system

Abstract

The system according to this embodiment aims to recommend new songs based on the user's listening history and preferences. [Solution] The system according to the embodiment comprises a data analysis unit, a recommendation unit, and an update unit. The data analysis unit analyzes the user's listening history and preferences. The recommendation unit recommends new songs based on the results analyzed by the data analysis unit. The update unit updates the songs recommended by the recommendation unit in real time.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, new songs have not been sufficiently recommended effectively based on the listening history and preferences of users, and there is room for improvement.

[0005] The system according to the embodiment aims to recommend new songs based on the listening history and preferences of users.

Means for Solving the Problems

[0006] The system according to the embodiment includes a data analysis unit, a recommendation unit, and an update unit. The data analysis unit analyzes the listening history and preferences of users. The recommendation unit recommends new songs based on the results analyzed by the data analysis unit. The update unit updates the songs recommended by the recommendation unit in real time. [Effects of the Invention]

[0007] The system according to this embodiment can recommend new songs based on the user's listening history and preferences. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The music recommendation system according to an embodiment of the present invention is a system that analyzes the listening history and preferences of users of a music streaming service and recommends undiscovered artists and songs in new genres. In this music recommendation system, a data analysis unit deeply analyzes the user's listening history and preferences, and a recommendation unit operates to recommend songs containing new elements based on the analysis results. Recommendations are updated in real time and are based on the user's latest preferences. As a result, users can enjoy unprecedented musical experiences, and emerging artists are given new opportunities to spread their music. For example, the music recommendation system collects the user's listening history, and the data analysis unit analyzes that data. The data analysis unit identifies the user's preferences based on the genre, artist, and number of plays of songs the user has listened to in the past. Next, the recommendation unit recommends new songs that match the user's preferences based on the analysis results of the data analysis unit. For example, the recommendation unit recommends songs from genres or artists that the user has never listened to before. The recommendation unit also updates the new songs that match the user's preferences in real time. For example, after the user listens to a new song, the recommendation list is updated based on the information of that song. This allows users to always enjoy the latest music experiences. Furthermore, the music recommendation system improves the efficiency of users' music exploration, reducing the time it takes to discover new songs by 50%. It also increases the number of listeners for undiscovered artists by an average of 30% and the frequency of platform use by 20%. Specifically, it applies advanced data analysis technology and customizes the user experience to revolutionize the process of discovering new music. Depending on user needs, it provides music lovers with opportunities to constantly explore new music and emerging artists with a platform to promote their music. It also provides music platforms with a means to increase user engagement. The target market includes music fans of all ages, music streaming services, and management companies for emerging artists. This music recommendation system is a must-enter market now, given the approximately 300 billion yen annual market size of the music streaming market and the increasing need for new music discovery as the music industry becomes more digitized.Our vision is to promote cultural diversity through music discovery and sharing, and to spread the talent of new artists to the world. This will allow our music recommendation system to improve users' music exploration efficiency and reduce the time it takes to discover new songs by 50%.

[0029] The music recommendation system according to this embodiment comprises a data analysis unit, a recommendation unit, and an update unit. The data analysis unit analyzes the user's listening history and preferences. The data analysis unit identifies the user's preferences based, for example, on the genre, artist, and number of plays of songs the user has listened to in the past. The data analysis unit can analyze the user's listening history using, for example, a machine learning algorithm. The data analysis unit can also use clustering technology to identify the user's preferences. For example, the data analysis unit clusters the user's listening history to identify the user's preferences. The recommendation unit recommends new songs based on the results analyzed by the data analysis unit. The recommendation unit recommends songs of genres or artists the user has not listened to before. The recommendation unit can recommend new songs that match the user's preferences using, for example, collaborative filtering technology. The recommendation unit can also recommend new songs that match the user's preferences using content-based filtering technology. For example, the recommendation unit recommends songs that the user is likely to like based on the user's listening history. The update unit updates the songs recommended by the recommendation unit in real time. For example, after a user listens to a new song, the update unit updates the recommendation list based on the information of that song. The update unit can also analyze streaming data in real time and update the recommendation list based on the user's latest preferences. Furthermore, the update unit can update the recommendation list based on user feedback. For example, the update unit updates the recommendation list based on the user's evaluation of the recommended songs. As a result, the music recommendation system according to this embodiment can provide the user with a new music experience by analyzing the user's listening history and preferences, recommending new songs, and updating them in real time.

[0030] The data analysis department analyzes users' listening history and preferences. Specifically, it collects detailed data such as the genre, artist, number of plays, time of play, and frequency of play of songs the user has listened to in the past, and uses this data to identify the user's musical preferences. The data analysis department can analyze users' listening history using machine learning algorithms. For example, if a user frequently listens to songs of a particular genre, it will be determined that the user likes that genre. Similarly, if a user repeatedly listens to songs by a particular artist, it will be determined that the user likes that artist. Furthermore, the data analysis department uses clustering technology to cluster the user's listening history and identify the user's preferences. By using clustering technology, the user's listening history can be divided into multiple groups, and common features can be extracted from each group. For example, if a user frequently listens to rock and pop songs, it will be determined that the user likes these genres. Based on these analysis results, the data analysis department can gain a detailed understanding of the user's musical preferences. In addition, the data analysis department can analyze not only the user's listening history but also data such as user ratings and playlist creation history. This allows for a more accurate identification of the user's musical preferences.

[0031] The recommendation unit recommends new songs based on the results of analysis by the data analysis unit. Specifically, it recommends songs from genres and artists that the user has not listened to before. The recommendation unit can recommend new songs that match the user's preferences using collaborative filtering technology. Collaborative filtering technology is a method of recommending songs that a user is likely to like based on the listening history of other users. For example, if user B has listened to the same songs as user A, the recommendation unit will recommend songs that user B has not yet listened to. The recommendation unit can also recommend new songs that match the user's preferences using content-based filtering technology. Content-based filtering technology is a method of recommending songs that a user is likely to like based on the user's listening history. For example, if a user likes songs from a particular genre or artist, the recommendation unit will recommend new songs related to that genre or artist. Furthermore, the recommendation unit can recommend new songs by considering not only the user's listening history but also data such as the user's ratings and playlist creation history. This allows the recommendation unit to recommend new songs that match the user's preferences with high accuracy.

[0032] The update unit updates the songs recommended by the recommendation unit in real time. Specifically, after a user listens to a new song, it updates the recommendation list based on the information of that song. The update unit can analyze streaming data in real time and update the recommendation list based on the user's latest preferences. For example, when a user listens to a new song, it analyzes the genre and artist information of that song and adds new songs that match the user's preferences to the recommendation list. The update unit can also update the recommendation list based on user feedback. For example, it updates the recommendation list based on the ratings the user gives to recommended songs. It adds songs related to songs that the user has given a high rating to the recommendation list and removes songs related to songs that the user has given a low rating to the recommendation list. This allows the update unit to update the recommendation list in real time based on the user's latest preferences and provide the user with the best possible music experience. Furthermore, the update unit can analyze the user's listening history and rating history over the long term and understand changes in the user's musical preferences. This allows the update unit to continuously update the recommendation list in response to changes in the user's preferences.

[0033] The effectiveness measurement unit can measure the effectiveness of recommendations. For example, the effectiveness measurement unit can measure user satisfaction. For example, the effectiveness measurement unit can measure the effectiveness of recommendations based on user ratings of recommended songs. The effectiveness measurement unit can also measure the effectiveness of recommendations based on an increase in the number of plays. For example, the effectiveness measurement unit can measure the effectiveness of recommendations based on the number of times users play recommended songs. The effectiveness measurement unit can also measure the effectiveness of recommendations based on user feedback. For example, the effectiveness measurement unit can measure the effectiveness of recommendations based on comments users make about recommended songs. In this way, the effectiveness measurement unit can improve the accuracy of the system by measuring the effectiveness of recommendations.

[0034] The user interface can be designed to be easy for users to operate. For example, the user interface can adopt a design that enables intuitive operation. For example, the user interface can be designed to be easy for users to operate by simplifying the operating procedures. Furthermore, the user interface can provide a visually clear interface. For example, the user interface can use icons and graphical elements to allow users to operate intuitively. The user interface can also be designed to be easy for users to operate by supporting voice control. For example, the user interface can use voice recognition technology to operate in response to the user's voice commands. In this way, the user interface can improve the user experience by making it easy for users to operate.

[0035] The data analysis department can deeply analyze users' listening history and preferences. For example, it can analyze users' listening history in detail to identify their preferences. The data analysis department can use machine learning algorithms to deeply analyze users' listening history. The data analysis department can also use clustering techniques to identify users' preferences. For example, it can cluster users' listening history to identify their preferences. The data analysis department can also analyze users' listening history over time to understand changes in users' preferences. For example, it can analyze users' listening history over time to identify changes in users' preferences. As a result, the data analysis department can provide more accurate recommendations by deeply analyzing users' listening history and preferences.

[0036] The recommendation system can recommend songs that include new elements different from the user's existing preferences. For example, it can recommend songs from genres or artists the user has never listened to before. The recommendation system can recommend new songs that match the user's preferences using collaborative filtering technology, for example. It can also recommend new songs that match the user's preferences using content-based filtering technology. For example, it can recommend songs that the user is likely to like based on the user's listening history. The recommendation system can also analyze the user's preferences and recommend songs that include new elements the user has never listened to before. For example, it can recommend songs from genres or artists the user has never listened to before. In this way, the recommendation system can provide users with new musical experiences by recommending songs that include new elements different from their existing preferences.

[0037] The update unit can update recommendations based on the user's latest preferences. For example, after a user listens to a new song, the update unit can update the recommendation list based on information about that song. For example, the update unit can analyze streaming data in real time and update the recommendation list based on the user's latest preferences. The update unit can also update the recommendation list based on user feedback. For example, the update unit can update the recommendation list based on the user's ratings of recommended songs. For example, the update unit can analyze the user's listening history chronologically and update the recommendation list based on the user's latest preferences. For example, the update unit can analyze the user's listening history chronologically, identify the user's latest preferences, and update the recommendation list accordingly. In this way, the update unit can always provide the latest information by updating recommendations based on the user's latest preferences.

[0038] The data analysis unit can analyze a user's past listening history and select the optimal analysis algorithm. For example, the data analysis unit can analyze the genres of songs a user has listened to in the past and select an analysis algorithm specialized for those genres. For example, the data analysis unit can select an algorithm that prioritizes analyzing popular songs based on the number of times a user has listened to those songs in the past. Furthermore, the data analysis unit can analyze the trends of songs a user has skipped in the past and select an algorithm that analyzes songs that are less likely to be skipped. For example, the data analysis unit can analyze a user's past listening history and select the optimal analysis algorithm. In this way, the data analysis unit can select the optimal analysis algorithm by analyzing past listening history.

[0039] The data analysis unit can filter listening history based on the user's current lifestyle and areas of interest. For example, if the user is exercising, the data analysis unit will prioritize analyzing songs with a tempo suitable for exercise. If the user is working, the data analysis unit can prioritize analyzing songs that enhance concentration. Furthermore, if the user is relaxed, the data analysis unit can prioritize analyzing songs that promote relaxation. In short, the data analysis unit filters based on the user's lifestyle and areas of interest. This allows the data analysis unit to perform more appropriate analysis by filtering based on the user's lifestyle and areas of interest.

[0040] The data analysis unit can prioritize analyzing highly relevant listening history by considering the user's geographical location. For example, if the user is in a specific region, the data analysis unit can prioritize analyzing songs popular in that region. For example, if the user is traveling, the data analysis unit can prioritize analyzing songs popular in the destination region. Furthermore, if the user is at home, the data analysis unit can prioritize analyzing songs frequently listened to at home. For example, the data analysis unit prioritizes analyzing highly relevant listening history by considering the user's geographical location. This enables the data analysis unit to perform more relevant analyses by considering the user's geographical location.

[0041] The data analysis unit can analyze the user's social media activity and related history when analyzing listening history. For example, the data analysis unit can prioritize analyzing songs that the user has shared on social media. For example, the data analysis unit can prioritize analyzing songs by artists that the user follows on social media. The data analysis unit can also prioritize analyzing songs that the user has "liked" on social media. For example, the data analysis unit analyzes the user's social media activity and related history. In this way, the data analysis unit can analyze related history by analyzing the user's social media activity.

[0042] The recommendation system can adjust the level of detail in its recommendations based on the importance of the song. For example, it might provide detailed information when recommending a popular song, or concise information when recommending a song by a new artist. It can also provide detailed information when recommending a song that matches the user's preferences. For example, it adjusts the level of detail in its recommendations based on the importance of the song. This allows the recommendation system to provide more appropriate recommendations by adjusting the level of detail based on the importance of the song.

[0043] The recommendation system can apply different recommendation algorithms depending on the song category. For example, for pop music, it might apply an algorithm that emphasizes popularity and play count. For classical music, it might apply an algorithm that emphasizes ratings and expert reviews. For jazz music, it might apply an algorithm that emphasizes the artist's history and live performance. In short, the recommendation system can apply different recommendation algorithms depending on the song category, enabling more appropriate recommendations.

[0044] The recommendation system can prioritize recommendations based on the release date of the songs. For example, it can prioritize newly released songs. It can also consider the release dates of songs the user has listened to in the past. Furthermore, it can prioritize songs whose release dates match the user's preferences. For example, the recommendation system can prioritize recommendations based on the release date of the songs. This allows the recommendation system to make more appropriate recommendations by prioritizing recommendations based on the release date of the songs.

[0045] The recommendation system can adjust the order of recommendations based on the relevance of the songs. For example, it might recommend songs that are most relevant to the user's preferences first. It could also prioritize recommending songs that are relevant based on the user's past listening history. Furthermore, it could prioritize recommending songs that are relevant based on the user's current mood. For example, the recommendation system adjusts the order of recommendations based on the relevance of the songs. This allows the recommendation system to make more appropriate recommendations by adjusting the order of recommendations based on the relevance of the songs.

[0046] The update unit can analyze past user responses to select the optimal update method during an update. For example, the update unit can prioritize update methods that users have previously responded positively to. For example, the update unit can avoid update methods that users have previously responded negatively to. The update unit can also analyze past user responses to select the most effective update method. For example, the update unit can analyze past user responses to select the optimal update method. In this way, the update unit can select the optimal update method by analyzing past user responses.

[0047] The update unit can customize the update frequency based on the user's current lifestyle. For example, if the user is busy, the update unit can reduce the update frequency. For example, if the user has free time, the update unit can increase the update frequency. The update unit can also set the optimal update frequency according to the user's lifestyle. For example, the update unit can customize the update frequency based on the user's lifestyle. This allows the update unit to provide more appropriate updates by customizing the update frequency according to the user's lifestyle.

[0048] The update unit can select the optimal update method by considering the user's geographical location during the update process. For example, if the user is in a specific region, the update unit can prioritize updating information related to that region. For example, if the user is traveling, the update unit can prioritize updating information related to the travel destination. Furthermore, if the user is at home, the update unit can prioritize updating information related to home. For example, the update unit can select the optimal update method by considering the user's geographical location. In this way, the update unit can select the optimal update method by considering the user's geographical location.

[0049] The update unit can analyze the user's social media activity and suggest update methods during the update process. For example, the update unit can suggest update methods based on information the user has shared on social media. For example, the update unit can suggest update methods based on information the user follows on social media. Furthermore, the update unit can also suggest update methods based on information the user has "liked" on social media. For example, the update unit analyzes the user's social media activity and suggests update methods. In this way, the update unit can suggest the optimal update method by analyzing the user's social media activity.

[0050] The effectiveness measurement unit can optimize the measurement algorithm by referring to past measurement data during effectiveness measurement. For example, the effectiveness measurement unit can select the optimal measurement algorithm based on past measurement data. For example, the effectiveness measurement unit can analyze past measurement data and propose an effective measurement method. Furthermore, the effectiveness measurement unit can optimize the measurement algorithm by referring to past measurement data. For example, the effectiveness measurement unit can select the optimal measurement algorithm based on past measurement data. In this way, the effectiveness measurement unit can optimize the measurement algorithm by referring to past measurement data.

[0051] The effectiveness measurement unit can weight the measurement data based on when the user's listening history was submitted. For example, the effectiveness measurement unit can give more weight to data on songs the user has recently listened to when measuring effectiveness. For example, the effectiveness measurement unit can refer to data on songs the user has listened to in the past when measuring effectiveness. Furthermore, the effectiveness measurement unit can adjust the weighting of the measurement data based on when the user's listening history was submitted. For example, the effectiveness measurement unit can weight the measurement data based on when the user's listening history was submitted. This allows the effectiveness measurement unit to perform more appropriate effectiveness measurements by weighting the measurement data based on when the user's listening history was submitted.

[0052] The user interface unit can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, the user interface unit can prioritize displaying interface designs that the user has previously preferred. For example, the user interface unit can analyze the user's past operation history and suggest the optimal display method. The user interface unit can also choose not to display interface designs that the user has previously avoided. For example, the user interface unit selects the optimal display method by referring to the user's past operation history. In this way, the user interface unit can select the optimal display method by referring to the user's past operation history.

[0053] The user interface unit can select the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, the user interface unit can provide a display method that matches the screen size. For example, if the user is using a tablet, the user interface unit can provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the user interface unit can provide a concise and highly visible display method. For example, the user interface unit selects the optimal display method by taking into account the user's device information. In this way, the user interface unit can select the optimal display method by taking into account the user's device information.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The data analysis department can consider a user's past music event attendance history when analyzing their listening history and preferences. For example, the data analysis department collects information on concerts and festivals a user has attended in the past and analyzes the songs and artists performed at those events. This allows the data analysis department to identify preferences with greater accuracy based on information about music events the user has actually experienced. The data analysis department can also analyze preference trends based on the type and scale of events a user has attended. For example, it can recommend songs of similar genres and artists to a user who has attended a large-scale festival. Furthermore, the data analysis department can analyze information on merchandise and related products purchased by users at events to help identify preferences. In this way, the data analysis department can provide more personalized music recommendations by considering a user's music event attendance history.

[0056] The data analysis department can consider users' social media activity when deeply analyzing their listening history and preferences. For example, the data analysis department collects and uses information on songs and artists that users have shared on social media. This allows the data analysis department to understand the trends in songs and artists that users want to share with others. The data analysis department can also analyze information on artists and music-related accounts that users follow on social media to help identify preferences. Furthermore, the data analysis department can analyze preference trends based on information on songs and artists that users have "liked" on social media. As a result, the data analysis department can provide more accurate music recommendations by considering users' social media activity.

[0057] The update function can consider the user's geographical location when updating recommendations based on the user's latest preferences. For example, if the user is in a specific region, the update function can prioritize recommending songs popular in that region. If the user is traveling, the update function can prioritize recommending songs popular in the destination region. Furthermore, if the user is at home, the update function can prioritize recommending songs that are frequently listened to at home. This allows the update function to provide more appropriate recommendations by considering the user's geographical location.

[0058] The data analysis unit can analyze a user's past listening history and select the optimal analysis algorithm, taking into account the user's lifestyle. For example, if the user is exercising, the data analysis unit will prioritize analyzing songs with a tempo suitable for exercise. If the user is working, the data analysis unit can prioritize analyzing songs that enhance concentration. Furthermore, if the user is relaxed, the data analysis unit can prioritize analyzing songs that promote relaxation. In this way, the data analysis unit can perform more appropriate analysis by considering the user's lifestyle.

[0059] The data analysis unit can filter listening history based on the user's current lifestyle and areas of interest. For example, if the user is exercising, the data analysis unit will prioritize analyzing songs with a tempo suitable for exercise. If the user is working, the data analysis unit can prioritize analyzing songs that enhance concentration. Furthermore, if the user is relaxed, the data analysis unit can prioritize analyzing songs that promote relaxation. In this way, the data analysis unit can perform more appropriate analysis by filtering based on the user's lifestyle and areas of interest.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data analysis department analyzes the user's listening history and preferences. For example, it identifies the user's preferences based on the genre, artist, and number of plays of songs the user has listened to in the past. The data analysis department uses machine learning algorithms and clustering techniques to analyze the user's listening history and identify preferences. Step 2: The recommendation team recommends new songs based on the results analyzed by the data analysis team. For example, it might recommend songs from genres or artists the user has never listened to before. The recommendation team uses collaborative filtering and content-based filtering technologies to recommend new songs that match the user's preferences. Step 3: The update unit updates the songs recommended by the recommendation unit in real time. For example, after a user listens to a new song, the recommendation list is updated based on the information about that song. The update unit analyzes streaming data in real time and updates the recommendation list based on the user's latest preferences. It can also update the recommendation list based on user feedback.

[0062] (Example of form 2) The music recommendation system according to an embodiment of the present invention is a system that analyzes the listening history and preferences of users of a music streaming service and recommends undiscovered artists and songs in new genres. In this music recommendation system, a data analysis unit deeply analyzes the user's listening history and preferences, and a recommendation unit operates to recommend songs containing new elements based on the analysis results. Recommendations are updated in real time and are based on the user's latest preferences. As a result, users can enjoy unprecedented musical experiences, and emerging artists are given new opportunities to spread their music. For example, the music recommendation system collects the user's listening history, and the data analysis unit analyzes that data. The data analysis unit identifies the user's preferences based on the genre, artist, and number of plays of songs the user has listened to in the past. Next, the recommendation unit recommends new songs that match the user's preferences based on the analysis results of the data analysis unit. For example, the recommendation unit recommends songs from genres or artists that the user has never listened to before. The recommendation unit also updates the new songs that match the user's preferences in real time. For example, after the user listens to a new song, the recommendation list is updated based on the information of that song. This allows users to always enjoy the latest music experiences. Furthermore, the music recommendation system improves the efficiency of users' music exploration, reducing the time it takes to discover new songs by 50%. It also increases the number of listeners for undiscovered artists by an average of 30% and the frequency of platform use by 20%. Specifically, it applies advanced data analysis technology and customizes the user experience to revolutionize the process of discovering new music. Depending on user needs, it provides music lovers with opportunities to constantly explore new music and emerging artists with a platform to promote their music. It also provides music platforms with a means to increase user engagement. The target market includes music fans of all ages, music streaming services, and management companies for emerging artists. This music recommendation system is a must-enter market now, given the approximately 300 billion yen annual market size of the music streaming market and the increasing need for new music discovery as the music industry becomes more digitized.Our vision is to promote cultural diversity through music discovery and sharing, and to spread the talent of new artists to the world. This will allow our music recommendation system to improve users' music exploration efficiency and reduce the time it takes to discover new songs by 50%.

[0063] The music recommendation system according to this embodiment comprises a data analysis unit, a recommendation unit, and an update unit. The data analysis unit analyzes the user's listening history and preferences. The data analysis unit identifies the user's preferences based, for example, on the genre, artist, and number of plays of songs the user has listened to in the past. The data analysis unit can analyze the user's listening history using, for example, a machine learning algorithm. The data analysis unit can also use clustering technology to identify the user's preferences. For example, the data analysis unit clusters the user's listening history to identify the user's preferences. The recommendation unit recommends new songs based on the results analyzed by the data analysis unit. The recommendation unit recommends songs of genres or artists the user has not listened to before. The recommendation unit can recommend new songs that match the user's preferences using, for example, collaborative filtering technology. The recommendation unit can also recommend new songs that match the user's preferences using content-based filtering technology. For example, the recommendation unit recommends songs that the user is likely to like based on the user's listening history. The update unit updates the songs recommended by the recommendation unit in real time. For example, after a user listens to a new song, the update unit updates the recommendation list based on the information of that song. The update unit can also analyze streaming data in real time and update the recommendation list based on the user's latest preferences. Furthermore, the update unit can update the recommendation list based on user feedback. For example, the update unit updates the recommendation list based on the user's evaluation of the recommended songs. As a result, the music recommendation system according to this embodiment can provide the user with a new music experience by analyzing the user's listening history and preferences, recommending new songs, and updating them in real time.

[0064] The data analysis department analyzes users' listening history and preferences. Specifically, it collects detailed data such as the genre, artist, number of plays, time of play, and frequency of play of songs the user has listened to in the past, and uses this data to identify the user's musical preferences. The data analysis department can analyze users' listening history using machine learning algorithms. For example, if a user frequently listens to songs of a particular genre, it will be determined that the user likes that genre. Similarly, if a user repeatedly listens to songs by a particular artist, it will be determined that the user likes that artist. Furthermore, the data analysis department uses clustering technology to cluster the user's listening history and identify the user's preferences. By using clustering technology, the user's listening history can be divided into multiple groups, and common features can be extracted from each group. For example, if a user frequently listens to rock and pop songs, it will be determined that the user likes these genres. Based on these analysis results, the data analysis department can gain a detailed understanding of the user's musical preferences. In addition, the data analysis department can analyze not only the user's listening history but also data such as user ratings and playlist creation history. This allows for a more accurate identification of the user's musical preferences.

[0065] The recommendation unit recommends new songs based on the results of analysis by the data analysis unit. Specifically, it recommends songs from genres and artists that the user has not listened to before. The recommendation unit can recommend new songs that match the user's preferences using collaborative filtering technology. Collaborative filtering technology is a method of recommending songs that a user is likely to like based on the listening history of other users. For example, if user B has listened to the same songs as user A, the recommendation unit will recommend songs that user B has not yet listened to. The recommendation unit can also recommend new songs that match the user's preferences using content-based filtering technology. Content-based filtering technology is a method of recommending songs that a user is likely to like based on the user's listening history. For example, if a user likes songs from a particular genre or artist, the recommendation unit will recommend new songs related to that genre or artist. Furthermore, the recommendation unit can recommend new songs by considering not only the user's listening history but also data such as the user's ratings and playlist creation history. This allows the recommendation unit to recommend new songs that match the user's preferences with high accuracy.

[0066] The update unit updates the songs recommended by the recommendation unit in real time. Specifically, after a user listens to a new song, it updates the recommendation list based on the information of that song. The update unit can analyze streaming data in real time and update the recommendation list based on the user's latest preferences. For example, when a user listens to a new song, it analyzes the genre and artist information of that song and adds new songs that match the user's preferences to the recommendation list. The update unit can also update the recommendation list based on user feedback. For example, it updates the recommendation list based on the ratings the user gives to recommended songs. It adds songs related to songs that the user has given a high rating to the recommendation list and removes songs related to songs that the user has given a low rating to the recommendation list. This allows the update unit to update the recommendation list in real time based on the user's latest preferences and provide the user with the best possible music experience. Furthermore, the update unit can analyze the user's listening history and rating history over the long term and understand changes in the user's musical preferences. This allows the update unit to continuously update the recommendation list in response to changes in the user's preferences.

[0067] The effectiveness measurement unit can measure the effectiveness of recommendations. For example, the effectiveness measurement unit can measure user satisfaction. For example, the effectiveness measurement unit can measure the effectiveness of recommendations based on user ratings of recommended songs. The effectiveness measurement unit can also measure the effectiveness of recommendations based on an increase in the number of plays. For example, the effectiveness measurement unit can measure the effectiveness of recommendations based on the number of times users play recommended songs. The effectiveness measurement unit can also measure the effectiveness of recommendations based on user feedback. For example, the effectiveness measurement unit can measure the effectiveness of recommendations based on comments users make about recommended songs. In this way, the effectiveness measurement unit can improve the accuracy of the system by measuring the effectiveness of recommendations.

[0068] The user interface can be designed to be easy for users to operate. For example, the user interface can adopt a design that enables intuitive operation. For example, the user interface can be designed to be easy for users to operate by simplifying the operating procedures. Furthermore, the user interface can provide a visually clear interface. For example, the user interface can use icons and graphical elements to allow users to operate intuitively. The user interface can also be designed to be easy for users to operate by supporting voice control. For example, the user interface can use voice recognition technology to operate in response to the user's voice commands. In this way, the user interface can improve the user experience by making it easy for users to operate.

[0069] The data analysis department can deeply analyze users' listening history and preferences. For example, it can analyze users' listening history in detail to identify their preferences. The data analysis department can use machine learning algorithms to deeply analyze users' listening history. The data analysis department can also use clustering techniques to identify users' preferences. For example, it can cluster users' listening history to identify their preferences. The data analysis department can also analyze users' listening history over time to understand changes in users' preferences. For example, it can analyze users' listening history over time to identify changes in users' preferences. As a result, the data analysis department can provide more accurate recommendations by deeply analyzing users' listening history and preferences.

[0070] The recommendation system can recommend songs that include new elements different from the user's existing preferences. For example, it can recommend songs from genres or artists the user has never listened to before. The recommendation system can recommend new songs that match the user's preferences using collaborative filtering technology, for example. It can also recommend new songs that match the user's preferences using content-based filtering technology. For example, it can recommend songs that the user is likely to like based on the user's listening history. The recommendation system can also analyze the user's preferences and recommend songs that include new elements the user has never listened to before. For example, it can recommend songs from genres or artists the user has never listened to before. In this way, the recommendation system can provide users with new musical experiences by recommending songs that include new elements different from their existing preferences.

[0071] The update unit can update recommendations based on the user's latest preferences. For example, after a user listens to a new song, the update unit can update the recommendation list based on information about that song. For example, the update unit can analyze streaming data in real time and update the recommendation list based on the user's latest preferences. The update unit can also update the recommendation list based on user feedback. For example, the update unit can update the recommendation list based on the user's ratings of recommended songs. For example, the update unit can analyze the user's listening history chronologically and update the recommendation list based on the user's latest preferences. For example, the update unit can analyze the user's listening history chronologically, identify the user's latest preferences, and update the recommendation list accordingly. In this way, the update unit can always provide the latest information by updating recommendations based on the user's latest preferences.

[0072] The data analysis unit can estimate the user's emotions and adjust the analysis method of the listening history based on the estimated emotions. For example, if the user is stressed, the data analysis unit can prioritize analyzing relaxing songs. For example, if the user is excited, the data analysis unit can prioritize analyzing energetic songs. Also, if the user is sad, the data analysis unit can prioritize analyzing mood-enhancing songs. For example, the data analysis unit estimates the user's emotions and determines the priority of songs to analyze based on the estimated emotions. This allows the data analysis unit to perform more appropriate analysis by adjusting the analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The data analysis unit can analyze a user's past listening history and select the optimal analysis algorithm. For example, the data analysis unit can analyze the genres of songs a user has listened to in the past and select an analysis algorithm specialized for those genres. For example, the data analysis unit can select an algorithm that prioritizes analyzing popular songs based on the number of times a user has listened to those songs in the past. Furthermore, the data analysis unit can analyze the trends of songs a user has skipped in the past and select an algorithm that analyzes songs that are less likely to be skipped. For example, the data analysis unit can analyze a user's past listening history and select the optimal analysis algorithm. In this way, the data analysis unit can select the optimal analysis algorithm by analyzing past listening history.

[0074] The data analysis unit can filter listening history based on the user's current lifestyle and areas of interest. For example, if the user is exercising, the data analysis unit will prioritize analyzing songs with a tempo suitable for exercise. If the user is working, the data analysis unit can prioritize analyzing songs that enhance concentration. Furthermore, if the user is relaxed, the data analysis unit can prioritize analyzing songs that promote relaxation. In short, the data analysis unit filters based on the user's lifestyle and areas of interest. This allows the data analysis unit to perform more appropriate analysis by filtering based on the user's lifestyle and areas of interest.

[0075] The data analysis unit can estimate the user's emotions and determine the priority of listening history to analyze based on the estimated emotions. For example, if the user is stressed, the data analysis unit can prioritize the analysis of listening history of relaxing songs. For example, if the user is excited, the data analysis unit can prioritize the analysis of listening history of energetic songs. Also, if the user is sad, the data analysis unit can prioritize the analysis of listening history of uplifting songs. For example, the data analysis unit estimates the user's emotions and determines the priority of songs to analyze based on the estimated emotions. This allows the data analysis unit to perform more appropriate analysis by prioritizing listening history based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The data analysis unit can prioritize analyzing highly relevant listening history by considering the user's geographical location. For example, if the user is in a specific region, the data analysis unit can prioritize analyzing songs popular in that region. For example, if the user is traveling, the data analysis unit can prioritize analyzing songs popular in the destination region. Furthermore, if the user is at home, the data analysis unit can prioritize analyzing songs frequently listened to at home. For example, the data analysis unit prioritizes analyzing highly relevant listening history by considering the user's geographical location. This enables the data analysis unit to perform more relevant analyses by considering the user's geographical location.

[0077] The data analysis unit can analyze the user's social media activity and related history when analyzing listening history. For example, the data analysis unit can prioritize analyzing songs that the user has shared on social media. For example, the data analysis unit can prioritize analyzing songs by artists that the user follows on social media. The data analysis unit can also prioritize analyzing songs that the user has "liked" on social media. For example, the data analysis unit analyzes the user's social media activity and related history. In this way, the data analysis unit can analyze related history by analyzing the user's social media activity.

[0078] The recommendation system can estimate the user's emotions and adjust the way recommendations are presented based on those emotions. For example, if the user is relaxed, the recommendation system will make recommendations using calm language. If the user is excited, the recommendation system will make recommendations using energetic language. Furthermore, if the user is sad, the recommendation system can make recommendations using encouraging language. For example, the recommendation system estimates the user's emotions and adjusts the way recommendations are presented based on those emotions. This allows the recommendation system to make more appropriate recommendations by adjusting the way recommendations are presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The recommendation system can adjust the level of detail in its recommendations based on the importance of the song. For example, it might provide detailed information when recommending a popular song, or concise information when recommending a song by a new artist. It can also provide detailed information when recommending a song that matches the user's preferences. For example, it adjusts the level of detail in its recommendations based on the importance of the song. This allows the recommendation system to provide more appropriate recommendations by adjusting the level of detail based on the importance of the song.

[0080] The recommendation system can apply different recommendation algorithms depending on the song category. For example, for pop music, it might apply an algorithm that emphasizes popularity and play count. For classical music, it might apply an algorithm that emphasizes ratings and expert reviews. For jazz music, it might apply an algorithm that emphasizes the artist's history and live performance. In short, the recommendation system can apply different recommendation algorithms depending on the song category, enabling more appropriate recommendations.

[0081] The recommendation system can estimate the user's emotions and adjust the length of recommendations based on those emotions. For example, if the user is relaxed, the recommendation system can provide a longer list of recommendations. If the user is in a hurry, the recommendation system can provide a shorter list of recommendations. Furthermore, if the user is excited, the recommendation system can provide a shorter list of recommendations, focusing on energetic songs. For example, the recommendation system estimates the user's emotions and adjusts the length of recommendations based on those emotions. This allows the recommendation system to provide more appropriate recommendations by adjusting the length of recommendations based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The recommendation system can prioritize recommendations based on the release date of the songs. For example, it can prioritize newly released songs. It can also consider the release dates of songs the user has listened to in the past. Furthermore, it can prioritize songs whose release dates match the user's preferences. For example, the recommendation system can prioritize recommendations based on the release date of the songs. This allows the recommendation system to make more appropriate recommendations by prioritizing recommendations based on the release date of the songs.

[0083] The recommendation system can adjust the order of recommendations based on the relevance of the songs. For example, it might recommend songs that are most relevant to the user's preferences first. It could also prioritize recommending songs that are relevant based on the user's past listening history. Furthermore, it could prioritize recommending songs that are relevant based on the user's current mood. For example, the recommendation system adjusts the order of recommendations based on the relevance of the songs. This allows the recommendation system to make more appropriate recommendations by adjusting the order of recommendations based on the relevance of the songs.

[0084] The update unit can estimate the user's emotions and adjust the timing of updates based on those emotions. For example, if the user is relaxed, the update unit will update at a slow pace. If the user is in a hurry, the update unit can update quickly. Furthermore, if the user is excited, the update unit can update frequently. For example, the update unit estimates the user's emotions and adjusts the timing of updates based on those emotions. This allows the update unit to provide more appropriate updates by adjusting the timing of updates based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The update unit can analyze past user responses to select the optimal update method during an update. For example, the update unit can prioritize update methods that users have previously responded positively to. For example, the update unit can avoid update methods that users have previously responded negatively to. The update unit can also analyze past user responses to select the most effective update method. For example, the update unit can analyze past user responses to select the optimal update method. In this way, the update unit can select the optimal update method by analyzing past user responses.

[0086] The update unit can customize the update frequency based on the user's current lifestyle. For example, if the user is busy, the update unit can reduce the update frequency. For example, if the user has free time, the update unit can increase the update frequency. The update unit can also set the optimal update frequency according to the user's lifestyle. For example, the update unit can customize the update frequency based on the user's lifestyle. This allows the update unit to provide more appropriate updates by customizing the update frequency according to the user's lifestyle.

[0087] The update unit can estimate the user's emotions and determine update priorities based on those emotions. For example, if the user is relaxed, the update unit will prioritize less important updates. If the user is in a hurry, the update unit can prioritize more important updates. Furthermore, if the user is excited, the update unit can prioritize energetic updates. For example, the update unit estimates the user's emotions and determines update priorities based on those emotions. This allows the update unit to provide more appropriate updates by prioritizing updates 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.

[0088] The update unit can select the optimal update method by considering the user's geographical location during the update process. For example, if the user is in a specific region, the update unit can prioritize updating information related to that region. For example, if the user is traveling, the update unit can prioritize updating information related to the travel destination. Furthermore, if the user is at home, the update unit can prioritize updating information related to home. For example, the update unit can select the optimal update method by considering the user's geographical location. In this way, the update unit can select the optimal update method by considering the user's geographical location.

[0089] The update unit can analyze the user's social media activity and suggest update methods during the update process. For example, the update unit can suggest update methods based on information the user has shared on social media. For example, the update unit can suggest update methods based on information the user follows on social media. Furthermore, the update unit can also suggest update methods based on information the user has "liked" on social media. For example, the update unit analyzes the user's social media activity and suggests update methods. In this way, the update unit can suggest the optimal update method by analyzing the user's social media activity.

[0090] The effectiveness measurement unit can estimate the user's emotions and adjust the effectiveness measurement method based on the estimated emotions. For example, if the user is relaxed, the effectiveness measurement unit can perform a detailed effectiveness measurement. For example, if the user is in a hurry, the effectiveness measurement unit can perform a concise effectiveness measurement. Furthermore, if the user is excited, the effectiveness measurement unit can perform an energetic effectiveness measurement. For example, the effectiveness measurement unit estimates the user's emotions and adjusts the effectiveness measurement method based on the estimated emotions. This allows the effectiveness measurement unit to perform more appropriate effectiveness measurements by adjusting the effectiveness measurement method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The effectiveness measurement unit can optimize the measurement algorithm by referring to past measurement data during effectiveness measurement. For example, the effectiveness measurement unit can select the optimal measurement algorithm based on past measurement data. For example, the effectiveness measurement unit can analyze past measurement data and propose an effective measurement method. Furthermore, the effectiveness measurement unit can optimize the measurement algorithm by referring to past measurement data. For example, the effectiveness measurement unit can select the optimal measurement algorithm based on past measurement data. In this way, the effectiveness measurement unit can optimize the measurement algorithm by referring to past measurement data.

[0092] The effectiveness measurement unit can estimate the user's emotions and adjust the frequency of effectiveness measurements based on the estimated emotions. For example, if the user is relaxed, the effectiveness measurement unit can perform effectiveness measurements frequently. For example, if the user is in a hurry, the effectiveness measurement unit can reduce the frequency of effectiveness measurements. Also, if the user is excited, the effectiveness measurement unit can perform energetic effectiveness measurements. For example, the effectiveness measurement unit estimates the user's emotions and adjusts the frequency of effectiveness measurements based on the estimated emotions. This allows the effectiveness measurement unit to perform more appropriate effectiveness measurements by adjusting the frequency of effectiveness measurements based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The effectiveness measurement unit can weight the measurement data based on when the user's listening history was submitted. For example, the effectiveness measurement unit can give more weight to data on songs the user has recently listened to when measuring effectiveness. For example, the effectiveness measurement unit can refer to data on songs the user has listened to in the past when measuring effectiveness. Furthermore, the effectiveness measurement unit can adjust the weighting of the measurement data based on when the user's listening history was submitted. For example, the effectiveness measurement unit can weight the measurement data based on when the user's listening history was submitted. This allows the effectiveness measurement unit to perform more appropriate effectiveness measurements by weighting the measurement data based on when the user's listening history was submitted.

[0094] The user interface unit can estimate the user's emotions and adjust the interface display method based on the estimated emotions. For example, if the user is tense, the user interface unit can provide an interface with calm colors. For example, if the user is enjoying themselves, the user interface unit can provide an interface with bright colors. Also, if the user is tired, the user interface unit can provide a simple and highly visible interface. For example, the user interface unit estimates the user's emotions and adjusts the interface display method based on the estimated emotions. In this way, the user interface unit can provide a more appropriate interface by adjusting the interface display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The user interface unit can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, the user interface unit can prioritize displaying interface designs that the user has previously preferred. For example, the user interface unit can analyze the user's past operation history and suggest the optimal display method. The user interface unit can also choose not to display interface designs that the user has previously avoided. For example, the user interface unit selects the optimal display method by referring to the user's past operation history. In this way, the user interface unit can select the optimal display method by referring to the user's past operation history.

[0096] The user interface unit can estimate the user's emotions and adjust the interface's operation procedures based on the estimated emotions. For example, if the user is nervous, the user interface unit can simplify the operation procedures. For example, if the user is enjoying themselves, the user interface unit can make the operation procedures more detailed. Furthermore, if the user is tired, the user interface unit can minimize the operation procedures. For example, the user interface unit estimates the user's emotions and adjusts the interface's operation procedures based on the estimated emotions. In this way, the user interface unit can provide more appropriate operation procedures by adjusting the interface's operation procedures based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The user interface unit can select the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, the user interface unit can provide a display method that matches the screen size. For example, if the user is using a tablet, the user interface unit can provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the user interface unit can provide a concise and highly visible display method. For example, the user interface unit selects the optimal display method by taking into account the user's device information. In this way, the user interface unit can select the optimal display method by taking into account the user's device information.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The data analysis department can consider a user's past music event attendance history when analyzing their listening history and preferences. For example, the data analysis department collects information on concerts and festivals a user has attended in the past and analyzes the songs and artists performed at those events. This allows the data analysis department to identify preferences with greater accuracy based on information about music events the user has actually experienced. The data analysis department can also analyze preference trends based on the type and scale of events a user has attended. For example, it can recommend songs of similar genres and artists to a user who has attended a large-scale festival. Furthermore, the data analysis department can analyze information on merchandise and related products purchased by users at events to help identify preferences. In this way, the data analysis department can provide more personalized music recommendations by considering a user's music event attendance history.

[0100] The effectiveness measurement unit can estimate the user's emotions and adjust the effectiveness measurement method based on the estimated emotions. For example, if the user is relaxed, the effectiveness measurement unit can perform a detailed effectiveness measurement. If the user is in a hurry, the effectiveness measurement unit can perform a concise effectiveness measurement. Furthermore, if the user is excited, the effectiveness measurement unit can perform an energetic effectiveness measurement. For example, the effectiveness measurement unit estimates the user's emotions and adjusts the effectiveness measurement method based on the estimated emotions. This allows the effectiveness measurement unit to perform more appropriate effectiveness measurements by adjusting the effectiveness measurement method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The user interface unit can estimate the user's emotions and adjust the interface display method based on the estimated emotions. For example, if the user interface unit is tense, it can provide an interface with calm colors. If the user interface unit is enjoying itself, it can provide an interface with bright colors. Furthermore, if the user interface unit is tired, it can provide a simple and highly visible interface. For example, the user interface unit estimates the user's emotions and adjusts the interface display method based on the estimated emotions. This allows the user interface unit to provide a more appropriate interface by adjusting the interface display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The data analysis department can consider users' social media activity when deeply analyzing their listening history and preferences. For example, the data analysis department collects and uses information on songs and artists that users have shared on social media. This allows the data analysis department to understand the trends in songs and artists that users want to share with others. The data analysis department can also analyze information on artists and music-related accounts that users follow on social media to help identify preferences. Furthermore, the data analysis department can analyze preference trends based on information on songs and artists that users have "liked" on social media. As a result, the data analysis department can provide more accurate music recommendations by considering users' social media activity.

[0103] The recommendation system can estimate the user's emotions and adjust the way recommendations are presented based on those emotions. For example, if the user is relaxed, the recommendation system will make recommendations using calm language. If the user is excited, the recommendation system can make recommendations using energetic language. Furthermore, if the user is sad, the recommendation system can make recommendations using encouraging language. For example, the recommendation system estimates the user's emotions and adjusts the way recommendations are presented based on those emotions. This allows the recommendation system to make more appropriate recommendations by adjusting the way recommendations are presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The update function can consider the user's geographical location when updating recommendations based on the user's latest preferences. For example, if the user is in a specific region, the update function can prioritize recommending songs popular in that region. If the user is traveling, the update function can prioritize recommending songs popular in the destination region. Furthermore, if the user is at home, the update function can prioritize recommending songs that are frequently listened to at home. This allows the update function to provide more appropriate recommendations by considering the user's geographical location.

[0105] The data analysis unit can estimate the user's emotions and adjust the analysis method of the listening history based on the estimated emotions. For example, if the user is stressed, the data analysis unit can prioritize analyzing relaxing songs. If the user is excited, the data analysis unit can prioritize analyzing energetic songs. Also, if the user is sad, the data analysis unit can prioritize analyzing mood-enhancing songs. For example, the data analysis unit estimates the user's emotions and determines the priority of songs to analyze based on the estimated emotions. This allows the data analysis unit to perform more appropriate analysis by adjusting the analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The data analysis unit can analyze a user's past listening history and select the optimal analysis algorithm, taking into account the user's lifestyle. For example, if the user is exercising, the data analysis unit will prioritize analyzing songs with a tempo suitable for exercise. If the user is working, the data analysis unit can prioritize analyzing songs that enhance concentration. Furthermore, if the user is relaxed, the data analysis unit can prioritize analyzing songs that promote relaxation. In this way, the data analysis unit can perform more appropriate analysis by considering the user's lifestyle.

[0107] The update unit can estimate the user's emotions and adjust the timing of updates based on those emotions. For example, if the user is relaxed, the update unit will update at a slow pace. If the user is in a hurry, the update unit can update quickly. Furthermore, if the user is excited, the update unit can update frequently. For example, the update unit estimates the user's emotions and adjusts the timing of updates based on those emotions. This allows the update unit to provide more appropriate updates by adjusting the timing of updates based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The data analysis unit can filter listening history based on the user's current lifestyle and areas of interest. For example, if the user is exercising, the data analysis unit will prioritize analyzing songs with a tempo suitable for exercise. If the user is working, the data analysis unit can prioritize analyzing songs that enhance concentration. Furthermore, if the user is relaxed, the data analysis unit can prioritize analyzing songs that promote relaxation. In this way, the data analysis unit can perform more appropriate analysis by filtering based on the user's lifestyle and areas of interest.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The data analysis department analyzes the user's listening history and preferences. For example, it identifies the user's preferences based on the genre, artist, and number of plays of songs the user has listened to in the past. The data analysis department uses machine learning algorithms and clustering techniques to analyze the user's listening history and identify preferences. Step 2: The recommendation team recommends new songs based on the results analyzed by the data analysis team. For example, it might recommend songs from genres or artists the user has never listened to before. The recommendation team uses collaborative filtering and content-based filtering technologies to recommend new songs that match the user's preferences. Step 3: The update unit updates the songs recommended by the recommendation unit in real time. For example, after a user listens to a new song, the recommendation list is updated based on the information about that song. The update unit analyzes streaming data in real time and updates the recommendation list based on the user's latest preferences. It can also update the recommendation list based on user feedback.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the data analysis unit, recommendation unit, update unit, effectiveness measurement unit, and user interface unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data analysis unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12. The update unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The effectiveness measurement unit is implemented by the specific processing unit 290 of the data processing unit 12. The user interface unit is implemented by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the data analysis unit, recommendation unit, update unit, effectiveness measurement unit, and user interface unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the data analysis unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12. The update unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The effectiveness measurement unit is implemented by the specific processing unit 290 of the data processing unit 12. The user interface unit is implemented by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the data analysis unit, recommendation unit, update unit, effectiveness measurement unit, and user interface unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data analysis unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12. The update unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The effectiveness measurement unit is implemented by the specific processing unit 290 of the data processing unit 12. The user interface unit is implemented by the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the data analysis unit, recommendation unit, update unit, effectiveness measurement unit, and user interface unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data analysis unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12. The update unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The effectiveness measurement unit is implemented by the specific processing unit 290 of the data processing unit 12. The user interface unit is implemented by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A data analysis unit analyzes the user's listening history and preferences, A recommendation unit recommends new songs based on the results of the data analysis unit, The system includes an update unit that updates the songs recommended by the aforementioned recommendation unit in real time. A system characterized by the following features. (Note 2) Equipped with an effect measurement unit, Measuring the effectiveness of recommendations The system described in Appendix 1, characterized by the features described herein. (Note 3) Equipped with a user interface section, To make it easy for users to operate The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned data analysis unit, In-depth analysis of user listening history and preferences The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned recommendation department, Recommend songs that include new elements that differ from your existing preferences. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned update unit is Recommendations are updated based on the user's latest preferences. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned data analysis unit, The system estimates the user's emotions and adjusts the listening history analysis method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned data analysis unit, The system analyzes the user's past listening history and selects the optimal analysis algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned data analysis unit, When analyzing listening history, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned data analysis unit, It estimates the user's emotions and determines the priority of listening history to analyze based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned data analysis unit, When analyzing listening history, the system prioritizes analyzing highly relevant history by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned data analysis unit, When analyzing listening history, the system analyzes the user's social media activity and related history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned recommendation department, It estimates the user's emotions and adjusts the way recommendations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned recommendation department, When making recommendations, adjust the level of detail based on the importance of the song. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned recommendation department, When making recommendations, different recommendation algorithms are applied depending on the song category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned recommendation department, It estimates the user's sentiment and adjusts the length of recommendations based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned recommendation department, When making recommendations, we prioritize them based on the release date of the songs. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned recommendation department, When making recommendations, the order of recommendations is adjusted based on the relevance of the songs. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned update unit is It estimates user sentiment and adjusts the timing of updates based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned update unit is During updates, the system analyzes past user feedback to select the optimal update method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned update unit is During updates, the update frequency is customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned update unit is It estimates user sentiment and determines update priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned update unit is During updates, the system will select the optimal update method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned update unit is When updating, we analyze the user's social media activity and suggest update methods. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned effect measurement unit is We estimate user emotions and adjust the effectiveness measurement method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned effect measurement unit is When measuring effectiveness, the measurement algorithm is optimized by referring to past measurement data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned effect measurement unit is We estimate the user's emotions and adjust the frequency of effectiveness measurement based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned effect measurement unit is During effectiveness measurement, the measurement data is weighted based on when the user's listening history was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 29) The user interface unit is It estimates the user's emotions and adjusts the interface display based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The user interface unit is When displaying the interface, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 31) The user interface unit is It estimates the user's emotions and adjusts the interface operation procedures based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The user interface unit is When displaying the interface, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 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 analysis unit analyzes the user's listening history and preferences, A recommendation unit recommends new songs based on the results of the data analysis unit, The system includes an update unit that updates the songs recommended by the aforementioned recommendation unit in real time. A system characterized by the following features.

2. Equipped with an effect measurement unit, Measuring the effectiveness of recommendations The system according to feature 1.

3. Equipped with a user interface section, To make it easy for users to operate The system according to feature 1.

4. The aforementioned data analysis unit, In-depth analysis of user listening history and preferences The system according to feature 1.

5. The aforementioned recommendation department, Recommend songs that include new elements that differ from your existing preferences. The system according to feature 1.

6. The aforementioned update unit is, Recommendations are updated based on the user's latest preferences. The system according to feature 1.

7. The aforementioned data analysis unit, The system estimates the user's emotions and adjusts the listening history analysis method based on the estimated emotions. The system according to feature 1.

8. The aforementioned data analysis unit, The system analyzes the user's past listening history and selects the optimal analysis algorithm. The system according to feature 1.

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