system

Abstract

The system according to this embodiment aims to generate and provide playlists based on the user's musical preferences, mood, and activities. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, and a learning unit. The collection unit collects information about the user's musical preferences, mood, and activities. The analysis unit analyzes the information collected by the collection unit. The generation unit generates a playlist based on the information analyzed by the analysis unit. The provision unit provides the user with the playlist generated by the generation unit. The learning unit learns the user's feedback on the playlist provided by the provision unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, playlist generation based on a user's music preferences, mood, and activities has not been sufficiently performed, and there is room for improvement.

[0005] The system according to the embodiment aims to generate and provide a playlist based on a user's music preferences, mood, and activities.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, and a learning unit. The collection unit collects information about the user's musical preferences, mood, and activities. The analysis unit analyzes the information collected by the collection unit. The generation unit generates a playlist based on the information analyzed by the analysis unit. The provision unit provides the playlist generated by the generation unit to the user. The learning unit learns the user's feedback on the playlist provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can generate and provide playlists based on the user's musical preferences, mood, and activities. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 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 reception 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 reception 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 playlist generation system according to an embodiment of the present invention is a system that generates a personalized playlist in real time based on the user's musical preferences, mood, and activities. This music playlist generation system collects information about the user's musical preferences, mood, and activities, and an AI analyzes the collected information to generate a playlist that is optimal for the user. Furthermore, it learns from the user's feedback and evolves the music experience. Through this mechanism, the user can enjoy a individually customized music experience. For example, the music playlist generation system collects information about the user's musical preferences, mood, and activities. In this process, it collects detailed data such as songs and playlists the user has selected in the past, as well as their current mood and activities. For example, by inputting the user's listening history and current mood, the AI ​​performs analysis based on that information. Next, the AI ​​analyzes the collected information. The AI ​​generates an optimal playlist based on the user's musical preferences, mood, and activities. For example, if the user wants to relax, the AI ​​selects relaxing songs and generates a playlist. Also, if the user is exercising, the AI ​​selects songs with a tempo suitable for exercise and generates a playlist. Furthermore, it learns from the user's feedback and evolves the music experience. By providing feedback on playlists, the AI ​​learns from that feedback and incorporates it into future playlist creation. For example, if a user likes a particular song, the AI ​​will use that information to add similar songs to the next playlist. This mechanism allows users to enjoy a personalized music experience, increasing user satisfaction and app usage time. It also increases opportunities for users to discover new music and artists, enriching their musical experience. For example, if a user enters "I want to relax," the AI ​​analyzes their past song selection history and current mood to create a playlist of relaxing songs. Similarly, if a user enters "I'm exercising," the AI ​​selects songs with a suitable tempo for exercise and creates a playlist. Furthermore, if a user provides feedback such as "I like this song" on a playlist, the AI ​​learns from that information and incorporates it into future playlists.In this way, users can enjoy a music experience tailored to their preferences, mood, and activities. As the AI ​​continuously learns from user feedback, the music experience evolves, and user satisfaction improves. This allows the music playlist generation system to create personalized playlists in real time based on the user's musical preferences, mood, and activities, thereby evolving the music experience.

[0029] The music playlist generation system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, and a learning unit. The collection unit collects information about the user's musical preferences, mood, and activities. For example, the collection unit collects the user's past song selection history, current mood, and activities. The collection unit collects detailed data such as songs and playlists previously selected by the user, as well as their current mood and activities. For example, the collection unit collects information by inputting the user's past song listening history and current mood. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected information and generates an optimal playlist based on the user's musical preferences, mood, and activities. If the user wants to relax, the analysis unit selects relaxing songs and generates a playlist. If the user is exercising, the analysis unit selects songs with a tempo suitable for exercise and generates a playlist. The generation unit generates a playlist based on the information analyzed by the analysis unit. For example, if the user wants to relax, the generation unit selects relaxing songs and generates a playlist. The generation unit, when the user is exercising, selects songs with a tempo suitable for exercise and generates a playlist. The provision unit provides the user with the playlist generated by the generation unit. The provision unit, for example, provides the generated playlist to the user. The provision unit collects feedback when the user provides feedback on the playlist. The learning unit learns the user's feedback on the playlist provided by the provision unit. The learning unit, for example, learns the feedback the user has provided on the playlist and reflects it in future playlist generation. If the user likes a particular song, the learning unit adds similar songs to the next playlist based on that information. As a result, the music playlist generation system according to this embodiment can generate personalized playlists in real time based on the user's musical preferences, mood, and activity, and evolve the music experience.

[0030] The data collection unit collects information about users' musical preferences, moods, and activities. Specifically, it collects detailed data such as users' past music selection history, current mood, and activities. For example, it retrieves the history of songs users have listened to in the past and analyzes which artists and genres they prefer. It also provides an interface for users to input their current mood, offering options such as "want to relax," "want to concentrate," or "exercising." Furthermore, it can utilize data from smartphones and wearable devices to understand users' activities. This allows it to collect information such as whether a user is jogging or working at a desk. The data collection unit collects this information in real time and transmits it to a central database. The collected data is organized by user and managed so that the analysis unit can access it. In addition, the data collection unit can also collect users' music streaming service usage history and music-related posts on social media. This allows for a more comprehensive understanding of users' musical preferences, moods, and activities. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the information collected by the data collection unit. Specifically, it analyzes the collected information and generates an optimal playlist based on the user's musical preferences, mood, and activities. The analysis unit uses AI to process data in real time and select songs that are best suited to the user's preferences, mood, and activities. For example, if the user wants to relax, it selects relaxing songs and generates a playlist. The AI ​​identifies songs suitable for relaxation based on past song selection history and user feedback. Also, if the user is exercising, it selects songs with a tempo suitable for exercise and generates a playlist. The AI ​​selects songs with the optimal tempo according to the heart rate and pace during exercise. Furthermore, the analysis unit integrates and analyzes multiple data sources to generate playlists tailored to the user's mood and activities. For example, in addition to the user's current mood and activities, it can also consider external data such as weather information and time of day. This allows the analysis unit to generate more accurate playlists. The analysis unit can also utilize past data and statistical information to analyze long-term user preferences and trends. For example, based on past song selection history, the system can identify song preferences for specific seasons and time periods, and reflect this in future playlist generation. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only understand the situation in real time but also to analyze long-term user preferences and trends, improving the overall reliability and accuracy of the system.

[0032] The generation unit generates playlists based on information analyzed by the analysis unit. Specifically, if the user wants to relax, it selects relaxing songs and generates a playlist. The generation unit uses AI to select songs best suited to the user's preferences, mood, and activity, and builds a playlist. For example, if the user is exercising, it selects songs with a tempo suitable for exercise and generates a playlist. The AI ​​selects songs with the optimal tempo according to the heart rate and pace during exercise. The generation unit integrates and analyzes multiple data sources to generate playlists tailored to the user's mood and activity. For example, in addition to the user's current mood and activity, it can also consider external data such as weather information and time of day. This allows the generation unit to generate more accurate playlists. The generation unit can also utilize past data and statistical information to analyze long-term user preferences and trends. For example, based on past song selection history, it can understand the trends in songs preferred during specific seasons or times of day and reflect this in future playlist generation. Furthermore, the generation unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. This allows the generation unit to not only grasp the situation in real time but also to analyze long-term user preferences and trends, thereby improving the reliability and accuracy of the entire system.

[0033] The delivery unit provides users with playlists generated by the generation unit. Specifically, it provides users with generated playlists and collects feedback when users provide feedback on the playlists. The delivery unit displays the generated playlists to the user through the user interface and starts playback. Users can provide feedback on the playlist, such as "like" or "dislike." The delivery unit collects this feedback and sends it to the learning unit. Furthermore, the delivery unit can reflect user feedback in real time and adjust the content of the playlist. For example, if a user rates a particular song as disliked, that song will be removed from the playlist and another song added instead. The delivery unit can also optimize the playlist's structure and song order based on user feedback. This allows the delivery unit to always provide users with the best possible music experience. The delivery unit supports multiple devices and platforms, allowing users to access playlists from any device. For example, playlists can be played on various devices such as smartphones, tablets, PCs, and smart speakers. This allows the delivery unit to improve user convenience and provide a seamless music experience.

[0034] The learning unit learns from user feedback on playlists provided by the service provider. Specifically, it learns the feedback users provide to playlists and incorporates this into future playlist generation. The learning unit uses AI to analyze user feedback and understand user preferences and trends. For example, if a user likes a particular song, it adds similar songs to the next playlist based on that information. Also, if a user likes a particular genre or artist, it generates a playlist based on that information. The learning unit continuously learns from user feedback to improve the accuracy of the playlists. Furthermore, the learning unit can optimize the playlist structure and song order based on user feedback. For example, if a user rates a particular song as disliked, it removes that song from the playlist and adds another song instead. Also, if a user likes a particular genre at a particular time of day, it generates a playlist based on that information. In this way, the learning unit can always provide users with the best possible music experience. The learning unit can also utilize past data and statistical information to analyze long-term user preferences and trends. For example, based on past song selection history, the system can identify song preferences for specific seasons or time periods and reflect this in future playlist generation. Furthermore, the learning unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the learning unit to not only understand real-time conditions but also analyze long-term user preferences and trends, improving the overall reliability and accuracy of the system.

[0035] The data collection unit can collect the user's past song selection history, current mood, and activities. For example, the data collection unit can collect the user's past song selection history. The data collection unit can collect information such as the number of plays, playback time, and number of skips. The data collection unit can collect the user's current mood. The data collection unit can collect the current mood through methods such as user input, sensor information, and facial recognition. The data collection unit can collect information about the user's activities. The data collection unit can collect information about activities such as exercise, studying, and working. As a result, the data collection unit can generate a more accurate playlist by collecting the user's past song selection history, current mood, and activities. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past song selection history into AI, and the AI ​​can analyze and collect that information.

[0036] The analysis unit can analyze the collected information and generate an optimal playlist based on the user's musical preferences, mood, and activities. For example, the analysis unit analyzes the collected information. The analysis unit generates an optimal playlist based on the user's musical preferences, mood, and activities. If the user wants to relax, the analysis unit selects relaxing songs and generates a playlist. If the user is exercising, the analysis unit selects songs with a tempo suitable for exercise and generates a playlist. In this way, the analysis unit can generate an optimal playlist based on the user's musical preferences, mood, and activities by analyzing the collected information. 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 collected information into an AI, which can then analyze the information and generate an optimal playlist.

[0037] The generation unit can select relaxing songs and generate a playlist when the user feels the need to relax. For example, if the user feels the need to relax, the generation unit can select relaxing songs and generate a playlist. The generation unit can select relaxing songs based on criteria such as tempo, melody, and lyrics. The generation unit can analyze the user's past song selection history and current mood to select relaxing songs. As a result, the generation unit can select relaxing songs and generate a playlist when the user feels the need to relax. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's mood into AI, and the AI ​​can analyze that information to select relaxing songs.

[0038] The generation unit can select songs with a tempo suitable for exercise and generate a playlist when the user is exercising. For example, the generation unit can select songs with a tempo suitable for exercise and generate a playlist when the user is exercising. The generation unit can select songs with a tempo suitable for exercise based on criteria such as BPM (beats per minute), rhythm, and energy level. The generation unit can analyze the user's past song selection history and current activity to select songs with a tempo suitable for exercise. As a result, the generation unit can select songs with a tempo suitable for exercise and generate a playlist when the user is exercising. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's activity into AI, and the AI ​​can analyze that information to select songs with a tempo suitable for exercise.

[0039] The learning unit can learn from the feedback users provide to playlists and incorporate that learning into future playlist generation. For example, the learning unit learns from the feedback users provide to playlists. If a user likes a particular song, the learning unit adds similar songs to the next playlist based on that information. The learning unit can collect and learn from user feedback such as ratings, comments, and play counts. This allows the learning unit to improve the music experience by learning from the feedback users provide to playlists and incorporating that learning into future playlist generation. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user feedback into AI, which can then analyze and learn from that information.

[0040] The collection unit can analyze the user's past music selection history and select the optimal collection method. For example, the collection unit may prioritize collecting songs that the user has listened to frequently in the past. The collection unit may analyze the trends in songs that the user listens to at a specific time of day and collect songs that are suitable for that time of day. If the user prefers a particular artist, the collection unit may prioritize collecting new songs by that artist. In this way, the collection unit can select the optimal collection method by analyzing the user's past music selection history. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's past music selection history into AI, which can then analyze that information and select the optimal collection method.

[0041] The data collection unit can filter the collected information on the user's current lifestyle and areas of interest when gathering information on musical preferences, mood, and activities. For example, if the user is working, the unit will collect music that enhances concentration. If the user is exercising, the unit will collect upbeat music. If the user wants to relax, the unit will collect calming music. In this way, the unit can collect more relevant information by filtering based on the user's current lifestyle and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the user's lifestyle and areas of interest into an AI, which can then analyze and filter that information.

[0042] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location when gathering information on musical preferences, mood, and activities. For example, if the user is traveling, the data collection unit will prioritize collecting music from that region. If the user is attending a specific event, the data collection unit will collect music related to that event. If the user is in a specific location, the data collection unit will collect music related to that location. In this way, the data collection unit can collect more appropriate information by prioritizing the collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's geographical location information into AI, which can then analyze that information and prioritize the collection of highly relevant information.

[0043] The data collection unit can collect relevant information by analyzing the user's social media activity when collecting information on their musical preferences, mood, and activities. For example, the data collection unit can collect music that the user has shared on social media. The data collection unit can collect new songs from artists that the user follows on social media. The data collection unit can collect music related to events that the user has participated in on social media. In this way, the data collection unit can collect relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media activity into AI, and the AI ​​can analyze that information and collect relevant information.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information during the analysis. For example, the analysis unit can analyze highly important information in detail and less important information in a simplified manner. The analysis unit prioritizes analyzing highly important information based on the user's past song selection history. The analysis unit also prioritizes analyzing highly important information based on the user's current mood and activities. As a result, the analysis unit can analyze more important information in detail by adjusting the level of detail of the analysis based on the importance of the collected information. 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 collected information into AI, which can analyze the information and adjust the level of detail of the analysis based on its importance.

[0045] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply different analysis algorithms depending on the genre of music. The analysis unit can apply different analysis algorithms depending on the user's mood. The analysis unit can apply different analysis algorithms depending on the user's activities. By doing so, the analysis unit can provide more appropriate analysis results by applying different analysis algorithms depending on the category of information. 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 category of information into the AI, and the AI ​​can analyze that information and apply different analysis algorithms.

[0046] The analysis unit can determine the priority of analysis based on when the information was collected. For example, the analysis unit may prioritize the analysis of the most recent information. The analysis unit may prioritize the analysis of important information based on the user's past song selection history. The analysis unit may prioritize the analysis of important information based on the user's current mood and activities. In this way, the analysis unit can prioritize the analysis of more important information by determining the priority of analysis based on when the information was collected. 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 information collection time into the AI, and the AI ​​can analyze that information to determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit prioritizes analyzing highly relevant information. The analysis unit prioritizes analyzing highly relevant information based on the user's past song selection history. The analysis unit prioritizes analyzing highly relevant information based on the user's current mood and activities. In this way, the analysis unit can prioritize the analysis of more relevant information by adjusting the order of analysis based on the relevance of the information. 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 information into the AI, which can then analyze the information and adjust the order of analysis.

[0048] The generation unit can analyze the user's past music selection history to select the most suitable songs when generating a playlist. For example, the generation unit can prioritize adding songs that the user has frequently listened to in the past to the playlist. The generation unit can analyze the user's listening habits at specific times of day and add songs suitable for those times to the playlist. If the user prefers a particular artist, the generation unit can prioritize adding songs by that artist to the playlist. In this way, the generation unit can select the most suitable songs by analyzing the user's past music selection history. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past music selection history into an AI, which can then analyze that information to select the most suitable songs.

[0049] The generation unit can customize song selection based on the user's current lifestyle when generating a playlist. For example, if the user is working, the generation unit adds songs to enhance concentration to the playlist. If the user is exercising, the generation unit adds upbeat songs to the playlist. If the user wants to relax, the generation unit adds calming songs to the playlist. In this way, the generation unit can generate a more appropriate playlist by customizing song selection based on the user's current lifestyle. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the user's lifestyle into AI, and the AI ​​can analyze that information to customize song selection.

[0050] The generation unit can select the most suitable songs when generating a playlist, taking into account the user's geographical location. For example, if the user is traveling, the generation unit will add music from that region to the playlist. If the user is attending a specific event, the generation unit will add music related to that event to the playlist. If the user is in a specific location, the generation unit will add music related to that location to the playlist. In this way, the generation unit can provide a more appropriate playlist by selecting the most suitable songs while considering the user's geographical location. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the user's geographical location information into AI, which can then analyze that information to select the most suitable songs.

[0051] The generation unit can analyze the user's social media activity to select songs when generating a playlist. For example, the generation unit can add music that the user has shared on social media to the playlist. The generation unit can add new songs from artists that the user follows on social media to the playlist. The generation unit can add music related to events that the user is participating in on social media to the playlist. In this way, the generation unit can select more appropriate songs by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the user's social media activity into AI, and the AI ​​can analyze that information to select songs.

[0052] The service provider can select the optimal service method when providing playlists by referring to the user's past feedback. For example, the service provider may prioritize service methods that the user has preferred in the past. The service provider may customize the service method based on the user's past feedback. The service provider may avoid service methods that the user has previously expressed dissatisfaction with. In this way, the service provider can select a more appropriate service method by referring to the user's past feedback. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider may input the user's past feedback into AI, which can then analyze the information and select the optimal service method.

[0053] The service provider can select the optimal service method when providing a playlist, taking into account the user's device information. For example, if the user is using a smartphone, the service provider will select a service method that matches the screen size. If the user is using a tablet, the service provider will select a service method optimized for a larger screen. If the user is using a smartwatch, the service provider will select a concise and highly visible service method. By selecting the optimal service method considering the user's device information, the service provider can provide a more appropriate music experience. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's device information into an AI, which can then analyze that information to select the optimal service method.

[0054] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit selects the optimal learning algorithm based on past learning data. The learning unit analyzes past learning data and adjusts the parameters of the learning algorithm. The learning unit improves the accuracy of the learning algorithm by referring to past learning data. As a result, the learning unit can perform more accurate learning by optimizing the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into AI, and the AI ​​can analyze that information to optimize the learning algorithm.

[0055] The learning unit can weight the training data based on when the collected information was submitted during the learning process. For example, the learning unit prioritizes learning the most recent information. The learning unit prioritizes learning important information based on the user's past song selection history. The learning unit prioritizes learning important information based on the user's current mood and activities. In this way, the learning unit can prioritize learning more important information by weighting the training data based on when the collected information was submitted. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the submission dates of the collected information into the AI, and the AI ​​can analyze that information to weight the training data.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The data collection unit can analyze users' social media activity and collect music they've shared and new releases from artists they follow. For example, it can collect music users have shared on social media, new releases from artists they follow, and music related to events they've attended. This allows the data collection unit to gather more relevant information by analyzing users' social media activity.

[0058] The generation unit can also generate playlists while considering the user's geographical location. For example, if the user is traveling, music from that region will be added to the playlist. If the user is attending a specific event, music related to that event will be added to the playlist. If the user is in a specific location, music related to that location will be added to the playlist. This allows the generation unit to provide more appropriate playlists by selecting the most suitable songs while considering the user's geographical location.

[0059] The service provider can also adjust how playlists are delivered, taking into account the user's device information. For example, if the user is using a smartphone, they can select a delivery method that matches the screen size. If the user is using a tablet, they can select a delivery method optimized for a larger screen. If the user is using a smartwatch, they can select a concise and highly visible delivery method. This allows the service provider to offer a more appropriate music experience by selecting the optimal delivery method based on the user's device information.

[0060] The analysis unit can also adjust the level of detail of the analysis based on the importance of the collected information. For example, it can analyze highly important information in detail and less important information in a simplified manner. It can prioritize the analysis of highly important information based on the user's past song selection history. It can also prioritize the analysis of highly important information based on the user's current mood and activities. In this way, the analysis unit can analyze more important information in detail by adjusting the level of detail of the analysis based on the importance of the collected information.

[0061] The service provider can also adjust how playlists are delivered by referring to past user feedback. For example, they can prioritize delivery methods that users have preferred in the past. They can customize delivery methods based on past user feedback. They can avoid delivery methods that users have previously expressed dissatisfaction with. This allows the service provider to select more appropriate delivery methods by referring to past user feedback.

[0062] The data collection unit can also collect information about the user's musical preferences, mood, and activities, taking into account their geographical location. For example, if the user is traveling, it can collect music from that region. If the user is attending a specific event, it can collect music related to that event. If the user is in a specific location, it can collect music related to that location. This allows the data collection unit to collect more relevant information by considering the user's geographical location, resulting in more appropriate information.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The data collection unit gathers information about the user's musical preferences, mood, and activities. For example, it collects the user's past song selection history, current mood, and activities. The data collection unit collects detailed data such as songs and playlists the user has selected in the past, as well as their current mood and activities. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it analyzes the collected information and generates an optimal playlist based on the user's musical preferences, mood, and activities. If the user wants to relax, the analysis unit selects relaxing songs and generates a playlist. If the user is exercising, it selects songs with a tempo suitable for exercise and generates a playlist. Step 3: The generation unit generates a playlist based on the information analyzed by the analysis unit. For example, if the user wants to relax, it selects relaxing songs and generates a playlist. If the user is exercising, it selects songs with a tempo suitable for exercise and generates a playlist. Step 4: The providing unit provides the user with the playlist generated by the generating unit. For example, it provides the generated playlist to the user and collects feedback by having the user provide feedback on the playlist. Step 5: The learning unit learns from user feedback on the playlists provided by the providing unit. For example, it learns the feedback users provide on playlists and reflects this in future playlist generation. If a user likes a particular song, it uses that information to add similar songs to the next playlist.

[0065] (Example of form 2) The music playlist generation system according to an embodiment of the present invention is a system that generates a personalized playlist in real time based on the user's musical preferences, mood, and activities. This music playlist generation system collects information about the user's musical preferences, mood, and activities, and an AI analyzes the collected information to generate a playlist that is optimal for the user. Furthermore, it learns from the user's feedback and evolves the music experience. Through this mechanism, the user can enjoy a individually customized music experience. For example, the music playlist generation system collects information about the user's musical preferences, mood, and activities. In this process, it collects detailed data such as songs and playlists the user has selected in the past, as well as their current mood and activities. For example, by inputting the user's listening history and current mood, the AI ​​performs analysis based on that information. Next, the AI ​​analyzes the collected information. The AI ​​generates an optimal playlist based on the user's musical preferences, mood, and activities. For example, if the user wants to relax, the AI ​​selects relaxing songs and generates a playlist. Also, if the user is exercising, the AI ​​selects songs with a tempo suitable for exercise and generates a playlist. Furthermore, it learns from the user's feedback and evolves the music experience. By providing feedback on playlists, the AI ​​learns from that feedback and incorporates it into future playlist creation. For example, if a user likes a particular song, the AI ​​will use that information to add similar songs to the next playlist. This mechanism allows users to enjoy a personalized music experience, increasing user satisfaction and app usage time. It also increases opportunities for users to discover new music and artists, enriching their musical experience. For example, if a user enters "I want to relax," the AI ​​analyzes their past song selection history and current mood to create a playlist of relaxing songs. Similarly, if a user enters "I'm exercising," the AI ​​selects songs with a suitable tempo for exercise and creates a playlist. Furthermore, if a user provides feedback such as "I like this song" on a playlist, the AI ​​learns from that information and incorporates it into future playlists.In this way, users can enjoy a music experience tailored to their preferences, mood, and activities. As the AI ​​continuously learns from user feedback, the music experience evolves, and user satisfaction improves. This allows the music playlist generation system to create personalized playlists in real time based on the user's musical preferences, mood, and activities, thereby evolving the music experience.

[0066] The music playlist generation system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, and a learning unit. The collection unit collects information about the user's musical preferences, mood, and activities. For example, the collection unit collects the user's past song selection history, current mood, and activities. The collection unit collects detailed data such as songs and playlists previously selected by the user, as well as their current mood and activities. For example, the collection unit collects information by inputting the user's past song listening history and current mood. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected information and generates an optimal playlist based on the user's musical preferences, mood, and activities. If the user wants to relax, the analysis unit selects relaxing songs and generates a playlist. If the user is exercising, the analysis unit selects songs with a tempo suitable for exercise and generates a playlist. The generation unit generates a playlist based on the information analyzed by the analysis unit. For example, if the user wants to relax, the generation unit selects relaxing songs and generates a playlist. The generation unit, when the user is exercising, selects songs with a tempo suitable for exercise and generates a playlist. The provision unit provides the user with the playlist generated by the generation unit. The provision unit, for example, provides the generated playlist to the user. The provision unit collects feedback when the user provides feedback on the playlist. The learning unit learns the user's feedback on the playlist provided by the provision unit. The learning unit, for example, learns the feedback the user has provided on the playlist and reflects it in future playlist generation. If the user likes a particular song, the learning unit adds similar songs to the next playlist based on that information. As a result, the music playlist generation system according to this embodiment can generate personalized playlists in real time based on the user's musical preferences, mood, and activity, and evolve the music experience.

[0067] The data collection unit collects information about users' musical preferences, moods, and activities. Specifically, it collects detailed data such as users' past music selection history, current mood, and activities. For example, it retrieves the history of songs users have listened to in the past and analyzes which artists and genres they prefer. It also provides an interface for users to input their current mood, offering options such as "want to relax," "want to concentrate," or "exercising." Furthermore, it can utilize data from smartphones and wearable devices to understand users' activities. This allows it to collect information such as whether a user is jogging or working at a desk. The data collection unit collects this information in real time and transmits it to a central database. The collected data is organized by user and managed so that the analysis unit can access it. In addition, the data collection unit can also collect users' music streaming service usage history and music-related posts on social media. This allows for a more comprehensive understanding of users' musical preferences, moods, and activities. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0068] The analysis unit analyzes the information collected by the data collection unit. Specifically, it analyzes the collected information and generates an optimal playlist based on the user's musical preferences, mood, and activities. The analysis unit uses AI to process data in real time and select songs that are best suited to the user's preferences, mood, and activities. For example, if the user wants to relax, it selects relaxing songs and generates a playlist. The AI ​​identifies songs suitable for relaxation based on past song selection history and user feedback. Also, if the user is exercising, it selects songs with a tempo suitable for exercise and generates a playlist. The AI ​​selects songs with the optimal tempo according to the heart rate and pace during exercise. Furthermore, the analysis unit integrates and analyzes multiple data sources to generate playlists tailored to the user's mood and activities. For example, in addition to the user's current mood and activities, it can also consider external data such as weather information and time of day. This allows the analysis unit to generate more accurate playlists. The analysis unit can also utilize past data and statistical information to analyze long-term user preferences and trends. For example, based on past song selection history, the system can identify song preferences for specific seasons and time periods, and reflect this in future playlist generation. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only understand the situation in real time but also to analyze long-term user preferences and trends, improving the overall reliability and accuracy of the system.

[0069] The generation unit generates playlists based on information analyzed by the analysis unit. Specifically, if the user wants to relax, it selects relaxing songs and generates a playlist. The generation unit uses AI to select songs best suited to the user's preferences, mood, and activity, and builds a playlist. For example, if the user is exercising, it selects songs with a tempo suitable for exercise and generates a playlist. The AI ​​selects songs with the optimal tempo according to the heart rate and pace during exercise. The generation unit integrates and analyzes multiple data sources to generate playlists tailored to the user's mood and activity. For example, in addition to the user's current mood and activity, it can also consider external data such as weather information and time of day. This allows the generation unit to generate more accurate playlists. The generation unit can also utilize past data and statistical information to analyze long-term user preferences and trends. For example, based on past song selection history, it can understand the trends in songs preferred during specific seasons or times of day and reflect this in future playlist generation. Furthermore, the generation unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. This allows the generation unit to not only grasp the situation in real time but also to analyze long-term user preferences and trends, thereby improving the reliability and accuracy of the entire system.

[0070] The delivery unit provides users with playlists generated by the generation unit. Specifically, it provides users with generated playlists and collects feedback when users provide feedback on the playlists. The delivery unit displays the generated playlists to the user through the user interface and starts playback. Users can provide feedback on the playlist, such as "like" or "dislike." The delivery unit collects this feedback and sends it to the learning unit. Furthermore, the delivery unit can reflect user feedback in real time and adjust the content of the playlist. For example, if a user rates a particular song as disliked, that song will be removed from the playlist and another song added instead. The delivery unit can also optimize the playlist's structure and song order based on user feedback. This allows the delivery unit to always provide users with the best possible music experience. The delivery unit supports multiple devices and platforms, allowing users to access playlists from any device. For example, playlists can be played on various devices such as smartphones, tablets, PCs, and smart speakers. This allows the delivery unit to improve user convenience and provide a seamless music experience.

[0071] The learning unit learns from user feedback on playlists provided by the service provider. Specifically, it learns the feedback users provide to playlists and incorporates this into future playlist generation. The learning unit uses AI to analyze user feedback and understand user preferences and trends. For example, if a user likes a particular song, it adds similar songs to the next playlist based on that information. Also, if a user likes a particular genre or artist, it generates a playlist based on that information. The learning unit continuously learns from user feedback to improve the accuracy of the playlists. Furthermore, the learning unit can optimize the playlist structure and song order based on user feedback. For example, if a user rates a particular song as disliked, it removes that song from the playlist and adds another song instead. Also, if a user likes a particular genre at a particular time of day, it generates a playlist based on that information. In this way, the learning unit can always provide users with the best possible music experience. The learning unit can also utilize past data and statistical information to analyze long-term user preferences and trends. For example, based on past song selection history, the system can identify song preferences for specific seasons or time periods and reflect this in future playlist generation. Furthermore, the learning unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the learning unit to not only understand real-time conditions but also analyze long-term user preferences and trends, improving the overall reliability and accuracy of the system.

[0072] The data collection unit can collect the user's past song selection history, current mood, and activities. For example, the data collection unit can collect the user's past song selection history. The data collection unit can collect information such as the number of plays, playback time, and number of skips. The data collection unit can collect the user's current mood. The data collection unit can collect the current mood through methods such as user input, sensor information, and facial recognition. The data collection unit can collect information about the user's activities. The data collection unit can collect information about activities such as exercise, studying, and working. As a result, the data collection unit can generate a more accurate playlist by collecting the user's past song selection history, current mood, and activities. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past song selection history into AI, and the AI ​​can analyze and collect that information.

[0073] The analysis unit can analyze the collected information and generate an optimal playlist based on the user's musical preferences, mood, and activities. For example, the analysis unit analyzes the collected information. The analysis unit generates an optimal playlist based on the user's musical preferences, mood, and activities. If the user wants to relax, the analysis unit selects relaxing songs and generates a playlist. If the user is exercising, the analysis unit selects songs with a tempo suitable for exercise and generates a playlist. In this way, the analysis unit can generate an optimal playlist based on the user's musical preferences, mood, and activities by analyzing the collected information. 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 collected information into an AI, which can then analyze the information and generate an optimal playlist.

[0074] The generation unit can select relaxing songs and generate a playlist when the user feels the need to relax. For example, if the user feels the need to relax, the generation unit can select relaxing songs and generate a playlist. The generation unit can select relaxing songs based on criteria such as tempo, melody, and lyrics. The generation unit can analyze the user's past song selection history and current mood to select relaxing songs. As a result, the generation unit can select relaxing songs and generate a playlist when the user feels the need to relax. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's mood into AI, and the AI ​​can analyze that information to select relaxing songs.

[0075] The generation unit can select songs with a tempo suitable for exercise and generate a playlist when the user is exercising. For example, the generation unit can select songs with a tempo suitable for exercise and generate a playlist when the user is exercising. The generation unit can select songs with a tempo suitable for exercise based on criteria such as BPM (beats per minute), rhythm, and energy level. The generation unit can analyze the user's past song selection history and current activity to select songs with a tempo suitable for exercise. As a result, the generation unit can select songs with a tempo suitable for exercise and generate a playlist when the user is exercising. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's activity into AI, and the AI ​​can analyze that information to select songs with a tempo suitable for exercise.

[0076] The learning unit can learn from the feedback users provide to playlists and incorporate that learning into future playlist generation. For example, the learning unit learns from the feedback users provide to playlists. If a user likes a particular song, the learning unit adds similar songs to the next playlist based on that information. The learning unit can collect and learn from user feedback such as ratings, comments, and play counts. This allows the learning unit to improve the music experience by learning from the feedback users provide to playlists and incorporating that learning into future playlist generation. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user feedback into AI, which can then analyze and learn from that information.

[0077] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions, such as music preferences, mood, and activities. For example, if the user is stressed, the data collection unit prioritizes collecting relaxing music. If the user is having fun, the data collection unit prioritizes collecting upbeat music. If the user is tired, the data collection unit prioritizes collecting calming music. In this way, the data collection unit can collect information at a more appropriate time by adjusting the collection timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into an AI, which can then analyze the information and adjust the collection timing.

[0078] The collection unit can analyze the user's past music selection history and select the optimal collection method. For example, the collection unit may prioritize collecting songs that the user has listened to frequently in the past. The collection unit may analyze the trends in songs that the user listens to at a specific time of day and collect songs that are suitable for that time of day. If the user prefers a particular artist, the collection unit may prioritize collecting new songs by that artist. In this way, the collection unit can select the optimal collection method by analyzing the user's past music selection history. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's past music selection history into AI, which can then analyze that information and select the optimal collection method.

[0079] The data collection unit can filter the collected information on the user's current lifestyle and areas of interest when gathering information on musical preferences, mood, and activities. For example, if the user is working, the unit will collect music that enhances concentration. If the user is exercising, the unit will collect upbeat music. If the user wants to relax, the unit will collect calming music. In this way, the unit can collect more relevant information by filtering based on the user's current lifestyle and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the user's lifestyle and areas of interest into an AI, which can then analyze and filter that information.

[0080] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting relaxing music. If the user is having fun, the data collection unit will prioritize collecting upbeat music. If the user is tired, the data collection unit will prioritize collecting calming music. In this way, the data collection unit can prioritize collecting more appropriate information by determining the priority of information to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the 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 an AI, and the AI ​​can analyze that information to determine the priority of information to collect.

[0081] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location when gathering information on musical preferences, mood, and activities. For example, if the user is traveling, the data collection unit will prioritize collecting music from that region. If the user is attending a specific event, the data collection unit will collect music related to that event. If the user is in a specific location, the data collection unit will collect music related to that location. In this way, the data collection unit can collect more appropriate information by prioritizing the collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's geographical location information into AI, which can then analyze that information and prioritize the collection of highly relevant information.

[0082] The data collection unit can collect relevant information by analyzing the user's social media activity when collecting information on their musical preferences, mood, and activities. For example, the data collection unit can collect music that the user has shared on social media. The data collection unit can collect new songs from artists that the user follows on social media. The data collection unit can collect music related to events that the user has participated in on social media. In this way, the data collection unit can collect relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media activity into AI, and the AI ​​can analyze that information and collect relevant information.

[0083] 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 displays the analysis results in visually calming colors. If the user is excited, the analysis unit displays the analysis results in visually stimulating colors. If the user is tired, the analysis unit displays the analysis results in a simple and easily readable format. In this way, the analysis unit can provide more appropriate analysis results by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into an AI, and the AI ​​can analyze that information to adjust the presentation of the analysis.

[0084] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information during the analysis. For example, the analysis unit can analyze highly important information in detail and less important information in a simplified manner. The analysis unit prioritizes analyzing highly important information based on the user's past song selection history. The analysis unit also prioritizes analyzing highly important information based on the user's current mood and activities. As a result, the analysis unit can analyze more important information in detail by adjusting the level of detail of the analysis based on the importance of the collected information. 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 collected information into AI, which can analyze the information and adjust the level of detail of the analysis based on its importance.

[0085] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply different analysis algorithms depending on the genre of music. The analysis unit can apply different analysis algorithms depending on the user's mood. The analysis unit can apply different analysis algorithms depending on the user's activities. By doing so, the analysis unit can provide more appropriate analysis results by applying different analysis algorithms depending on the category of information. 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 category of information into the AI, and the AI ​​can analyze that information and apply different analysis algorithms.

[0086] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis. If the user is relaxed, the analysis unit provides a detailed analysis. If the user is excited, the analysis unit provides a visually stimulating analysis. In this way, the analysis unit can provide more appropriate analysis results by adjusting the length of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into an AI, and the AI ​​can analyze that information and adjust the length of the analysis.

[0087] The analysis unit can determine the priority of analysis based on when the information was collected. For example, the analysis unit may prioritize the analysis of the most recent information. The analysis unit may prioritize the analysis of important information based on the user's past song selection history. The analysis unit may prioritize the analysis of important information based on the user's current mood and activities. In this way, the analysis unit can prioritize the analysis of more important information by determining the priority of analysis based on when the information was collected. 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 information collection time into the AI, and the AI ​​can analyze that information to determine the priority of analysis.

[0088] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit prioritizes analyzing highly relevant information. The analysis unit prioritizes analyzing highly relevant information based on the user's past song selection history. The analysis unit prioritizes analyzing highly relevant information based on the user's current mood and activities. In this way, the analysis unit can prioritize the analysis of more relevant information by adjusting the order of analysis based on the relevance of the information. 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 information into the AI, which can then analyze the information and adjust the order of analysis.

[0089] The generation unit can estimate the user's emotions and adjust the playlist generation method based on the estimated emotions. For example, if the user wants to relax, the generation unit will select relaxing songs and generate a playlist. If the user is exercising, the generation unit will select songs with a tempo suitable for exercise and generate a playlist. If the user wants to concentrate, the generation unit will select songs that enhance concentration and generate a playlist. In this way, the generation unit can generate more appropriate playlists by adjusting the playlist generation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into an AI, and the AI ​​can analyze that information to adjust the playlist generation method.

[0090] The generation unit can analyze the user's past music selection history to select the most suitable songs when generating a playlist. For example, the generation unit can prioritize adding songs that the user has frequently listened to in the past to the playlist. The generation unit can analyze the user's listening habits at specific times of day and add songs suitable for those times to the playlist. If the user prefers a particular artist, the generation unit can prioritize adding songs by that artist to the playlist. In this way, the generation unit can select the most suitable songs by analyzing the user's past music selection history. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past music selection history into an AI, which can then analyze that information to select the most suitable songs.

[0091] The generation unit can customize song selection based on the user's current lifestyle when generating a playlist. For example, if the user is working, the generation unit adds songs to enhance concentration to the playlist. If the user is exercising, the generation unit adds upbeat songs to the playlist. If the user wants to relax, the generation unit adds calming songs to the playlist. In this way, the generation unit can generate a more appropriate playlist by customizing song selection based on the user's current lifestyle. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the user's lifestyle into AI, and the AI ​​can analyze that information to customize song selection.

[0092] The generation unit can estimate the user's emotions and determine the priority of the playlist based on the estimated emotions. For example, if the user wants to relax, the generation unit will prioritize adding relaxing songs to the playlist. If the user is exercising, the generation unit will prioritize adding songs with a tempo suitable for exercise. If the user wants to concentrate, the generation unit will prioritize adding songs that enhance concentration. In this way, the generation unit can provide a more appropriate playlist by prioritizing the playlist based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into an AI, and the AI ​​can analyze that information to determine the priority of the playlist.

[0093] The generation unit can select the most suitable songs when generating a playlist, taking into account the user's geographical location. For example, if the user is traveling, the generation unit will add music from that region to the playlist. If the user is attending a specific event, the generation unit will add music related to that event to the playlist. If the user is in a specific location, the generation unit will add music related to that location to the playlist. In this way, the generation unit can provide a more appropriate playlist by selecting the most suitable songs while considering the user's geographical location. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the user's geographical location information into AI, which can then analyze that information to select the most suitable songs.

[0094] The generation unit can analyze the user's social media activity to select songs when generating a playlist. For example, the generation unit can add music that the user has shared on social media to the playlist. The generation unit can add new songs from artists that the user follows on social media to the playlist. The generation unit can add music related to events that the user is participating in on social media to the playlist. In this way, the generation unit can select more appropriate songs by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the user's social media activity into AI, and the AI ​​can analyze that information to select songs.

[0095] The service provider can estimate the user's emotions and adjust how playlists are presented based on those emotions. For example, if the user is relaxed, the service provider will provide calming music. If the user is exercising, the service provider will provide upbeat music. If the user wants to concentrate, the service provider will provide music that enhances concentration. In this way, the service provider can provide a more appropriate musical experience by adjusting how playlists are presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into an AI, and the AI ​​can analyze that information to adjust how playlists are presented.

[0096] The service provider can select the optimal service method when providing playlists by referring to the user's past feedback. For example, the service provider may prioritize service methods that the user has preferred in the past. The service provider may customize the service method based on the user's past feedback. The service provider may avoid service methods that the user has previously expressed dissatisfaction with. In this way, the service provider can select a more appropriate service method by referring to the user's past feedback. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider may input the user's past feedback into AI, which can then analyze the information and select the optimal service method.

[0097] The playback unit can estimate the user's emotions and adjust the order in which the playlist is presented based on the estimated emotions. For example, if the user is relaxed, the playback unit will present calming songs first. If the user is exercising, the playback unit will present upbeat songs first. If the user wants to concentrate, the playback unit will present songs that enhance concentration first. In this way, the playback unit can provide a more appropriate musical experience by adjusting the order in which the playlist is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the playback unit may be performed using AI or not using AI. For example, the playback unit can input user emotion data into an AI, and the AI ​​can analyze that information to adjust the order in which the playlist is presented.

[0098] The service provider can select the optimal service method when providing a playlist, taking into account the user's device information. For example, if the user is using a smartphone, the service provider will select a service method that matches the screen size. If the user is using a tablet, the service provider will select a service method optimized for a larger screen. If the user is using a smartwatch, the service provider will select a concise and highly visible service method. By selecting the optimal service method considering the user's device information, the service provider can provide a more appropriate music experience. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's device information into an AI, which can then analyze that information to select the optimal service method.

[0099] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is relaxed, the learning unit will prioritize learning feedback for relaxing music. If the user is exercising, the learning unit will prioritize learning feedback for music suitable for exercise. If the user wants to concentrate, the learning unit will prioritize learning feedback for music that enhances concentration. In this way, the learning unit can perform more appropriate learning by selecting training data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user emotion data into an AI, and the AI ​​can analyze that information to select training data.

[0100] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit selects the optimal learning algorithm based on past learning data. The learning unit analyzes past learning data and adjusts the parameters of the learning algorithm. The learning unit improves the accuracy of the learning algorithm by referring to past learning data. As a result, the learning unit can perform more accurate learning by optimizing the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into AI, and the AI ​​can analyze that information to optimize the learning algorithm.

[0101] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is relaxed, the learning unit will set the learning frequency low. If the user is exercising, the learning unit will set the learning frequency high. If the user wants to concentrate, the learning unit will set the learning frequency to a medium level. In this way, the learning unit can perform more appropriate learning by adjusting the learning frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input user emotion data into an AI, and the AI ​​can analyze that information to adjust the learning frequency.

[0102] The learning unit can weight the training data based on when the collected information was submitted during the learning process. For example, the learning unit prioritizes learning the most recent information. The learning unit prioritizes learning important information based on the user's past song selection history. The learning unit prioritizes learning important information based on the user's current mood and activities. In this way, the learning unit can prioritize learning more important information by weighting the training data based on when the collected information was submitted. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the submission dates of the collected information into the AI, and the AI ​​can analyze that information to weight the training data.

[0103] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0104] The analysis unit can also estimate the user's emotions and adjust the order of songs in the playlist based on those emotions. For example, if the user wants to relax, relaxing songs will be placed at the beginning of the playlist. If the user is exercising, songs with a tempo suitable for exercise will be placed at the beginning of the playlist. If the user wants to concentrate, songs that enhance concentration will be placed at the beginning of the playlist. In this way, the analysis unit can provide a more appropriate musical experience by adjusting the order of songs in the playlist based on the user's emotions.

[0105] The data collection unit can analyze users' social media activity and collect music they've shared and new releases from artists they follow. For example, it can collect music users have shared on social media, new releases from artists they follow, and music related to events they've attended. This allows the data collection unit to gather more relevant information by analyzing users' social media activity.

[0106] The generation unit can also generate playlists while considering the user's geographical location. For example, if the user is traveling, music from that region will be added to the playlist. If the user is attending a specific event, music related to that event will be added to the playlist. If the user is in a specific location, music related to that location will be added to the playlist. This allows the generation unit to provide more appropriate playlists by selecting the most suitable songs while considering the user's geographical location.

[0107] The service provider can also adjust how playlists are delivered, taking into account the user's device information. For example, if the user is using a smartphone, they can select a delivery method that matches the screen size. If the user is using a tablet, they can select a delivery method optimized for a larger screen. If the user is using a smartwatch, they can select a concise and highly visible delivery method. This allows the service provider to offer a more appropriate music experience by selecting the optimal delivery method based on the user's device information.

[0108] The learning unit can also estimate the user's emotions and select training data based on those estimated emotions. For example, if the user is relaxed, it will prioritize learning feedback for relaxing music. If the user is exercising, it will prioritize learning feedback for music suitable for exercise. If the user wants to concentrate, it will prioritize learning feedback for music that enhances concentration. In this way, the learning unit can perform more appropriate learning by selecting training data based on the user's emotions.

[0109] The analysis unit can also adjust the level of detail of the analysis based on the importance of the collected information. For example, it can analyze highly important information in detail and less important information in a simplified manner. It can prioritize the analysis of highly important information based on the user's past song selection history. It can also prioritize the analysis of highly important information based on the user's current mood and activities. In this way, the analysis unit can analyze more important information in detail by adjusting the level of detail of the analysis based on the importance of the collected information.

[0110] The generation unit can also estimate the user's emotions and prioritize playlists based on those emotions. For example, if the user wants to relax, relaxing songs will be added to the playlist first. If the user is exercising, songs with a tempo suitable for exercise will be added to the playlist first. If the user wants to concentrate, songs that enhance concentration will be added to the playlist first. In this way, the generation unit can provide a more appropriate playlist by prioritizing playlists based on the user's emotions.

[0111] The service provider can also adjust how playlists are delivered by referring to past user feedback. For example, they can prioritize delivery methods that users have preferred in the past. They can customize delivery methods based on past user feedback. They can avoid delivery methods that users have previously expressed dissatisfaction with. This allows the service provider to select more appropriate delivery methods by referring to past user feedback.

[0112] The learning unit can also estimate the user's emotions and adjust the learning frequency based on those emotions. For example, if the user is relaxed, the learning frequency can be set low. If the user is exercising, the learning frequency can be set high. If the user wants to concentrate, the learning frequency can be set to a medium level. In this way, the learning unit can provide more appropriate learning by adjusting the learning frequency based on the user's emotions.

[0113] The data collection unit can also collect information about the user's musical preferences, mood, and activities, taking into account their geographical location. For example, if the user is traveling, it can collect music from that region. If the user is attending a specific event, it can collect music related to that event. If the user is in a specific location, it can collect music related to that location. This allows the data collection unit to collect more relevant information by considering the user's geographical location, resulting in more appropriate information.

[0114] The following briefly describes the processing flow for example form 2.

[0115] Step 1: The data collection unit gathers information about the user's musical preferences, mood, and activities. For example, it collects the user's past song selection history, current mood, and activities. The data collection unit collects detailed data such as songs and playlists the user has selected in the past, as well as their current mood and activities. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it analyzes the collected information and generates an optimal playlist based on the user's musical preferences, mood, and activities. If the user wants to relax, the analysis unit selects relaxing songs and generates a playlist. If the user is exercising, it selects songs with a tempo suitable for exercise and generates a playlist. Step 3: The generation unit generates a playlist based on the information analyzed by the analysis unit. For example, if the user wants to relax, it selects relaxing songs and generates a playlist. If the user is exercising, it selects songs with a tempo suitable for exercise and generates a playlist. Step 4: The providing unit provides the user with the playlist generated by the generating unit. For example, it provides the generated playlist to the user and collects feedback by having the user provide feedback on the playlist. Step 5: The learning unit learns from user feedback on the playlists provided by the providing unit. For example, it learns the feedback users provide on playlists and reflects this in future playlist generation. If a user likes a particular song, it uses that information to add similar songs to the next playlist.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and learning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 38B of the smart device 14 to collect information about the user's music preferences, mood, and activities. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected information to generate a playlist optimized for the user. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, and generates a playlist based on the analysis results. The provision unit is implemented in the control unit 46A of the smart device 14, and provides the generated playlist to the user. The learning unit is implemented in the specific processing unit 290 of the data processing unit 12, and learns user feedback to reflect in subsequent playlist generation. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0120] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and learning unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to collect information about the user's music preferences, mood, and activities. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected information to generate a playlist optimized for the user. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, and generates a playlist based on the analysis results. The provision unit is implemented in the control unit 46A of the smart glasses 214, and provides the generated playlist to the user. The learning unit is implemented in the specific processing unit 290 of the data processing unit 12, and learns user feedback to reflect in subsequent playlist generation. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0136] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In 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.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 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.

[0151] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and learning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the headset terminal 314 to collect information about the user's music preferences, mood, and activities. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected information to generate a playlist optimized for the user. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, and generates a playlist based on the analysis results. The provision unit is implemented in the control unit 46A of the headset terminal 314, and provides the generated playlist to the user. The learning unit is implemented in the specific processing unit 290 of the data processing unit 12, and learns user feedback to reflect in subsequent playlist generation. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0152] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and learning unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the robot 414 to collect information about the user's musical preferences, mood, and activities. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected information to generate a playlist optimized for the user. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, and generates a playlist based on the analysis results. The provision unit is implemented in the control unit 46A of the robot 414, and provides the generated playlist to the user. The learning unit is implemented in the specific processing unit 290 of the data processing unit 12, and learns user feedback to reflect in subsequent playlist generation. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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."

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] (Note 1) A collection unit that collects information about the user's music preferences, mood, and activities, An analysis unit analyzes the information collected by the aforementioned collection unit, A generation unit generates a playlist based on the information analyzed by the analysis unit, A providing unit that provides the user with the playlist generated by the generation unit, The system includes a learning unit that learns user feedback on the playlist provided by the aforementioned providing unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system collects the user's past music selection history, current mood, and activities. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected information is analyzed to generate optimal playlists based on the user's musical preferences, mood, and activities. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is If the user wants to relax, select relaxing songs and generate a playlist. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is If the user is exercising, the system will select songs with a suitable tempo for exercise and generate a playlist. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned learning unit, The system learns from user feedback on playlists and incorporates it into future playlist creation. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of data collection regarding music preferences, mood, and activities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The system analyzes the user's past song selection history and selects the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting information on music preferences, moods, and activities, 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 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting information on music preferences, moods, and activities, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting information on music preferences, moods, and activities, the system analyzes users' social media activity to gather relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the collected information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts how playlists are generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating a playlist, the system analyzes the user's past song selection history to select the most suitable songs. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating a playlist, the song selection is customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and prioritizes playlists based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating a playlist, the system selects the most suitable songs by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating playlists, the system analyzes the user's social media activity to select songs. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts how playlists are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing playlists, we select the optimal delivery method by referring to past user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and adjusts the order in which playlists are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing playlists, the optimal delivery method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned learning unit, During training, the training data is weighted based on when the collected information was submitted. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0188] 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 information about the user's music preferences, mood, and activities, An analysis unit analyzes the information collected by the aforementioned collection unit, A generation unit generates a playlist based on the information analyzed by the analysis unit, A providing unit that provides the user with the playlist generated by the generation unit, The system includes a learning unit that learns user feedback on the playlist provided by the aforementioned providing unit. A system characterized by the following features.

2. The aforementioned collection unit is The system collects the user's past music selection history, current mood, and activities. The system according to feature 1.

3. The aforementioned analysis unit, The collected information is analyzed to generate optimal playlists based on the user's musical preferences, mood, and activities. The system according to feature 1.

4. The generating unit is If the user wants to relax, select relaxing songs and generate a playlist. The system according to feature 1.

5. The generating unit is If the user is exercising, the system will select songs with a suitable tempo for exercise and generate a playlist. The system according to feature 1.

6. The aforementioned learning unit, The system learns from user feedback on playlists and incorporates it into future playlist creation. The system according to feature 1.

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

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

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

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

Images