system

Abstract

The system according to this embodiment aims to match users with each other based on their musical preferences and listening history, and to provide interaction functions. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a matching unit, and a provision unit. The collection unit collects the user's music preferences, listening history, and activity data. The analysis unit analyzes the data collected by the collection unit. The matching unit matches users based on the analysis results obtained by the analysis unit. The provision unit provides interaction functions such as live chat, playlist sharing, and real-time recommendations between users matched by the matching unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that users have not been sufficiently effectively matched based on music preferences and listening histories, and an interaction has not been sufficiently provided.

[0005] The system according to the embodiment aims to match users based on music preferences and listening histories and provide an interaction function.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a matching unit, and a provision unit. The collection unit collects users' music preferences, listening history, and activity data. The analysis unit analyzes the data collected by the collection unit. The matching unit matches users based on the analysis results obtained by the analysis unit. The provision unit provides interaction functions such as live chat, playlist sharing, and real-time recommendations between users matched by the matching unit. [Effects of the Invention]

[0007] The system according to this embodiment can match users with each other based on their musical preferences and listening history, and provide interaction functions. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applicable 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 uses generative AI to match users based on their musical preferences and activity data, and forms communities among users who share common musical tastes. This music matching system uses generative AI to analyze users' musical preferences, listening history, and activity data, and matches users with common musical tastes, allowing users to easily connect with like-minded individuals and form communities. Furthermore, various interaction functions such as live chat, playlist sharing, and real-time recommendations are added to enrich the user's music experience. For example, the live chat function allows users to exchange opinions and discuss music in real time. The playlist sharing function allows users to share their favorite songs with other users and create shared playlists. Additionally, the real-time recommendation function allows users to discover new songs and artists that match their musical tastes. This mechanism allows users to connect with other users through music, form communities, and enrich their music experience. For example, when a user attends a live event of their favorite artist, they can enjoy it together with other users who like the same artist. Furthermore, when users discover new songs, they can share them with other users and create shared playlists. This allows users to discover and enjoy new music. The music matching system matches users based on their musical preferences and activity data, enabling the formation of communities among users with shared musical tastes.

[0029] The music matching system according to this embodiment comprises a collection unit, an analysis unit, a matching unit, and a provision unit. The collection unit collects the user's music preferences, listening history, and activity data. For example, to collect the user's music preferences, the collection unit records the genre, artist, and tempo of songs played by the user. The collection unit can also record data such as the number of plays, playback time, and playback frequency to collect the user's listening history. Furthermore, the collection unit can record exercise data, location information, and social media activity to collect the user's activity data. For example, the collection unit records the genre and artist of songs played by the user to understand the user's music preferences. The collection unit can also record the number of plays and playback time to understand the user's listening history. Furthermore, the collection unit can record the user's exercise data and location information to understand the user's activity data. The analysis unit analyzes the data collected by the collection unit using a generative AI. The analysis unit analyzes users' musical preferences, listening history, and activity data using, for example, data mining, statistical analysis, and machine learning algorithms. The analysis unit can analyze users' musical preferences using, for example, data mining techniques. It can also analyze users' listening history using statistical analysis techniques. Furthermore, it can analyze users' activity data using machine learning algorithms. For example, the analysis unit analyzes users' musical preferences using data mining techniques and matches users with similar musical tastes. It can also analyze users' listening history using statistical analysis techniques and match users with similar musical tastes. Furthermore, it can analyze users' activity data using machine learning algorithms and match users with similar musical tastes. The matching unit matches users based on the analysis results obtained by the analysis unit. The matching unit matches users using, for example, similarity scores or common interests. The matching function, for example, uses a similarity score to match users who share the same musical tastes.Furthermore, the matching unit can also match users with shared musical tastes using common interests. For example, the matching unit can use similarity scores to match users with shared musical tastes and form communities. The service provider provides interaction functions such as live chat, playlist sharing, and real-time recommendations to users matched by the matching unit. For example, by providing a live chat function, the service provider can enable users to exchange opinions and discuss music in real time. Furthermore, by providing a playlist sharing function, the service provider can enable users to share their favorite songs with other users and create common playlists. In addition, by providing a real-time recommendation function, the service provider can enable users to discover new songs and artists that suit their musical tastes. For example, by providing a live chat function, the service provider can enable users to exchange opinions and discuss music in real time. Furthermore, by providing a playlist sharing function, the service provider can enable users to share their favorite songs with other users and create common playlists. Furthermore, the service provider offers a real-time recommendation function, allowing users to discover new songs and artists that match their musical tastes. This enables the music matching system according to the embodiment to match users based on their musical preferences and activity data, forming communities among users with shared musical tastes.

[0030] The data collection unit collects users' musical preferences, listening history, and activity data. Specifically, it meticulously records data such as the genre and artist of songs played by the user, tempo, number of plays, playback time, and playback frequency. For example, if a user frequently plays songs by a particular artist, it can be determined that they tend to like related songs by that artist or songs of the same genre. By analyzing the user's playback time and frequency, it is possible to understand what kind of music the user listens to and at what times of day. Furthermore, the data collection unit also collects user activity data. This includes the user's exercise data, location information, and social media activity. For example, it can record the tempo and genre of music a user listens to while running and suggest music suitable for exercise. It is also possible to use location information to provide music related to specific regions or events. By collecting social media activity data, it is possible to understand what kind of music users post and share, enabling more personalized music suggestions. In this way, the data collection unit can centrally manage diverse user data and gain a detailed understanding of the user's musical preferences and behavioral patterns.

[0031] The analysis unit uses generative AI to analyze data collected by the collection unit. Specifically, it utilizes data mining, statistical analysis, and machine learning algorithms to analyze users' musical preferences, listening history, and activity data in detail. By using data mining technology, it is possible to delve deeper into users' musical preferences and discover hidden patterns and trends. For example, it is possible to analyze users' preferences for specific genres or artists and match users with similar musical tastes. By using statistical analysis technology, it is possible to analyze users' listening history in detail and understand the distribution of playback counts and playback times. This allows for predictions of what kind of music users listen to and at what times, enabling optimal music recommendations. Furthermore, by using machine learning algorithms, it is possible to analyze users' activity data and understand their musical preferences during exercise or in specific locations. For example, it is possible to analyze the tempo and genre of music listened to while running and suggest music suitable for exercise. Based on these analysis results, the generative AI predicts users' musical preferences and behavioral patterns and provides data for optimal matching. This allows the analysis unit to quickly and accurately analyze diverse user data and provide a foundation for matching users with shared musical tastes.

[0032] The matching unit matches users based on the analysis results obtained by the analysis unit. Specifically, it optimally matches users using criteria such as similarity scores and shared interests. The similarity score is calculated based on users' musical preferences, listening history, and activity data, and serves as an indicator for accurately matching users with shared musical tastes. For example, it can match users who like the same artists or genres, promoting the exchange of opinions and discussions about music. Furthermore, by using shared interests, it is possible to match users who have commonalities other than music. For example, it can match users who have attended the same event, allowing them to share music and memories related to the event. Using these criteria, the matching unit optimally matches users, enabling the formation of communities among users with shared musical tastes. In addition, the matching unit can collect user feedback and continuously improve the accuracy of the matching algorithm. This allows the matching unit to promote interaction between users and create new connections through music.

[0033] The service provider offers interaction features such as live chat, playlist sharing, and real-time recommendations to users matched by the matching service provider. Specifically, the live chat function allows users to exchange opinions and discuss music in real time. For example, users with similar musical tastes can discuss the latest albums and concert information. The playlist sharing function allows users to share their favorite songs with other users and create shared playlists. This enables users to discover new music and share the enjoyment of music. Furthermore, the real-time recommendation function allows users to discover new songs and artists that suit their musical tastes. For example, based on the user's listening history and activity data, a generative AI can suggest the most suitable music in real time. Through these interaction features, the service provider strengthens connections between users and supports the formation of communities through music. The service provider can also collect user feedback and continuously improve the accuracy and effectiveness of the interaction features. In this way, the service provider can provide users with a better music experience and create new connections through music.

[0034] The data collection unit can analyze the user's past music listening history and select the optimal collection method. For example, the data collection unit can prioritize collecting similar music based on artists and genres the user has frequently listened to in the past. The data collection unit can also analyze the user's listening habits at specific times of day and collect music appropriate for those times. Furthermore, the data collection unit can analyze the user's past skipped songs and avoid collecting similar songs. For example, the data collection unit can prioritize collecting similar music based on artists and genres the user has frequently listened to in the past. The data collection unit can also analyze the user's listening habits at specific times of day and collect music appropriate for those times. Furthermore, the data collection unit can analyze the user's past skipped songs and avoid collecting similar songs. This allows the optimal method for collecting music data to be selected by analyzing the user's past music listening history. 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 past music listening history data into a generating AI and have the generating AI select the optimal collection method.

[0035] 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 prioritize collecting energetic music. Similarly, if the user is reading, it can prioritize collecting relaxing music. Furthermore, if the user is working, it can prioritize collecting music that enhances concentration. This allows for the collection of more appropriate music data by filtering it 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 and have the generating AI perform the filtering.

[0036] 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 will prioritize collecting music popular in that region. Furthermore, if the user is traveling, the data collection unit can collect music that matches the culture and atmosphere of the travel destination. Additionally, if the user is at home, the data collection unit can collect music based on the user's past musical listening habits at home. This allows for the collection of 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 information into a generating AI and have the generating AI collect highly relevant data.

[0037] 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 similar music based on music shared by the user on social media. The data collection unit can also prioritize collecting new songs by artists and bands that the user follows. Furthermore, the data collection unit can collect music related to music events and festivals that the user has attended. For example, the data collection unit can collect similar music based on music shared by the user on social media. Furthermore, the data collection unit can prioritize collecting new songs by artists and bands that the user follows. Furthermore, the data collection unit can also collect music related to music events and festivals that the user has attended. This allows for the collection of relevant music data by analyzing 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 activity data into a generating AI and have the generating AI collect the relevant data.

[0038] 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 performs a detailed analysis on artists and genres that the user frequently listens to. The analysis unit can also perform a simplified analysis on songs that the user has only listened to once. Furthermore, the analysis unit can also perform a detailed analysis on songs that the user has added to their playlist. By adjusting the level of detail of the analysis based on the importance of the music data, more appropriate analysis results can be provided. 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 have the generating AI adjust the level of detail of the analysis.

[0039] The analysis unit can apply different analysis algorithms depending on the music category during analysis. For example, for classical music, the analysis unit performs analysis based on the structure of the piece and the use of instruments. For pop music, the analysis unit can also perform analysis based on the catchiness of the lyrics and melody. Furthermore, for jazz music, the analysis unit can also perform analysis that takes into account the elements of improvisation. By applying different analysis algorithms depending on the music category, more appropriate analysis results can be provided. 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 and have the generating AI execute the application of different analysis algorithms.

[0040] The analysis unit can determine the priority of analysis based on the timing of music data collection during the analysis process. For example, the analysis unit may prioritize the analysis of recently collected music data. It can also prioritize the analysis of music data collected by the user at specific events or festivals. Furthermore, it can prioritize the analysis of music data listened to by the user during specific time periods. By determining the priority of analysis based on the timing of music data collection, more appropriate analysis results can be provided. 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 timing of music data collection into a generating AI and have the generating AI determine the priority of analysis.

[0041] 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 prioritize analyzing data related to artists and genres that the user frequently listens to. It can also prioritize analyzing data related to songs that the user has added to playlists. Furthermore, the analysis unit can prioritize analyzing data related to music that the user has shared on social media. By adjusting the order of analysis based on the relevance of the music data, more appropriate analysis results can be provided. 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 relevance of the music data into a generating AI and have the generating AI adjust the order of analysis.

[0042] 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 a user with other users who like the same artists. It can also match a user with other users who like the same genre of music. Furthermore, the matching unit can match a user with other users who share the same playlist. This allows for improved matching accuracy 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 the interrelationships of music data into a generating AI and have the generating AI perform the matching accuracy improvement.

[0043] The matching unit can perform matching while considering the user's attribute information. For example, the matching unit can match users with similar attributes based on the user's age and gender. The matching unit can also match users with nearby users based on the user's place of residence. Furthermore, the matching unit can match users with many commonalities based on the user's occupation and hobbies. For example, the matching unit can match users with similar attributes based on the user's age and gender. The matching unit can also match users with nearby users based on the user's place of residence. Furthermore, the matching unit can also match users with many commonalities based on the user's occupation and hobbies. This allows for the provision of more appropriate matching results by considering the user's 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 the user's attribute information into a generating AI and have the generating AI perform the matching.

[0044] The matching unit can perform matching while considering the geographical distribution of music data. For example, if a user is in a specific region, the matching unit will match them with other users who like music popular in that region. Furthermore, if a user is traveling, the matching unit can match them with other users who like music popular in their travel destination. Additionally, if a user is at home, the matching unit can match them with other users who live in the same region. This allows for more appropriate matching results by considering the geographical distribution 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 the geographical distribution of music data into a generating AI and have the generating AI perform the matching.

[0045] The matching unit can improve the accuracy of matching by referring to relevant literature on music data during the matching process. For example, the matching unit can refer to literature on artists that a user likes and match them with other users who like the same artists. It can also refer to literature on genres that a user likes and match them with other users who like the same genres. Furthermore, the matching unit can refer to literature on music events that a user has attended and match them with other users who attended the same events. In this way, the accuracy of matching can be improved by referring to relevant literature on 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 on music data into a generating AI and have the generating AI perform the matching accuracy improvement.

[0046] The service provider can select the optimal display method by referring to the user's past operation history when providing interaction functions. For example, the service provider can provide a similar design based on the interface design the user has preferred to use in the past. The service provider can also prioritize the display of functions that the user has frequently used in the past. Furthermore, the service provider can hide or place in an inconspicuous location functions that the user has avoided in the past. For example, the service provider can provide a similar design based on the interface design the user has preferred to use in the past. The service provider can also prioritize the display of functions that the user has frequently used in the past. Furthermore, the service provider can hide or place in an inconspicuous location functions that the user has avoided in the past. This allows the service provider to provide the optimal display method by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user operation history data into a generating AI and have the generating AI select the optimal display method.

[0047] The service provider can select the optimal display method by considering the user's current activity when providing interaction functions. For example, if the user is exercising, the service provider can provide a simple and highly visible interface. It can also provide an interface with relaxing colors if the user is reading. Furthermore, it can provide a simple interface that enhances concentration if the user is working. This allows the service provider to provide the optimal display method by considering the user's current activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user activity data into a generating AI and have the generating AI select the optimal display method.

[0048] The service provider can select the optimal display method by considering the user's device information when providing interaction functions. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can also provide a display method optimized for a larger screen. In addition, if the user is using a smartwatch, the service provider can also provide a concise and highly visible display method. This allows the service provider to provide the optimal display method by considering the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0049] The service provider can provide a multilingual interface according to the user's language settings when providing interaction functions. For example, the service provider can automatically set the interface language based on the language settings of the user's device. The service provider can also provide a language switching function if the user uses multiple languages. Furthermore, the service provider can provide the interface in a specific language if the user selects a particular language. By providing a multilingual interface according to the user's language settings, a more appropriate interface can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's language setting data into a generating AI and have the generating AI perform the provision of a multilingual interface.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The data collection unit can adjust the timing of data collection, taking into account the user's device's battery level, when collecting the user's music preferences, listening history, and activity data. For example, if the user's device battery is low, the data collection unit can temporarily stop data collection and resume it after the battery has been charged. The data collection unit can also prioritize data collection if the user's device is charging. Furthermore, if the user's device has sufficient battery power, the data collection unit can proceed with normal data collection. This allows for efficient data collection without compromising the user experience of the device, by considering the user's device's battery level. 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 device's battery level data into a generating AI and have the generating AI adjust the data collection timing.

[0052] 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, it can prioritize collecting energetic music. If the user is reading, it can prioritize collecting relaxing music. Furthermore, if the user is working, it can prioritize collecting music that enhances concentration. By filtering music data based on the user's current activity and areas of interest, more appropriate music data can be collected. 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 activity data into a generating AI and have the generating AI perform the filtering.

[0053] 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, it can prioritize the collection of music popular in that region. If the user is traveling, it can also collect music that matches the culture and atmosphere of the travel destination. Furthermore, if the user is at home, it can collect music based on the types of music they have listened to at home in the past. In this way, highly relevant music data can be collected 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 information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0054] The data collection unit can analyze the user's social media activity and collect relevant data when collecting music data. For example, it can collect similar music based on music the user has shared on social media. It can also prioritize collecting new songs from artists and bands the user follows. Furthermore, it can collect music related to music events and festivals the user has attended. In this way, relevant music data can be collected by analyzing 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 activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0055] 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, it can perform a detailed analysis on artists and genres that the user frequently listens to. It can also perform a simplified analysis on songs that the user has only listened to once. Furthermore, it can perform a detailed analysis on songs that the user has added to their playlist. By adjusting the level of detail of the analysis based on the importance of the music data, it is possible to provide more appropriate analysis results. 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 have the generating AI perform the adjustment of the level of detail of the analysis.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data collection unit collects the user's music preferences, listening history, and activity data. For example, it records the genre and artist of songs the user plays, the tempo of the songs, the number of plays, the playback time, the frequency of playback, exercise data, location information, and social media activity. Step 2: The analysis unit uses generative AI to analyze the data collected by the collection unit. For example, it uses data mining, statistical analysis, and machine learning algorithms to analyze the user's music preferences, listening history, and activity data. Step 3: The matching unit matches users based on the analysis results obtained by the analysis unit. For example, it matches users with shared musical tastes using criteria such as similarity scores or common interests. Step 4: The service provider provides interaction features such as live chat, playlist sharing, and real-time recommendations between users matched by the matching service provider. This allows users to exchange opinions and discuss music in real time, share their favorite songs with other users, and discover new songs and artists.

[0058] (Example of form 2) The music matching system according to an embodiment of the present invention is a system that uses generative AI to match users based on their musical preferences and activity data, and forms communities among users who share common musical tastes. This music matching system uses generative AI to analyze users' musical preferences, listening history, and activity data, and matches users with common musical tastes, allowing users to easily connect with like-minded individuals and form communities. Furthermore, various interaction functions such as live chat, playlist sharing, and real-time recommendations are added to enrich the user's music experience. For example, the live chat function allows users to exchange opinions and discuss music in real time. The playlist sharing function allows users to share their favorite songs with other users and create shared playlists. Additionally, the real-time recommendation function allows users to discover new songs and artists that match their musical tastes. This mechanism allows users to connect with other users through music, form communities, and enrich their music experience. For example, when a user attends a live event of their favorite artist, they can enjoy it together with other users who like the same artist. Furthermore, when users discover new songs, they can share them with other users and create shared playlists. This allows users to discover and enjoy new music. The music matching system matches users based on their musical preferences and activity data, enabling the formation of communities among users with shared musical tastes.

[0059] The music matching system according to this embodiment comprises a collection unit, an analysis unit, a matching unit, and a provision unit. The collection unit collects the user's music preferences, listening history, and activity data. For example, to collect the user's music preferences, the collection unit records the genre, artist, and tempo of songs played by the user. The collection unit can also record data such as the number of plays, playback time, and playback frequency to collect the user's listening history. Furthermore, the collection unit can record exercise data, location information, and social media activity to collect the user's activity data. For example, the collection unit records the genre and artist of songs played by the user to understand the user's music preferences. The collection unit can also record the number of plays and playback time to understand the user's listening history. Furthermore, the collection unit can record the user's exercise data and location information to understand the user's activity data. The analysis unit analyzes the data collected by the collection unit using a generative AI. The analysis unit analyzes users' musical preferences, listening history, and activity data using, for example, data mining, statistical analysis, and machine learning algorithms. The analysis unit can analyze users' musical preferences using, for example, data mining techniques. It can also analyze users' listening history using statistical analysis techniques. Furthermore, it can analyze users' activity data using machine learning algorithms. For example, the analysis unit analyzes users' musical preferences using data mining techniques and matches users with similar musical tastes. It can also analyze users' listening history using statistical analysis techniques and match users with similar musical tastes. Furthermore, it can analyze users' activity data using machine learning algorithms and match users with similar musical tastes. The matching unit matches users based on the analysis results obtained by the analysis unit. The matching unit matches users using, for example, similarity scores or common interests. The matching function, for example, uses a similarity score to match users who share the same musical tastes.Furthermore, the matching unit can also match users with shared musical tastes using common interests. For example, the matching unit can use similarity scores to match users with shared musical tastes and form communities. The service provider provides interaction functions such as live chat, playlist sharing, and real-time recommendations to users matched by the matching unit. For example, by providing a live chat function, the service provider can enable users to exchange opinions and discuss music in real time. Furthermore, by providing a playlist sharing function, the service provider can enable users to share their favorite songs with other users and create common playlists. In addition, by providing a real-time recommendation function, the service provider can enable users to discover new songs and artists that suit their musical tastes. For example, by providing a live chat function, the service provider can enable users to exchange opinions and discuss music in real time. Furthermore, by providing a playlist sharing function, the service provider can enable users to share their favorite songs with other users and create common playlists. Furthermore, the service provider offers a real-time recommendation function, allowing users to discover new songs and artists that match their musical tastes. This enables the music matching system according to the embodiment to match users based on their musical preferences and activity data, forming communities among users with shared musical tastes.

[0060] The data collection unit collects users' musical preferences, listening history, and activity data. Specifically, it meticulously records data such as the genre and artist of songs played by the user, tempo, number of plays, playback time, and playback frequency. For example, if a user frequently plays songs by a particular artist, it can be determined that they tend to like related songs by that artist or songs of the same genre. By analyzing the user's playback time and frequency, it is possible to understand what kind of music the user listens to and at what times of day. Furthermore, the data collection unit also collects user activity data. This includes the user's exercise data, location information, and social media activity. For example, it can record the tempo and genre of music a user listens to while running and suggest music suitable for exercise. It is also possible to use location information to provide music related to specific regions or events. By collecting social media activity data, it is possible to understand what kind of music users post and share, enabling more personalized music suggestions. In this way, the data collection unit can centrally manage diverse user data and gain a detailed understanding of the user's musical preferences and behavioral patterns.

[0061] The analysis unit uses generative AI to analyze data collected by the collection unit. Specifically, it utilizes data mining, statistical analysis, and machine learning algorithms to analyze users' musical preferences, listening history, and activity data in detail. By using data mining technology, it is possible to delve deeper into users' musical preferences and discover hidden patterns and trends. For example, it is possible to analyze users' preferences for specific genres or artists and match users with similar musical tastes. By using statistical analysis technology, it is possible to analyze users' listening history in detail and understand the distribution of playback counts and playback times. This allows for predictions of what kind of music users listen to and at what times, enabling optimal music recommendations. Furthermore, by using machine learning algorithms, it is possible to analyze users' activity data and understand their musical preferences during exercise or in specific locations. For example, it is possible to analyze the tempo and genre of music listened to while running and suggest music suitable for exercise. Based on these analysis results, the generative AI predicts users' musical preferences and behavioral patterns and provides data for optimal matching. This allows the analysis unit to quickly and accurately analyze diverse user data and provide a foundation for matching users with shared musical tastes.

[0062] The matching unit matches users based on the analysis results obtained by the analysis unit. Specifically, it optimally matches users using criteria such as similarity scores and shared interests. The similarity score is calculated based on users' musical preferences, listening history, and activity data, and serves as an indicator for accurately matching users with shared musical tastes. For example, it can match users who like the same artists or genres, promoting the exchange of opinions and discussions about music. Furthermore, by using shared interests, it is possible to match users who have commonalities other than music. For example, it can match users who have attended the same event, allowing them to share music and memories related to the event. Using these criteria, the matching unit optimally matches users, enabling the formation of communities among users with shared musical tastes. In addition, the matching unit can collect user feedback and continuously improve the accuracy of the matching algorithm. This allows the matching unit to promote interaction between users and create new connections through music.

[0063] The service provider offers interaction features such as live chat, playlist sharing, and real-time recommendations to users matched by the matching service provider. Specifically, the live chat function allows users to exchange opinions and discuss music in real time. For example, users with similar musical tastes can discuss the latest albums and concert information. The playlist sharing function allows users to share their favorite songs with other users and create shared playlists. This enables users to discover new music and share the enjoyment of music. Furthermore, the real-time recommendation function allows users to discover new songs and artists that suit their musical tastes. For example, based on the user's listening history and activity data, a generative AI can suggest the most suitable music in real time. Through these interaction features, the service provider strengthens connections between users and supports the formation of communities through music. The service provider can also collect user feedback and continuously improve the accuracy and effectiveness of the interaction features. In this way, the service provider can provide users with a better music experience and create new connections through music.

[0064] 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 will collect music data at night and prioritize music that matches the relaxed atmosphere. If the user is stressed, the data collection unit can also prioritize collecting daytime activity data to collect music that helps reduce stress. Furthermore, if the user is excited, the data collection unit can prioritize collecting data during exercise to collect energetic music. This allows for the collection of more appropriate music data by adjusting the timing of music data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the collection unit may be performed using AI, or not using AI. For example, the collection unit may input user emotion data into the generating AI and have the generating AI perform emotion estimation.

[0065] The data collection unit can analyze the user's past music listening history and select the optimal collection method. For example, the data collection unit can prioritize collecting similar music based on artists and genres the user has frequently listened to in the past. The data collection unit can also analyze the user's listening habits at specific times of day and collect music appropriate for those times. Furthermore, the data collection unit can analyze the user's past skipped songs and avoid collecting similar songs. For example, the data collection unit can prioritize collecting similar music based on artists and genres the user has frequently listened to in the past. The data collection unit can also analyze the user's listening habits at specific times of day and collect music appropriate for those times. Furthermore, the data collection unit can analyze the user's past skipped songs and avoid collecting similar songs. This allows the optimal method for collecting music data to be selected by analyzing the user's past music listening history. 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 past music listening history data into a generating AI and have the generating AI select the optimal collection method.

[0066] 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 prioritize collecting energetic music. Similarly, if the user is reading, it can prioritize collecting relaxing music. Furthermore, if the user is working, it can prioritize collecting music that enhances concentration. This allows for the collection of more appropriate music data by filtering it 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 and have the generating AI perform the filtering.

[0067] The collection unit can estimate the user's emotions and determine the priority of music data to collect based on the estimated emotions. For example, if the user is relaxed, the collection unit will prioritize collecting relaxing music. It can also prioritize collecting music that helps reduce stress if the user is stressed. Furthermore, if the user is excited, the collection unit can prioritize collecting energetic music. This allows for the collection of more appropriate music data by prioritizing music data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into a generating AI, allowing the generating AI to perform emotion estimation.

[0068] 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 will prioritize collecting music popular in that region. Furthermore, if the user is traveling, the data collection unit can collect music that matches the culture and atmosphere of the travel destination. Additionally, if the user is at home, the data collection unit can collect music based on the user's past musical listening habits at home. This allows for the collection of 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 information into a generating AI and have the generating AI collect highly relevant data.

[0069] 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 similar music based on music shared by the user on social media. The data collection unit can also prioritize collecting new songs by artists and bands that the user follows. Furthermore, the data collection unit can collect music related to music events and festivals that the user has attended. For example, the data collection unit can collect similar music based on music shared by the user on social media. Furthermore, the data collection unit can prioritize collecting new songs by artists and bands that the user follows. Furthermore, the data collection unit can also collect music related to music events and festivals that the user has attended. This allows for the collection of relevant music data by analyzing 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 activity data into a generating AI and have the generating AI collect the relevant data.

[0070] 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 display the analysis results in visually calming colors. If the user is stressed, the analysis unit can also display the analysis results in a simple and intuitively easy-to-understand format. Furthermore, if the user is excited, the analysis unit can display the analysis results with visually stimulating effects. For example, if the user is relaxed, the analysis unit can display the analysis results in visually calming colors. If the user is stressed, the analysis unit can also display the analysis results in a simple and intuitively easy-to-understand format. Furthermore, if the user is excited, the analysis unit can also display the analysis results with visually stimulating effects. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0071] 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 performs a detailed analysis on artists and genres that the user frequently listens to. The analysis unit can also perform a simplified analysis on songs that the user has only listened to once. Furthermore, the analysis unit can also perform a detailed analysis on songs that the user has added to their playlist. By adjusting the level of detail of the analysis based on the importance of the music data, more appropriate analysis results can be provided. 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 have the generating AI adjust the level of detail of the analysis.

[0072] The analysis unit can apply different analysis algorithms depending on the music category during analysis. For example, for classical music, the analysis unit performs analysis based on the structure of the piece and the use of instruments. For pop music, the analysis unit can also perform analysis based on the catchiness of the lyrics and melody. Furthermore, for jazz music, the analysis unit can also perform analysis that takes into account the elements of improvisation. By applying different analysis algorithms depending on the music category, more appropriate analysis results can be provided. 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 and have the generating AI execute the application of different analysis algorithms.

[0073] 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 provides a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can also provide an analysis result with visually stimulating effects. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0074] The analysis unit can determine the priority of analysis based on the timing of music data collection during the analysis process. For example, the analysis unit may prioritize the analysis of recently collected music data. It can also prioritize the analysis of music data collected by the user at specific events or festivals. Furthermore, it can prioritize the analysis of music data listened to by the user during specific time periods. By determining the priority of analysis based on the timing of music data collection, more appropriate analysis results can be provided. 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 timing of music data collection into a generating AI and have the generating AI determine the priority of analysis.

[0075] 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 prioritize analyzing data related to artists and genres that the user frequently listens to. It can also prioritize analyzing data related to songs that the user has added to playlists. Furthermore, the analysis unit can prioritize analyzing data related to music that the user has shared on social media. By adjusting the order of analysis based on the relevance of the music data, more appropriate analysis results can be provided. 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 relevance of the music data into a generating AI and have the generating AI adjust the order of analysis.

[0076] 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 will match them with other users who are also relaxed. If a user is stressed, the matching unit can also match them with other users who prefer music that helps reduce stress. Furthermore, if a user is excited, the matching unit can also match them with other users who prefer energetic music. This allows for more appropriate matching results by adjusting the matching criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0077] 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 a user with other users who like the same artists. It can also match a user with other users who like the same genre of music. Furthermore, the matching unit can match a user with other users who share the same playlist. This allows for improved matching accuracy 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 the interrelationships of music data into a generating AI and have the generating AI perform the matching accuracy improvement.

[0078] The matching unit can perform matching while considering the user's attribute information. For example, the matching unit can match users with similar attributes based on the user's age and gender. The matching unit can also match users with nearby users based on the user's place of residence. Furthermore, the matching unit can match users with many commonalities based on the user's occupation and hobbies. For example, the matching unit can match users with similar attributes based on the user's age and gender. The matching unit can also match users with nearby users based on the user's place of residence. Furthermore, the matching unit can also match users with many commonalities based on the user's occupation and hobbies. This allows for the provision of more appropriate matching results by considering the user's 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 the user's attribute information into a generating AI and have the generating AI perform the matching.

[0079] 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 will prioritize displaying users with a relaxed atmosphere. Similarly, if a user is stressed, the matching unit can prioritize displaying users who prefer music that helps reduce stress. Furthermore, if a user is excited, the matching unit can prioritize displaying users who prefer energetic music. This allows for more appropriate matching results by adjusting the order in which matching results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0080] The matching unit can perform matching while considering the geographical distribution of music data. For example, if a user is in a specific region, the matching unit will match them with other users who like music popular in that region. Furthermore, if a user is traveling, the matching unit can match them with other users who like music popular in their travel destination. Additionally, if a user is at home, the matching unit can match them with other users who live in the same region. This allows for more appropriate matching results by considering the geographical distribution 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 the geographical distribution of music data into a generating AI and have the generating AI perform the matching.

[0081] The matching unit can improve the accuracy of matching by referring to relevant literature on music data during the matching process. For example, the matching unit can refer to literature on artists that a user likes and match them with other users who like the same artists. It can also refer to literature on genres that a user likes and match them with other users who like the same genres. Furthermore, the matching unit can refer to literature on music events that a user has attended and match them with other users who attended the same events. In this way, the accuracy of matching can be improved by referring to relevant literature on 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 on music data into a generating AI and have the generating AI perform the matching accuracy improvement.

[0082] The system can estimate the user's emotions and adjust how interaction features are displayed based on those emotions. For example, if the user is relaxed, the system can provide an interface with calming colors to reduce visual stress. If the user is excited, the system can also provide an interface with visually stimulating effects. Furthermore, if the user is stressed, the system can provide a simple and intuitive interface. This allows for a more appropriate interface to be provided by adjusting how interaction features are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provision unit may be performed using AI, for example, or without AI. For example, the service provision unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0083] The service provider can select the optimal display method by referring to the user's past operation history when providing interaction functions. For example, the service provider can provide a similar design based on the interface design the user has preferred to use in the past. The service provider can also prioritize the display of functions that the user has frequently used in the past. Furthermore, the service provider can hide or place in an inconspicuous location functions that the user has avoided in the past. For example, the service provider can provide a similar design based on the interface design the user has preferred to use in the past. The service provider can also prioritize the display of functions that the user has frequently used in the past. Furthermore, the service provider can hide or place in an inconspicuous location functions that the user has avoided in the past. This allows the service provider to provide the optimal display method by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user operation history data into a generating AI and have the generating AI select the optimal display method.

[0084] The service provider can select the optimal display method by considering the user's current activity when providing interaction functions. For example, if the user is exercising, the service provider can provide a simple and highly visible interface. It can also provide an interface with relaxing colors if the user is reading. Furthermore, it can provide a simple interface that enhances concentration if the user is working. This allows the service provider to provide the optimal display method by considering the user's current activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user activity data into a generating AI and have the generating AI select the optimal display method.

[0085] The service provider can estimate the user's emotions and adjust the operation procedures of the interaction function based on the estimated user emotions. For example, if the user is relaxed, the service provider can provide detailed operation procedures. If the user is in a hurry, the service provider can also provide concise and quick operation procedures. Furthermore, if the user is stressed, the service provider can also provide intuitive and easy-to-use procedures. For example, if the user is relaxed, the service provider can provide detailed operation procedures. If the user is in a hurry, the service provider can also provide concise and quick operation procedures. Furthermore, if the user is stressed, the service provider can also provide intuitive and easy-to-use procedures. This allows for the provision of more appropriate operation procedures by adjusting the operation procedures of the interaction function according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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 service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into a generating AI and have the AI ​​perform emotion estimation.

[0086] The service provider can select the optimal display method by considering the user's device information when providing interaction functions. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can also provide a display method optimized for a larger screen. In addition, if the user is using a smartwatch, the service provider can also provide a concise and highly visible display method. This allows the service provider to provide the optimal display method by considering the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0087] The service provider can provide a multilingual interface according to the user's language settings when providing interaction functions. For example, the service provider can automatically set the interface language based on the language settings of the user's device. The service provider can also provide a language switching function if the user uses multiple languages. Furthermore, the service provider can provide the interface in a specific language if the user selects a particular language. By providing a multilingual interface according to the user's language settings, a more appropriate interface can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's language setting data into a generating AI and have the generating AI perform the provision of a multilingual interface.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The data collection unit can adjust the timing of data collection, taking into account the user's device's battery level, when collecting the user's music preferences, listening history, and activity data. For example, if the user's device battery is low, the data collection unit can temporarily stop data collection and resume it after the battery has been charged. The data collection unit can also prioritize data collection if the user's device is charging. Furthermore, if the user's device has sufficient battery power, the data collection unit can proceed with normal data collection. This allows for efficient data collection without compromising the user experience of the device, by considering the user's device's battery level. 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 device's battery level data into a generating AI and have the generating AI adjust the data collection timing.

[0090] 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, music data can be collected at night, prioritizing music that matches the relaxed atmosphere. If the user is stressed, activity data from the daytime can be prioritized to collect music that helps reduce stress. Furthermore, if the user is excited, data from exercise can be prioritized to collect energetic music. By adjusting the timing of music data collection according to the user's emotions, more appropriate music data can be collected. 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, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0091] 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 results can be displayed in visually calming colors. If the user is stressed, the analysis results can be displayed in a simple and intuitively easy-to-understand format. Furthermore, if the user is excited, the analysis results can be displayed with visually stimulating effects. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0092] 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, it can prioritize collecting energetic music. If the user is reading, it can prioritize collecting relaxing music. Furthermore, if the user is working, it can prioritize collecting music that enhances concentration. By filtering music data based on the user's current activity and areas of interest, more appropriate music data can be collected. 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 activity data into a generating AI and have the generating AI perform the filtering.

[0093] The data collection unit can estimate the user's emotions and determine the priority of music data to collect based on the estimated emotions. For example, if the user is relaxed, it can prioritize collecting relaxing music. If the user is stressed, it can prioritize collecting music that helps reduce stress. Furthermore, if the user is excited, it can prioritize collecting energetic music. In this way, more appropriate music data can be collected by prioritizing music data 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, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] 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, it can prioritize the collection of music popular in that region. If the user is traveling, it can also collect music that matches the culture and atmosphere of the travel destination. Furthermore, if the user is at home, it can collect music based on the types of music they have listened to at home in the past. In this way, highly relevant music data can be collected 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 information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0095] The data collection unit can analyze the user's social media activity and collect relevant data when collecting music data. For example, it can collect similar music based on music the user has shared on social media. It can also prioritize collecting new songs from artists and bands the user follows. Furthermore, it can collect music related to music events and festivals the user has attended. In this way, relevant music data can be collected by analyzing 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 activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0096] 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, it can perform a detailed analysis on artists and genres that the user frequently listens to. It can also perform a simplified analysis on songs that the user has only listened to once. Furthermore, it can perform a detailed analysis on songs that the user has added to their playlist. By adjusting the level of detail of the analysis based on the importance of the music data, it is possible to provide more appropriate analysis results. 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 have the generating AI perform the adjustment of the level of detail of the analysis.

[0097] 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, it will be matched with other users who are also relaxed. If a user is stressed, it can be matched with users who prefer music that helps reduce stress. Furthermore, if a user is excited, it can be matched with users who prefer energetic music. By adjusting the matching criteria according to the user's emotions, more appropriate matching results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the matching unit may be performed using AI, or not using AI. For example, the matching unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0098] The service provider can estimate the user's emotions and adjust the display method of interaction functions based on the estimated user emotions. For example, if the user is relaxed, it can provide an interface with calming colors to reduce visual stress. If the user is excited, it can also provide an interface with visually stimulating effects. Furthermore, if the user is stressed, it can provide a simple and intuitive interface. In this way, a more appropriate interface can be provided by adjusting the display method of interaction functions 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 service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The data collection unit collects the user's music preferences, listening history, and activity data. For example, it records the genre and artist of songs the user plays, the tempo of the songs, the number of plays, the playback time, the frequency of playback, exercise data, location information, and social media activity. Step 2: The analysis unit uses generative AI to analyze the data collected by the collection unit. For example, it uses data mining, statistical analysis, and machine learning algorithms to analyze the user's music preferences, listening history, and activity data. Step 3: The matching unit matches users based on the analysis results obtained by the analysis unit. For example, it matches users with shared musical tastes using criteria such as similarity scores or common interests. Step 4: The service provider provides interaction features such as live chat, playlist sharing, and real-time recommendations between users matched by the matching service provider. This allows users to exchange opinions and discuss music in real time, share their favorite songs with other users, and discover new songs and artists.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] Each of the multiple elements described above, including the collection unit, analysis unit, matching unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the user's music preferences, listening history, and activity data using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using a generating AI. The matching unit is implemented in the specific processing unit 290 of the data processing unit 12 and matches users based on the analysis results. The provision unit is implemented in the control unit 46A of the smart device 14 and provides interaction functions 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.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements described above, including the collection unit, analysis unit, matching unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's music preferences, listening history, and activity data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using a generating AI. The matching unit is implemented in the identification processing unit 290 of the data processing unit 12 and matches users based on the analysis results. The provision unit is implemented in the control unit 46A of the smart glasses 214 and provides interaction functions 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 changed in various ways.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the collection unit, analysis unit, matching unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the user's music preferences, listening history, and activity data using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using a generating AI. The matching unit is implemented in the specific processing unit 290 of the data processing unit 12 and matches users based on the analysis results. The provision unit is implemented in the control unit 46A of the headset terminal 314 and provides interaction functions 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.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] Each of the multiple elements described above, including the collection unit, analysis unit, matching unit, and provision unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the robot 414 to collect the user's music preferences, listening history, and activity data. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using a generating AI. The matching unit is implemented in the identification processing unit 290 of the data processing unit 12 and matches users based on the analysis results. The provision unit is implemented in the control unit 46A of the robot 414 and provides interaction functions 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] (Note 1) A data collection unit that collects users' music preferences, listening history, and activity data, An analysis unit analyzes the data collected by the aforementioned collection unit, A matching unit that matches users based on the analysis results obtained by the aforementioned analysis unit, The system includes a provisioning unit that provides interaction functions such as live chat, playlist sharing, and real-time recommendations between users matched by the matching unit. A system characterized by the following features. (Note 2) 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 3) The aforementioned collection unit is The system analyzes the user's past music listening history and selects the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 4) 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 5) 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 6) 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 7) 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 8) 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 9) 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 10) 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 11) 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 12) 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 13) 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 14) 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 15) 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 16) 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 17) 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 18) 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 19) 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 20) The aforementioned supply unit is, It estimates the user's emotions and adjusts how interaction features are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing interaction features, 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 22) The aforementioned supply unit is, When providing interaction features, the optimal display method is selected considering the user's current activity status. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, It estimates the user's emotions and adjusts the operation procedures of the interaction function based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing interaction features, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing interaction features, a multilingual interface is provided according to the user's language settings. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0173] 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' music preferences, listening history, and activity data, An analysis unit analyzes the data collected by the aforementioned collection unit, A matching unit that matches users based on the analysis results obtained by the aforementioned analysis unit, The system includes a provisioning unit that provides interaction functions such as live chat, playlist sharing, and real-time recommendations between users matched by the matching unit. A system characterized by the following features.

2. 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.

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

4. 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.

5. 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.

6. 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 according to feature 1.

7. The aforementioned collection unit is When collecting music data, we analyze users' social media activity and collect relevant data. The system according to feature 1.

8. 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 according to feature 1.

9. The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the music data. The system according to feature 1.

10. The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the music category. The system according to feature 1.

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