system

Abstract

The system according to this embodiment aims to match users with each other based on their musical preferences and activity data, and to promote interaction between them. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a matching unit, and an interaction unit. The collection unit collects users' musical preferences and activity data. The analysis unit analyzes the data collected by the collection unit in real time. The matching unit matches users with common musical tastes based on the analysis results obtained by the analysis unit. The interaction unit allows users matched by the matching unit to interact with each other, such as through live chat, playlist sharing, and real-time recommendations.

Description

Technical Field

[0006] , , ,

[0005] , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, users have not been sufficiently matched effectively based on music preferences and activity data to promote interaction, and there is room for improvement.

[0005] The system according to the embodiment aims to match users based on music preferences and activity data and promote interaction.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a matching unit, and an interaction unit. The data collection unit collects users' musical preferences and activity data. The analysis unit analyzes the data collected by the data collection unit in real time. The matching unit matches users with common musical tastes based on the analysis results obtained by the analysis unit. The interaction unit enables users matched by the matching unit to interact with each other, such as through live chat, playlist sharing, and real-time recommendations. [Effects of the Invention]

[0007] The system according to this embodiment can match users with each other based on their musical preferences and activity data, thereby promoting interaction. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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 matching system according to an embodiment of the present invention is a system that matches users of a music streaming service based on their musical preferences and activity data, and forms communities among users who share common musical tastes. This music matching system collects users' musical preferences and activity data, and an AI analyzes this data in real time. Next, based on the analysis results, it matches users who share common musical tastes with each other. This allows users to deepen their interactions through music through interactions such as live chat, playlist sharing, and real-time recommendations. First, the music matching system collects users' musical preferences and activity data. For example, it collects data such as which songs users have played, which artists they follow, and which playlists they have created. This allows the music matching system to understand the users' musical preferences. Next, the music matching system's AI analyzes the collected data in real time. The AI ​​analyzes users' musical preferences and activity data and matches users who share common musical tastes with each other. For example, it can match users who follow the same artists. Matched users can deepen their interactions through interactions such as live chat, playlist sharing, and real-time recommendations. For example, through live chat, users with shared musical tastes can communicate in real time. They can also share their favorite songs through playlist sharing. Furthermore, real-time recommendations allow them to introduce new songs and artists to each other. This mechanism allows music matching systems to deepen user interaction through music and enrich the musical experience. For instance, users with shared musical tastes can form communities and share information about music, leading to the discovery of new music and a deeper understanding of music. Additionally, interactions such as live chat, playlist sharing, and real-time recommendations strengthen connections between users and promote social connections through music.This allows the music matching system to facilitate interaction by matching users with shared musical tastes based on their musical preferences and activity data.

[0029] The music matching system according to this embodiment comprises a collection unit, an analysis unit, a matching unit, and an interaction unit. The collection unit collects user music preferences and activity data. For example, the collection unit collects data such as which songs the user has played, which artists the user follows, and which playlists the user has created. By collecting this data, the collection unit can understand the user's music preferences. For example, the collection unit can collect playback history to understand which songs the user plays frequently. The collection unit can collect data on followed artists to understand which artists the user is interested in. The collection unit can collect data on created playlists to understand what kind of playlists the user is creating. The analysis unit analyzes the data collected by the collection unit in real time. For example, the analysis unit analyzes user music preferences and activity data and matches users with common musical tastes. For example, the analysis unit can match users who follow the same artists. The analysis unit can match users who like the same genre of music. The analysis unit can match users who have created the same playlists. The matching unit matches users with shared musical tastes based on the analysis results obtained by the analysis unit. For example, the matching unit can match users who follow the same artists, users who like the same genre of music, or users who have created the same playlists. The interaction unit provides users matched by the matching unit with opportunities to interact, such as live chat, playlist sharing, and real-time recommendations. For example, the interaction unit allows users with shared musical tastes to communicate in real time through live chat, share their favorite songs through playlist sharing, and introduce new songs and artists to each other through real-time recommendations.As a result, the music matching system according to this embodiment can match users with common musical tastes based on their musical preferences and activity data, thereby promoting interaction.

[0030] The data collection unit collects user musical preferences and activity data. Specifically, it collects data such as which songs users play, which artists they follow, and which playlists they create. This allows for a detailed understanding of users' musical preferences. For example, the data collection unit collects users' playback history to understand which songs they play frequently. This allows for the identification of songs and artists that users particularly like. The data collection unit also collects data on artists that users follow to understand which artists users are interested in. Furthermore, the data collection unit collects data on playlists that users create to understand what kind of playlists they create. This allows for a detailed analysis of users' musical preferences and trends. The data collection unit can centrally manage and update this data in real time. For example, if a user plays a new song, follows a new artist, or creates a new playlist, that data is collected and reflected in the system. This allows the data collection unit to always understand users' musical preferences based on the latest data. In addition, the data collection unit can anonymize user data and take measures to protect privacy. This allows users to use the system with peace of mind.

[0031] The analysis unit analyzes the data collected by the collection unit in real time. Specifically, it analyzes users' musical preferences and activity data and matches users with similar musical tastes. For example, the analysis unit can match users who follow the same artists, allowing users interested in the same artists to connect. It can also match users who prefer the same genre of music, allowing users interested in the same genre to connect. Furthermore, the analysis unit can match users who have created the same playlists, allowing users who create the same playlists to connect. The analysis unit uses AI to analyze this data, providing a detailed analysis of users' musical preferences and activity data. For example, the AI ​​analyzes users' playback history and followed artists to identify users with similar musical tastes. The AI ​​also analyzes users' playlist data to identify users who create the same playlists. As a result, the analysis unit can analyze users' musical preferences and activity data in detail and match users with similar musical tastes. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term trends and patterns. This allows the analysis unit to analyze users' musical preferences and activity data in detail and match users with similar musical tastes.

[0032] The matching unit matches users with shared musical tastes based on the analysis results obtained by the analysis unit. Specifically, it can match users who follow the same artists, allowing users interested in the same artists to connect. It can also match users who prefer the same genre of music, allowing users interested in the same genre to connect. Furthermore, it can match users who create the same playlists, allowing users who create the same playlists to connect. The matching unit uses AI to analyze this data, conducting a detailed analysis of users' musical preferences and activity data. For example, the AI ​​analyzes users' playback history and followed artists to identify users with shared musical tastes. The AI ​​also analyzes users' playlist data to identify users who create the same playlists. As a result, the matching unit can conduct a detailed analysis of users' musical preferences and activity data, enabling it to match users with shared musical tastes. In addition, the matching unit can utilize past data and statistical information to analyze long-term trends and patterns. This allows the matching unit to analyze users' musical preferences and activity data in detail, enabling it to match users with similar musical tastes.

[0033] The Interaction Department provides users matched by the Matching Department with features such as live chat, playlist sharing, and real-time recommendations. Specifically, live chat allows users with shared musical tastes to communicate in real time, enabling them to exchange opinions and share information about music. Playlist sharing allows users to share their favorite songs, helping them discover new songs and artists. Real-time recommendations allow users to introduce new songs and artists to each other, enabling them to discover and enjoy new music. By providing these features, the Interaction Department promotes user interaction and revitalizes communication through music. Furthermore, the Interaction Department can collect user feedback and make improvements to enhance the quality of interaction. For example, based on user feedback, it can improve the live chat function or expand the playlist sharing function. In this way, the Interaction Department can further enrich user interaction and revitalize communication through music.

[0034] The data collection unit can collect data such as which songs a user has played, which artists they follow, and which playlists they have created. For example, the data collection unit can collect which songs a user has played. The data collection unit can collect which artists a user follows. The data collection unit can collect which playlists a user has created. This allows the data collection unit to collect detailed data on the user's musical preferences and activities. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's playback history data into a generating AI, which can then analyze the playback history data to understand the user's musical preferences.

[0035] The analysis unit can analyze the collected data in real time and match users who share common musical tastes. For example, the analysis unit analyzes the collected data in real time. The analysis unit can match users who share common musical tastes. This allows the analysis unit to analyze data in real time and match users who share common musical tastes. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input the collected data into a generating AI, which can analyze the data and match users who share common musical tastes.

[0036] The interaction unit can provide real-time communication between users who share a common musical taste through live chat. The interaction unit can, for example, enable real-time communication between users who share a common musical taste through live chat. This allows the interaction unit to enable real-time communication between users who share a common musical taste. Some or all of the above-described processing in the interaction unit may be performed using AI, or not. For example, the interaction unit can input live chat data into a generating AI, which can then analyze the data to support communication between users.

[0037] The interaction unit can provide the ability to share each other's favorite songs through playlist sharing. For example, the interaction unit can share each other's favorite songs through playlist sharing. This allows users to share playlists and their favorite songs with each other. Some or all of the above processing in the interaction unit may be performed using AI, or not. For example, the interaction unit can input playlist sharing data into a generating AI, which can then analyze the data to support playlist sharing between users.

[0038] The interaction unit can provide a way for users to share new songs and artists through real-time recommendations. For example, the interaction unit can share new songs and artists through real-time recommendations. This allows users to share new songs and artists with each other in real time. Some or all of the above processing in the interaction unit may be performed using AI, or not. For example, the interaction unit can input real-time recommendation data into a generating AI, which can then analyze the data to support user recommendations.

[0039] The collection unit can analyze the user's past music playback history and select the optimal collection method. For example, the collection unit can analyze the genres of songs the user has played in the past and collect new songs of the same genre. The collection unit can prioritize collecting new songs by artists the user has followed in the past. The collection unit can analyze the trends of playlists the user has created in the past and collect songs with similar trends. In this way, the collection unit can select the optimal collection method based on the user's past music playback history. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's past playback history data into a generating AI, which can analyze the data and select the optimal collection method.

[0040] The data collection unit can filter music data based on the user's current activity and areas of interest. For example, if the user is exercising, the data collection unit can collect music data with a tempo suitable for exercise. If the user is studying, the data collection unit can collect music data that enhances concentration. If the user is relaxing, the data collection unit can collect music data that promotes relaxation. This allows the data collection unit to filter music data based on the user's current activity and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user activity data into a generating AI, which can then analyze and filter the data.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting music data. For example, if the user is in a specific region, the data collection unit can collect popular songs from that region. If the user is traveling, the data collection unit can collect music from their travel destination. If the user is attending a specific event, the data collection unit can collect music related to that event. In this way, the data collection unit can collect highly relevant music data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI, which can then analyze the data and collect highly relevant data.

[0042] The data collection unit can analyze the user's social media activity and collect relevant data when collecting music data. For example, the data collection unit can collect relevant music data based on songs the user has shared on social media. The data collection unit can collect new songs from artists the user follows. The data collection unit can collect songs related to music events the user has attended. In this way, the data collection unit can collect relevant music data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI, which can then analyze the data and collect relevant music data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the music data during the analysis. For example, the analysis unit can perform a detailed analysis on songs that the user frequently plays. The analysis unit can perform a detailed analysis on songs by artists that the user follows. The analysis unit can perform a detailed analysis on songs included in playlists created by the user. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the music data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the music data into a generating AI, and the generating AI can analyze the data and adjust the level of detail.

[0044] The analysis unit can apply different analysis algorithms depending on the music category during analysis. For example, in the case of classical music, the analysis unit can perform analysis based on the structure of the piece and performance techniques. In the case of pop music, the analysis unit can perform analysis based on the catchiness of the lyrics and melody. In the case of jazz music, the analysis unit can perform analysis based on the elements of improvisation. This allows the analysis unit to apply different analysis algorithms depending on the music category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input music category data into a generating AI, which can analyze the data and apply an appropriate algorithm.

[0045] The analysis unit can determine the priority of analysis based on the collection date of the music data during the analysis. For example, the analysis unit may prioritize the analysis of the most recent music data. The analysis unit may prioritize the analysis of songs recently played by the user. The analysis unit may prioritize the analysis of songs by artists recently followed by the user. This allows the analysis unit to determine the priority of analysis based on the collection date of the music data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input music data collection date data into a generating AI, which can then analyze the data and determine the priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the music data during analysis. For example, the analysis unit can determine the order of analysis based on the relevance of songs played by the user. The analysis unit can determine the order of analysis based on the relevance of artists followed by the user. The analysis unit can determine the order of analysis based on the relevance of playlists created by the user. In this way, the analysis unit can adjust the order of analysis based on the relevance of the music data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input music data relevance data into a generating AI, and the generating AI can analyze the data and adjust the order.

[0047] The matching unit can improve the accuracy of matching by considering the interrelationships of music data during the matching process. For example, the matching unit can match users who follow the same artist with each other. The matching unit can match users who like the same genre of music with each other. The matching unit can match users who have created the same playlist with each other. In this way, the matching unit can improve the accuracy of matching by considering the interrelationships of music data. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input data on the interrelationships of music data into a generating AI, which can then analyze the data to improve the accuracy of matching.

[0048] The matching unit can perform matching while considering user attribute information. For example, the matching unit can match users of similar ages. The matching unit can match users of the same gender. The matching unit can match users of similar residential areas. In this way, the matching unit can perform more appropriate matching by considering user attribute information. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input user attribute information data into a generating AI, and the generating AI can analyze the data and perform matching.

[0049] The matching unit can perform matching while considering the geographical distribution of music data. For example, the matching unit can match users who live in the same region. The matching unit can match users who live in the same country. The matching unit can match users who live in the same city. By considering the geographical distribution of music data, the matching unit can perform more appropriate matching. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input geographical distribution data of music data into a generating AI, and the generating AI can analyze the data and perform matching.

[0050] The matching unit can improve the accuracy of matching by referring to relevant literature for music data during the matching process. For example, the matching unit can improve the accuracy of matching by referring to papers related to music data. The matching unit can improve the accuracy of matching by referring to books related to music data. The matching unit can improve the accuracy of matching by referring to articles related to music data. In this way, the matching unit can improve the accuracy of matching by referring to relevant literature for music data. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input relevant literature data for music data into a generating AI, and the generating AI can analyze the data to improve the accuracy of matching.

[0051] The interaction unit can select the optimal display method by referring to the user's past operation history during interaction. For example, the interaction unit can prioritize providing display methods that the user has preferred in the past. The interaction unit can also exclude display methods that the user has avoided in the past. The interaction unit can suggest the optimal display method based on the user's past operation history. In this way, the interaction unit can select the optimal display method based on the user's past operation history. Some or all of the above processing in the interaction unit may be performed using AI, for example, or without AI. For example, the interaction unit can input user operation history data into a generating AI, and the generating AI can analyze the data and select the optimal display method.

[0052] The interaction unit can customize the means of interaction based on the user's current activity during interaction. For example, if the user is exercising, the interaction unit can provide audio interaction. If the user is studying, the interaction unit can provide visual interaction. If the user is relaxed, the interaction unit can provide gentle interaction. In this way, the interaction unit can customize the means of interaction based on the user's current activity. Some or all of the above processing in the interaction unit may be performed using AI, for example, or without AI. For example, the interaction unit can input user activity data into a generating AI, which can then analyze the data to customize the means of interaction.

[0053] The interaction unit can select the optimal interaction method by considering the user's geographical location information during interaction. For example, if the user is in a specific region, the interaction unit can provide interactions related to the music of that region. If the user is traveling, the interaction unit can provide interactions related to the music of the travel destination. If the user is participating in a specific event, the interaction unit can provide interactions related to that event. In this way, the interaction unit can select the optimal interaction method by considering the user's geographical location information. Some or all of the above processing in the interaction unit may be performed using AI, for example, or without AI. For example, the interaction unit can input the user's geographical location data into a generating AI, and the generating AI can analyze the data to select the optimal interaction method.

[0054] The interaction unit can analyze the user's social media activity during interaction and propose means of interaction. For example, the interaction unit can propose interactions based on songs the user has shared on social media. The interaction unit can propose interactions based on artists the user follows. The interaction unit can propose interactions based on music events the user has participated in. In this way, the interaction unit can propose means of interaction based on the user's social media activity. Some or all of the above processing in the interaction unit may be performed using AI, for example, or without AI. For example, the interaction unit can input the user's social media data into a generating AI, which can then analyze the data and propose means of interaction.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The data collection unit can collect data tailored to the user's environment and situation by utilizing sensor data from the user's device when collecting user music preferences and activity data. For example, the data collection unit can acquire the user's location information and collect music data corresponding to the user's location. The data collection unit can acquire the user's heart rate and exercise level and collect music data corresponding to the user's activity level. The data collection unit can acquire sounds around the user and collect music data corresponding to the surrounding environmental sounds. As a result, the data collection unit can collect music data tailored to the user's environment and situation, providing a more personalized music experience.

[0057] The analysis unit can predict future musical preferences by considering the user's past musical tastes and activity data when analyzing collected data. For example, the analysis unit can analyze the trends of songs the user has played in the past and predict songs that the user is likely to play in the future. The analysis unit can predict new songs from artists the user has followed in the past. The analysis unit can analyze the trends of playlists the user has created in the past and predict playlists that the user is likely to create in the future. As a result, the analysis unit can predict the user's future musical preferences and provide more appropriate music data.

[0058] The data collection unit can analyze users' social media activity to collect relevant music data when gathering data on users' musical preferences and activities. For example, the data collection unit can collect relevant music data based on songs that users have shared on social media. The data collection unit can collect new songs from artists that users follow. The data collection unit can collect songs related to music events that users have attended. In this way, the data collection unit can collect relevant music data based on users' social media activity, enabling it to provide a more personalized music experience.

[0059] The analysis unit can determine the priority of analysis based on when the music data was collected when analyzing the collected data. For example, the analysis unit can prioritize the analysis of the most recent music data. The analysis unit can prioritize the analysis of songs recently played by the user. The analysis unit can prioritize the analysis of songs by artists recently followed by the user. This allows the analysis unit to determine the priority of analysis based on when the music data was collected, and to provide more appropriate music data.

[0060] The matching system can consider users' geographical location when matching users with shared musical tastes. For example, the matching system can match users living in the same region, users living in the same country, or users living in the same city. By considering users' geographical location, the matching system can perform more appropriate matches and promote interaction between users.

[0061] The interaction unit can select the optimal interaction method by referring to the user's past operation history when providing interactions such as live chat, playlist sharing, and real-time recommendations. For example, the interaction unit can prioritize providing interaction methods that the user has preferred in the past. The interaction unit can also eliminate interaction methods that the user has avoided in the past. The interaction unit can suggest the optimal interaction method based on the user's past operation history. As a result, the interaction unit can select the optimal interaction method based on the user's past operation history, providing a more personalized music experience.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects user music preferences and activity data. For example, it collects data such as which songs the user has played, which artists they follow, and which playlists they have created. This allows the system to understand the user's music preferences. Step 2: The analysis unit analyzes the data collected by the collection unit in real time. For example, it analyzes users' musical preferences and activity data to match users with similar musical tastes. This allows for matching users who follow the same artists, prefer the same genre of music, or create the same playlists. Step 3: The matching unit matches users with shared musical tastes based on the analysis results obtained by the analysis unit. For example, it can match users who follow the same artists, users who like the same genre of music, or users who create the same playlists. Step 4: The interaction section provides users matched by the matching section with opportunities to interact with each other through live chat, playlist sharing, and real-time recommendations. For example, users with shared musical tastes can communicate in real time through live chat, and they can share their favorite songs through playlist sharing. They can also introduce new songs and artists to each other through real-time recommendations.

[0064] (Example of form 2) The music matching system according to an embodiment of the present invention is a system that matches users of a music streaming service based on their musical preferences and activity data, and forms communities among users who share common musical tastes. This music matching system collects users' musical preferences and activity data, and an AI analyzes this data in real time. Next, based on the analysis results, it matches users who share common musical tastes with each other. This allows users to deepen their interactions through music through interactions such as live chat, playlist sharing, and real-time recommendations. First, the music matching system collects users' musical preferences and activity data. For example, it collects data such as which songs users have played, which artists they follow, and which playlists they have created. This allows the music matching system to understand the users' musical preferences. Next, the music matching system's AI analyzes the collected data in real time. The AI ​​analyzes users' musical preferences and activity data and matches users who share common musical tastes with each other. For example, it can match users who follow the same artists. Matched users can deepen their interactions through interactions such as live chat, playlist sharing, and real-time recommendations. For example, through live chat, users with shared musical tastes can communicate in real time. They can also share their favorite songs through playlist sharing. Furthermore, real-time recommendations allow them to introduce new songs and artists to each other. This mechanism allows music matching systems to deepen user interaction through music and enrich the musical experience. For instance, users with shared musical tastes can form communities and share information about music, leading to the discovery of new music and a deeper understanding of music. Additionally, interactions such as live chat, playlist sharing, and real-time recommendations strengthen connections between users and promote social connections through music.This allows the music matching system to facilitate interaction by matching users with shared musical tastes based on their musical preferences and activity data.

[0065] The music matching system according to this embodiment comprises a collection unit, an analysis unit, a matching unit, and an interaction unit. The collection unit collects user music preferences and activity data. For example, the collection unit collects data such as which songs the user has played, which artists the user follows, and which playlists the user has created. By collecting this data, the collection unit can understand the user's music preferences. For example, the collection unit can collect playback history to understand which songs the user plays frequently. The collection unit can collect data on followed artists to understand which artists the user is interested in. The collection unit can collect data on created playlists to understand what kind of playlists the user is creating. The analysis unit analyzes the data collected by the collection unit in real time. For example, the analysis unit analyzes user music preferences and activity data and matches users with common musical tastes. For example, the analysis unit can match users who follow the same artists. The analysis unit can match users who like the same genre of music. The analysis unit can match users who have created the same playlists. The matching unit matches users with shared musical tastes based on the analysis results obtained by the analysis unit. For example, the matching unit can match users who follow the same artists, users who like the same genre of music, or users who have created the same playlists. The interaction unit provides users matched by the matching unit with opportunities to interact, such as live chat, playlist sharing, and real-time recommendations. For example, the interaction unit allows users with shared musical tastes to communicate in real time through live chat, share their favorite songs through playlist sharing, and introduce new songs and artists to each other through real-time recommendations.As a result, the music matching system according to this embodiment can match users with common musical tastes based on their musical preferences and activity data, thereby promoting interaction.

[0066] The data collection unit collects user musical preferences and activity data. Specifically, it collects data such as which songs users play, which artists they follow, and which playlists they create. This allows for a detailed understanding of users' musical preferences. For example, the data collection unit collects users' playback history to understand which songs they play frequently. This allows for the identification of songs and artists that users particularly like. The data collection unit also collects data on artists that users follow to understand which artists users are interested in. Furthermore, the data collection unit collects data on playlists that users create to understand what kind of playlists they create. This allows for a detailed analysis of users' musical preferences and trends. The data collection unit can centrally manage and update this data in real time. For example, if a user plays a new song, follows a new artist, or creates a new playlist, that data is collected and reflected in the system. This allows the data collection unit to always understand users' musical preferences based on the latest data. In addition, the data collection unit can anonymize user data and take measures to protect privacy. This allows users to use the system with peace of mind.

[0067] The analysis unit analyzes the data collected by the collection unit in real time. Specifically, it analyzes users' musical preferences and activity data and matches users with similar musical tastes. For example, the analysis unit can match users who follow the same artists, allowing users interested in the same artists to connect. It can also match users who prefer the same genre of music, allowing users interested in the same genre to connect. Furthermore, the analysis unit can match users who have created the same playlists, allowing users who create the same playlists to connect. The analysis unit uses AI to analyze this data, providing a detailed analysis of users' musical preferences and activity data. For example, the AI ​​analyzes users' playback history and followed artists to identify users with similar musical tastes. The AI ​​also analyzes users' playlist data to identify users who create the same playlists. As a result, the analysis unit can analyze users' musical preferences and activity data in detail and match users with similar musical tastes. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term trends and patterns. This allows the analysis unit to analyze users' musical preferences and activity data in detail and match users with similar musical tastes.

[0068] The matching unit matches users with shared musical tastes based on the analysis results obtained by the analysis unit. Specifically, it can match users who follow the same artists, allowing users interested in the same artists to connect. It can also match users who prefer the same genre of music, allowing users interested in the same genre to connect. Furthermore, it can match users who create the same playlists, allowing users who create the same playlists to connect. The matching unit uses AI to analyze this data, conducting a detailed analysis of users' musical preferences and activity data. For example, the AI ​​analyzes users' playback history and followed artists to identify users with shared musical tastes. The AI ​​also analyzes users' playlist data to identify users who create the same playlists. As a result, the matching unit can conduct a detailed analysis of users' musical preferences and activity data, enabling it to match users with shared musical tastes. In addition, the matching unit can utilize past data and statistical information to analyze long-term trends and patterns. This allows the matching unit to analyze users' musical preferences and activity data in detail, enabling it to match users with similar musical tastes.

[0069] The Interaction Department provides users matched by the Matching Department with features such as live chat, playlist sharing, and real-time recommendations. Specifically, live chat allows users with shared musical tastes to communicate in real time, enabling them to exchange opinions and share information about music. Playlist sharing allows users to share their favorite songs, helping them discover new songs and artists. Real-time recommendations allow users to introduce new songs and artists to each other, enabling them to discover and enjoy new music. By providing these features, the Interaction Department promotes user interaction and revitalizes communication through music. Furthermore, the Interaction Department can collect user feedback and make improvements to enhance the quality of interaction. For example, based on user feedback, it can improve the live chat function or expand the playlist sharing function. In this way, the Interaction Department can further enrich user interaction and revitalize communication through music.

[0070] The data collection unit can collect data such as which songs a user has played, which artists they follow, and which playlists they have created. For example, the data collection unit can collect which songs a user has played. The data collection unit can collect which artists a user follows. The data collection unit can collect which playlists a user has created. This allows the data collection unit to collect detailed data on the user's musical preferences and activities. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's playback history data into a generating AI, which can then analyze the playback history data to understand the user's musical preferences.

[0071] The analysis unit can analyze the collected data in real time and match users who share common musical tastes. For example, the analysis unit analyzes the collected data in real time. The analysis unit can match users who share common musical tastes. This allows the analysis unit to analyze data in real time and match users who share common musical tastes. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input the collected data into a generating AI, which can analyze the data and match users who share common musical tastes.

[0072] The interaction unit can provide real-time communication between users who share a common musical taste through live chat. The interaction unit can, for example, enable real-time communication between users who share a common musical taste through live chat. This allows the interaction unit to enable real-time communication between users who share a common musical taste. Some or all of the above-described processing in the interaction unit may be performed using AI, or not. For example, the interaction unit can input live chat data into a generating AI, which can then analyze the data to support communication between users.

[0073] The interaction unit can provide the ability to share each other's favorite songs through playlist sharing. For example, the interaction unit can share each other's favorite songs through playlist sharing. This allows users to share playlists and their favorite songs with each other. Some or all of the above processing in the interaction unit may be performed using AI, or not. For example, the interaction unit can input playlist sharing data into a generating AI, which can then analyze the data to support playlist sharing between users.

[0074] The interaction unit can provide a way for users to share new songs and artists through real-time recommendations. For example, the interaction unit can share new songs and artists through real-time recommendations. This allows users to share new songs and artists with each other in real time. Some or all of the above processing in the interaction unit may be performed using AI, or not. For example, the interaction unit can input real-time recommendation data into a generating AI, which can then analyze the data to support user recommendations.

[0075] The data collection unit can estimate the user's emotions and adjust the timing of music data collection based on the estimated emotions. For example, if the user is relaxed, the data collection unit can collect relaxing music data at night. If the user is stressed, the data collection unit can collect music data that helps relieve stress. If the user is excited, the data collection unit can collect energetic music data. This allows the data collection unit to adjust the timing of music data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's emotion data into a generative AI, which can analyze the emotion data and adjust the collection timing.

[0076] The collection unit can analyze the user's past music playback history and select the optimal collection method. For example, the collection unit can analyze the genres of songs the user has played in the past and collect new songs of the same genre. The collection unit can prioritize collecting new songs by artists the user has followed in the past. The collection unit can analyze the trends of playlists the user has created in the past and collect songs with similar trends. In this way, the collection unit can select the optimal collection method based on the user's past music playback history. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's past playback history data into a generating AI, which can analyze the data and select the optimal collection method.

[0077] The data collection unit can filter music data based on the user's current activity and areas of interest. For example, if the user is exercising, the data collection unit can collect music data with a tempo suitable for exercise. If the user is studying, the data collection unit can collect music data that enhances concentration. If the user is relaxing, the data collection unit can collect music data that promotes relaxation. This allows the data collection unit to filter music data based on the user's current activity and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user activity data into a generating AI, which can then analyze and filter the data.

[0078] The collection unit can estimate the user's emotions and determine the priority of music data to collect based on the estimated user emotions. For example, if the user is sad, the collection unit can prioritize collecting music data that lifts the user's mood. If the user is happy, the collection unit can prioritize collecting music data that maintains that mood. If the user is tired, the collection unit can prioritize collecting music data that helps them relax. In this way, the collection unit can determine the priority of music data based on 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 collection unit may be performed using AI, for example, or not using AI. For example, the collection unit can input the user's emotion data into a generative AI, which can analyze the emotion data and determine the priority.

[0079] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting music data. For example, if the user is in a specific region, the data collection unit can collect popular songs from that region. If the user is traveling, the data collection unit can collect music from their travel destination. If the user is attending a specific event, the data collection unit can collect music related to that event. In this way, the data collection unit can collect highly relevant music data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI, which can then analyze the data and collect highly relevant data.

[0080] The data collection unit can analyze the user's social media activity and collect relevant data when collecting music data. For example, the data collection unit can collect relevant music data based on songs the user has shared on social media. The data collection unit can collect new songs from artists the user follows. The data collection unit can collect songs related to music events the user has attended. In this way, the data collection unit can collect relevant music data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI, which can then analyze the data and collect relevant music data.

[0081] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can present the results in a calm manner. If the user is excited, the analysis unit can present the results in an energetic manner. If the user is stressed, the analysis unit can present the results in a simple and easy-to-understand manner. In this way, the analysis unit can adjust the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into a generative AI, which can then analyze the emotion data and adjust the presentation.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the music data during the analysis. For example, the analysis unit can perform a detailed analysis on songs that the user frequently plays. The analysis unit can perform a detailed analysis on songs by artists that the user follows. The analysis unit can perform a detailed analysis on songs included in playlists created by the user. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the music data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the music data into a generating AI, and the generating AI can analyze the data and adjust the level of detail.

[0083] The analysis unit can apply different analysis algorithms depending on the music category during analysis. For example, in the case of classical music, the analysis unit can perform analysis based on the structure of the piece and performance techniques. In the case of pop music, the analysis unit can perform analysis based on the catchiness of the lyrics and melody. In the case of jazz music, the analysis unit can perform analysis based on the elements of improvisation. This allows the analysis unit to apply different analysis algorithms depending on the music category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input music category data into a generating AI, which can analyze the data and apply an appropriate algorithm.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can present a short, concise analysis result. If the user is relaxed, the analysis unit can present a detailed analysis result. If the user is excited, the analysis unit can present a visually stimulating analysis result. In this way, the analysis unit can adjust the length of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's emotion data into a generative AI, which can then analyze the emotion data and adjust the length of the analysis.

[0085] The analysis unit can determine the priority of analysis based on the collection date of the music data during the analysis. For example, the analysis unit may prioritize the analysis of the most recent music data. The analysis unit may prioritize the analysis of songs recently played by the user. The analysis unit may prioritize the analysis of songs by artists recently followed by the user. This allows the analysis unit to determine the priority of analysis based on the collection date of the music data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input music data collection date data into a generating AI, which can then analyze the data and determine the priority.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the music data during analysis. For example, the analysis unit can determine the order of analysis based on the relevance of songs played by the user. The analysis unit can determine the order of analysis based on the relevance of artists followed by the user. The analysis unit can determine the order of analysis based on the relevance of playlists created by the user. In this way, the analysis unit can adjust the order of analysis based on the relevance of the music data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input music data relevance data into a generating AI, and the generating AI can analyze the data and adjust the order.

[0087] The matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated emotions. For example, if a user is relaxed, the matching unit can prioritize matching other users who are also relaxed. If a user is excited, the matching unit can prioritize matching other excited users. If a user is stressed, the matching unit can prioritize matching other relaxed users. In this way, the matching unit can adjust the matching criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 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, which can analyze the emotion data and adjust the matching criteria.

[0088] The matching unit can improve the accuracy of matching by considering the interrelationships of music data during the matching process. For example, the matching unit can match users who follow the same artist with each other. The matching unit can match users who like the same genre of music with each other. The matching unit can match users who have created the same playlist with each other. In this way, the matching unit can improve the accuracy of matching by considering the interrelationships of music data. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input data on the interrelationships of music data into a generating AI, which can then analyze the data to improve the accuracy of matching.

[0089] The matching unit can perform matching while considering user attribute information. For example, the matching unit can match users of similar ages. The matching unit can match users of the same gender. The matching unit can match users of similar residential areas. In this way, the matching unit can perform more appropriate matching by considering user attribute information. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input user attribute information data into a generating AI, and the generating AI can analyze the data and perform matching.

[0090] The matching unit can estimate the user's emotions and adjust the order in which matching results are displayed based on the estimated emotions. For example, if a user is relaxed, the matching unit can prioritize displaying relaxed users. If a user is excited, the matching unit can prioritize displaying excited users. If a user is stressed, the matching unit can prioritize displaying relaxed users. In this way, the matching unit can adjust the order in which matching results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 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 the generative AI can analyze the emotion data and adjust the display order.

[0091] The matching unit can perform matching while considering the geographical distribution of music data. For example, the matching unit can match users who live in the same region. The matching unit can match users who live in the same country. The matching unit can match users who live in the same city. By considering the geographical distribution of music data, the matching unit can perform more appropriate matching. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input geographical distribution data of music data into a generating AI, and the generating AI can analyze the data and perform matching.

[0092] The matching unit can improve the accuracy of matching by referring to relevant literature for music data during the matching process. For example, the matching unit can improve the accuracy of matching by referring to papers related to music data. The matching unit can improve the accuracy of matching by referring to books related to music data. The matching unit can improve the accuracy of matching by referring to articles related to music data. In this way, the matching unit can improve the accuracy of matching by referring to relevant literature for music data. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input relevant literature data for music data into a generating AI, and the generating AI can analyze the data to improve the accuracy of matching.

[0093] The interaction unit can estimate the user's emotions and adjust the way the interaction is displayed based on the estimated emotions. For example, if the user is relaxed, the interaction unit can provide an interaction in a calm manner. If the user is excited, the interaction unit can provide an interaction in an energetic manner. If the user is stressed, the interaction unit can provide an interaction in a simple and easy-to-understand manner. In this way, the interaction unit can adjust the way the interaction is displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 above processing in the interaction unit may be performed using AI, for example, or not using AI. For example, the interaction unit can input user emotion data into a generative AI, and the generative AI can analyze the emotion data and adjust the display method.

[0094] The interaction unit can select the optimal display method by referring to the user's past operation history during interaction. For example, the interaction unit can prioritize providing display methods that the user has preferred in the past. The interaction unit can also exclude display methods that the user has avoided in the past. The interaction unit can suggest the optimal display method based on the user's past operation history. In this way, the interaction unit can select the optimal display method based on the user's past operation history. Some or all of the above processing in the interaction unit may be performed using AI, for example, or without AI. For example, the interaction unit can input user operation history data into a generating AI, and the generating AI can analyze the data and select the optimal display method.

[0095] The interaction unit can customize the means of interaction based on the user's current activity during interaction. For example, if the user is exercising, the interaction unit can provide audio interaction. If the user is studying, the interaction unit can provide visual interaction. If the user is relaxed, the interaction unit can provide gentle interaction. In this way, the interaction unit can customize the means of interaction based on the user's current activity. Some or all of the above processing in the interaction unit may be performed using AI, for example, or without AI. For example, the interaction unit can input user activity data into a generating AI, which can then analyze the data to customize the means of interaction.

[0096] The interaction unit can estimate the user's emotions and determine the priority of interactions based on the estimated emotions. For example, if the user is relaxed, the interaction unit can prioritize relaxing interactions. If the user is excited, the interaction unit can prioritize energetic interactions. If the user is stressed, the interaction unit can prioritize interactions that help relieve stress. In this way, the interaction unit can determine the priority of interactions based on 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 interaction unit may be performed using AI, for example, or not using AI. For example, the interaction unit can input user emotion data into a generative AI, which can analyze the emotion data and determine the priority.

[0097] The interaction unit can select the optimal interaction method by considering the user's geographical location information during interaction. For example, if the user is in a specific region, the interaction unit can provide interactions related to the music of that region. If the user is traveling, the interaction unit can provide interactions related to the music of the travel destination. If the user is participating in a specific event, the interaction unit can provide interactions related to that event. In this way, the interaction unit can select the optimal interaction method by considering the user's geographical location information. Some or all of the above processing in the interaction unit may be performed using AI, for example, or without AI. For example, the interaction unit can input the user's geographical location data into a generating AI, and the generating AI can analyze the data to select the optimal interaction method.

[0098] The interaction unit can analyze the user's social media activity during interaction and propose means of interaction. For example, the interaction unit can propose interactions based on songs the user has shared on social media. The interaction unit can propose interactions based on artists the user follows. The interaction unit can propose interactions based on music events the user has participated in. In this way, the interaction unit can propose means of interaction based on the user's social media activity. Some or all of the above processing in the interaction unit may be performed using AI, for example, or without AI. For example, the interaction unit can input the user's social media data into a generating AI, which can then analyze the data and propose means of interaction.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The data collection unit can collect data tailored to the user's environment and situation by utilizing sensor data from the user's device when collecting user music preferences and activity data. For example, the data collection unit can acquire the user's location information and collect music data corresponding to the user's location. The data collection unit can acquire the user's heart rate and exercise level and collect music data corresponding to the user's activity level. The data collection unit can acquire sounds around the user and collect music data corresponding to the surrounding environmental sounds. As a result, the data collection unit can collect music data tailored to the user's environment and situation, providing a more personalized music experience.

[0101] The analysis unit can predict future musical preferences by considering the user's past musical tastes and activity data when analyzing collected data. For example, the analysis unit can analyze the trends of songs the user has played in the past and predict songs that the user is likely to play in the future. The analysis unit can predict new songs from artists the user has followed in the past. The analysis unit can analyze the trends of playlists the user has created in the past and predict playlists that the user is likely to create in the future. As a result, the analysis unit can predict the user's future musical preferences and provide more appropriate music data.

[0102] The analysis unit can estimate the user's emotions when analyzing collected data and adjust the analysis results based on the estimated emotions. For example, if the user is relaxed, the analysis unit can prioritize analyzing relaxing music. If the user is stressed, the analysis unit can prioritize analyzing music that helps relieve stress. If the user is excited, the analysis unit can prioritize analyzing energetic music. This allows the analysis unit to provide analysis results that match the user's emotions, thus providing a more personalized music experience.

[0103] The interaction unit can estimate the user's emotions when providing interactions such as live chat, playlist sharing, and real-time recommendations, and adjust the content of the interaction based on the estimated emotions. For example, if the user is relaxed, the interaction unit can recommend relaxing music. If the user is stressed, the interaction unit can recommend music that helps relieve stress. If the user is excited, the interaction unit can recommend energetic music. In this way, the interaction unit can provide interactions that respond to the user's emotions, providing a more personalized music experience.

[0104] The matching unit can estimate users' emotions when matching users with shared musical tastes, and adjust the matching criteria based on these estimated emotions. For example, if a user is relaxed, the matching unit will prioritize matching them with other users who are also relaxed. If a user is excited, the matching unit will prioritize matching them with other excited users. If a user is stressed, the matching unit will prioritize matching them with other relaxed users. This allows the matching unit to provide matching that is tailored to the user's emotions, offering a more personalized music experience.

[0105] The data collection unit can analyze users' social media activity to collect relevant music data when gathering data on users' musical preferences and activities. For example, the data collection unit can collect relevant music data based on songs that users have shared on social media. The data collection unit can collect new songs from artists that users follow. The data collection unit can collect songs related to music events that users have attended. In this way, the data collection unit can collect relevant music data based on users' social media activity, enabling it to provide a more personalized music experience.

[0106] The analysis unit can determine the priority of analysis based on when the music data was collected when analyzing the collected data. For example, the analysis unit can prioritize the analysis of the most recent music data. The analysis unit can prioritize the analysis of songs recently played by the user. The analysis unit can prioritize the analysis of songs by artists recently followed by the user. This allows the analysis unit to determine the priority of analysis based on when the music data was collected, and to provide more appropriate music data.

[0107] The matching system can consider users' geographical location when matching users with shared musical tastes. For example, the matching system can match users living in the same region, users living in the same country, or users living in the same city. By considering users' geographical location, the matching system can perform more appropriate matches and promote interaction between users.

[0108] The interaction unit can select the optimal interaction method by referring to the user's past operation history when providing interactions such as live chat, playlist sharing, and real-time recommendations. For example, the interaction unit can prioritize providing interaction methods that the user has preferred in the past. The interaction unit can also eliminate interaction methods that the user has avoided in the past. The interaction unit can suggest the optimal interaction method based on the user's past operation history. As a result, the interaction unit can select the optimal interaction method based on the user's past operation history, providing a more personalized music experience.

[0109] The interaction unit can estimate the user's emotions when providing interactions such as live chat, playlist sharing, and real-time recommendations, and prioritize interactions based on those estimated emotions. For example, if the user is relaxed, the interaction unit will prioritize relaxing interactions. If the user is excited, the interaction unit can prioritize energetic interactions. If the user is stressed, the interaction unit can prioritize interactions that help relieve stress. This allows the interaction unit to prioritize interactions based on the user's emotions, providing a more personalized music experience.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The data collection unit collects user music preferences and activity data. For example, it collects data such as which songs the user has played, which artists they follow, and which playlists they have created. This allows the system to understand the user's music preferences. Step 2: The analysis unit analyzes the data collected by the collection unit in real time. For example, it analyzes users' musical preferences and activity data to match users with similar musical tastes. This allows for matching users who follow the same artists, prefer the same genre of music, or create the same playlists. Step 3: The matching unit matches users with shared musical tastes based on the analysis results obtained by the analysis unit. For example, it can match users who follow the same artists, users who like the same genre of music, or users who create the same playlists. Step 4: The interaction section provides users matched by the matching section with opportunities to interact with each other through live chat, playlist sharing, and real-time recommendations. For example, users with shared musical tastes can communicate in real time through live chat, and they can share their favorite songs through playlist sharing. They can also introduce new songs and artists to each other through real-time recommendations.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the data collection unit, analysis unit, matching unit, and interaction unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14 and collects the user's music preferences and activity data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data in real time. The matching unit is implemented by the specific processing unit 290 of the data processing unit 12 and matches users based on the analysis results. The interaction unit is implemented by the control unit 46A of the smart device 14 and provides matching users with the ability to interact with each other, such as live chat, playlist sharing, and real-time recommendations. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the data collection unit, analysis unit, matching unit, and interaction unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and collects the user's music preferences and activity data. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data in real time. The matching unit is implemented by the identification processing unit 290 of the data processing unit 12 and matches users based on the analysis results. The interaction unit is implemented by the control unit 46A of the smart glasses 214 and provides matching users with the ability to interact with each other, such as live chat, playlist sharing, and real-time recommendations. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the data collection unit, analysis unit, matching unit, and interaction unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 and collects the user's music preferences and activity data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data in real time. The matching unit is implemented by the specific processing unit 290 of the data processing unit 12 and matches users based on the analysis results. The interaction unit is implemented by the control unit 46A of the headset terminal 314 and provides matching users with the ability to interact with each other, such as live chat, playlist sharing, and real-time recommendations. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the data collection unit, analysis unit, matching unit, and interaction unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the robot 414 and collects the user's music preferences and activity data. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data in real time. The matching unit is implemented by the identification processing unit 290 of the data processing unit 12 and matches users based on the analysis results. The interaction unit is implemented by the control unit 46A of the robot 414 and provides matching users with the ability to interact with each other, such as live chat, playlist sharing, and real-time recommendations. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) A data collection unit that collects users' musical preferences and activity data, An analysis unit analyzes the data collected by the aforementioned collection unit in real time, A matching unit matches users who share common musical tastes based on the analysis results obtained by the aforementioned analysis unit, The system includes an interaction unit that allows users matched by the matching unit to interact with each other, such as through live chat, playlist sharing, and real-time recommendations. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system collects data such as which songs users play, which artists they follow, and which playlists they create. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed in real time to match users with shared musical tastes. The system described in Appendix 1, characterized by the features described herein. (Note 4) The interaction unit is, It provides real-time communication between users who share a common interest in music through live chat. The system described in Appendix 1, characterized by the features described herein. (Note 5) The interaction unit is, It offers the opportunity to share each other's favorite songs through playlist sharing. The system described in Appendix 1, characterized by the features described herein. (Note 6) The interaction unit is, It provides a platform for sharing new songs and artists through real-time recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of music data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The system analyzes the user's past music playback history and selects the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting music data, filtering is performed based on the user's current activity and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of music data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting music data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting music data, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the music data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the music category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the music data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the music data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The matching unit is It estimates the user's emotions and adjusts the matching criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The matching unit is During the matching process, the accuracy of the matching is improved by considering the interrelationships between music data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The matching unit is During the matching process, user attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The matching unit is It estimates the user's sentiment and adjusts the order in which matching results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The matching unit is During the matching process, the geographical distribution of music data is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The matching unit is During the matching process, we improve the accuracy of the matching by referring to relevant literature for the music data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The interaction unit is, It estimates the user's emotions and adjusts how interactions are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The interaction unit is, During interaction, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The interaction unit is, During interaction, the means of interaction are customized based on the user's current activity. The system described in Appendix 1, characterized by the features described herein. (Note 28) The interaction unit is, It estimates the user's emotions and prioritizes interactions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The interaction unit is, During interaction, the system selects the optimal interaction method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The interaction unit is, During interaction, the system analyzes the user's social media activity and suggests ways to interact. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 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 data collection unit that collects users' musical preferences and activity data, An analysis unit analyzes the data collected by the aforementioned collection unit in real time, A matching unit matches users who share common musical tastes based on the analysis results obtained by the aforementioned analysis unit, The system includes an interaction unit that allows users matched by the matching unit to interact with each other, such as through live chat, playlist sharing, and real-time recommendations. A system characterized by the following features.

2. The aforementioned collection unit is The system collects data such as which songs users play, which artists they follow, and which playlists they create. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed in real time to match users with shared musical tastes. The system according to feature 1.

4. The interaction unit is, It provides real-time communication between users who share a common interest in music through live chat. The system according to feature 1.

5. The interaction unit is, It offers the opportunity to share each other's favorite songs through playlist sharing. The system according to feature 1.

6. The interaction unit is, It provides a platform for sharing new songs and artists through real-time recommendations. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of music data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is The system analyzes the user's past music playback history and selects the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting music data, filtering is performed based on the user's current activity and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and determines the priority of music data to collect based on the estimated user emotions. The system according to feature 1.

Images