system

Abstract

The system according to this embodiment aims to analyze the user's listening history and activity data and recommend music tailored to their individual preferences. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a recommendation unit. The collection unit collects the user's listening history and activity data. The analysis unit analyzes the data collected by the collection unit. The recommendation unit recommends music based on the analysis results obtained by the analysis 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 the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that a system for recommending music based on a user's listening history and activity data has not been sufficiently provided and there is room for improvement.

[0005] The system according to the embodiment aims to analyze a user's listening history and activity data and recommend music according to individual preferences.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a recommendation unit. The collection unit collects a user's listening history and activity data. The analysis unit analyzes the data collected by the collection unit. The recommendation unit recommends music based on the analysis result obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's listening history and activity data and recommend music tailored to their individual preferences. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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) An AI agent system according to an embodiment of the present invention is a system that automatically collects and analyzes a user's listening history and activity data, and provides music lists and playlists tailored to the user's individual preferences. This AI agent system collects the user's listening history and activity data, and the AI ​​analyzes it to understand the user's preferences and tendencies. The AI ​​then recommends music that suits the user's mood and time of day. Furthermore, it also provides customized messages from artists and notifications of live events. For example, the AI ​​agent system collects the user's listening history and activity data. For example, the AI ​​agent system can collect data such as a list of songs the user has played, the number of plays, and the playback time. Next, the AI ​​agent system analyzes the collected data to understand the user's preferences and tendencies. For example, the AI ​​can identify genres and artists that the user likes to listen to. Next, the AI ​​agent system recommends music that suits the user's mood and time of day. For example, the AI ​​can recommend relaxing music when the user wants to relax. The AI ​​agent system also provides customized messages from artists and notifications of live events. For example, the AI ​​can notify the user of messages and live event information from artists that the user follows. This allows the AI ​​agent system to improve user engagement and increase repeat purchase rates and premium service upgrade rates. Furthermore, by leveraging real-time data processing and feedback loops, the AI ​​agent system can optimize its music recommendation algorithms. This enables the AI ​​agent system to personalize the music experience users desire, providing music tailored to the time of day and mood. By analyzing users' listening history and activity data, the AI ​​agent system can recommend music that matches individual preferences.

[0029] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, and a recommendation unit. The collection unit collects the user's listening history and activity data. The user's listening history includes, but is not limited to, a list of songs played, the number of plays, and the playback time. For example, the collection unit collects a list of songs played by the user. The collection unit can also collect the number of plays by the user. The collection unit can also collect the user's playback time. For example, the collection unit stores a list of songs played by the user in a database. The number of plays counts the number of times each song has been played. The playback time records the duration for which each song was played. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses AI to understand the user's preferences and tendencies. For example, the analysis unit identifies genres and artists that the user likes to listen to. The analysis unit can also analyze the user's playback history to understand the user's musical preferences. The analysis unit can also analyze the user's activity data to understand the user's behavioral patterns. For example, the analysis unit classifies the genre of music played by the user and identifies the user's preferences. The analysis unit identifies the artist of the music played by the user and understands the user's preferences. The analysis unit analyzes the user's playback history and understands the user's musical preferences. The analysis unit analyzes the user's activity data and understands the user's behavioral patterns. The recommendation unit recommends music based on the analysis results obtained by the analysis unit. The recommendation unit, for example, uses AI to recommend music that suits the user's mood and time of day. For example, the recommendation unit recommends relaxing music when the user wants to relax. The recommendation unit can also recommend music that the user wants to listen to while exercising. The recommendation unit can also recommend music that the user wants to listen to when they want to concentrate. For example, the recommendation unit recommends relaxing music when the user wants to relax. The recommendation unit recommends music that the user wants to listen to while exercising. The recommendation unit recommends music that the user wants to listen to when they want to concentrate. As a result, the AI ​​agent system according to this embodiment can analyze the user's listening history and activity data and recommend music tailored to individual preferences.

[0030] The data collection unit collects user listening history and activity data. User listening history includes, but is not limited to, a list of played songs, play counts, and playback time. For example, the unit collects a list of songs played by the user. It can also collect the user's play counts. Furthermore, it can collect the user's playback time. For example, the unit stores a list of songs played by the user in a database. Play counts the number of times each song has been played. Play time records the duration each song was played. The unit collects this data in real time and transmits it to a central database. User listening history is important for understanding which songs a user listens to and how often. For example, if a user frequently plays songs by a particular artist, it can be determined that the artist is a user preference. Analyzing the user's playback time can also reveal when users tend to listen to music. This allows the data collection unit to gain a detailed understanding of the user's musical preferences and listening patterns. In addition, the data collection unit also collects user activity data. Activity data includes user location information, exercise data, and device usage. For example, if a user listens to music while running, collecting that exercise data allows the system to understand the type of music the user prefers to listen to during exercise. Collecting user location information also allows the system to understand where the user is listening to music. This enables the data collection unit to understand not only the user's music preferences and listening patterns, but also their behavioral patterns in detail. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and recommendation departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI, for example, to understand user preferences and trends. For instance, the analysis unit identifies genres and artists that users enjoy listening to. It can also analyze users' playback history to understand their musical preferences. Furthermore, the analysis unit can analyze user activity data to understand user behavior patterns. For example, the analysis unit classifies the genres of songs played by users to identify their preferences. The analysis unit identifies the artists of songs played by users to understand their preferences. The analysis unit analyzes users' playback history to understand their musical preferences. The analysis unit analyzes user activity data to understand user behavior patterns. The analysis unit uses AI to process collected data in real time and understand user preferences and trends. Specifically, the AI ​​uses machine learning algorithms to analyze users' playback history and activity data to identify user preferences and trends. For example, the AI ​​classifies the genres and artists of songs played by users to identify their preferences. Furthermore, AI can analyze a user's playback history to understand their musical preferences. It can also analyze user activity data to understand their behavioral patterns. For example, AI can identify the types of music a user listens to while running, and pinpoint the music they want to listen to during exercise. AI can also analyze a user's location information to understand where they are listening to music. This allows the analysis unit to gain a detailed understanding not only of the user's musical preferences and listening patterns, but also their behavioral patterns. Moreover, the analysis unit can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past listening data, it can predict fluctuations in user preferences during specific times of day or seasons, and develop future countermeasures. Additionally, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This enables the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and security of the system.

[0032] The recommendation unit recommends music based on the analysis results obtained by the analysis unit. For example, the recommendation unit uses AI to recommend music that suits the user's mood and time of day. For instance, it can recommend music that helps the user relax when they want to relax. It can also recommend music that the user wants to listen to while exercising. Furthermore, it can recommend music that the user wants to listen to when they want to concentrate. Specifically, the AI ​​uses machine learning algorithms to analyze the user's playback history and activity data to identify music that suits the user's mood and time of day. For example, the AI ​​identifies and recommends music that helps the user relax when they want to relax. It can also identify and recommend music that the user wants to listen to while exercising. Furthermore, it can identify and recommend music that the user wants to listen to when they want to concentrate. This allows the recommendation system to recommend music with high accuracy based on the user's mood and time of day. Furthermore, the recommendation system can continuously revise its recommendation results based on real-time updated data to adapt to the latest situation. For example, if the user's mood or activity changes, the recommendation system immediately incorporates the new data and updates the recommendation results. The recommendation system can also collect user feedback and continuously improve the accuracy and effectiveness of its recommendations. For example, users can rate the recommended music to improve the accuracy of the recommendation algorithm. As a result, the recommendation system can always provide highly accurate music recommendations based on the latest information, thereby improving user satisfaction.

[0033] The recommendation unit can recommend music that suits the user's mood and time of day. For example, the recommendation unit can recommend relaxing music when the user wants to relax. For example, the recommendation unit can recommend music that the user wants to listen to while exercising. For example, the recommendation unit can recommend music that the user wants to listen to when they want to concentrate. For example, the recommendation unit can recommend relaxing music when the user wants to relax. For example, the recommendation unit can recommend music that the user wants to listen to while exercising. For example, the recommendation unit can recommend music that the user wants to listen to when they want to concentrate. This allows for a more personalized music experience by recommending music that suits the user's mood and time of day. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input prompts to a generating AI to recommend music that suits the user's mood and time of day, and output music recommended by the generating AI.

[0034] The notification unit can deliver customized messages from artists and announcements about live events. For example, the notification unit can notify users of text messages from artists. The notification unit can also notify users of voice messages from artists. The notification unit can also notify users of video messages from artists. For example, the notification unit can notify users of text messages from artists. The notification unit can notify users of voice messages from artists. The notification unit can notify users of video messages from artists. This can improve user engagement by delivering customized messages from artists and announcements about live events. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input customized messages from artists and announcements about live events into a generating AI, and the generating AI can output the content to be notified.

[0035] The data collection unit can analyze the user's past listening history and select the optimal data collection method. For example, the data collection unit adjusts the collection frequency based on songs the user has frequently listened to in the past. The data collection unit can also collect data during specific time periods if the user tends to listen at those times. The data collection unit can also prioritize collecting data related to specific genres if the user prefers them. For example, the data collection unit adjusts the collection frequency based on songs the user has frequently listened to in the past. The data collection unit collects data during specific time periods if the user tends to listen at those times. The data collection unit prioritizes collecting data related to specific genres if the user prefers them. This allows the optimal data collection method to be selected by analyzing the user's past 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 listening history into a generating AI, and the generating AI can output information on how to select the optimal data collection method.

[0036] The data collection unit can filter listening history and activity data based on the user's current lifestyle and areas of interest. For example, if the user is exercising, the data collection unit can collect data on music suitable for exercise. For example, if the user is working, the data collection unit can collect data on music that enhances concentration. For example, if the user is relaxing, the data collection unit can collect data on music that promotes relaxation. By filtering the data based on the user's lifestyle and areas of interest, more relevant 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 prompts to a generating AI to filter data based on the user's lifestyle and areas of interest, and the generating AI can output the content to be filtered.

[0037] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting listening history and activity data. For example, if the user is traveling, the data collection unit can collect music data from that region. For example, if the user is attending a specific event, the data collection unit can also collect music data related to that event. For example, if the user is at home, the data collection unit can also collect music data that promotes relaxation. For example, if the user is traveling, the data collection unit can collect music data from that region. For example, if the user is attending a specific event, the data collection unit can collect music data related to that event. For example, if the user is at home, the data collection unit can collect music data that promotes relaxation. This allows for the priority collection of highly relevant 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, which can then output content that prioritizes the collection of highly relevant data.

[0038] The data collection unit can analyze a user's social media activity and collect relevant data when collecting listening history and activity data. For example, the data collection unit can collect relevant data based on music shared by the user on social media. The data collection unit can also collect data on new songs by artists the user follows. The data collection unit can also collect data on music events the user has attended. For example, the data collection unit can collect relevant data based on music shared by the user on social media. The data collection unit can collect data on new songs by artists the user follows. The data collection unit can collect data on music events the user has attended. This allows relevant data to 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 into a generating AI, and the generating AI can output content that collects relevant data.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data. For example, the analysis unit can perform a standard analysis on general data. For example, the analysis unit can perform a simplified analysis on less important data. For example, the analysis unit can perform a detailed analysis on important data. For example, the analysis unit can perform a standard analysis on general data. For example, the analysis unit can perform a simplified analysis on less important data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input prompts to the generating AI to adjust the level of detail of the analysis based on the importance of the data, and the generating AI can output content that adjusts the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a music analysis algorithm to music data. For example, the analysis unit can apply a behavior analysis algorithm to activity data. For example, the analysis unit can apply an emotion analysis algorithm to emotion data. For example, the analysis unit can apply a music analysis algorithm to music data. For example, the analysis unit can apply a behavior analysis algorithm to activity data. For example, the analysis unit can apply an emotion analysis algorithm to emotion data. By applying different analysis algorithms depending on the data category, more accurate data analysis becomes possible. 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 prompts to a generating AI to apply different analysis algorithms depending on the data category, and the generating AI can output the analysis algorithm to be applied.

[0041] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may also perform analysis while referring to past data. The analysis unit may also focus on analyzing data from a specific period. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may perform analysis while referring to past data. The analysis unit may focus on analyzing data from a specific period. This enables efficient data analysis by determining the priority of analysis based on the data collection period. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input prompts to a generating AI to determine the priority of analysis based on the data collection period, and the generating AI may output content that determines the priority of analysis.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. The analysis unit may also postpone the analysis of less relevant data. The analysis unit may also evaluate the relevance of the data and analyze it in the optimal order. For example, the analysis unit may prioritize the analysis of highly relevant data. The analysis unit may postpone the analysis of less relevant data. The analysis unit may evaluate the relevance of the data and analyze it in the optimal order. This enables efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input prompts to a generating AI to adjust the order of analysis based on the relevance of the data, and the generating AI may output content to adjust the order of analysis.

[0043] The recommendation unit can adjust the level of detail of recommendations based on the importance of the music. For example, the recommendation unit will provide detailed recommendations for important music. For example, the recommendation unit can provide standard recommendations for general music. For example, the recommendation unit can provide simplified recommendations for less important music. For example, the recommendation unit will provide detailed recommendations for important music. For example, the recommendation unit will provide standard recommendations for general music. For example, the recommendation unit will provide simplified recommendations for less important music. This allows for efficient music recommendation by adjusting the level of detail of recommendations based on the importance of the music. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input prompts to a generating AI to adjust the level of detail of recommendations based on the importance of the music, and the generating AI can output content to adjust the level of detail of the recommendations.

[0044] The recommendation unit can apply different recommendation algorithms depending on the music category during the recommendation process. For example, the recommendation unit can apply a pop recommendation algorithm to pop music. For example, the recommendation unit can apply a classical recommendation algorithm to classical music. For example, the recommendation unit can apply a jazz recommendation algorithm to jazz music. For example, the recommendation unit can apply a pop recommendation algorithm to pop music. For example, the recommendation unit can apply a classical recommendation algorithm to classical music. For example, the recommendation unit can apply a jazz recommendation algorithm to jazz music. By applying different recommendation algorithms depending on the music category, more accurate music recommendations become possible. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input prompts to a generating AI to apply different recommendation algorithms depending on the music category, and the generating AI can output the recommendation algorithm to be applied.

[0045] The recommendation unit can determine the priority of recommendations based on the release date of the music. For example, the recommendation unit may prioritize recommending the latest music. The recommendation unit may also make recommendations while referring to past hit songs. The recommendation unit may also focus on recommending music from a specific period. For example, the recommendation unit may prioritize recommending the latest music. The recommendation unit may make recommendations while referring to past hit songs. The recommendation unit may focus on recommending music from a specific period. This allows the recommendation unit to prioritize recommending the latest music by determining the priority of recommendations based on the release date of the music. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit may input a prompt to a generating AI to determine the priority of recommendations based on the release date of the music, and the generating AI may output the content used to determine the priority of recommendations.

[0046] The recommendation unit can adjust the order of recommendations based on the relevance of the music. For example, the recommendation unit may prioritize recommending highly relevant music. The recommendation unit may also postpone recommending less relevant music. The recommendation unit may also evaluate the relevance of the music and recommend it in the optimal order. For example, the recommendation unit may prioritize recommending highly relevant music. The recommendation unit may postpone recommending less relevant music. The recommendation unit may evaluate the relevance of the music and recommend it in the optimal order. This allows the recommendation unit to prioritize recommending more relevant music by adjusting the order of recommendations based on the relevance of the music. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit may input a prompt to a generating AI to adjust the order of recommendations based on the relevance of the music, and the generating AI may output content to adjust the order of recommendations.

[0047] The notification unit can select the optimal notification method by referring to the user's past response history when sending a notification. For example, the notification unit can prioritize notification methods that the user has preferred in the past. The notification unit can also avoid notification methods that the user has ignored in the past. The notification unit can also analyze the user's past response history and select the optimal notification method. For example, the notification unit can prioritize notification methods that the user has preferred in the past. The notification unit can avoid notification methods that the user has ignored in the past. The notification unit can analyze the user's past response history and select the optimal notification method. This allows the notification unit to select the optimal notification method by referring to the user's past response history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's past response history into a generating AI, and output content that the generating AI will use to select the optimal notification method.

[0048] The notification unit can select the optimal notification method by considering the user's device information when sending a notification. For example, if the user is using a smartphone, the notification unit will send a push notification. For example, if the user is using a tablet, the notification unit can also send a notification optimized for a larger screen. For example, if the user is using a smartwatch, the notification unit can also send a concise and highly visible notification. For example, if the user is using a smartphone, the notification unit will send a push notification. If the user is using a tablet, the notification unit will send a notification optimized for a larger screen. If the user is using a smartwatch, the notification unit will send a concise and highly visible notification. This allows the notification unit to select the optimal notification method by considering the user's device information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's device information into a generating AI, and the generating AI can output content that selects the optimal notification method.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The analysis unit can consider the user's past music rating data when analyzing the user's listening history and activity data. For example, it can prioritize analyzing songs and artists that the user has previously given high ratings to. The analysis unit can also exclude songs and artists that the user has given low ratings to. Furthermore, the analysis unit can focus on analyzing songs that the user has added to specific playlists. By considering the user's rating data, a more accurate analysis becomes possible.

[0051] The data collection unit can consider the user's device's battery level when collecting user listening history and activity data. For example, if the battery level is low, data collection can be temporarily stopped. If the battery level is sufficient, data collection can be resumed. Furthermore, if the battery level is low, the frequency of data collection can be reduced. This allows for efficient data collection by considering the device's battery level.

[0052] The data collection unit can consider the user's internet connection status when collecting user listening history and activity data. For example, if the internet connection is unstable, data collection can be temporarily stopped. If the internet connection is stable, data collection can be resumed. Furthermore, if the internet connection is unstable, the frequency of data collection can be reduced. This allows for more efficient data collection by considering the internet connection status.

[0053] The analysis unit can consider the user's music playback device when analyzing the user's listening history and activity data. For example, if the user is playing music on a smartphone, the analysis will be optimized for the smartphone. If the user is playing music on a smart speaker, the analysis can be optimized for the smart speaker. Furthermore, if the user is playing music on a car audio system, the analysis can be optimized for the car audio system. By considering the playback device, more accurate analysis becomes possible.

[0054] The data collection unit can take into account the user's privacy settings when collecting user listening history and activity data. For example, if the user has high privacy settings, the scope of data collection can be limited. If the user has low privacy settings, the scope of data collection can be expanded. Also, if the user has refused to have certain data collected, that data can not be collected. In this way, by taking privacy settings into consideration, data collection is possible while protecting the user's privacy.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The collection unit collects the user's listening history and activity data. The user's listening history includes a list of songs played, the number of plays, and the total playback time. The collection unit stores this data in a database and records the number of plays and the total playback time for each song. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI to understand the user's preferences and trends, and identifies the genres and artists that the user likes to listen to. It also analyzes the user's playback history and activity data to understand the user's behavioral patterns. Step 3: The recommendation unit recommends music based on the analysis results obtained by the analysis unit. The recommendation unit uses AI to recommend music that suits the user's mood and time of day, providing music suitable for when you want to relax, exercise, or concentrate.

[0057] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that automatically collects and analyzes a user's listening history and activity data, and provides music lists and playlists tailored to the user's individual preferences. This AI agent system collects the user's listening history and activity data, and the AI ​​analyzes it to understand the user's preferences and tendencies. The AI ​​then recommends music that suits the user's mood and time of day. Furthermore, it also provides customized messages from artists and notifications of live events. For example, the AI ​​agent system collects the user's listening history and activity data. For example, the AI ​​agent system can collect data such as a list of songs the user has played, the number of plays, and the playback time. Next, the AI ​​agent system analyzes the collected data to understand the user's preferences and tendencies. For example, the AI ​​can identify genres and artists that the user likes to listen to. Next, the AI ​​agent system recommends music that suits the user's mood and time of day. For example, the AI ​​can recommend relaxing music when the user wants to relax. The AI ​​agent system also provides customized messages from artists and notifications of live events. For example, the AI ​​can notify the user of messages and live event information from artists that the user follows. This allows the AI ​​agent system to improve user engagement and increase repeat purchase rates and premium service upgrade rates. Furthermore, by leveraging real-time data processing and feedback loops, the AI ​​agent system can optimize its music recommendation algorithms. This enables the AI ​​agent system to personalize the music experience users desire, providing music tailored to the time of day and mood. By analyzing users' listening history and activity data, the AI ​​agent system can recommend music that matches individual preferences.

[0058] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, and a recommendation unit. The collection unit collects the user's listening history and activity data. The user's listening history includes, but is not limited to, a list of songs played, the number of plays, and the playback time. For example, the collection unit collects a list of songs played by the user. The collection unit can also collect the number of plays by the user. The collection unit can also collect the user's playback time. For example, the collection unit stores a list of songs played by the user in a database. The number of plays counts the number of times each song has been played. The playback time records the duration for which each song was played. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses AI to understand the user's preferences and tendencies. For example, the analysis unit identifies genres and artists that the user likes to listen to. The analysis unit can also analyze the user's playback history to understand the user's musical preferences. The analysis unit can also analyze the user's activity data to understand the user's behavioral patterns. For example, the analysis unit classifies the genre of music played by the user and identifies the user's preferences. The analysis unit identifies the artist of the music played by the user and understands the user's preferences. The analysis unit analyzes the user's playback history and understands the user's musical preferences. The analysis unit analyzes the user's activity data and understands the user's behavioral patterns. The recommendation unit recommends music based on the analysis results obtained by the analysis unit. The recommendation unit, for example, uses AI to recommend music that suits the user's mood and time of day. For example, the recommendation unit recommends relaxing music when the user wants to relax. The recommendation unit can also recommend music that the user wants to listen to while exercising. The recommendation unit can also recommend music that the user wants to listen to when they want to concentrate. For example, the recommendation unit recommends relaxing music when the user wants to relax. The recommendation unit recommends music that the user wants to listen to while exercising. The recommendation unit recommends music that the user wants to listen to when they want to concentrate. As a result, the AI ​​agent system according to this embodiment can analyze the user's listening history and activity data and recommend music tailored to individual preferences.

[0059] The data collection unit collects user listening history and activity data. User listening history includes, but is not limited to, a list of played songs, play counts, and playback time. For example, the unit collects a list of songs played by the user. It can also collect the user's play counts. Furthermore, it can collect the user's playback time. For example, the unit stores a list of songs played by the user in a database. Play counts the number of times each song has been played. Play time records the duration each song was played. The unit collects this data in real time and transmits it to a central database. User listening history is important for understanding which songs a user listens to and how often. For example, if a user frequently plays songs by a particular artist, it can be determined that the artist is a user preference. Analyzing the user's playback time can also reveal when users tend to listen to music. This allows the data collection unit to gain a detailed understanding of the user's musical preferences and listening patterns. In addition, the data collection unit also collects user activity data. Activity data includes user location information, exercise data, and device usage. For example, if a user listens to music while running, collecting that exercise data allows the system to understand the type of music the user prefers to listen to during exercise. Collecting user location information also allows the system to understand where the user is listening to music. This enables the data collection unit to understand not only the user's music preferences and listening patterns, but also their behavioral patterns in detail. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and recommendation departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0060] The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI, for example, to understand user preferences and trends. For instance, the analysis unit identifies genres and artists that users enjoy listening to. It can also analyze users' playback history to understand their musical preferences. Furthermore, the analysis unit can analyze user activity data to understand user behavior patterns. For example, the analysis unit classifies the genres of songs played by users to identify their preferences. The analysis unit identifies the artists of songs played by users to understand their preferences. The analysis unit analyzes users' playback history to understand their musical preferences. The analysis unit analyzes user activity data to understand user behavior patterns. The analysis unit uses AI to process collected data in real time and understand user preferences and trends. Specifically, the AI ​​uses machine learning algorithms to analyze users' playback history and activity data to identify user preferences and trends. For example, the AI ​​classifies the genres and artists of songs played by users to identify their preferences. Furthermore, AI can analyze a user's playback history to understand their musical preferences. It can also analyze user activity data to understand their behavioral patterns. For example, AI can identify the types of music a user listens to while running, and pinpoint the music they want to listen to during exercise. AI can also analyze a user's location information to understand where they are listening to music. This allows the analysis unit to gain a detailed understanding not only of the user's musical preferences and listening patterns, but also their behavioral patterns. Moreover, the analysis unit can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past listening data, it can predict fluctuations in user preferences during specific times of day or seasons, and develop future countermeasures. Additionally, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This enables the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and security of the system.

[0061] The recommendation unit recommends music based on the analysis results obtained by the analysis unit. For example, the recommendation unit uses AI to recommend music that suits the user's mood and time of day. For instance, it can recommend music that helps the user relax when they want to relax. It can also recommend music that the user wants to listen to while exercising. Furthermore, it can recommend music that the user wants to listen to when they want to concentrate. Specifically, the AI ​​uses machine learning algorithms to analyze the user's playback history and activity data to identify music that suits the user's mood and time of day. For example, the AI ​​identifies and recommends music that helps the user relax when they want to relax. It can also identify and recommend music that the user wants to listen to while exercising. Furthermore, it can identify and recommend music that the user wants to listen to when they want to concentrate. This allows the recommendation system to recommend music with high accuracy based on the user's mood and time of day. Furthermore, the recommendation system can continuously revise its recommendation results based on real-time updated data to adapt to the latest situation. For example, if the user's mood or activity changes, the recommendation system immediately incorporates the new data and updates the recommendation results. The recommendation system can also collect user feedback and continuously improve the accuracy and effectiveness of its recommendations. For example, users can rate the recommended music to improve the accuracy of the recommendation algorithm. As a result, the recommendation system can always provide highly accurate music recommendations based on the latest information, thereby improving user satisfaction.

[0062] The recommendation unit can recommend music that suits the user's mood and time of day. For example, the recommendation unit can recommend relaxing music when the user wants to relax. For example, the recommendation unit can recommend music that the user wants to listen to while exercising. For example, the recommendation unit can recommend music that the user wants to listen to when they want to concentrate. For example, the recommendation unit can recommend relaxing music when the user wants to relax. For example, the recommendation unit can recommend music that the user wants to listen to while exercising. For example, the recommendation unit can recommend music that the user wants to listen to when they want to concentrate. This allows for a more personalized music experience by recommending music that suits the user's mood and time of day. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input prompts to a generating AI to recommend music that suits the user's mood and time of day, and output music recommended by the generating AI.

[0063] The notification unit can deliver customized messages from artists and announcements about live events. For example, the notification unit can notify users of text messages from artists. The notification unit can also notify users of voice messages from artists. The notification unit can also notify users of video messages from artists. For example, the notification unit can notify users of text messages from artists. The notification unit can notify users of voice messages from artists. The notification unit can notify users of video messages from artists. This can improve user engagement by delivering customized messages from artists and announcements about live events. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input customized messages from artists and announcements about live events into a generating AI, and the generating AI can output the content to be notified.

[0064] The data collection unit can estimate the user's emotions and adjust the timing of collecting listening history and activity data based on the estimated user emotions. For example, if the user is relaxed, the data collection unit may collect listening history at night. For example, if the user is stressed, the data collection unit may collect activity data during the day. For example, if the user is excited, the data collection unit may collect data in real time. For example, if the user is relaxed, the data collection unit may collect listening history at night. If the user is stressed, the data collection unit may collect activity data during the day. If the user is excited, the data collection unit may collect data in real time. This allows for more appropriate data collection by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input prompts to the generating AI to estimate the user's emotions, and output the emotions estimated by the generating AI.

[0065] The data collection unit can analyze the user's past listening history and select the optimal data collection method. For example, the data collection unit adjusts the collection frequency based on songs the user has frequently listened to in the past. The data collection unit can also collect data during specific time periods if the user tends to listen at those times. The data collection unit can also prioritize collecting data related to specific genres if the user prefers them. For example, the data collection unit adjusts the collection frequency based on songs the user has frequently listened to in the past. The data collection unit collects data during specific time periods if the user tends to listen at those times. The data collection unit prioritizes collecting data related to specific genres if the user prefers them. This allows the optimal data collection method to be selected by analyzing the user's past 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 listening history into a generating AI, and the generating AI can output information on how to select the optimal data collection method.

[0066] The data collection unit can filter listening history and activity data based on the user's current lifestyle and areas of interest. For example, if the user is exercising, the data collection unit can collect data on music suitable for exercise. For example, if the user is working, the data collection unit can collect data on music that enhances concentration. For example, if the user is relaxing, the data collection unit can collect data on music that promotes relaxation. By filtering the data based on the user's lifestyle and areas of interest, more relevant 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 prompts to a generating AI to filter data based on the user's lifestyle and areas of interest, and the generating AI can output the content to be filtered.

[0067] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is sad, the data collection unit may prioritize collecting data on uplifting music. For example, if the user is happy, the data collection unit may prioritize collecting data on music that helps maintain that mood. For example, if the user is tired, the data collection unit may prioritize collecting data on relaxing music. For example, if the user is sad, the data collection unit may prioritize collecting data on uplifting music. If the user is happy, the data collection unit may prioritize collecting data on music that helps maintain that mood. If the user is tired, the data collection unit may prioritize collecting data on relaxing music. This allows for more appropriate data collection by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input prompts to the generating AI to estimate the user's emotions, and output the emotions estimated by the generating AI.

[0068] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting listening history and activity data. For example, if the user is traveling, the data collection unit can collect music data from that region. For example, if the user is attending a specific event, the data collection unit can also collect music data related to that event. For example, if the user is at home, the data collection unit can also collect music data that promotes relaxation. For example, if the user is traveling, the data collection unit can collect music data from that region. For example, if the user is attending a specific event, the data collection unit can collect music data related to that event. For example, if the user is at home, the data collection unit can collect music data that promotes relaxation. This allows for the priority collection of highly relevant 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, which can then output content that prioritizes the collection of highly relevant data.

[0069] The data collection unit can analyze a user's social media activity and collect relevant data when collecting listening history and activity data. For example, the data collection unit can collect relevant data based on music shared by the user on social media. The data collection unit can also collect data on new songs by artists the user follows. The data collection unit can also collect data on music events the user has attended. For example, the data collection unit can collect relevant data based on music shared by the user on social media. The data collection unit can collect data on new songs by artists the user follows. The data collection unit can collect data on music events the user has attended. This allows relevant data to 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 into a generating AI, and the generating AI can output content that collects relevant data.

[0070] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed data analysis. For example, if the user is in a hurry, the analysis unit can perform a simplified data analysis. For example, if the user is excited, the analysis unit can perform a real-time data analysis. For example, if the user is relaxed, the analysis unit can perform a detailed data analysis. If the user is in a hurry, the analysis unit can perform a simplified data analysis. If the user is excited, the analysis unit can perform a real-time data analysis. This allows for more appropriate data analysis by adjusting the data analysis method based on 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-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input prompts to the generating AI to estimate the user's emotions, and output the emotions estimated by the generating AI.

[0071] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data. For example, the analysis unit can perform a standard analysis on general data. For example, the analysis unit can perform a simplified analysis on less important data. For example, the analysis unit can perform a detailed analysis on important data. For example, the analysis unit can perform a standard analysis on general data. For example, the analysis unit can perform a simplified analysis on less important data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input prompts to the generating AI to adjust the level of detail of the analysis based on the importance of the data, and the generating AI can output content that adjusts the level of detail of the analysis.

[0072] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a music analysis algorithm to music data. For example, the analysis unit can apply a behavior analysis algorithm to activity data. For example, the analysis unit can apply an emotion analysis algorithm to emotion data. For example, the analysis unit can apply a music analysis algorithm to music data. For example, the analysis unit can apply a behavior analysis algorithm to activity data. For example, the analysis unit can apply an emotion analysis algorithm to emotion data. By applying different analysis algorithms depending on the data category, more accurate data analysis becomes possible. 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 prompts to a generating AI to apply different analysis algorithms depending on the data category, and the generating AI can output the analysis algorithm to be applied.

[0073] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, if the user is relaxed, the analysis unit can display detailed analysis results. For example, if the user is in a hurry, the analysis unit can also display concise analysis results. For example, if the user is excited, the analysis unit can also display visually stimulating analysis results. For example, if the user is relaxed, the analysis unit can display detailed analysis results. If the user is in a hurry, the analysis unit can display concise analysis results. If the user is excited, the analysis unit can display visually stimulating analysis results. This allows for the provision of more appropriate information by adjusting how the analysis results are displayed based on 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-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input prompts to the generating AI to estimate the user's emotions, and output the emotions estimated by the generating AI.

[0074] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may also perform analysis while referring to past data. The analysis unit may also focus on analyzing data from a specific period. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may perform analysis while referring to past data. The analysis unit may focus on analyzing data from a specific period. This enables efficient data analysis by determining the priority of analysis based on the data collection period. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input prompts to a generating AI to determine the priority of analysis based on the data collection period, and the generating AI may output content that determines the priority of analysis.

[0075] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. The analysis unit may also postpone the analysis of less relevant data. The analysis unit may also evaluate the relevance of the data and analyze it in the optimal order. For example, the analysis unit may prioritize the analysis of highly relevant data. The analysis unit may postpone the analysis of less relevant data. The analysis unit may evaluate the relevance of the data and analyze it in the optimal order. This enables efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input prompts to a generating AI to adjust the order of analysis based on the relevance of the data, and the generating AI may output content to adjust the order of analysis.

[0076] The recommendation system can estimate the user's emotions and adjust the way music recommendations are presented based on those emotions. For example, if the user is relaxed, the recommendation system will recommend music using calming language. If the user is excited, the recommendation system may recommend music using energetic language. If the user is sad, the recommendation system may recommend music using uplifting language. For example, if the user is relaxed, the recommendation system will recommend music using calming language. If the user is excited, the recommendation system will recommend music using energetic language. If the user is sad, the recommendation system will recommend music using uplifting language. By adjusting the way music recommendations are presented based on the user's emotions, more appropriate music recommendations become possible. 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 recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input prompts into a generative AI to estimate the user's emotions, and the generative AI can output the emotions it estimates.

[0077] The recommendation unit can adjust the level of detail of recommendations based on the importance of the music. For example, the recommendation unit will provide detailed recommendations for important music. For example, the recommendation unit can provide standard recommendations for general music. For example, the recommendation unit can provide simplified recommendations for less important music. For example, the recommendation unit will provide detailed recommendations for important music. For example, the recommendation unit will provide standard recommendations for general music. For example, the recommendation unit will provide simplified recommendations for less important music. This allows for efficient music recommendation by adjusting the level of detail of recommendations based on the importance of the music. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input prompts to a generating AI to adjust the level of detail of recommendations based on the importance of the music, and the generating AI can output content to adjust the level of detail of the recommendations.

[0078] The recommendation unit can apply different recommendation algorithms depending on the music category during the recommendation process. For example, the recommendation unit can apply a pop recommendation algorithm to pop music. For example, the recommendation unit can apply a classical recommendation algorithm to classical music. For example, the recommendation unit can apply a jazz recommendation algorithm to jazz music. For example, the recommendation unit can apply a pop recommendation algorithm to pop music. For example, the recommendation unit can apply a classical recommendation algorithm to classical music. For example, the recommendation unit can apply a jazz recommendation algorithm to jazz music. By applying different recommendation algorithms depending on the music category, more accurate music recommendations become possible. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input prompts to a generating AI to apply different recommendation algorithms depending on the music category, and the generating AI can output the recommendation algorithm to be applied.

[0079] The recommendation section can estimate the user's emotions and adjust the length of recommendations based on the estimated emotions. For example, if the user is relaxed, the recommendation section will provide longer recommendations. If the user is in a hurry, the recommendation section may provide shorter recommendations. If the user is excited, the recommendation section may provide recommendations of moderate length. For example, if the user is relaxed, the recommendation section will provide longer recommendations. If the user is in a hurry, the recommendation section will provide shorter recommendations. If the user is excited, the recommendation section will provide recommendations of moderate length. By adjusting the length of recommendations based on the user's emotions, more appropriate music recommendations become possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AIs include, but are not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation section may be performed using AI, for example, or without AI. For example, the recommendation system can input prompts into a generative AI to estimate the user's emotions, and the generative AI can output the emotions it estimates.

[0080] The recommendation unit can determine the priority of recommendations based on the release date of the music. For example, the recommendation unit may prioritize recommending the latest music. The recommendation unit may also make recommendations while referring to past hit songs. The recommendation unit may also focus on recommending music from a specific period. For example, the recommendation unit may prioritize recommending the latest music. The recommendation unit may make recommendations while referring to past hit songs. The recommendation unit may focus on recommending music from a specific period. This allows the recommendation unit to prioritize recommending the latest music by determining the priority of recommendations based on the release date of the music. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit may input a prompt to a generating AI to determine the priority of recommendations based on the release date of the music, and the generating AI may output the content used to determine the priority of recommendations.

[0081] The recommendation unit can adjust the order of recommendations based on the relevance of the music. For example, the recommendation unit may prioritize recommending highly relevant music. The recommendation unit may also postpone recommending less relevant music. The recommendation unit may also evaluate the relevance of the music and recommend it in the optimal order. For example, the recommendation unit may prioritize recommending highly relevant music. The recommendation unit may postpone recommending less relevant music. The recommendation unit may evaluate the relevance of the music and recommend it in the optimal order. This allows the recommendation unit to prioritize recommending more relevant music by adjusting the order of recommendations based on the relevance of the music. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit may input a prompt to a generating AI to adjust the order of recommendations based on the relevance of the music, and the generating AI may output content to adjust the order of recommendations.

[0082] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, the notification unit can send notifications at night if the user is relaxed. For example, the notification unit can send notifications during the day if the user is stressed. For example, the notification unit can send notifications in real time if the user is excited. For example, the notification unit can send notifications at night if the user is relaxed. For example, the notification unit can send notifications during the day if the user is stressed. For example, the notification unit can send notifications in real time if the user is excited. By adjusting the timing of notifications based on the user's emotions, notifications can be sent at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input a prompt to the generative AI to estimate the user's emotions and output the emotion estimated by the generative AI.

[0083] The notification unit can select the optimal notification method by referring to the user's past response history when sending a notification. For example, the notification unit can prioritize notification methods that the user has preferred in the past. The notification unit can also avoid notification methods that the user has ignored in the past. The notification unit can also analyze the user's past response history and select the optimal notification method. For example, the notification unit can prioritize notification methods that the user has preferred in the past. The notification unit can avoid notification methods that the user has ignored in the past. The notification unit can analyze the user's past response history and select the optimal notification method. This allows the notification unit to select the optimal notification method by referring to the user's past response history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's past response history into a generating AI, and output content that the generating AI will use to select the optimal notification method.

[0084] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, if the user is sad, the notification unit will prioritize uplifting notifications. For example, if the user is happy, the notification unit may also prioritize notifications that help maintain that mood. For example, if the user is tired, the notification unit may also prioritize relaxing notifications. For example, if the user is sad, the notification unit will prioritize uplifting notifications. If the user is happy, the notification unit will prioritize notifications that help maintain that mood. If the user is tired, the notification unit will prioritize relaxing notifications. This allows for more appropriate notifications to be delivered by determining the priority of notifications based on 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 notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input a prompt to a generating AI to estimate the user's emotions, and output the emotions estimated by the generating AI.

[0085] The notification unit can select the optimal notification method by considering the user's device information when sending a notification. For example, if the user is using a smartphone, the notification unit will send a push notification. For example, if the user is using a tablet, the notification unit can also send a notification optimized for a larger screen. For example, if the user is using a smartwatch, the notification unit can also send a concise and highly visible notification. For example, if the user is using a smartphone, the notification unit will send a push notification. If the user is using a tablet, the notification unit will send a notification optimized for a larger screen. If the user is using a smartwatch, the notification unit will send a concise and highly visible notification. This allows the notification unit to select the optimal notification method by considering the user's device information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's device information into a generating AI, and the generating AI can output content that selects the optimal notification method.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The analysis unit can consider the user's past music rating data when analyzing the user's listening history and activity data. For example, it can prioritize analyzing songs and artists that the user has previously given high ratings to. The analysis unit can also exclude songs and artists that the user has given low ratings to. Furthermore, the analysis unit can focus on analyzing songs that the user has added to specific playlists. By considering the user's rating data, a more accurate analysis becomes possible.

[0088] The recommendation system can estimate the user's emotions and adjust the music playback order based on those emotions. For example, if the user is relaxed, calming songs will be played first. If the user is excited, faster-paced songs may be played first. If the user is sad, uplifting songs may be played first. By adjusting the playback order based on the user's emotions, a more appropriate music experience can be provided.

[0089] The notification unit can estimate the user's emotions and customize notification content based on those emotions. For example, if the user is relaxed, it can send a calming message. If the user is excited, it can send an energetic message. If the user is sad, it can send an uplifting message. By customizing notification content based on the user's emotions, more appropriate communication becomes possible.

[0090] The data collection unit can consider the user's device's battery level when collecting user listening history and activity data. For example, if the battery level is low, data collection can be temporarily stopped. If the battery level is sufficient, data collection can be resumed. Furthermore, if the battery level is low, the frequency of data collection can be reduced. This allows for efficient data collection by considering the device's battery level.

[0091] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated emotions. For example, if the user is relaxed, detailed analysis results can be provided. If the user is in a hurry, concise analysis results can be provided. If the user is excited, visually stimulating analysis results can be provided. By adjusting the notification method of analysis results based on the user's emotions, it becomes possible to provide more appropriate information.

[0092] The data collection unit can consider the user's internet connection status when collecting user listening history and activity data. For example, if the internet connection is unstable, data collection can be temporarily stopped. If the internet connection is stable, data collection can be resumed. Furthermore, if the internet connection is unstable, the frequency of data collection can be reduced. This allows for more efficient data collection by considering the internet connection status.

[0093] The recommendation system can estimate the user's emotions and adjust the music volume based on those estimates. For example, if the user is relaxed, the volume can be set lower. If the user is excited, the volume can be set higher. If the user is sad, the volume can be set to a moderate level. This allows for a more appropriate musical experience by adjusting the music volume based on the user's emotions.

[0094] The analysis unit can consider the user's music playback device when analyzing the user's listening history and activity data. For example, if the user is playing music on a smartphone, the analysis will be optimized for the smartphone. If the user is playing music on a smart speaker, the analysis can be optimized for the smart speaker. Furthermore, if the user is playing music on a car audio system, the analysis can be optimized for the car audio system. By considering the playback device, more accurate analysis becomes possible.

[0095] The notification unit can estimate the user's emotions and adjust the notification frequency based on that estimation. For example, if the user is relaxed, the notification frequency can be reduced. If the user is excited, the notification frequency can be increased. If the user is sad, notifications can be sent at a moderate frequency. By adjusting the notification frequency based on the user's emotions, notifications can be delivered at a more appropriate time.

[0096] The data collection unit can take into account the user's privacy settings when collecting user listening history and activity data. For example, if the user has high privacy settings, the scope of data collection can be limited. If the user has low privacy settings, the scope of data collection can be expanded. Also, if the user has refused to have certain data collected, that data can not be collected. In this way, by taking privacy settings into consideration, data collection is possible while protecting the user's privacy.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The collection unit collects the user's listening history and activity data. The user's listening history includes a list of songs played, the number of plays, and the total playback time. The collection unit stores this data in a database and records the number of plays and the total playback time for each song. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI to understand the user's preferences and trends, and identifies the genres and artists that the user likes to listen to. It also analyzes the user's playback history and activity data to understand the user's behavioral patterns. Step 3: The recommendation unit recommends music based on the analysis results obtained by the analysis unit. The recommendation unit uses AI to recommend music that suits the user's mood and time of day, providing music suitable for when you want to relax, exercise, or concentrate.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] Each of the multiple elements described above, including the collection unit, analysis unit, recommendation unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart device 14 and collects the user's listening history and activity data. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to understand the user's preferences and tendencies. The recommendation unit is implemented by the control unit 46A of the smart device 14 and recommends music according to the user's mood and time of day. The notification unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides customized messages from artists and notifications of live events. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the data collection unit, analysis unit, recommendation unit, and notification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the smart glasses 214 and collects the user's listening history and activity data. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to understand the user's preferences and tendencies. The recommendation unit is implemented by the control unit 46A of the smart glasses 214 and recommends music according to the user's mood and time of day. The notification unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides customized messages from artists and notifications of live events. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the collection unit, analysis unit, recommendation unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the headset terminal 314 and collects the user's listening history and activity data. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to understand the user's preferences and tendencies. The recommendation unit is implemented by the control unit 46A of the headset terminal 314 and recommends music according to the user's mood and time of day. The notification unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides customized messages from artists and notifications of live events. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the collection unit, analysis unit, recommendation unit, and notification unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the robot 414 and collects the user's listening history and activity data. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to understand the user's preferences and tendencies. The recommendation unit is implemented by the control unit 46A of the robot 414 and recommends music according to the user's mood and time of day. The notification unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides customized messages from artists and notifications of live events. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] (Note 1) A collection unit that collects user listening history and activity data, An analysis unit analyzes the data collected by the aforementioned collection unit, The system includes a recommendation unit that recommends music based on the analysis results obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned recommendation department, It recommends music based on the user's mood and time of day. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features a notification section that displays customized messages from artists and announcements about live events. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting listening history and activity data based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Analyze the user's past listening history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting listening history and activity data, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting listening history and activity 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 9) The aforementioned collection unit is When collecting listening history and activity data, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, We estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned recommendation department, It estimates the user's emotions and adjusts the way music recommendations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned recommendation department, When making recommendations, adjust the level of detail based on the importance of the music. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned recommendation department, When making recommendations, different recommendation algorithms are applied depending on the music category. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned recommendation department, It estimates the user's sentiment and adjusts the length of recommendations based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned recommendation department, When making recommendations, we prioritize recommendations based on the release date of the music. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned recommendation department, When making recommendations, the order of recommendations will be adjusted based on the relevance of the music. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned notification unit, When sending a notification, the system will refer to the user's past response history to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned notification unit, When sending notifications, the system selects the most suitable notification method, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0171] 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 collection unit that collects user listening history and activity data, An analysis unit analyzes the data collected by the aforementioned collection unit, The system includes a recommendation unit that recommends music based on the analysis results obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned recommendation department, It recommends music based on the user's mood and time of day. The system according to feature 1.

3. It features a notification section that displays customized messages from artists and announcements about live events. The system according to feature 1.

4. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting listening history and activity data based on the estimated emotions. The system according to feature 1.

5. The aforementioned collection unit is Analyze the user's past listening history and select the optimal data collection method. The system according to feature 1.

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

7. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is When collecting listening history and activity data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system according to feature 1.

9. The aforementioned collection unit is When collecting listening history and activity data, the system analyzes users' social media activity and collects relevant data. The system according to feature 1.

10. The aforementioned analysis unit, We estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. The system according to feature 1.

Images