows the negotiation unit to provide the most suitable negotiation method based on the user's geographical location. Some or all of the above processing in the negotiation unit may be performed using, for example, generative AI, or without generative AI. For example, the negotiation unit can input the user's geographical location data into generative AI to optimize the negotiation process.

[0096] The negotiation unit can analyze the user's social media activity during negotiations and support the progress of those negotiations. For example, the negotiation unit can analyze the trends of negotiations shared by the user on social media and display similar negotiations. The negotiation unit can also prioritize displaying negotiations from accounts that the user follows. The negotiation unit can also display negotiations of interest based on the user's social media activity. This allows the negotiation unit to provide the optimal negotiation method based on the user's social media activity. Some or all of the above processing in the negotiation unit may be performed using, for example, generative AI, or not using generative AI. For example, the negotiation unit can input the user's social media activity data into generative AI to support the progress of negotiations.

[0097] The matching unit can estimate the user's emotions and adjust the matching process based on the estimated emotions. For example, if the user is stressed, the matching unit can proceed with matching using a simple interface to reduce visual burden. If the user is relaxed, the matching unit can also proceed with matching that includes detailed information to broaden the options. If the user is in a hurry, the matching unit can prioritize the most relevant matches to support quick selection. In this way, the matching unit can provide a matching process that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the matching unit may be performed using AI or not. For example, the matching unit can input user emotion data into a generative AI and have the generative AI adjust the matching process based on emotions.

[0098] The matching unit can optimize its matching algorithm by referring to past matching history during the matching process. For example, the matching unit can suggest the optimal matching method based on the user's past matching history. The matching unit can also prioritize displaying frequently used matching methods based on the user's past matching history. The matching unit can also analyze the user's past matching history to improve matching efficiency. This allows the matching unit to provide the optimal matching method based on past matching history. Some or all of the above-described processes in the matching unit may be performed using, for example, a generative AI, or without a generative AI. For example, the matching unit can input past matching history data into a generative AI and have the generative AI perform the optimization of the matching algorithm.

[0099] The matching unit can adjust the matching priority based on the user's current situation during the matching process. For example, if the user is in a hurry, the matching unit will prioritize the most important matches. If the user is relaxed, the matching unit can also provide a detailed matching description and proceed with the matching. The matching unit can also adjust how the matching process proceeds according to the user's current situation. This allows the matching unit to provide matching priorities that are appropriate to the user's current situation. Some or all of the above-described processes in the matching unit may be performed using, for example, a generative AI, or without a generative AI. For example, the matching unit can input the user's current situation data into a generative AI and have the generative AI adjust the matching priority.

[0100] The matching unit can estimate the user's emotions and adjust the matching notification method based on the estimated user emotions. For example, if the user is stressed, the matching unit can provide a simple notification method to reduce visual burden. For example, if the user is relaxed, the matching unit can provide a detailed notification method to support the matching process. For example, if the user is in a hurry, the matching unit can provide a rapid notification method to expedite the matching process. In this way, the matching unit can provide matching notification methods that are appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the matching unit may be performed using AI, for example, or not using AI. For example, the matching unit can input user emotion data into a generative AI and have the generative AI perform adjustments to the matching notification method based on emotions.

[0101] The matching unit can optimize the matching process by considering the user's geographical location information during the matching process. For example, if the user is in a specific region, the matching unit can prioritize displaying matching methods for that region. For example, if the user is traveling, the matching unit can also display matching methods for the travel destination. For example, if the user is participating in a specific event, the matching unit can also display matching methods related to that event. This allows the matching unit to provide the most suitable matching method based on the user's geographical location information. Some or all of the above processing in the matching unit may be performed using, for example, a generative AI, or without a generative AI. For example, the matching unit can input the user's geographical location data into a generative AI to optimize the matching process.

[0102] The matching unit can analyze the user's social media activity during the matching process and support the matching process. For example, the matching unit can analyze the trends of matches shared by the user on social media and display similar matches. The matching unit can also prioritize displaying matches from accounts that the user follows. For example, the matching unit can display matches of interest based on the user's social media activity. This allows the matching unit to provide the optimal matching method based on the user's social media activity. Some or all of the above processing in the matching unit may be performed using, for example, a generative AI, or without a generative AI. For example, the matching unit can input the user's social media activity data into a generative AI to support the matching process.

[0103] The tracking unit can estimate the user's emotions and adjust how the tracking is displayed based on the estimated emotions. For example, if the user is stressed, the tracking unit can display tracking information in a simple interface to reduce visual burden. For example, if the user is relaxed, the tracking unit can display tracking information with more detailed information to broaden the user's options. For example, if the user is in a hurry, the tracking unit can prioritize displaying the most important tracking information to support quick confirmation. In this way, the tracking unit can provide a way to display tracking that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the tracking unit may be performed using AI or not using AI. For example, the tracking unit can input user emotion data into a generative AI and have the generative AI perform adjustments to how the tracking is displayed based on emotions.

[0104] The tracking unit can optimize the tracking algorithm by referring to past tracking history during tracking. For example, the tracking unit can suggest the optimal tracking method based on the user's past tracking history. The tracking unit can also prioritize displaying frequently used tracking methods based on the user's past tracking history. The tracking unit can also analyze the user's past tracking history to improve tracking efficiency. This allows the tracking unit to provide the optimal tracking method based on past tracking history. Some or all of the above processing in the tracking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the tracking unit can input past tracking history data into a generative AI and have the generative AI perform the optimization of the tracking algorithm.

[0105] The tracking unit can adjust tracking priorities based on the user's current situation during tracking. For example, if the user is in a hurry, the tracking unit will prioritize displaying the most important tracking information. If the user is relaxed, for example, the tracking unit can provide detailed tracking information and proceed with tracking accordingly. The tracking unit can also adjust how tracking proceeds according to the user's current situation. This allows the tracking unit to provide tracking priorities that are appropriate to the user's current situation. Some or all of the above processing in the tracking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the tracking unit can input the user's current situation data into a generative AI and have the generative AI adjust the tracking priorities.

[0106] The tracking unit can estimate the user's emotions and adjust the tracking notification method based on the estimated user emotions. For example, if the user is stressed, the tracking unit can provide a simple notification method to reduce visual burden. For example, if the user is relaxed, the tracking unit can provide a detailed notification method to support the progress of tracking. For example, if the user is in a hurry, the tracking unit can provide a rapid notification method to expedite the tracking process. In this way, the tracking unit can provide tracking notification methods that are appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input user emotion data into a generative AI and have the generative AI perform adjustments to the tracking notification method based on emotions.

[0107] The tracking unit can optimize the tracking process by considering the user's geographical location information during tracking. For example, if the user is in a specific region, the tracking unit can prioritize displaying the tracking method for that region. For example, if the user is traveling, the tracking unit can also display the tracking method for the travel destination. For example, if the user is participating in a specific event, the tracking unit can also display tracking related to that event. In this way, the tracking unit can provide the optimal tracking method based on the user's geographical location information. Some or all of the above processing in the tracking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the tracking unit can input the user's geographical location data into a generative AI to optimize the tracking process.

[0108] The tracking unit can analyze the user's social media activity during tracking and support the progress of tracking. For example, the tracking unit can analyze the tracking trends of the user's social media shares and display similar tracking. For example, the tracking unit can also prioritize displaying tracking of accounts that the user follows. For example, the tracking unit can display tracking of interest based on the user's social media activity. This allows the tracking unit to provide the optimal tracking method based on the user's social media activity. Some or all of the above processing in the tracking unit may be performed using, for example, generative AI, or without generative AI. For example, the tracking unit can input the user's social media activity data into generative AI to support the progress of tracking.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The music licensing support system can also include an emotion estimation unit that estimates the user's emotions and optimizes the licensing process based on those emotions. For example, if the user is stressed, the emotion estimation unit can simplify the procedural steps and adjust them to proceed quickly. If the user is relaxed, it can provide detailed explanations and support the process. Furthermore, if the user is in a hurry, it can suggest the shortest procedural route and complete it quickly. In this way, the emotion estimation unit can provide an optimal licensing process tailored to the user's emotions.

[0111] The music license acquisition support system can also include a history reference unit that optimizes the license acquisition process by referring to the user's past license acquisition history. For example, it can suggest the most suitable procedure based on the user's past license acquisition history. It can also prioritize displaying frequently used procedures from the user's past license acquisition history. Furthermore, it can analyze the user's past license acquisition history to improve the efficiency of the procedure. As a result, the history reference unit can provide the most suitable procedure based on the user's past license acquisition history.

[0112] The music licensing support system can also include a project reference section that optimizes the licensing process based on the user's current projects and areas of interest. For example, it can prioritize displaying music that matches the theme of the user's current project. It can also filter and display relevant music based on the user's areas of interest. Furthermore, it can suggest the most suitable music based on the requirements of the project specified by the user. In this way, the project reference section can provide an optimal licensing process based on the user's current projects and areas of interest.

[0113] The music licensing support system can further include a location information reference unit that optimizes the licensing process by considering the user's geographical location. For example, if the user is in a specific region, music from that region can be displayed preferentially. If the user is traveling, music related to the culture of their travel destination can be displayed. Furthermore, if the user is attending a specific event, music related to that event can be displayed. This allows the location information reference unit to provide an optimal licensing process based on the user's geographical location.

[0114] The music licensing support system can also include a social media reference section that analyzes the user's social media activity and optimizes the licensing process. For example, it can analyze the trends of songs the user shares on social media and display similar songs. It can also prioritize displaying new songs from artists the user follows. Furthermore, it can display songs in genres of interest based on the user's social media activity. In this way, the social media reference section can provide an optimal licensing process based on the user's social media activity.

[0115] The music licensing support system can further include an emotion estimation unit that estimates the user's emotions and adjusts the display method of search results based on the estimated emotions. For example, if the user is stressed, the search results can be displayed with a simple interface to reduce visual burden. If the user is relaxed, the search results can be displayed with more detailed information to broaden the range of music options. Furthermore, if the user is in a hurry, the most relevant songs can be prioritized to support quick selection. In this way, the emotion estimation unit can provide a search result display method that is tailored to the user's emotions.

[0116] The music licensing support system can further include an emotion estimation unit that estimates the user's emotions and adjusts the procedure based on those emotions. For example, if the user is stressed, the procedure steps can be simplified and the process can be expedited. If the user is relaxed, detailed explanations can be provided to support the procedure. Furthermore, if the user is in a hurry, the shortest procedure route can be suggested to allow for quick completion. In this way, the emotion estimation unit can provide a procedure that is tailored to the user's emotions.

[0117] The music licensing support system can further include an emotion estimation unit that estimates the user's emotions and adjusts the negotiation process based on those emotions. For example, if the user is stressed, the negotiation steps can be simplified and the process can be expedited. If the user is relaxed, detailed explanations can be provided to support the negotiation process. Furthermore, if the user is in a hurry, the shortest negotiation route can be suggested to complete it quickly. In this way, the emotion estimation unit can provide a negotiation process that is tailored to the user's emotions.

[0118] The music licensing support system can also include an emotion estimation unit that estimates the user's emotions and adjusts the matching process based on those emotions. For example, if the user is stressed, the matching process can proceed with a simple interface to reduce visual burden. If the user is relaxed, the matching process can proceed with more detailed information to broaden the options. Furthermore, if the user is in a hurry, the system can prioritize the most relevant matches to support quick selection. In this way, the emotion estimation unit can provide a matching process that is tailored to the user's emotions.

[0119] The music licensing support system can also include an emotion estimation unit that estimates the user's emotions and adjusts the tracking display method based on the estimated emotions. For example, if the user is stressed, tracking information can be displayed with a simple interface to reduce visual burden. If the user is relaxed, tracking information including detailed information can be displayed to broaden their options. Furthermore, if the user is in a hurry, the most important tracking information can be prioritized to support quick confirmation. In this way, the emotion estimation unit can provide a tracking display method that is appropriate to the user's emotions.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The search unit searches for music licensing information. For example, it presents suitable music and licensing conditions from a vast music database. If an advertising agency is looking for music that fits a specific theme, the AI ​​will search for music that matches those criteria and present the licensing conditions. Step 2: The Procedures Department applies for and pays for the license agreement based on the license terms retrieved by the Search Department. For example, the application and payment for the license agreement can be completed online. This prevents delays and errors in the process, ensuring that licenses are obtained quickly and reliably. Step 3: The Negotiation Department assists in price negotiations with rights holders based on the license agreements made by the Procedures Department. For example, AI proposes a fair price based on past transaction data and supports price negotiations with rights holders. If an advertising agency wants to license music within its budget, the AI ​​proposes a fair price and supports the negotiations. Step 4: The matching department matches creators with rights holders based on price negotiations supported by the negotiation department. For example, AI compares the creator's requests with the rights holder's terms to perform the matching. If a video producer wants to use a specific song, the AI ​​identifies the rights holder of that song and performs the matching. Step 5: The tracking unit tracks the usage of licenses matched by the matching unit. For example, AI manages license periods and conditions to ensure compliance. It also provides notifications before the license period expires and supports the renewal process.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the search unit, procedure unit, negotiation unit, matching unit, and tracking unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the search unit is implemented by the control unit 46A of the smart device 14 and presents suitable music and license conditions from a vast music database. The procedure unit is implemented by the specific processing unit 290 of the data processing unit 12 and completes the application and payment of license agreements online. The negotiation unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a fair price based on past transaction data and supports price negotiations with rights holders. The matching unit is implemented by the control unit 46A of the smart device 14 and performs matching by comparing the creator's requests with the conditions offered by rights holders. The tracking unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages license periods and conditions to ensure compliance. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the search unit, procedure unit, negotiation unit, matching unit, and tracking unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the search unit is implemented by the control unit 46A of the smart glasses 214 and presents suitable music and license conditions from a vast music database. The procedure unit is implemented by the specific processing unit 290 of the data processing unit 12 and completes the application and payment of license agreements online. The negotiation unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a fair price based on past transaction data and supports price negotiations with rights holders. The matching unit is implemented by the control unit 46A of the smart glasses 214 and performs matching by comparing the creator's requests with the conditions offered by rights holders. The tracking unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages license periods and conditions to ensure compliance. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the search unit, procedure unit, negotiation unit, matching unit, and tracking unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the search unit is implemented by the control unit 46A of the headset terminal 314 and presents suitable music and license conditions from a vast music database. The procedure unit is implemented by the specific processing unit 290 of the data processing unit 12 and completes the application and payment of license agreements online. The negotiation unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a fair price based on past transaction data and supports price negotiations with rights holders. The matching unit is implemented by the control unit 46A of the headset terminal 314 and performs matching by comparing the creator's requests with the conditions offered by rights holders. The tracking unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages license periods and conditions to ensure compliance. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] Each of the multiple elements described above, including the search unit, procedure unit, negotiation unit, matching unit, and tracking unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the search unit is implemented by the control unit 46A of the robot 414 and presents suitable music and license conditions from a vast music database. The procedure unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and completes the application and payment of license agreements online. The negotiation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes a fair price based on past transaction data and supports price negotiations with rights holders. The matching unit is implemented by, for example, the control unit 46A of the robot 414 and performs matching by comparing the creator's requests with the conditions offered by rights holders. The tracking unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and manages license periods and conditions to ensure compliance. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] (Note 1) A search unit for searching song licensing information, A procedure unit that handles license agreement applications and payments based on the license conditions retrieved by the aforementioned search unit, A negotiation department that assists in price negotiations with rights holders based on license agreements made by the aforementioned procedural department, Based on price negotiations supported by the aforementioned negotiation department, the matching department matches creators with rights holders. The system includes a tracking unit that tracks the usage status of licenses matched by the matching unit. A system characterized by the following features. (Note 2) The aforementioned search unit, We present songs and licensing conditions that suit your purpose from our vast music database. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned procedural department, Complete license agreement applications and payments online. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned negotiating body said, We propose a fair price based on past transaction data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The matching unit is We compare the creator's requests with the rights holder's terms of service to facilitate matching. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned tracking unit is Manage license periods and terms, and ensure strict compliance. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned search unit, It estimates the user's sentiment and adjusts how search results are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned search unit, The search algorithm is optimized by referencing the user's past search history during the search process. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned search unit, Filter search results based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned search unit, It estimates the user's emotions and determines the priority of search results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned search unit, When searching, the system prioritizes displaying songs that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned search unit, The system analyzes the user's social media activity during searches and displays relevant songs. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned procedural department, It estimates the user's emotions and adjusts the procedure based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned procedural department, The system optimizes procedures by referencing past procedure history during the process. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned procedural department, The priority of procedures will be adjusted based on the user's current status during the process. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned procedural department, The system estimates the user's emotions and adjusts the notification method for procedures based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned procedural department, The system optimizes the process by taking into account the user's geographical location during the procedure. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned procedural department, Analyze the user's social media activity during the process and support the progress of the procedure. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned negotiating body said, It estimates the user's emotions and adjusts the negotiation process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned negotiating body said, During negotiations, the negotiation algorithm is optimized by referring to past negotiation history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned negotiating body said, Prioritize negotiations based on the user's current situation during negotiations. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned negotiating body said, The system estimates the user's emotions and adjusts the negotiation notification method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned negotiating body said, The system optimizes the negotiation process by considering the user's geographical location during negotiations. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned negotiating body said, Analyze users' social media activity during negotiations to support the progress of those negotiations. The system described in Appendix 1, characterized by the features described herein. (Note 25) The matching unit is It estimates the user's emotions and adjusts the matching process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The matching unit is The matching algorithm is optimized by referring to past matching history during the matching process. The system described in Appendix 1, characterized by the features described herein. (Note 27) The matching unit is The matching process adjusts the matching priority based on the user's current status. The system described in Appendix 1, characterized by the features described herein. (Note 28) The matching unit is It estimates the user's emotions and adjusts the matching notification method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The matching unit is The system optimizes the matching process by taking into account the user's geographical location during the matching process. The system described in Appendix 1, characterized by the features described herein. (Note 30) The matching unit is Analyzes users' social media activity during the matching process to support the matching process. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned tracking unit is It estimates the user's emotions and adjusts how tracking is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned tracking unit is During tracking, the tracking algorithm is optimized by referring to past tracking history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned tracking unit is Prioritize tracking based on the user's current status during tracking. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned tracking unit is It estimates the user's emotions and adjusts the tracking notification method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned tracking unit is The tracking process is optimized by taking into account the user's geographical location during tracking. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned tracking unit is Analyzes users' social media activity during tracking and supports the tracking process. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0194] 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 search unit for searching song licensing information, A procedure unit that handles license agreement applications and payments based on the license conditions retrieved by the aforementioned search unit, A negotiation department that assists in price negotiations with rights holders based on license agreements made by the aforementioned procedural department, Based on price negotiations supported by the aforementioned negotiation department, the matching department matches creators with rights holders. The system includes a tracking unit that tracks the usage status of licenses matched by the matching unit. A system characterized by the following features.

2. The aforementioned search unit, We present songs and licensing conditions that suit your purpose from our vast music database. The system according to feature 1.

3. The aforementioned procedural department, Complete license agreement applications and payments online. The system according to feature 1.

4. The aforementioned negotiating body said, We propose a fair price based on past transaction data. The system according to feature 1.

5. The matching unit is We compare the creator's requests with the rights holder's terms of service to facilitate matching. The system according to feature 1.

6. The aforementioned tracking unit is Manage license periods and terms, and ensure strict compliance. The system according to feature 1.

7. The aforementioned search unit, It estimates the user's sentiment and adjusts how search results are displayed based on that estimated sentiment. The system according to feature 1.

8. The aforementioned search unit, The search algorithm is optimized by referencing the user's past search history during the search process. The system according to feature 1.

9. The aforementioned search unit, Filter search results based on the user's current projects and areas of interest. The system according to feature 1.

10. The aforementioned search unit, It estimates the user's emotions and determines the priority of search results based on those estimated emotions. The system according to feature 1.

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