system

Abstract

The system according to this embodiment aims to analyze the user's listening history and activity data and provide individually customized music lists and playlists. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a recommendation unit, and a notification 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. The notification unit notifies the user of the music list or playlist recommended by the recommendation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, music recommendations based on a user's listening history and activity data have not been sufficiently performed, 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 provide individually customized music lists or playlists.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a recommendation unit, and a notification 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. The notification unit notifies the user of the music list or playlist recommended by the recommendation unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's listening history and activity data and provide individually customized music lists and playlists. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The music streaming system according to an embodiment of the present invention is a system that provides individually customized music lists, playlists, and related information based on the user's listening history and activity data. This music streaming system collects the user's listening history and activity data, and a generating AI analyzes this data to recommend music that suits the user's mood and time of day. The generating AI also notifies the user of customized messages from artists and announcements of live events. For example, if a user is looking for music to listen to during their morning commute, the generating AI will recommend music that the user likes based on their past listening history and activity data. Also, if the user follows a specific artist, they can receive customized messages from that artist and announcements of live events. This mechanism allows users to easily find music that suits their mood and time of day, enriching their musical experience. It also strengthens the connection with artists and improves fan engagement. In this way, the music streaming system can improve the user's musical experience.

[0029] The music streaming system according to this embodiment comprises a collection unit, an analysis unit, a recommendation unit, and a notification unit. The collection unit collects the user's listening history and activity data. For example, the collection unit collects listening history such as the number of times a song has been played, the duration of playback, and the type of song played. The collection unit can also collect activity data such as the user's exercise data, location information, and app usage history. For example, the collection unit can collect music data listened to by the user during exercise and associate it with the exercise data. The analysis unit analyzes the data collected by the collection unit using generative AI. For example, the analysis unit can analyze the data using data mining, statistical analysis, and machine learning algorithms. For example, the analysis unit can analyze the user's listening history and identify the user's music preferences and listening patterns. The recommendation unit recommends music based on the analysis results obtained by the analysis unit. The recommendation unit uses generative AI to generate music lists and playlists tailored to the user's mood and time of day. For example, the recommendation unit can recommend music for when the user wants to relax. Furthermore, the recommendation unit can also recommend music based on the user's past behavior patterns and trends. The notification unit notifies the user of music lists and playlists recommended by the recommendation unit. The notification unit notifies the user by methods such as push notifications, email notifications, and in-app notifications. For example, the notification unit can notify the user of customized messages from artists the user follows or announcements of live events. As a result, the music streaming system according to the embodiment can provide individually customized music lists and playlists based on the user's listening history and activity data.

[0030] The data collection unit collects user listening history and activity data. Specifically, it collects detailed listening history, such as the number of times a song is played, the duration of playback, and the types of songs played. This allows the system to understand which artists and genres a user prefers, and what kind of music they tend to listen to at different times of the day. The data collection unit also collects activity data such as exercise data, location information, and app usage history. For example, by collecting music data a user listens to while jogging and linking it with exercise data, it is possible to analyze the types of music preferred during exercise. Furthermore, by utilizing location information, it becomes possible to understand the types of music a user listens to in specific locations and provide music that is appropriate for those locations. The data collection unit collects this data in real time and transmits it to a central database. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit uses generative AI to analyze data collected by the collection unit. Specifically, it analyzes data using data mining, statistical analysis, and machine learning algorithms to identify users' musical preferences and listening patterns. For example, the generative AI analyzes a user's listening history to extract preferences for specific artists or genres. It can also analyze user activity data to understand musical preferences in specific situations, such as during exercise or relaxation. Furthermore, the generative AI can analyze user location information to identify trends in music listened to in specific locations. This allows the analysis unit to comprehensively analyze diverse user data and provide a foundation for generating individually customized music lists and playlists. The analysis unit can also utilize historical data and statistical information to analyze long-term trends and patterns. For example, based on past listening data, it can predict musical preferences in specific seasons or events, which can be used for future music recommendations. This allows the analysis unit to not only grasp real-time situations but also handle long-term trend analysis, improving the overall reliability and accuracy of the system.

[0032] The recommendation unit recommends music based on the analysis results obtained by the analysis unit. Specifically, it uses generative AI to generate music lists and playlists tailored to the user's mood and time of day. For example, to recommend music for when a user wants to relax, the generative AI analyzes the user's past listening history and activity data to identify music preferred during relaxation. It can also recommend music based on the user's past behavior patterns and trends. For example, if a user frequently listens to a particular artist, it will recommend new songs by that artist or songs by related artists. Furthermore, the recommendation unit can utilize the user's location information and exercise data to recommend music suitable for specific places and situations. For example, if a user is exercising at the gym, it can recommend energetic music. In this way, the recommendation unit can provide music lists and playlists that meet the diverse needs of users, improving the user's music experience.

[0033] The notification unit notifies users of music lists and playlists recommended by the recommendation unit. Specifically, it notifies users via methods such as push notifications, email notifications, and in-app notifications. For example, the notification unit can notify users of customized messages from artists they follow or announcements of live events. Furthermore, the notification unit can provide individually customized notifications based on the user's listening history and activity data. For example, if a user tends to listen to a particular genre of music at a specific time, it can notify them of a music list tailored to that time. The notification unit can also collect user feedback and continuously improve the accuracy and effectiveness of its notifications. For example, it can analyze user responses to received notifications and optimize the content of future notifications. This allows the notification unit to provide users with timely and appropriate information and improve their music experience.

[0034] 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 can adjust the timing of data collection based on songs the user has frequently listened to in the past. The data collection unit can also analyze the user's listening patterns and suggest the optimal data collection method. Furthermore, the data collection unit can collect data at specific time periods based on the user's past listening history. This allows the optimal data collection method to be selected based on 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 listening history data into a generating AI and have the generating AI select the optimal data collection method.

[0035] 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 at work, the data collection unit will prioritize collecting music data related to work. It can also collect music data suitable for exercise if the user is exercising. Furthermore, if the user is relaxing, the data collection unit can collect music data suitable for relaxation. This allows for the collection of data tailored to the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's lifestyle data into a generating AI and have the generating AI perform the filtering.

[0036] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location when collecting listening history and activity data. For example, if the user is in a specific region, the data collection unit can collect music data related to that region. Furthermore, if the user is traveling, the data collection unit can collect music data related to their travel destination. Additionally, if the user is at home, the data collection unit can collect music data related to their activities at home. This allows for the collection of highly relevant data based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI collect highly relevant data.

[0037] The data collection unit can analyze the user's social media activity and collect relevant data when collecting 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. It can also collect relevant data based on the activities of artists followed by the user. Furthermore, the data collection unit can collect relevant data based on music events attended by the user. This allows for the collection of relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI perform the collection of relevant data.

[0038] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on important data. It can also perform a simplified analysis on less important data. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. This allows the level of detail of the analysis to be adjusted according to the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0039] The analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a music-specific analysis algorithm to music data. It can also apply an activity-specific analysis algorithm to activity data. Furthermore, it can apply a social media-specific analysis algorithm to social media data. This enables analysis according to the data category. 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 the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0040] The analysis unit can determine the priority of analysis based on the data submission date. For example, the analysis unit may prioritize the analysis of the most recent data. It may also postpone the analysis of older data. Furthermore, the analysis unit can determine the priority of analysis according to the data submission date. This allows the analysis priority to be determined according to the data submission date. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data submission date into a generating AI and have the generating AI perform the determination of the analysis priority.

[0041] The analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the data. This allows the order of analysis to be adjusted according to 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 can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0042] The recommendation system can adjust the level of detail of recommendations based on the importance of the music. For example, it can provide detailed recommendations for important music, and simplified recommendations for less important music. Furthermore, the recommendation system can determine the priority of recommendations based on the importance of the music, thereby adjusting the level of detail of recommendations according to the importance of the music. Some or all of the above processes in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input the importance of the music into a generating AI and have the generating AI perform the adjustment of the level of detail of the recommendations.

[0043] The recommendation system can apply different recommendation algorithms depending on the music category. For example, it can apply a pop-specific recommendation algorithm to pop music, a classical-specific recommendation algorithm to classical music, and a jazz-specific recommendation algorithm to jazz music. This enables recommendations tailored to the music category. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input music categories into a generating AI and have the generating AI apply different recommendation algorithms.

[0044] The recommendation department can determine the priority of recommendations based on the submission date of the music. For example, the recommendation department may prioritize recommending the latest music. It may also postpone recommending older music. Furthermore, the recommendation department may also determine the priority of recommendations based on the submission date of the music. This allows the recommendation department to determine the priority of recommendations according to the submission date of the music. Some or all of the above processes in the recommendation department may be performed using AI, for example, or not using AI. For example, the recommendation department can input the submission dates of the music into a generating AI and have the generating AI perform the determination of the recommendation priority.

[0045] The recommendation system can adjust the order of recommendations based on the relevance of the music. For example, the recommendation system will prioritize recommending music that is highly relevant. It can also postpone recommending music that is less relevant. Furthermore, the recommendation system can adjust the order of recommendations according to the relevance of the music. This allows the recommendation system to adjust the order of recommendations according to the relevance of the music. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input the relevance of the music into a generating AI and have the generating AI perform the adjustment of the recommendation order.

[0046] The notification unit can select the optimal notification method by referring to the user's past operation history. For example, the notification unit will prioritize using notification methods that the user has frequently checked in the past. The notification unit can also select the optimal notification timing from the user's operation history. Furthermore, the notification unit can customize the content of notifications based on the user's operation history. This allows the system to select the optimal notification method based on the user's past operation 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 user operation history data into a generating AI and have the generating AI select the optimal notification method.

[0047] The notification unit can select the optimal notification method based on the user's device information. For example, if the user is using a smartphone, the notification unit will prioritize push notifications. It can also provide a notification method optimized for larger screens if the user is using a tablet. Furthermore, if the user is using a smartwatch, the notification unit can provide a concise and highly visible notification method. This allows the notification unit to select the optimal notification method based on 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 have the generating AI select the optimal notification method.

[0048] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0049] The music streaming system can also collect user health data and analyze it in its analysis unit. For example, it can collect the user's heart rate and sleep data and recommend music tailored to the user's physical condition based on this data. When the user wants to relax, it can recommend music that lowers their heart rate, and during exercise, it can recommend music that maintains their heart rate. It can also provide music that helps the user sleep comfortably based on their sleep data. In this way, it can provide a music experience that is tailored to the user's health condition.

[0050] Music streaming systems can further analyze users' social media activity and recommend music based on their interests. For example, they can recommend relevant music based on the music users share on social media and the artists they follow. They can also collect information on music events and concerts users attend and recommend live recordings and music by related artists based on this information. This allows for a music experience tailored to the user's social media activity.

[0051] Music streaming systems can also provide region-specific music and event information based on the user's geographical location. For example, if a user is in a specific region, the system can recommend traditional music or music by local artists. If a user is traveling, the system can also provide information on music festivals and live events in their destination. This allows for a music experience tailored to the user's geographical location.

[0052] Music streaming systems can further provide optimal sound quality settings based on the user's device information. For example, if a user is using high-quality headphones, the system can provide high-quality music data. Also, if a user is using their smartphone's speakers, the system can automatically adjust equalizer settings to optimize sound quality. This allows for the provision of an optimal music experience based on the user's device information.

[0053] Music streaming systems can further provide an optimal interface design based on the user's past operation history. For example, they can prioritize displaying operations that the user has frequently used in the past. They can also suggest optimal button layouts and menu configurations based on the user's operation history. This allows for the provision of an optimal interface design based on the user's past operation history.

[0054] The following briefly describes the processing flow for example form 1.

[0055] Step 1: The data collection unit collects the user's listening history and activity data. Specifically, it collects listening history such as the number of times a song has been played, the duration of playback, and the types of songs played. The data collection unit can also collect activity data such as the user's exercise data, location information, and app usage history. For example, it can collect music data that a user listened to while exercising and associate it with exercise data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses generative AI to perform data mining, statistical analysis, and machine learning algorithms to analyze the data. For example, it can analyze a user's listening history to identify the user's musical preferences and listening patterns. Step 3: The recommendation unit recommends music based on the analysis results obtained by the analysis unit. The recommendation unit uses generative AI to generate music lists and playlists tailored to the user's mood and time of day. For example, it can recommend music for when the user wants to relax. It can also recommend music based on the user's past behavior patterns and trends. Step 4: The notification unit notifies users of music lists and playlists recommended by the recommendation unit. The notification unit notifies users via methods such as push notifications, email notifications, and in-app notifications. For example, it can notify users of customized messages from artists they follow or announcements of live events.

[0056] (Example of form 2) The music streaming system according to an embodiment of the present invention is a system that provides individually customized music lists, playlists, and related information based on the user's listening history and activity data. This music streaming system collects the user's listening history and activity data, and a generating AI analyzes this data to recommend music that suits the user's mood and time of day. The generating AI also notifies the user of customized messages from artists and announcements of live events. For example, if a user is looking for music to listen to during their morning commute, the generating AI will recommend music that the user likes based on their past listening history and activity data. Also, if the user follows a specific artist, they can receive customized messages from that artist and announcements of live events. This mechanism allows users to easily find music that suits their mood and time of day, enriching their musical experience. It also strengthens the connection with artists and improves fan engagement. In this way, the music streaming system can improve the user's musical experience.

[0057] The music streaming system according to this embodiment comprises a collection unit, an analysis unit, a recommendation unit, and a notification unit. The collection unit collects the user's listening history and activity data. For example, the collection unit collects listening history such as the number of times a song has been played, the duration of playback, and the type of song played. The collection unit can also collect activity data such as the user's exercise data, location information, and app usage history. For example, the collection unit can collect music data listened to by the user during exercise and associate it with the exercise data. The analysis unit analyzes the data collected by the collection unit using generative AI. For example, the analysis unit can analyze the data using data mining, statistical analysis, and machine learning algorithms. For example, the analysis unit can analyze the user's listening history and identify the user's music preferences and listening patterns. The recommendation unit recommends music based on the analysis results obtained by the analysis unit. The recommendation unit uses generative AI to generate music lists and playlists tailored to the user's mood and time of day. For example, the recommendation unit can recommend music for when the user wants to relax. Furthermore, the recommendation unit can also recommend music based on the user's past behavior patterns and trends. The notification unit notifies the user of music lists and playlists recommended by the recommendation unit. The notification unit notifies the user by methods such as push notifications, email notifications, and in-app notifications. For example, the notification unit can notify the user of customized messages from artists the user follows or announcements of live events. As a result, the music streaming system according to the embodiment can provide individually customized music lists and playlists based on the user's listening history and activity data.

[0058] The data collection unit collects user listening history and activity data. Specifically, it collects detailed listening history, such as the number of times a song is played, the duration of playback, and the types of songs played. This allows the system to understand which artists and genres a user prefers, and what kind of music they tend to listen to at different times of the day. The data collection unit also collects activity data such as exercise data, location information, and app usage history. For example, by collecting music data a user listens to while jogging and linking it with exercise data, it is possible to analyze the types of music preferred during exercise. Furthermore, by utilizing location information, it becomes possible to understand the types of music a user listens to in specific locations and provide music that is appropriate for those locations. The data collection unit collects this data in real time and transmits it to a central database. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0059] The analysis unit uses generative AI to analyze data collected by the collection unit. Specifically, it analyzes data using data mining, statistical analysis, and machine learning algorithms to identify users' musical preferences and listening patterns. For example, the generative AI analyzes a user's listening history to extract preferences for specific artists or genres. It can also analyze user activity data to understand musical preferences in specific situations, such as during exercise or relaxation. Furthermore, the generative AI can analyze user location information to identify trends in music listened to in specific locations. This allows the analysis unit to comprehensively analyze diverse user data and provide a foundation for generating individually customized music lists and playlists. The analysis unit can also utilize historical data and statistical information to analyze long-term trends and patterns. For example, based on past listening data, it can predict musical preferences in specific seasons or events, which can be used for future music recommendations. This allows the analysis unit to not only grasp real-time situations but also handle long-term trend analysis, improving the overall reliability and accuracy of the system.

[0060] The recommendation unit recommends music based on the analysis results obtained by the analysis unit. Specifically, it uses generative AI to generate music lists and playlists tailored to the user's mood and time of day. For example, to recommend music for when a user wants to relax, the generative AI analyzes the user's past listening history and activity data to identify music preferred during relaxation. It can also recommend music based on the user's past behavior patterns and trends. For example, if a user frequently listens to a particular artist, it will recommend new songs by that artist or songs by related artists. Furthermore, the recommendation unit can utilize the user's location information and exercise data to recommend music suitable for specific places and situations. For example, if a user is exercising at the gym, it can recommend energetic music. In this way, the recommendation unit can provide music lists and playlists that meet the diverse needs of users, improving the user's music experience.

[0061] The notification unit notifies users of music lists and playlists recommended by the recommendation unit. Specifically, it notifies users via methods such as push notifications, email notifications, and in-app notifications. For example, the notification unit can notify users of customized messages from artists they follow or announcements of live events. Furthermore, the notification unit can provide individually customized notifications based on the user's listening history and activity data. For example, if a user tends to listen to a particular genre of music at a specific time, it can notify them of a music list tailored to that time. The notification unit can also collect user feedback and continuously improve the accuracy and effectiveness of its notifications. For example, it can analyze user responses to received notifications and optimize the content of future notifications. This allows the notification unit to provide users with timely and appropriate information and improve their music experience.

[0062] The data collection unit can estimate the user's emotions and adjust the timing of data collection for listening history and activity based on the estimated emotions. For example, if the user is relaxed, the data collection unit can collect data regularly and perform detailed analysis. If the user is stressed, the data collection unit can reduce the frequency of data collection to lessen the user's burden. Furthermore, if the user is excited, the data collection unit can collect data in real time and reflect it immediately. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0063] 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 can adjust the timing of data collection based on songs the user has frequently listened to in the past. The data collection unit can also analyze the user's listening patterns and suggest the optimal data collection method. Furthermore, the data collection unit can collect data at specific time periods based on the user's past listening history. This allows the optimal data collection method to be selected based on 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 listening history data into a generating AI and have the generating AI select the optimal data collection method.

[0064] 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 at work, the data collection unit will prioritize collecting music data related to work. It can also collect music data suitable for exercise if the user is exercising. Furthermore, if the user is relaxing, the data collection unit can collect music data suitable for relaxation. This allows for the collection of data tailored to the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's lifestyle data into a generating AI and have the generating AI perform the filtering.

[0065] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit will prioritize collecting data related to relaxation. If the user is stressed, the data collection unit can also prioritize collecting data that helps reduce stress. Furthermore, if the user is excited, the data collection unit can also prioritize collecting data that helps maintain excitement. This allows for the prioritization of data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data.

[0066] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location when collecting listening history and activity data. For example, if the user is in a specific region, the data collection unit can collect music data related to that region. Furthermore, if the user is traveling, the data collection unit can collect music data related to their travel destination. Additionally, if the user is at home, the data collection unit can collect music data related to their activities at home. This allows for the collection of highly relevant data based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI collect highly relevant data.

[0067] The data collection unit can analyze the 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. It can also collect relevant data based on the activities of artists followed by the user. Furthermore, the data collection unit can collect relevant data based on music events attended by the user. This allows for the collection of relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI perform the collection of relevant data.

[0068] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can display the analysis results in a visually easy-to-understand manner. If the user is stressed, the analysis unit can also display the analysis results concisely. Furthermore, if the user is excited, the analysis unit can display the analysis results in detail. This allows the presentation of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0069] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on important data. It can also perform a simplified analysis on less important data. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. This allows the level of detail of the analysis to be adjusted according to the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0070] The analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a music-specific analysis algorithm to music data. It can also apply an activity-specific analysis algorithm to activity data. Furthermore, it can apply a social media-specific analysis algorithm to social media data. This enables analysis according to the data category. 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 the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0071] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis. If the user is stressed, the analysis unit can also perform a concise analysis. Furthermore, if the user is excited, the analysis unit can also perform a detailed analysis. This allows the length of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0072] The analysis unit can determine the priority of analysis based on the data submission date. For example, the analysis unit may prioritize the analysis of the most recent data. It may also postpone the analysis of older data. Furthermore, the analysis unit can determine the priority of analysis according to the data submission date. This allows the analysis priority to be determined according to the data submission date. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data submission date into a generating AI and have the generating AI perform the determination of the analysis priority.

[0073] The analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the data. This allows the order of analysis to be adjusted according to 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 can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0074] The recommendation system can estimate the user's emotions and adjust the way recommendations are presented based on those emotions. For example, if the user is relaxed, the recommendation system can provide visually clear recommendations. If the user is stressed, it can provide concise recommendations. Furthermore, if the user is excited, it can provide detailed recommendations. This allows the recommendation system to adjust the way recommendations are presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation system may be performed using AI or not. For example, the recommendation system can input user emotion data into a generative AI and have the generative AI adjust the way recommendations are presented.

[0075] The recommendation system can adjust the level of detail of recommendations based on the importance of the music. For example, it can provide detailed recommendations for important music, and simplified recommendations for less important music. Furthermore, the recommendation system can determine the priority of recommendations based on the importance of the music, thereby adjusting the level of detail of recommendations according to the importance of the music. Some or all of the above processes in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input the importance of the music into a generating AI and have the generating AI perform the adjustment of the level of detail of the recommendations.

[0076] The recommendation system can apply different recommendation algorithms depending on the music category. For example, it can apply a pop-specific recommendation algorithm to pop music, a classical-specific recommendation algorithm to classical music, and a jazz-specific recommendation algorithm to jazz music. This enables recommendations tailored to the music category. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input music categories into a generating AI and have the generating AI apply different recommendation algorithms.

[0077] The recommendation unit 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 unit will provide detailed recommendations. If the user is stressed, the recommendation unit can provide concise recommendations. Furthermore, if the user is excited, the recommendation unit can also provide detailed recommendations. This allows the length of recommendations to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit can input user emotion data into a generative AI and have the generative AI adjust the length of recommendations.

[0078] The recommendation department can determine the priority of recommendations based on the submission date of the music. For example, the recommendation department may prioritize recommending the latest music. It may also postpone recommending older music. Furthermore, the recommendation department may also determine the priority of recommendations based on the submission date of the music. This allows the recommendation department to determine the priority of recommendations according to the submission date of the music. Some or all of the above processes in the recommendation department may be performed using AI, for example, or not using AI. For example, the recommendation department can input the submission dates of the music into a generating AI and have the generating AI perform the determination of the recommendation priority.

[0079] The recommendation system can adjust the order of recommendations based on the relevance of the music. For example, the recommendation system will prioritize recommending music that is highly relevant. It can also postpone recommending music that is less relevant. Furthermore, the recommendation system can adjust the order of recommendations according to the relevance of the music. This allows the recommendation system to adjust the order of recommendations according to the relevance of the music. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input the relevance of the music into a generating AI and have the generating AI perform the adjustment of the recommendation order.

[0080] The notification unit can estimate the user's emotions and adjust the way notifications are displayed based on the estimated emotions. For example, if the user is relaxed, the notification unit can provide a visually clear notification. If the user is stressed, the notification unit can provide a concise notification. Furthermore, if the user is agitated, the notification unit can provide a detailed notification. This allows the notification display method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, or not using AI. For example, the notification unit can input user emotion data into the generative AI and have the generative AI adjust the way notifications are displayed.

[0081] The notification unit can select the optimal notification method by referring to the user's past operation history. For example, the notification unit will prioritize using notification methods that the user has frequently checked in the past. The notification unit can also select the optimal notification timing from the user's operation history. Furthermore, the notification unit can customize the content of notifications based on the user's operation history. This allows the system to select the optimal notification method based on the user's past operation 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 user operation history data into a generating AI and have the generating AI select the optimal notification method.

[0082] The notification unit can estimate the user's emotions and adjust the notification procedure based on the estimated emotions. For example, if the user is relaxed, the notification unit can provide detailed instructions. If the user is stressed, the notification unit can also provide concise instructions. Furthermore, if the user is agitated, the notification unit can also provide detailed instructions. This allows the notification procedure to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI adjust the notification procedure.

[0083] The notification unit can select the optimal notification method based on the user's device information. For example, if the user is using a smartphone, the notification unit will prioritize push notifications. It can also provide a notification method optimized for larger screens if the user is using a tablet. Furthermore, if the user is using a smartwatch, the notification unit can provide a concise and highly visible notification method. This allows the notification unit to select the optimal notification method based on 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 have the generating AI select the optimal notification method.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The music streaming system can also collect user health data and analyze it in its analysis unit. For example, it can collect the user's heart rate and sleep data and recommend music tailored to the user's physical condition based on this data. When the user wants to relax, it can recommend music that lowers their heart rate, and during exercise, it can recommend music that maintains their heart rate. It can also provide music that helps the user sleep comfortably based on their sleep data. In this way, it can provide a music experience that is tailored to the user's health condition.

[0086] Music streaming systems can also estimate the user's emotions and adjust the tempo and genre of music based on those estimates. For example, if a user is feeling sad, a slow ballad can be recommended; if they are excited, fast-paced rock or electronica can be recommended. Furthermore, if a user wants to relax, calming genres such as jazz or classical music can be recommended. This allows for a music experience tailored to the user's emotions.

[0087] Music streaming systems can further analyze users' social media activity and recommend music based on their interests. For example, they can recommend relevant music based on the music users share on social media and the artists they follow. They can also collect information on music events and concerts users attend and recommend live recordings and music by related artists based on this information. This allows for a music experience tailored to the user's social media activity.

[0088] Music streaming systems can also estimate the user's emotions and adjust the music volume and effects based on those estimates. For example, if the user is relaxed, the volume can be lowered and effects such as echo and reverb can be added. Conversely, if the user is excited, the volume can be increased and the bass and drums can be emphasized. This allows for a music experience tailored to the user's emotions.

[0089] Music streaming systems can also provide region-specific music and event information based on the user's geographical location. For example, if a user is in a specific region, the system can recommend traditional music or music by local artists. If a user is traveling, the system can also provide information on music festivals and live events in their destination. This allows for a music experience tailored to the user's geographical location.

[0090] Music streaming systems can also estimate the user's emotions and adjust the playback order of music based on those emotions. For example, if the user is relaxed, the system can set the playback order to start with slower songs and gradually increase the tempo. Conversely, if the user is excited, the system can set the playback order to start with faster songs and gradually decrease the tempo. This allows for a music experience tailored to the user's emotions.

[0091] Music streaming systems can further provide optimal sound quality settings based on the user's device information. For example, if a user is using high-quality headphones, the system can provide high-quality music data. Also, if a user is using their smartphone's speakers, the system can automatically adjust equalizer settings to optimize sound quality. This allows for the provision of an optimal music experience based on the user's device information.

[0092] Music streaming systems can also estimate the user's emotions and adjust the display of lyrics based on those emotions. For example, if the user is relaxed, the lyrics can be displayed in a visually easy-to-understand manner. If the user is stressed, the lyrics can be displayed concisely. Furthermore, if the user is excited, the lyrics can be displayed in detail. This allows for the provision of lyrics that are tailored to the user's emotions.

[0093] Music streaming systems can further provide an optimal interface design based on the user's past operation history. For example, they can prioritize displaying operations that the user has frequently used in the past. They can also suggest optimal button layouts and menu configurations based on the user's operation history. This allows for the provision of an optimal interface design based on the user's past operation history.

[0094] Music streaming systems can also estimate the user's emotions and adjust the visual effects of the music based on those emotions. For example, if the user is relaxed, calming visual effects can be displayed. Conversely, if the user is excited, dynamic and colorful visual effects can be displayed. This allows for the provision of visual effects that respond to the user's emotions.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The data collection unit collects the user's listening history and activity data. Specifically, it collects listening history such as the number of times a song has been played, the duration of playback, and the types of songs played. The data collection unit can also collect activity data such as the user's exercise data, location information, and app usage history. For example, it can collect music data that a user listened to while exercising and associate it with exercise data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses generative AI to perform data mining, statistical analysis, and machine learning algorithms to analyze the data. For example, it can analyze a user's listening history to identify the user's musical preferences and listening patterns. Step 3: The recommendation unit recommends music based on the analysis results obtained by the analysis unit. The recommendation unit uses generative AI to generate music lists and playlists tailored to the user's mood and time of day. For example, it can recommend music for when the user wants to relax. It can also recommend music based on the user's past behavior patterns and trends. Step 4: The notification unit notifies users of music lists and playlists recommended by the recommendation unit. The notification unit notifies users via methods such as push notifications, email notifications, and in-app notifications. For example, it can notify users of customized messages from artists they follow or announcements of live events.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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 collects the user's listening history and activity data using the control unit 46A of the smart device 14. The analysis unit analyzes the data using generated AI using the specific processing unit 290 of the data processing unit 12. The recommendation unit recommends music based on the analysis results using the specific processing unit 290 of the data processing unit 12. The notification unit notifies the user of the recommended music list or playlist using the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the collection unit, analysis unit, recommendation unit, and notification unit, is implemented in, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's listening history and activity data using the control unit 46A of the smart glasses 214. The analysis unit analyzes the data using generated AI using, for example, the identification processing unit 290 of the data processing unit 12. The recommendation unit recommends music based on the analysis results using, for example, the identification processing unit 290 of the data processing unit 12. The notification unit notifies the user of the recommended music list or playlist using, for example, the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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 collects the user's listening history and activity data using the control unit 46A of the headset terminal 314. The analysis unit analyzes the data using generated AI using the specific processing unit 290 of the data processing unit 12. The recommendation unit recommends music based on the analysis results using the specific processing unit 290 of the data processing unit 12. The notification unit notifies the user of the recommended music list or playlist using the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the collection unit, analysis unit, recommendation unit, and notification unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the user's listening history and activity data by the control unit 46A of the robot 414. The analysis unit analyzes the data using generated AI by, for example, the identification processing unit 290 of the data processing unit 12. The recommendation unit recommends music based on the analysis results by, for example, the identification processing unit 290 of the data processing unit 12. The notification unit notifies the user of the recommended music list or playlist by, for example, the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] (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, A recommendation unit recommends music based on the analysis results obtained by the aforementioned analysis unit, The system includes a notification unit that notifies the user of music lists and playlists recommended by the aforementioned recommendation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting listening history and activity data based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) 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 4) 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 5) 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 6) The aforementioned collection unit is When collecting listening history and activity data, the system prioritizes collecting highly relevant data based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting 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 8) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, Adjust the level of detail in the analysis based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, Apply different analysis algorithms depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, Prioritize analysis based on data submission timing. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, 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 14) The aforementioned recommendation department, It estimates the user's emotions and adjusts the way recommendations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned recommendation department, Adjust the level of detail in recommendations based on the importance of the music. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned recommendation department, Apply different recommendation algorithms depending on the music category. The system described in Appendix 1, characterized by the features described herein. (Note 17) 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 18) The aforementioned recommendation department, Prioritizing recommendations based on the submission date of music. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned recommendation department, The recommendation order is adjusted based on the relevance of the music. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned notification unit, It estimates the user's emotions and adjusts how notifications are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned notification unit, The system selects the optimal notification method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned notification unit, It estimates the user's emotions and adjusts the notification procedure based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned notification unit, The optimal notification method is selected based on the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0169] 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, A recommendation unit recommends music based on the analysis results obtained by the aforementioned analysis unit, The system includes a notification unit that notifies the user of music lists and playlists recommended by the aforementioned recommendation unit. A system characterized by the following features.

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

3. 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.

4. 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.

5. 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.

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

7. 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.

8. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system according to feature 1.

9. The aforementioned analysis unit, Adjust the level of detail in the analysis based on the importance of the data. The system according to feature 1.

10. The aforementioned analysis unit, Apply different analysis algorithms depending on the data category. The system according to feature 1.

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