system

Abstract

The system according to this embodiment aims to analyze user data and generate and provide an optimal playlist. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, an adjustment unit, and a provision unit. The collection unit collects user data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a playlist based on the analysis results obtained by the analysis unit. The adjustment unit adjusts the playlist generated by the generation unit in real time. The provision unit provides the playlist adjusted by the adjustment unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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, it has not been fully achieved to provide an optimal playlist based on a user's music preferences and activity patterns, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze user data and generate and provide an optimal playlist.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, an adjustment unit, and a provision unit. The collection unit collects user data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a playlist based on the analysis results obtained by the analysis unit. The adjustment unit adjusts the playlist generated by the generation unit in real time. The provision unit provides the playlist adjusted by the adjustment unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze user data and generate and provide an optimal playlist. [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 manages 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 receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The music recommendation system according to an embodiment of the present invention is a system that learns a user's musical preferences, time of day, activity patterns, and emotional state, and generates an optimal playlist. This music recommendation system uses AI to learn the user's past listening data and activity patterns, analyzes the user's emotional state, and recommends music that matches that mood. Furthermore, the AI ​​adjusts the playlist in real time based on the time of day and location. This provides a personalized music experience tailored to each user, offering optimal support for their mood and activities throughout the day through music. For example, it can recommend uplifting music during the morning commute and calming music during evening relaxation time. Also, if the user is feeling stressed, it can recommend music with a relaxing effect. This allows users to enjoy music that matches their mood and activities, saving them the trouble of choosing music. In addition, the music recommendation system supports the discovery of new songs and artists. By recommending new music based on the user's preferences, users can enjoy the discovery and surprise of new music. This enriches the user's musical experience. The music recommendation system uses natural language processing and machine learning to analyze user behavior and adjusts music in real time using emotion recognition technology. This enables the generation of dynamic playlists based on the user's preferences. For example, if a user enters "I want to relax," the AI ​​will recommend relaxing music based on that request. In this way, the music recommendation system provides interactive features that deepen the user's music experience, resulting in concrete effects such as increased user engagement, improved user satisfaction, and an increase in new registrations. For example, it is expected that personalized playlists will increase usage frequency by 30%, and customized music experiences will increase satisfaction by 50%. In addition, unique features are expected to increase the number of new users by 20%. The music recommendation system supports a richer life by accompanying users in their daily lives through music and providing the optimal music experience tailored to their emotions and activities. For example, if a user is feeling stressed, recommending relaxing music can help reduce the user's stress.Furthermore, when users want to discover new music, the system enriches their musical experience by recommending new music based on their preferences. This allows the music recommendation system to enhance the user's musical experience and bring about concrete effects such as increased user engagement, improved user satisfaction, and an increase in new registrations.

[0029] The music recommendation system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, an adjustment unit, and a provision unit. The collection unit collects user data. User data includes, but is not limited to, listening history, activity patterns, and emotional states. The collection unit collects, for example, the user's past listening data. The collection unit may also collect, for example, the user's activity patterns. The collection unit may also collect, for example, the user's emotional states. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the collected data to identify the user's preferences. The analysis unit may also analyze the collected data to identify the user's emotional states. The analysis unit may also analyze the collected data to identify the user's activity patterns. The generation unit generates playlists based on the analysis results obtained by the analysis unit. The generation unit generates playlists based on, for example, the user's preferences. The generation unit may also generate playlists based on, for example, the user's emotional states. The generation unit may also generate playlists based on, for example, the user's activity patterns. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input user preference data into the generation AI and have the generation AI generate a playlist. The adjustment unit adjusts the playlist generated by the generation unit in real time. The adjustment unit adjusts the playlist based on, for example, time of day. The adjustment unit can also adjust the playlist based on, for example, location. The adjustment unit can also adjust the playlist based on, for example, the user's emotional state. Some or all of the above-described processes in the adjustment unit may be performed using, for example, an AI, or without a generation AI. For example, the adjustment unit can input the generated playlist into the AI ​​and have the AI ​​perform real-time adjustments. The provision unit provides the playlist adjusted by the adjustment unit. The provision unit provides the adjusted playlist to the user. The provision unit can also send the adjusted playlist to the user's device.The delivery unit can, for example, save the customized playlist to the user's account. Some or all of the processing described above in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the customized playlist into the AI ​​and have the AI ​​select the delivery method. In this way, the music recommendation system according to the embodiment can provide a personalized music experience by collecting, analyzing, generating, customizing, and providing user data.

[0030] The data collection unit collects user data. User data includes, but is not limited to, listening history, activity patterns, and emotional states. For example, the data collection unit collects the user's past listening data. Specifically, it collects detailed data such as the title, artist name, playback time, and playback frequency of songs the user has played in the past. This allows the user to understand their musical preferences and tendencies. The data collection unit can also collect, for example, the user's activity patterns. Activity patterns include the time of day, location, and type of device the user uses to listen to music. For example, if a user listens to music on their smartphone while commuting, the unit collects information on the time of day, travel route, and device used. This allows the user to understand their daily rhythm and the situations in which they listen to music. The data collection unit can also collect, for example, the user's emotional state. Emotional state is data that indicates the mood and emotions the user feels when listening to music, and can be collected using biometric data such as heart rate, skin electrochemistry, and facial recognition obtained from smartphone sensors or wearable devices. This enables music recommendations tailored to the user's emotional state. The data collection unit can centrally manage and update this data in real time, enabling it to respond to the latest user status. Furthermore, the data collection unit is required to handle data securely by anonymizing and encrypting it to protect user privacy. This allows the data collection unit to efficiently collect diverse user data and improve the overall system performance.

[0031] The analysis department analyzes the data collected by the data collection department. For example, the analysis department analyzes the collected data to identify user preferences. Specifically, it uses machine learning algorithms to extract preferred genres, artists, and song characteristics from the user's listening history and models the user's musical preferences. The analysis department can also analyze the collected data to identify the user's emotional state. To identify emotional states, it uses emotion recognition technology to estimate emotions from the user's biometric and voice data. For example, it can determine whether the user is relaxed or excited from fluctuations in heart rate and tone of voice. The analysis department can also analyze the collected data to identify user activity patterns. To identify activity patterns, it uses time-series data analysis to understand the trends in the time of day and location where the user listens to music. This allows it to identify the situations in which the user most often listens to music. Furthermore, the analysis department comprehensively analyzes this data to understand the user's overall musical preferences and behavioral patterns. For example, if a user tends to prefer a particular genre of music at a particular time of day, it can generate a playlist based on that information. The analysis department can use AI to analyze data, enabling it to identify user preferences and emotional states with high accuracy and speed. This allows the analysis department to build a foundation for providing users with the optimal music experience.

[0032] The generation unit generates playlists based on the analysis results obtained by the analysis unit. The generation unit generates playlists based on user preferences, for example. Specifically, it combines songs from genres and artists that the user likes to create an appealing playlist for the user. The generation unit can also generate playlists based on the user's emotional state, for example. To generate playlists according to emotional state, emotion recognition technology is used to select songs that match the user's current mood. For example, calm songs are selected when the user wants to relax, and upbeat songs are selected when the user wants to feel energized. The generation unit can also generate playlists based on the user's activity patterns, for example. To generate playlists according to activity patterns, songs are selected that match the situation in which the user listens to music. For example, energetic songs are selected during commutes, and calming songs are selected during relaxation time. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user preference data into a generation AI and have the generation AI perform playlist generation. The generation AI selects songs best suited to the user's preferences and emotional state from a vast music database and automatically generates a playlist. This allows the generation unit to provide users with personalized playlists.

[0033] The adjustment unit adjusts the playlist generated by the generation unit in real time. For example, the adjustment unit adjusts the playlist based on the time of day. Specifically, it selects songs suitable for waking up in the morning and songs that promote relaxation in the evening. The adjustment unit can also adjust the playlist based on location. To adjust the playlist based on location, it uses the user's current location information to select songs suitable for specific locations, such as during a commute or while training at the gym. The adjustment unit can also adjust the playlist based on the user's emotional state. To adjust the playlist based on emotional state, it selects songs that match the user's current mood based on the user's emotional data acquired in real time. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the generated playlist into the AI ​​and have the AI ​​perform real-time adjustments. The AI ​​analyzes the user's latest data and dynamically changes the contents of the playlist. This allows the adjustment unit to provide the optimal music experience according to the user's situation and mood.

[0034] The service provider provides playlists that have been adjusted by the adjustment unit. The service provider provides the adjusted playlists to the user, for example. Specifically, it streams the playlists to the user's device, allowing them to enjoy music in real time. The service provider can also, for example, send the adjusted playlists to the user's device. Users can play pre-downloaded playlists even in environments without an internet connection. The service provider can also, for example, save the adjusted playlists to the user's account. This allows the user to play previously provided playlists at any time. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the adjusted playlists into the AI ​​and have the AI ​​select the delivery method. The AI ​​selects the optimal delivery method considering the user's device status and network environment. This allows the service provider to provide the playlists to the user in the most optimal way, realizing a comfortable music experience.

[0035] The data collection unit can collect the user's past listening data and activity patterns. For example, the data collection unit can collect the user's past listening data. The data collection unit can also collect the user's activity patterns. The data collection unit can also analyze the user's past listening data and select the optimal collection method. By collecting the user's past listening data and activity patterns, it is possible to generate more accurate playlists. Past listening data includes, but is not limited to, listening history and playback counts. Activity patterns include, but are not limited to, activities by time of day and activities by location. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's past listening data into AI and have AI perform data collection.

[0036] The analysis unit can analyze the collected data and analyze the user's emotional state. For example, the analysis unit can analyze the collected data and identify the user's emotional state. The analysis unit can also analyze the collected data and apply algorithms to identify the user's emotional state. The analysis unit can also analyze the collected data and use emotion recognition technology to identify the user's emotional state. This makes it possible to generate emotion-based playlists by analyzing the user's emotional state. Emotional state includes, but is not limited to, the type of emotion and the intensity of the emotion. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the collected data into AI and have the AI ​​perform the emotional state analysis.

[0037] The generation unit can generate playlists based on the analysis results. For example, the generation unit can generate playlists that match the user's preferences based on the analysis results. The generation unit can also generate playlists that match the user's emotional state based on the analysis results. The generation unit can also generate playlists that match the user's activity patterns based on the analysis results. In this way, by generating playlists based on the analysis results, it is possible to provide music that matches the user's preferences. The analysis results include, but are not limited to, the user's preferences and emotional state. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the analysis results into a generation AI and have the generation AI perform playlist generation.

[0038] The adjustment unit can adjust the playlist in real time based on time of day and location. For example, the adjustment unit adjusts the playlist based on time of day. The adjustment unit can also adjust the playlist based on location. The adjustment unit can also adjust the playlist based on the user's emotional state. This allows the system to provide music tailored to the user's situation by adjusting the playlist in real time based on time of day and location. Time of day includes, but is not limited to, morning, noon, and night. Location includes, but is not limited to, home, work, and out and about. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or not using AI. For example, the adjustment unit can input the generated playlist into the AI ​​and have the AI ​​perform real-time adjustments.

[0039] The service provider can provide users with customized playlists. The service provider can, for example, provide users with customized playlists. The service provider can, for example, send customized playlists to the user's device. The service provider can, for example, save customized playlists to the user's account. This allows for a personalized music experience by providing users with customized playlists. The service includes, but is not limited to, the means and timing of the service. Some or all of the above-described processes in the service provider may be performed, for example, using AI or not using AI. For example, the service provider can input customized playlists into AI and have the AI ​​select the method of service.

[0040] The data collection unit can analyze the user's past listening data and select the optimal collection method. For example, the data collection unit may prioritize collecting songs that the user has listened to frequently in the past. The data collection unit may also analyze songs that the user listened to during a specific time period and collect songs that are appropriate for that time period. The data collection unit may also analyze songs that the user listened to during a specific activity and collect songs that are appropriate for that activity. In this way, the optimal collection method can be selected by analyzing the user's past listening data. The optimal collection method includes, but is not limited to, the type of data and the means of collection. 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 may input the user's past listening data into AI and have AI select the optimal collection method.

[0041] The data collection unit can filter listening data based on the user's current activity status and areas of interest. For example, if the user is exercising, the data collection unit can collect energetic songs. If the user is relaxing, the data collection unit can also collect calming songs. If the user is studying, the data collection unit can also collect songs that enhance concentration. This allows for the collection of more appropriate data by filtering based on the user's current activity status and areas of interest. Activity status includes, but is not limited to, examples such as exercising or resting. Areas of interest include, but is not limited to, examples such as music genres or artists. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input user activity data into AI and have the AI ​​perform the filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting listening data. For example, if the user is traveling, the data collection unit can collect popular songs from that region. For example, if the user is attending a specific event, the data collection unit can also collect songs related to that event. For example, if the user is in a specific city, the data collection unit can also collect songs by artists from that city. This allows for the collection of more appropriate data by prioritizing the collection of highly relevant data by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's geographical location information into AI and have AI perform the collection of highly relevant data.

[0043] The data collection unit can analyze the user's social media activity and collect relevant data when collecting listening data. For example, the data collection unit can collect songs that the user has shared on social media. The data collection unit can also collect new songs by artists that the user follows. The data collection unit can also collect songs related to music events that the user has attended. This allows the collection of relevant data by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and the number of likes. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into AI and have AI perform the collection of relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data. For example, the analysis unit can perform a simplified analysis on less important data. For example, the analysis unit can prioritize the analysis of highly important data. This allows for efficient analysis by adjusting the level of detail based on the importance of the data. Data importance includes, but is not limited to, the impact and relevance of the data. Adjusting the level of detail includes, but is not limited to, the depth and scope of the analysis. Some or all of the above processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the data importance into the AI ​​and have the AI ​​perform the level of detail adjustment.

[0045] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply different analysis algorithms for each genre. For example, the analysis unit can also apply different analysis algorithms for each artist. For example, the analysis unit can also apply different analysis algorithms for each time period. By applying different analysis algorithms depending on the data category, more appropriate analysis can be performed. Data categories include, but are not limited to, music genres and user attributes. Analysis algorithms include, but are not limited to, clustering and regression analysis. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input data categories into the AI ​​and have the AI ​​perform the application of analysis algorithms.

[0046] The analysis department can determine the priority of analysis based on the data submission date. For example, the analysis department may prioritize the analysis of the most recent data. The analysis department may also perform analysis while referring to historical data. For example, the analysis department may simplify the analysis of older data. This allows for efficient analysis by determining the priority of analysis based on the data submission date. The submission date includes, but is not limited to, the submission date and time, and the submission frequency. The priority includes, but is not limited to, the importance and urgency of the data. Some or all of the above processes in the analysis department may be performed using, for example, AI, or not using AI. For example, the analysis department can input the data submission dates into AI and have the AI ​​perform the priority determination.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. For example, the analysis unit may also analyze highly relevant data in detail. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Relevance includes, but is not limited to, data correlation and relevance. Adjustment of the order includes, but is not limited to, prioritizing analyses and determining the order of analyses. Some or all of the above processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the data relevance into AI and have AI perform the order adjustment.

[0048] The generation unit can generate an optimal playlist by considering the user's geographical location information when generating a playlist. For example, if the user is traveling, the generation unit can generate a playlist that includes popular songs from that region. For example, if the user is attending a specific event, the generation unit can also generate a playlist that includes songs related to that event. For example, if the user is in a specific city, the generation unit can also generate a playlist that includes songs by artists from that city. By generating an optimal playlist that considers the user's geographical location information, a more appropriate music experience can be provided. Geographical location information includes, but is not limited to, GPS data and location services. An optimal playlist includes, but is not limited to, the user's preferences and activity status. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI and have the generation AI perform the generation of an optimal playlist.

[0049] The generation unit can analyze the user's social media activity and adjust the playlist content when generating a playlist. For example, the generation unit can include songs that the user has shared on social media in the playlist. The generation unit can also include new songs by artists that the user follows in the playlist. The generation unit can also include songs related to music events that the user is attending in the playlist. By analyzing the user's social media activity and adjusting the playlist content, a more appropriate music experience can be provided. Social media activity includes, but is not limited to, posts and the number of likes. Adjustments to the playlist content include, but are not limited to, song selection criteria and playlist length. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input the user's social media data into a generation AI and have the generation AI perform the adjustment of the playlist content.

[0050] The adjustment unit can select the optimal adjustment method when adjusting a playlist by referring to the user's past listening history. For example, the adjustment unit can add songs that the user has listened to frequently in the past. The adjustment unit can also add songs that the user listened to during a specific time period. The adjustment unit can also add songs that the user listened to during a specific activity. By referring to the user's past listening history and selecting the optimal adjustment method, a more appropriate music experience can be provided. Listening history includes, but is not limited to, the number of plays and playback time. The optimal adjustment method includes, but is not limited to, the type of data and adjustment method. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or not using AI. For example, the adjustment unit can input the user's listening history data into AI and have AI select the optimal adjustment method.

[0051] The adjustment unit can customize the playlist content based on the user's current activity level when adjusting the playlist. For example, if the user is exercising, the adjustment unit can add energetic songs. For example, if the user is relaxing, the adjustment unit can add calming songs. For example, if the user is studying, the adjustment unit can add songs to enhance concentration. This allows for a more appropriate music experience by customizing the playlist content based on the user's current activity level. Activity levels include, but are not limited to, exercising or resting. Customization of playlist content includes, but is not limited to, song selection criteria and playlist length. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or not. For example, the adjustment unit can input user activity data into AI and have the AI ​​customize the playlist content.

[0052] The adjustment unit can select the optimal adjustment method when adjusting a playlist, taking into account the user's geographical location information. For example, if the user is traveling, the adjustment unit can add popular songs from that region. For example, if the user is attending a specific event, the adjustment unit can add songs related to that event. For example, if the user is in a specific city, the adjustment unit can add songs by artists from that city. By selecting the optimal adjustment method while considering the user's geographical location information, a more appropriate music experience can be provided. Geographical location information includes, but is not limited to, GPS data and location services. The optimal adjustment method includes, but is not limited to, data type and adjustment method. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or not using AI. For example, the adjustment unit can input the user's geographical location information into AI and have the AI ​​select the optimal adjustment method.

[0053] The adjustment unit can analyze the user's social media activity and adjust the playlist content when adjusting the playlist. For example, the adjustment unit can add songs that the user has shared on social media to the playlist. The adjustment unit can also add new songs by artists that the user follows to the playlist. The adjustment unit can also add songs related to music events that the user is attending to the playlist. By analyzing the user's social media activity and adjusting the playlist content, a more appropriate music experience can be provided. Social media activity includes, but is not limited to, posts and the number of likes. Adjusting the playlist content includes, but is not limited to, song selection criteria and playlist length. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or not using AI. For example, the adjustment unit can input the user's social media data into AI and have AI perform the adjustment of the playlist content.

[0054] The service provider can select the optimal service method when providing a playlist by referring to the user's past listening history. For example, the service provider can provide a playlist containing songs that the user has frequently listened to in the past. The service provider can also provide a playlist containing songs that the user listened to during a specific time period. The service provider can also provide a playlist containing songs that the user listened to during a specific activity. By selecting the optimal service method by referring to the user's past listening history, a more appropriate music experience can be provided. Listening history includes, but is not limited to, the number of plays and playback time. The optimal service method includes, but is not limited to, the means of service and the timing of service. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's listening history data into AI and have the AI ​​select the optimal service method.

[0055] The service provider can customize the content of a playlist based on the user's current activity when providing it. For example, if the user is exercising, the service provider can provide a playlist containing energetic songs. If the user is relaxing, the service provider can also provide a playlist containing calming songs. If the user is studying, the service provider can also provide a playlist containing songs that help improve concentration. By customizing the playlist content based on the user's current activity, a more appropriate music experience can be provided. Activity status includes, but is not limited to, exercising or resting. Customization of playlist content includes, but is not limited to, criteria for song selection and playlist length. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input user activity data into AI and have the AI ​​customize the playlist content.

[0056] The service provider can select the optimal delivery method when providing playlists, taking into account the user's geographical location information. For example, if the user is traveling, the service provider can provide a playlist containing popular songs from that region. For example, if the user is attending a specific event, the service provider can also provide songs related to that event. For example, if the user is in a specific city, the service provider can also provide songs by artists from that city. By selecting the optimal delivery method considering the user's geographical location information, a more appropriate music experience can be provided. Geographical location information includes, but is not limited to, GPS data and location services. The optimal delivery method includes, but is not limited to, the means of delivery and the timing of delivery. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's geographical location information into AI and have the AI ​​select the optimal delivery method.

[0057] The service provider can analyze the user's social media activity and adjust the playlist content when providing a playlist. For example, the service provider can include songs that the user has shared on social media in the playlist. For example, the service provider can also include new songs by artists that the user follows in the playlist. For example, the service provider can also include songs related to music events that the user is attending in the playlist. By analyzing the user's social media activity and adjusting the playlist content, a more appropriate music experience can be provided. Social media activity includes, but is not limited to, posts and the number of likes. Adjustments to the playlist content include, but are not limited to, song selection criteria and playlist length. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's social media data into AI and have the AI ​​perform the adjustment of the playlist content.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The data collection unit collects sensor data from the user's device, allowing for a more detailed understanding of the user's activity level. For example, it can collect heart rate data from a smartwatch to determine if the user is exercising. It can also use the accelerometer sensor of a smartphone to determine whether the user is walking or sitting. Furthermore, it can use GPS data to identify the user's location and provide music appropriate for that location. This enables more accurate music recommendations based on the user's activity level.

[0060] The generation unit can improve the playlist generation method by utilizing the user's past playlist rating data. For example, it can analyze the characteristics of playlists that the user has previously given high ratings to and generate new playlists with those characteristics. It can also provide playlists that match the user's preferences by avoiding the characteristics of playlists that the user has given low ratings to. Furthermore, by continuously training the playlist generation algorithm using user rating data, it becomes possible to generate playlists with higher accuracy.

[0061] The service provider can adjust how playlists are delivered, taking into account the battery level of the user's device. For example, if the battery level is low, it can provide music at a lower bitrate to reduce data usage. If the battery level is insufficient, the playlist can also be downloaded for offline playback. Furthermore, if the battery level is sufficient, high-quality music can be provided to enhance the user's music experience. This enables optimal music delivery tailored to the user's device status.

[0062] The analysis unit can analyze a user's past music playback patterns and detect changes in their musical preferences. For example, if a user shifts from a genre they frequently listened to in the past to a different genre, the unit can detect this change and prioritize recommending music from the new genre. Furthermore, if a user starts listening to a particular artist frequently, the unit can recommend music related to that artist. In addition, by continuously monitoring changes in the user's playback patterns, the unit can quickly respond to changes in the user's musical preferences.

[0063] The generation unit can optimize the order of songs in a playlist based on the user's musical preferences. For example, if a user likes a particular song, that song can be placed at the beginning of the playlist. Similarly, if a user likes a particular artist, songs by that artist can be placed consecutively. Furthermore, by analyzing the user's playback history and adjusting the playlist order based on the tempo and key of songs the user prefers, a smoother music experience can be provided.

[0064] The adjustment unit can adjust the playlist content considering the user's device connection status. For example, if the user is connected to Wi-Fi, high-quality music can be provided. If the user is using mobile data, music with a lower bitrate can be provided to reduce data usage. Furthermore, if the user is offline, pre-downloaded music can be provided to ensure an uninterrupted music experience. This enables optimal music delivery according to the user's connection status.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The data collection unit collects user data. User data includes, for example, listening history, activity patterns, and emotional state. The data collection unit can collect the user's past listening data, activity patterns, and emotional state. Step 2: The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the collected data to identify user preferences, emotional states, and activity patterns. Step 3: The generation unit generates a playlist based on the analysis results obtained by the analysis unit. The generation unit can generate a playlist based on the user's preferences, emotional state, and activity patterns. Processing in the generation unit may also be performed using a generation AI. Step 4: The adjustment unit adjusts the playlist generated by the generation unit in real time. The adjustment unit can adjust the playlist based on the time of day, location, and the user's emotional state. Processing in the adjustment unit may also be performed using AI. Step 5: The service provider provides the playlist that has been adjusted by the adjustment provider. The service provider can provide the adjusted playlist to the user and send it to the user's device or save it to the user's account. Processing in the service provider may also be performed using AI.

[0067] (Example of form 2) The music recommendation system according to an embodiment of the present invention is a system that learns a user's musical preferences, time of day, activity patterns, and emotional state, and generates an optimal playlist. This music recommendation system uses AI to learn the user's past listening data and activity patterns, analyzes the user's emotional state, and recommends music that matches that mood. Furthermore, the AI ​​adjusts the playlist in real time based on the time of day and location. This provides a personalized music experience tailored to each user, offering optimal support for their mood and activities throughout the day through music. For example, it can recommend uplifting music during the morning commute and calming music during evening relaxation time. Also, if the user is feeling stressed, it can recommend music with a relaxing effect. This allows users to enjoy music that matches their mood and activities, saving them the trouble of choosing music. In addition, the music recommendation system supports the discovery of new songs and artists. By recommending new music based on the user's preferences, users can enjoy the discovery and surprise of new music. This enriches the user's musical experience. The music recommendation system uses natural language processing and machine learning to analyze user behavior and adjusts music in real time using emotion recognition technology. This enables the generation of dynamic playlists based on the user's preferences. For example, if a user enters "I want to relax," the AI ​​will recommend relaxing music based on that request. In this way, the music recommendation system provides interactive features that deepen the user's music experience, resulting in concrete effects such as increased user engagement, improved user satisfaction, and an increase in new registrations. For example, it is expected that personalized playlists will increase usage frequency by 30%, and customized music experiences will increase satisfaction by 50%. In addition, unique features are expected to increase the number of new users by 20%. The music recommendation system supports a richer life by accompanying users in their daily lives through music and providing the optimal music experience tailored to their emotions and activities. For example, if a user is feeling stressed, recommending relaxing music can help reduce the user's stress.Furthermore, when users want to discover new music, the system enriches their musical experience by recommending new music based on their preferences. This allows the music recommendation system to enhance the user's musical experience and bring about concrete effects such as increased user engagement, improved user satisfaction, and an increase in new registrations.

[0068] The music recommendation system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, an adjustment unit, and a provision unit. The collection unit collects user data. User data includes, but is not limited to, listening history, activity patterns, and emotional states. The collection unit collects, for example, the user's past listening data. The collection unit may also collect, for example, the user's activity patterns. The collection unit may also collect, for example, the user's emotional states. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the collected data to identify the user's preferences. The analysis unit may also analyze the collected data to identify the user's emotional states. The analysis unit may also analyze the collected data to identify the user's activity patterns. The generation unit generates playlists based on the analysis results obtained by the analysis unit. The generation unit generates playlists based on, for example, the user's preferences. The generation unit may also generate playlists based on, for example, the user's emotional states. The generation unit may also generate playlists based on, for example, the user's activity patterns. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input user preference data into the generation AI and have the generation AI generate a playlist. The adjustment unit adjusts the playlist generated by the generation unit in real time. The adjustment unit adjusts the playlist based on, for example, time of day. The adjustment unit can also adjust the playlist based on, for example, location. The adjustment unit can also adjust the playlist based on, for example, the user's emotional state. Some or all of the above-described processes in the adjustment unit may be performed using, for example, an AI, or without a generation AI. For example, the adjustment unit can input the generated playlist into the AI ​​and have the AI ​​perform real-time adjustments. The provision unit provides the playlist adjusted by the adjustment unit. The provision unit provides the adjusted playlist to the user. The provision unit can also send the adjusted playlist to the user's device.The delivery unit can, for example, save the customized playlist to the user's account. Some or all of the processing described above in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the customized playlist into the AI ​​and have the AI ​​select the delivery method. In this way, the music recommendation system according to the embodiment can provide a personalized music experience by collecting, analyzing, generating, customizing, and providing user data.

[0069] The data collection unit collects user data. User data includes, but is not limited to, listening history, activity patterns, and emotional states. For example, the data collection unit collects the user's past listening data. Specifically, it collects detailed data such as the title, artist name, playback time, and playback frequency of songs the user has played in the past. This allows the user to understand their musical preferences and tendencies. The data collection unit can also collect, for example, the user's activity patterns. Activity patterns include the time of day, location, and type of device the user uses to listen to music. For example, if a user listens to music on their smartphone while commuting, the unit collects information on the time of day, travel route, and device used. This allows the user to understand their daily rhythm and the situations in which they listen to music. The data collection unit can also collect, for example, the user's emotional state. Emotional state is data that indicates the mood and emotions the user feels when listening to music, and can be collected using biometric data such as heart rate, skin electrochemistry, and facial recognition obtained from smartphone sensors or wearable devices. This enables music recommendations tailored to the user's emotional state. The data collection unit can centrally manage and update this data in real time, enabling it to respond to the latest user status. Furthermore, the data collection unit is required to handle data securely by anonymizing and encrypting it to protect user privacy. This allows the data collection unit to efficiently collect diverse user data and improve the overall system performance.

[0070] The analysis department analyzes the data collected by the data collection department. For example, the analysis department analyzes the collected data to identify user preferences. Specifically, it uses machine learning algorithms to extract preferred genres, artists, and song characteristics from the user's listening history and models the user's musical preferences. The analysis department can also analyze the collected data to identify the user's emotional state. To identify emotional states, it uses emotion recognition technology to estimate emotions from the user's biometric and voice data. For example, it can determine whether the user is relaxed or excited from fluctuations in heart rate and tone of voice. The analysis department can also analyze the collected data to identify user activity patterns. To identify activity patterns, it uses time-series data analysis to understand the trends in the time of day and location where the user listens to music. This allows it to identify the situations in which the user most often listens to music. Furthermore, the analysis department comprehensively analyzes this data to understand the user's overall musical preferences and behavioral patterns. For example, if a user tends to prefer a particular genre of music at a particular time of day, it can generate a playlist based on that information. The analysis department can use AI to analyze data, enabling it to identify user preferences and emotional states with high accuracy and speed. This allows the analysis department to build a foundation for providing users with the optimal music experience.

[0071] The generation unit generates playlists based on the analysis results obtained by the analysis unit. The generation unit generates playlists based on user preferences, for example. Specifically, it combines songs from genres and artists that the user likes to create an appealing playlist for the user. The generation unit can also generate playlists based on the user's emotional state, for example. To generate playlists according to emotional state, emotion recognition technology is used to select songs that match the user's current mood. For example, calm songs are selected when the user wants to relax, and upbeat songs are selected when the user wants to feel energized. The generation unit can also generate playlists based on the user's activity patterns, for example. To generate playlists according to activity patterns, songs are selected that match the situation in which the user listens to music. For example, energetic songs are selected during commutes, and calming songs are selected during relaxation time. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user preference data into a generation AI and have the generation AI perform playlist generation. The generation AI selects songs best suited to the user's preferences and emotional state from a vast music database and automatically generates a playlist. This allows the generation unit to provide users with personalized playlists.

[0072] The adjustment unit adjusts the playlist generated by the generation unit in real time. For example, the adjustment unit adjusts the playlist based on the time of day. Specifically, it selects songs suitable for waking up in the morning and songs that promote relaxation in the evening. The adjustment unit can also adjust the playlist based on location. To adjust the playlist based on location, it uses the user's current location information to select songs suitable for specific locations, such as during a commute or while training at the gym. The adjustment unit can also adjust the playlist based on the user's emotional state. To adjust the playlist based on emotional state, it selects songs that match the user's current mood based on the user's emotional data acquired in real time. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the generated playlist into the AI ​​and have the AI ​​perform real-time adjustments. The AI ​​analyzes the user's latest data and dynamically changes the contents of the playlist. This allows the adjustment unit to provide the optimal music experience according to the user's situation and mood.

[0073] The service provider provides playlists that have been adjusted by the adjustment unit. The service provider provides the adjusted playlists to the user, for example. Specifically, it streams the playlists to the user's device, allowing them to enjoy music in real time. The service provider can also, for example, send the adjusted playlists to the user's device. Users can play pre-downloaded playlists even in environments without an internet connection. The service provider can also, for example, save the adjusted playlists to the user's account. This allows the user to play previously provided playlists at any time. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the adjusted playlists into the AI ​​and have the AI ​​select the delivery method. The AI ​​selects the optimal delivery method considering the user's device status and network environment. This allows the service provider to provide the playlists to the user in the most optimal way, realizing a comfortable music experience.

[0074] The data collection unit can collect the user's past listening data and activity patterns. For example, the data collection unit can collect the user's past listening data. The data collection unit can also collect the user's activity patterns. The data collection unit can also analyze the user's past listening data and select the optimal collection method. By collecting the user's past listening data and activity patterns, it is possible to generate more accurate playlists. Past listening data includes, but is not limited to, listening history and playback counts. Activity patterns include, but are not limited to, activities by time of day and activities by location. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's past listening data into AI and have AI perform data collection.

[0075] The analysis unit can analyze the collected data and analyze the user's emotional state. For example, the analysis unit can analyze the collected data and identify the user's emotional state. The analysis unit can also analyze the collected data and apply algorithms to identify the user's emotional state. The analysis unit can also analyze the collected data and use emotion recognition technology to identify the user's emotional state. This makes it possible to generate emotion-based playlists by analyzing the user's emotional state. Emotional state includes, but is not limited to, the type of emotion and the intensity of the emotion. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the collected data into AI and have the AI ​​perform the emotional state analysis.

[0076] The generation unit can generate playlists based on the analysis results. For example, the generation unit can generate playlists that match the user's preferences based on the analysis results. The generation unit can also generate playlists that match the user's emotional state based on the analysis results. The generation unit can also generate playlists that match the user's activity patterns based on the analysis results. In this way, by generating playlists based on the analysis results, it is possible to provide music that matches the user's preferences. The analysis results include, but are not limited to, the user's preferences and emotional state. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the analysis results into a generation AI and have the generation AI perform playlist generation.

[0077] The adjustment unit can adjust the playlist in real time based on time of day and location. For example, the adjustment unit adjusts the playlist based on time of day. The adjustment unit can also adjust the playlist based on location. The adjustment unit can also adjust the playlist based on the user's emotional state. This allows the system to provide music tailored to the user's situation by adjusting the playlist in real time based on time of day and location. Time of day includes, but is not limited to, morning, noon, and night. Location includes, but is not limited to, home, work, and out and about. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or not using AI. For example, the adjustment unit can input the generated playlist into the AI ​​and have the AI ​​perform real-time adjustments.

[0078] The service provider can provide users with customized playlists. The service provider can, for example, provide users with customized playlists. The service provider can, for example, send customized playlists to the user's device. The service provider can, for example, save customized playlists to the user's account. This allows for a personalized music experience by providing users with customized playlists. The service includes, but is not limited to, the means and timing of the service. Some or all of the above-described processes in the service provider may be performed, for example, using AI or not using AI. For example, the service provider can input customized playlists into AI and have the AI ​​select the method of service.

[0079] The data collection unit can estimate the user's emotions and adjust the timing of listening data collection based on the estimated emotions. For example, if the user is relaxed, the data collection unit can collect relaxing music at night. For example, if the user is stressed, the data collection unit can also collect stress-reducing music during the day. For example, if the user is excited, the data collection unit can also collect energetic music during exercise. By adjusting the timing of listening data collection based on the user's emotions, more appropriate data can be collected. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Collection timing includes, but is not limited to, regular intervals or specific events. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into AI and have the AI ​​adjust the collection timing.

[0080] The data collection unit can analyze the user's past listening data and select the optimal collection method. For example, the data collection unit may prioritize collecting songs that the user has listened to frequently in the past. The data collection unit may also analyze songs that the user listened to during a specific time period and collect songs that are appropriate for that time period. The data collection unit may also analyze songs that the user listened to during a specific activity and collect songs that are appropriate for that activity. In this way, the optimal collection method can be selected by analyzing the user's past listening data. The optimal collection method includes, but is not limited to, the type of data and the means of collection. 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 may input the user's past listening data into AI and have AI select the optimal collection method.

[0081] The data collection unit can filter listening data based on the user's current activity status and areas of interest. For example, if the user is exercising, the data collection unit can collect energetic songs. If the user is relaxing, the data collection unit can also collect calming songs. If the user is studying, the data collection unit can also collect songs that enhance concentration. This allows for the collection of more appropriate data by filtering based on the user's current activity status and areas of interest. Activity status includes, but is not limited to, examples such as exercising or resting. Areas of interest include, but is not limited to, examples such as music genres or artists. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input user activity data into AI and have the AI ​​perform the filtering.

[0082] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting relaxing songs. For example, if the user is relaxed, the data collection unit may also prioritize collecting new songs. For example, if the user is excited, the data collection unit may also prioritize collecting energetic songs. This allows for the collection of more appropriate data by prioritizing data collection 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Prioritization may include, but is not limited to, importance or urgency. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into an AI and have the AI ​​perform the priority determination.

[0083] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting listening data. For example, if the user is traveling, the data collection unit can collect popular songs from that region. For example, if the user is attending a specific event, the data collection unit can also collect songs related to that event. For example, if the user is in a specific city, the data collection unit can also collect songs by artists from that city. This allows for the collection of more appropriate data by prioritizing the collection of highly relevant data by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's geographical location information into AI and have AI perform the collection of highly relevant data.

[0084] The data collection unit can analyze the user's social media activity and collect relevant data when collecting listening data. For example, the data collection unit can collect songs that the user has shared on social media. The data collection unit can also collect new songs by artists that the user follows. The data collection unit can also collect songs related to music events that the user has attended. This allows the collection of relevant data by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and the number of likes. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into AI and have AI perform the collection of relevant data.

[0085] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis. For example, if the user is in a hurry, the analysis unit can perform a simplified analysis. For example, if the user is excited, the analysis unit can prioritize energetic songs. This allows for more appropriate analysis by adjusting the data analysis method 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Adjusting the analysis method includes, but is not limited to, algorithm selection and level of detail of analysis. Some or all of the above processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input user emotion data into an AI and have the AI ​​perform the adjustment of the analysis method.

[0086] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data. For example, the analysis unit can perform a simplified analysis on less important data. For example, the analysis unit can prioritize the analysis of highly important data. This allows for efficient analysis by adjusting the level of detail based on the importance of the data. Data importance includes, but is not limited to, the impact and relevance of the data. Adjusting the level of detail includes, but is not limited to, the depth and scope of the analysis. Some or all of the above processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the data importance into the AI ​​and have the AI ​​perform the level of detail adjustment.

[0087] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply different analysis algorithms for each genre. For example, the analysis unit can also apply different analysis algorithms for each artist. For example, the analysis unit can also apply different analysis algorithms for each time period. By applying different analysis algorithms depending on the data category, more appropriate analysis can be performed. Data categories include, but are not limited to, music genres and user attributes. Analysis algorithms include, but are not limited to, clustering and regression analysis. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input data categories into the AI ​​and have the AI ​​perform the application of analysis algorithms.

[0088] The analysis unit can estimate the user's emotions and determine the priority of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit may prioritize analyzing songs with a relaxing effect. For example, if the user is relaxed, the analysis unit may also prioritize analyzing new songs. For example, if the user is excited, the analysis unit may also prioritize analyzing energetic songs. This allows for more appropriate analysis by determining the priority of the analysis 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Prioritization may include, but is not limited to, importance or urgency. 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 have the AI ​​perform the priority determination.

[0089] The analysis department can determine the priority of analysis based on the data submission date. For example, the analysis department may prioritize the analysis of the most recent data. The analysis department may also perform analysis while referring to historical data. For example, the analysis department may simplify the analysis of older data. This allows for efficient analysis by determining the priority of analysis based on the data submission date. The submission date includes, but is not limited to, the submission date and time, and the submission frequency. The priority includes, but is not limited to, the importance and urgency of the data. Some or all of the above processes in the analysis department may be performed using, for example, AI, or not using AI. For example, the analysis department can input the data submission dates into AI and have the AI ​​perform the priority determination.

[0090] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. For example, the analysis unit may also analyze highly relevant data in detail. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Relevance includes, but is not limited to, data correlation and relevance. Adjustment of the order includes, but is not limited to, prioritizing analyses and determining the order of analyses. Some or all of the above processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the data relevance into AI and have AI perform the order adjustment.

[0091] 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 is relaxed, the generation unit can generate a playlist centered on relaxing songs. If the user is stressed, the generation unit can also generate a playlist centered on relaxing songs. If the user is excited, the generation unit can also generate a playlist centered on energetic songs. By adjusting the playlist generation method based on the user's emotions, a more appropriate playlist can be generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Adjusting the generation method includes, but is not limited to, algorithm selection and level of detail in generation. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform the adjustment of the generation method.

[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 is stressed, the generation unit may prioritize including relaxing songs in the playlist. For example, if the user is relaxed, the generation unit may also prioritize including new songs in the playlist. For example, if the user is excited, the generation unit may also prioritize including energetic songs in the playlist. In this way, a more appropriate playlist can be generated by determining the priority of 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, a text generation AI (e.g., LLM) or a multimodal generation AI. Prioritization includes, but is not limited to, importance or urgency. Some or all of the above processing in the generation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the generation unit can input user emotion data into a generative AI and have the generative AI perform the priority determination.

[0093] The generation unit can generate an optimal playlist by considering the user's geographical location information when generating a playlist. For example, if the user is traveling, the generation unit can generate a playlist that includes popular songs from that region. For example, if the user is attending a specific event, the generation unit can also generate a playlist that includes songs related to that event. For example, if the user is in a specific city, the generation unit can also generate a playlist that includes songs by artists from that city. By generating an optimal playlist that considers the user's geographical location information, a more appropriate music experience can be provided. Geographical location information includes, but is not limited to, GPS data and location services. An optimal playlist includes, but is not limited to, the user's preferences and activity status. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI and have the generation AI perform the generation of an optimal playlist.

[0094] The generation unit can analyze the user's social media activity and adjust the playlist content when generating a playlist. For example, the generation unit can include songs that the user has shared on social media in the playlist. The generation unit can also include new songs by artists that the user follows in the playlist. The generation unit can also include songs related to music events that the user is attending in the playlist. By analyzing the user's social media activity and adjusting the playlist content, a more appropriate music experience can be provided. Social media activity includes, but is not limited to, posts and the number of likes. Adjustments to the playlist content include, but are not limited to, song selection criteria and playlist length. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input the user's social media data into a generation AI and have the generation AI perform the adjustment of the playlist content.

[0095] The adjustment unit can estimate the user's emotions and adjust the playlist adjustment method based on the estimated user emotions. For example, if the user is relaxed, the adjustment unit can add relaxing songs. For example, if the user is stressed, the adjustment unit can add relaxing songs. For example, if the user is excited, the adjustment unit can add energetic songs. In this way, by adjusting the playlist adjustment method based on the user's emotions, a more appropriate musical experience can be provided. 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. Adjustment of the adjustment method includes, but is not limited to, algorithm selection and level of detail of adjustment. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input user emotion data into AI and have the AI ​​perform the adjustment of the adjustment method.

[0096] The adjustment unit can select the optimal adjustment method when adjusting a playlist by referring to the user's past listening history. For example, the adjustment unit can add songs that the user has listened to frequently in the past. The adjustment unit can also add songs that the user listened to during a specific time period. The adjustment unit can also add songs that the user listened to during a specific activity. By referring to the user's past listening history and selecting the optimal adjustment method, a more appropriate music experience can be provided. Listening history includes, but is not limited to, the number of plays and playback time. The optimal adjustment method includes, but is not limited to, the type of data and adjustment method. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or not using AI. For example, the adjustment unit can input the user's listening history data into AI and have AI select the optimal adjustment method.

[0097] The adjustment unit can customize the playlist content based on the user's current activity level when adjusting the playlist. For example, if the user is exercising, the adjustment unit can add energetic songs. For example, if the user is relaxing, the adjustment unit can add calming songs. For example, if the user is studying, the adjustment unit can add songs to enhance concentration. This allows for a more appropriate music experience by customizing the playlist content based on the user's current activity level. Activity levels include, but are not limited to, exercising or resting. Customization of playlist content includes, but is not limited to, song selection criteria and playlist length. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or not. For example, the adjustment unit can input user activity data into AI and have the AI ​​customize the playlist content.

[0098] The adjustment unit can estimate the user's emotions and determine the priority of the playlist adjustments based on the estimated user emotions. For example, if the user is feeling stressed, the adjustment unit may prioritize adding relaxing songs. For example, if the user is relaxed, the adjustment unit may also prioritize adding new songs. For example, if the user is excited, the adjustment unit may also prioritize adding energetic songs. This allows for a more appropriate musical experience by determining the playlist adjustment priority 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Prioritization may include, but is not limited to, importance or urgency. Some or all of the above processing in the adjustment unit may be performed using AI, or not using AI. For example, the adjustment unit can input user emotion data into an AI and have the AI ​​perform the priority determination.

[0099] The adjustment unit can select the optimal adjustment method when adjusting a playlist, taking into account the user's geographical location information. For example, if the user is traveling, the adjustment unit can add popular songs from that region. For example, if the user is attending a specific event, the adjustment unit can add songs related to that event. For example, if the user is in a specific city, the adjustment unit can add songs by artists from that city. By selecting the optimal adjustment method while considering the user's geographical location information, a more appropriate music experience can be provided. Geographical location information includes, but is not limited to, GPS data and location services. The optimal adjustment method includes, but is not limited to, data type and adjustment method. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or not using AI. For example, the adjustment unit can input the user's geographical location information into AI and have the AI ​​select the optimal adjustment method.

[0100] The adjustment unit can analyze the user's social media activity and adjust the playlist content when adjusting the playlist. For example, the adjustment unit can add songs that the user has shared on social media to the playlist. The adjustment unit can also add new songs by artists that the user follows to the playlist. The adjustment unit can also add songs related to music events that the user is attending to the playlist. By analyzing the user's social media activity and adjusting the playlist content, a more appropriate music experience can be provided. Social media activity includes, but is not limited to, posts and the number of likes. Adjusting the playlist content includes, but is not limited to, song selection criteria and playlist length. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or not using AI. For example, the adjustment unit can input the user's social media data into AI and have AI perform the adjustment of the playlist content.

[0101] The service provider can estimate the user's emotions and adjust how the playlist is delivered based on those emotions. For example, if the user is relaxed, the service provider may primarily deliver relaxing songs. If the user is stressed, the service provider may primarily deliver relaxing songs. If the user is excited, the service provider may primarily deliver energetic songs. By adjusting how the playlist is delivered based on the user's emotions, a more appropriate musical experience can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Adjustment of the delivery method may include, but is not limited to, the means of delivery and the timing of delivery. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into AI and have the AI ​​perform the adjustment of the delivery method.

[0102] The service provider can select the optimal service method when providing a playlist by referring to the user's past listening history. For example, the service provider can provide a playlist containing songs that the user has frequently listened to in the past. The service provider can also provide a playlist containing songs that the user listened to during a specific time period. The service provider can also provide a playlist containing songs that the user listened to during a specific activity. By selecting the optimal service method by referring to the user's past listening history, a more appropriate music experience can be provided. Listening history includes, but is not limited to, the number of plays and playback time. The optimal service method includes, but is not limited to, the means of service and the timing of service. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's listening history data into AI and have the AI ​​select the optimal service method.

[0103] The service provider can customize the content of a playlist based on the user's current activity when providing it. For example, if the user is exercising, the service provider can provide a playlist containing energetic songs. If the user is relaxing, the service provider can also provide a playlist containing calming songs. If the user is studying, the service provider can also provide a playlist containing songs that help improve concentration. By customizing the playlist content based on the user's current activity, a more appropriate music experience can be provided. Activity status includes, but is not limited to, exercising or resting. Customization of playlist content includes, but is not limited to, criteria for song selection and playlist length. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input user activity data into AI and have the AI ​​customize the playlist content.

[0104] The service provider can estimate the user's emotions and determine the priority of playlist recommendations based on those emotions. For example, if the user is stressed, the service provider may prioritize providing relaxing songs. If the user is relaxed, the service provider may also prioritize providing new songs. If the user is excited, the service provider may also prioritize providing energetic songs. This allows for a more appropriate music experience by determining the priority of playlist recommendations 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 may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Prioritization may include, but is not limited to, importance or urgency. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input user emotion data into an AI and have the AI ​​determine the priorities.

[0105] The service provider can select the optimal delivery method when providing playlists, taking into account the user's geographical location information. For example, if the user is traveling, the service provider can provide a playlist containing popular songs from that region. For example, if the user is attending a specific event, the service provider can also provide songs related to that event. For example, if the user is in a specific city, the service provider can also provide songs by artists from that city. By selecting the optimal delivery method considering the user's geographical location information, a more appropriate music experience can be provided. Geographical location information includes, but is not limited to, GPS data and location services. The optimal delivery method includes, but is not limited to, the means of delivery and the timing of delivery. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's geographical location information into AI and have the AI ​​select the optimal delivery method.

[0106] The service provider can analyze the user's social media activity and adjust the playlist content when providing a playlist. For example, the service provider can include songs that the user has shared on social media in the playlist. For example, the service provider can also include new songs by artists that the user follows in the playlist. For example, the service provider can also include songs related to music events that the user is attending in the playlist. By analyzing the user's social media activity and adjusting the playlist content, a more appropriate music experience can be provided. Social media activity includes, but is not limited to, posts and the number of likes. Adjustments to the playlist content include, but are not limited to, song selection criteria and playlist length. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's social media data into AI and have the AI ​​perform the adjustment of the playlist content.

[0107] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0108] The data collection unit collects sensor data from the user's device, allowing for a more detailed understanding of the user's activity level. For example, it can collect heart rate data from a smartwatch to determine if the user is exercising. It can also use the accelerometer sensor of a smartphone to determine whether the user is walking or sitting. Furthermore, it can use GPS data to identify the user's location and provide music appropriate for that location. This enables more accurate music recommendations based on the user's activity level.

[0109] The analytics department can analyze users' social media posts to estimate their current emotional state. For example, it can analyze text and images posted on social media to identify positive and negative emotions. It can also estimate the emotions associated with specific hashtags if the user uses them. Furthermore, it can analyze the content of accounts users follow and groups they participate in to understand their interests. This enables more personalized music recommendations based on the user's emotional state.

[0110] The generation unit can improve the playlist generation method by utilizing the user's past playlist rating data. For example, it can analyze the characteristics of playlists that the user has previously given high ratings to and generate new playlists with those characteristics. It can also provide playlists that match the user's preferences by avoiding the characteristics of playlists that the user has given low ratings to. Furthermore, by continuously training the playlist generation algorithm using user rating data, it becomes possible to generate playlists with higher accuracy.

[0111] The adjustment unit can dynamically adjust the playlist content using the user's real-time biometric data. For example, if the user's heart rate increases, energetic songs can be added. Similarly, if the user's skin temperature rises, relaxing songs can be added. Furthermore, if the user's respiratory rate increases, calming songs can be added to promote relaxation. This allows for a more appropriate music experience based on the user's biometric data.

[0112] The service provider can adjust how playlists are delivered, taking into account the battery level of the user's device. For example, if the battery level is low, it can provide music at a lower bitrate to reduce data usage. If the battery level is insufficient, the playlist can also be downloaded for offline playback. Furthermore, if the battery level is sufficient, high-quality music can be provided to enhance the user's music experience. This enables optimal music delivery tailored to the user's device status.

[0113] The data collection unit can analyze user voice commands and collect data in accordance with user requests. For example, if a user enters the voice command "I want to relax," it can collect music with relaxing effects. If a user requests "I want to listen to new songs," it can prioritize collecting newly released songs. Furthermore, if a user requests "I want to listen to music while exercising," it can collect energetic music, providing a music experience tailored to the user's request.

[0114] The analysis unit can analyze a user's past music playback patterns and detect changes in their musical preferences. For example, if a user shifts from a genre they frequently listened to in the past to a different genre, the unit can detect this change and prioritize recommending music from the new genre. Furthermore, if a user starts listening to a particular artist frequently, the unit can recommend music related to that artist. In addition, by continuously monitoring changes in the user's playback patterns, the unit can quickly respond to changes in the user's musical preferences.

[0115] The generation unit can optimize the order of songs in a playlist based on the user's musical preferences. For example, if a user likes a particular song, that song can be placed at the beginning of the playlist. Similarly, if a user likes a particular artist, songs by that artist can be placed consecutively. Furthermore, by analyzing the user's playback history and adjusting the playlist order based on the tempo and key of songs the user prefers, a smoother music experience can be provided.

[0116] The adjustment unit can adjust the playlist content considering the user's device connection status. For example, if the user is connected to Wi-Fi, high-quality music can be provided. If the user is using mobile data, music with a lower bitrate can be provided to reduce data usage. Furthermore, if the user is offline, pre-downloaded music can be provided to ensure an uninterrupted music experience. This enables optimal music delivery according to the user's connection status.

[0117] The playback system can estimate the user's emotions and adjust the timing of playlist delivery based on those estimates. For example, if a user is feeling stressed, it can immediately provide relaxing music. If the user is relaxed, it can also adjust the timing of new music delivery. Furthermore, if the user is excited, it can adjust the timing of energetic music delivery, providing an optimal musical experience tailored to the user's emotions. This enables more appropriate music delivery based on the user's feelings.

[0118] The following briefly describes the processing flow for example form 2.

[0119] Step 1: The data collection unit collects user data. User data includes, for example, listening history, activity patterns, and emotional state. The data collection unit can collect the user's past listening data, activity patterns, and emotional state. Step 2: The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the collected data to identify user preferences, emotional states, and activity patterns. Step 3: The generation unit generates a playlist based on the analysis results obtained by the analysis unit. The generation unit can generate a playlist based on the user's preferences, emotional state, and activity patterns. Processing in the generation unit may also be performed using a generation AI. Step 4: The adjustment unit adjusts the playlist generated by the generation unit in real time. The adjustment unit can adjust the playlist based on the time of day, location, and the user's emotional state. Processing in the adjustment unit may also be performed using AI. Step 5: The service provider provides the playlist that has been adjusted by the adjustment provider. The service provider can provide the adjusted playlist to the user and send it to the user's device or save it to the user's account. Processing in the service provider may also be performed using AI.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, adjustment unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects user data using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. 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 adjustment unit is implemented in the control unit 46A of the smart device 14 and adjusts the generated playlist in real time. The provision unit is implemented in the control unit 46A of the smart device 14 and provides the adjusted playlist to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0124] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, adjustment unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects user data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. 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 adjustment unit is implemented in the control unit 46A of the smart glasses 214 and adjusts the generated playlist in real time. The provision unit is implemented in the control unit 46A of the smart glasses 214 and provides the adjusted playlist to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0140] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, adjustment unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects user data using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. 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 adjustment unit is implemented in the control unit 46A of the headset terminal 314 and adjusts the generated playlist in real time. The provision unit is implemented in the control unit 46A of the headset terminal 314 and provides the adjusted playlist to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0156] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, adjustment unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects user data using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data. The generation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and generates a playlist based on the analysis results. The adjustment unit is implemented, for example, in the control unit 46A of the robot 414, and adjusts the generated playlist in real time. The provision unit is implemented, for example, in the control unit 46A of the robot 414, and provides the adjusted playlist to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] (Note 1) A data collection unit that collects user data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit generates a playlist based on the analysis results obtained by the analysis unit, An adjustment unit that adjusts the playlist generated by the generation unit in real time, The system includes a providing unit that provides a playlist adjusted by the adjustment unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect users' past listening data and activity patterns. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is We analyze the collected data to understand the user's emotional state. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate a playlist based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The adjustment unit is, Adjust playlists in real time based on time of day and location. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Provide users with customized playlists. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of listening data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past listening data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting listening data, filtering is performed based on the user's current activities 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 data 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 listening data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting listening data, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is We estimate user sentiment and adjust the data analysis method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is We estimate the user's emotions and prioritize the analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 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 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 21) The generating unit is When generating a playlist, the system takes the user's geographical location into consideration to create the most optimal playlist. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating a playlist, the system analyzes the user's social media activity and adjusts the playlist content accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 23) The adjustment unit is, It estimates the user's emotions and adjusts how the playlist is adjusted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The adjustment unit is, When adjusting playlists, the system refers to the user's past listening history to select the optimal adjustment method. The system described in Appendix 1, characterized by the features described herein. (Note 25) The adjustment unit is, When adjusting a playlist, customize the playlist content based on the user's current activity. The system described in Appendix 1, characterized by the features described herein. (Note 26) The adjustment unit is, It estimates the user's emotions and determines the priority of playlist adjustments based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The adjustment unit is, When adjusting playlists, the system selects the optimal adjustment method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 28) The adjustment unit is, When adjusting playlists, we analyze the user's social media activity to adjust the playlist content. The system described in Appendix 1, characterized by the features described herein. (Note 29) 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 30) The aforementioned supply unit is, When providing playlists, the system selects the optimal delivery method by referring to the user's past listening history. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing a playlist, customize its contents based on the user's current activity level. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, The system estimates the user's emotions and determines the priority of playlist recommendations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing playlists, the optimal delivery method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, When providing playlists, we analyze users' social media activity and adjust the playlist content accordingly. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects user data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit generates a playlist based on the analysis results obtained by the analysis unit, An adjustment unit that adjusts the playlist generated by the generation unit in real time, The system includes a providing unit that provides a playlist adjusted by the adjustment unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect users' past listening data and activity patterns. The system according to feature 1.

3. The aforementioned analysis unit is We analyze the collected data to understand the user's emotional state. The system according to feature 1.

4. The generating unit is Generate a playlist based on the analysis results. The system according to feature 1.

5. The adjustment unit is, Adjust playlists in real time based on time of day and location. The system according to feature 1.

6. The aforementioned supply unit is, Provide users with customized playlists. The system according to feature 1.

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

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

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