System

Abstract

An object of the system according to the embodiment is to allow a user to efficiently find new music and artists.SOLUTION: A system includes a listening history analysis unit and a recommendation unit. The listening history analysis unit analyzes the user's music listening history and preferences. The recommendation unit recommends an unexplored artist or music having low similarity on the basis of the user's preference analyzed by the listening history analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem of making it difficult for users to efficiently discover new music and artists.

[0005] The system according to the embodiment aims to enable users to efficiently discover new music and artists. [Means for solving the problem]

[0006] The system according to the embodiment includes a listening history analysis unit and a recommendation unit. The listening history analysis unit analyzes a user's music listening history and preferences. The recommendation unit recommends undiscovered artists and songs that have low similarity to the user's preferences, based on the user's preferences analyzed by the listening history analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to efficiently discover new music and artists. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A music recommendation system according to an embodiment of the present invention analyzes a user's music listening history and preferences, and uses a generation AI to recommend undiscovered artists and songs with low similarity. This allows users to efficiently discover new music and artists, broadening their musical experience. It also provides emerging artists with more opportunities to promote their music.

[0029] A music recommendation system according to an embodiment includes a listening history analysis unit and a recommendation unit. The listening history analysis unit analyzes a user's music listening history and preferences. For example, the listening history analysis unit collects data on songs and artists the user has listened to in the past and analyzes the trends. The listening history analysis unit can also analyze data such as the user's playback count, playback time, and the genres of the songs played. For example, if the user primarily listens to rock, the listening history analysis unit identifies this tendency. The recommendation unit recommends undiscovered artists and songs with low similarity based on the user's preferences analyzed by the listening history analysis unit. For example, if the user primarily listens to rock, the recommendation unit recommends artists and songs from different genres, such as jazz or classical. The recommendation unit can also make recommendations based on data on the user's preferences and data on undiscovered artists and songs. For example, the recommendation unit recommends undiscovered artists and songs with low similarity based on genres and artists the user has listened to at least once in the past. As a result, the music recommendation system according to the embodiment allows users to efficiently discover new music and artists, broadening the range of music experiences, and also increasing opportunities for emerging artists to spread their music.

[0030] The listening history analysis unit analyzes the user's lifestyle data and can grasp the user's preference trends with greater accuracy. For example, the listening history analysis unit collects activity data when the user listens to music and analyzes the differences between music listened to while exercising and music listened to while relaxing. For example, the listening history analysis unit grasps the tendency of music listened to while exercising and the tendency of music listened to while relaxing. Furthermore, the listening history analysis unit can grasp the user's preference trends with greater accuracy based on the user's lifestyle data. For example, the listening history analysis unit grasps the tendency of music listened to while commuting and analyzes the user's preferences. This makes it possible to grasp the user's music preferences according to their lifestyle.

[0031] The listening history analysis unit can analyze a user's preferences for other entertainment based on their music listening history and build a comprehensive entertainment recommendation system. The listening history analysis unit, for example, analyzes a user's music listening history and movie viewing history to build a comprehensive entertainment recommendation system. For example, the listening history analysis unit recommends movies related to a user who likes a particular music genre. The listening history analysis unit can also analyze a user's game preferences based on their music listening history. For example, the listening history analysis unit recommends games related to a user who likes a particular music genre. This can improve the user's overall entertainment experience.

[0032] The listening history analysis unit can match a user with other users who share the same tastes based on the user's listening history, thereby forming a music community. The listening history analysis unit, for example, analyzes the user's music listening history and identifies other users who share the same tastes. For example, the listening history analysis unit matches users who frequently listen to the same artists. The listening history analysis unit can also form a music community based on user preference data. For example, the listening history analysis unit connects users who like the same genre of music. This promotes interaction between users and forms a music community.

[0033] The recommendation unit can make recommendations based on genres and artists that are different from the user's preferences but that the user has shown interest in at least once in the past. For example, the recommendation unit recommends undiscovered artists or songs that are less similar to genres and artists the user has listened to at least once in the past. For example, the recommendation unit recommends a new jazz artist based on a jazz artist the user has listened to only once in the past. The recommendation unit can also recommend genres and artists the user has shown interest in at least once in the past based on the user's preference data. For example, the recommendation unit recommends a new classical artist based on a classical artist the user has listened to only once in the past. This makes it possible to recommend new music and artists that will interest the user.

[0034] The recommendation unit can recommend music from countries or regions that the user has not listened to before, based on the user's music listening history. The recommendation unit, for example, analyzes the user's listening history and recommends music from countries or regions that the user has not listened to before. For example, the recommendation unit recommends African music to a user who only listens to American music. The recommendation unit can also recommend music from regions that the user has not listened to before, based on the user's listening history. For example, the recommendation unit recommends Asian music to a user who only listens to European music. This makes it possible to introduce music from new cultural spheres to the user.

[0035] The recommendation unit can recommend undiscovered artists and songs by referring to the listening history of the user's friends and family. The recommendation unit, for example, analyzes the listening history of the user's friends and family and recommends undiscovered artists and songs based on that data. For example, the recommendation unit recommends undiscovered artists that friends listen to. The recommendation unit can also recommend undiscovered artists and songs based on the listening history of the user's friends and family. For example, the recommendation unit recommends undiscovered artists that family members listen to. This makes it possible to recommend new music and artists based on the preferences of the user's friends and family.

[0036] The recommendation unit can recommend undiscovered artists and songs by utilizing data from live events and concerts that the user has attended in the past. For example, the recommendation unit analyzes data from live events and concerts that the user has attended in the past and recommends undiscovered artists and songs based on that data. For example, the recommendation unit recommends other artists who performed at the same event. The recommendation unit can also recommend undiscovered artists and songs based on the user's history of attending live events and concerts. For example, the recommendation unit recommends new artists based on songs played at concerts that the user has attended in the past. This makes it possible to recommend new music and artists based on the user's history of attending live events and concerts.

[0037] The recommendation unit can predict music and artists that the user is likely to discover next based on the user's past discovery history. The recommendation unit, for example, analyzes the user's past discovery history and predicts music and artists that the user is likely to discover next. For example, the recommendation unit recommends new artists based on trends in artists discovered in the past. The recommendation unit can also predict music and artists that the user is likely to discover next based on the user's discovery history. For example, the recommendation unit recommends new songs based on trends in songs discovered in the past. This makes it possible to predict music and artists that the user will discover next based on the user's past discovery history.

[0038] When a user discovers new music or an artist, the recommendation unit can provide the user with a backstory or an interview with the artist related to the discovery. For example, when a user discovers new music or an artist, the recommendation unit can provide the user with a backstory of the artist. For example, the recommendation unit can introduce the artist's background and musical career. Furthermore, when a user discovers new music or an artist, the recommendation unit can provide the user with an interview with the artist. For example, the recommendation unit can display the contents of the artist's interview. This can provide the user with relevant information when they discover new music or an artist.

[0039] The recommendation unit can add a function for discovering new music and artists based on recommendations from the user's friends and family. The recommendation unit can add a function for discovering new music and artists based on recommendations from the user's friends and family, for example. For example, the recommendation unit displays new artists recommended by friends. The recommendation unit can also discover new music and artists based on recommendations from the user's friends and family. For example, the recommendation unit displays new artists recommended by family members. This allows the user to discover new music and artists based on recommendations from the user's friends and family.

[0040] The recommendation unit can add a function to discover new music and artists based on music from places the user has visited in the past or at travel destinations. The recommendation unit can add a function to discover new music and artists based on music from places the user has visited in the past or at travel destinations. For example, the recommendation unit recommends new artists based on music listened to at travel destinations. The recommendation unit can also discover new music and artists based on the user's past visit history. For example, the recommendation unit recommends new artists based on music listened to at places the user has visited in the past. This makes it possible to discover new music and artists based on music from travel destinations the user has visited in the past.

[0041] The recommendation unit can introduce targeted advertisements based on the user's listening history and promote emerging artists. The recommendation unit, for example, analyzes the user's listening history and displays targeted advertisements for emerging artists based on that data. For example, the recommendation unit advertises emerging artists of a particular genre to a user who likes that genre. The recommendation unit can also introduce targeted advertisements based on the user's listening history. For example, the recommendation unit advertises emerging artists of a genre that the user frequently listens to. This makes it possible to display targeted advertisements for emerging artists based on the user's listening history.

[0042] The recommendation unit can analyze the user's past promotion response data and suggest the optimal promotion method. The recommendation unit, for example, analyzes the user's past promotion response data and suggests the optimal promotion method for an up-and-coming artist based on that data. For example, the recommendation unit reuses promotion methods that have received good responses in the past. The recommendation unit can also suggest the optimal promotion method based on the user's promotion response data. For example, the recommendation unit suggests a new promotion method based on the tendency of advertisements that the user has clicked in the past. This makes it possible to suggest the optimal promotion method based on the user's past promotion response data.

[0043] The recommendation unit can promote emerging artists by referring to the listening history of the user's friends and family. The recommendation unit, for example, analyzes the listening history of the user's friends and family and promotes emerging artists based on that data. For example, the recommendation unit recommends emerging artists that friends are listening to to the user. The recommendation unit can also promote emerging artists based on the listening history of the user's friends and family. For example, the recommendation unit recommends emerging artists that family members are listening to to the user. This makes it possible to promote emerging artists based on the listening history of the user's friends and family.

[0044] The recommendation unit can promote emerging artists by utilizing data on live events and concerts that the user has previously attended. For example, the recommendation unit analyzes data on live events and concerts that the user has previously attended and promotes emerging artists based on that data. For example, the recommendation unit recommends other emerging artists who performed at the same event. The recommendation unit can also promote emerging artists based on the user's history of attending live events and concerts. For example, the recommendation unit recommends new artists based on songs that were played at concerts that the user has previously attended. This makes it possible to promote emerging artists based on the user's history of attending live events and concerts.

[0045] The recommendation unit can add a function to provide users' feedback to artists in real time. The recommendation unit builds a system that provides users with feedback on songs they have listened to in real time to artists. For example, the recommendation unit immediately notifies artists of users' comments and ratings. The recommendation unit can also provide users' feedback to artists in real time based on the user's feedback. For example, if a user gives positive feedback on a song, the recommendation unit communicates that information to the artist. This allows users' feedback to be provided to artists in real time.

[0046] The recommendation unit can provide information about live events of artists based on the user's listening history. For example, the recommendation unit analyzes the user's listening history and provides information about live events of artists based on that data. For example, the recommendation unit notifies the user of live performance information of artists that the user frequently listens to. The recommendation unit can also provide information about live events of artists based on the user's listening history. For example, the recommendation unit provides information about live events of artists that the user has listened to in the past. This makes it possible to provide information about live events of artists based on the user's listening history.

[0047] The recommendation unit can add a function to provide feedback from the user's friends and family to the artist. The recommendation unit, for example, analyzes the feedback from the user's friends and family and provides the artist with the data based on that. For example, if a friend gives positive feedback about an artist's song, the recommendation unit communicates that information to the artist. The recommendation unit can also provide the artist with feedback from the user's friends and family. For example, if a family member gives positive feedback about an artist's song, the recommendation unit communicates that information to the artist. This makes it possible to provide the artist with feedback from the user's friends and family.

[0048] The recommendation unit can provide artists with data on live events and concerts that the user has attended in the past. For example, the recommendation unit analyzes data on live events and concerts that the user has attended in the past and provides the artists with information based on that data. For example, the recommendation unit provides the artists with feedback from users who attended the same event. The recommendation unit can also provide artists with information based on the user's participation history in live events and concerts. For example, the recommendation unit provides the artists with feedback from concerts that the user has attended in the past. This makes it possible to provide artists with data based on the user's participation history in live events and concerts.

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

[0050] The listening history analysis unit can recommend music from places or travel destinations visited by the user in the past based on the user's music listening history. For example, the listening history analysis unit can analyze music from countries or regions visited by the user in the past and recommend undiscovered artists and songs from those regions. The listening history analysis unit can also grasp trends in the music the user listened to while traveling and recommend new music based on those trends. This allows the user to enjoy the culture and music of the travel destination more deeply.

[0051] The listening history analysis unit can analyze the music that a user listens to at a particular time period based on the user's music listening history and recommend music that is suitable for that time period. For example, the listening history analysis unit can identify the music that a user listens to in the morning and recommend music that is suitable for that time period. The listening history analysis unit can also analyze the music that a user listens to to relax in the evening and recommend music that is suitable for the evening. This allows the user to enjoy music that is best suited to each time period of the day.

[0052] The listening history analysis unit can analyze the music the user listens to in a particular season based on the user's music listening history and recommend music suitable for that season. For example, the listening history analysis unit can identify the music the user listens to in the summer and recommend songs suitable for summer. The listening history analysis unit can also analyze the music the user listens to to relax in the winter and recommend songs suitable for winter. This allows the user to enjoy the best music for each season.

[0053] The listening history analysis unit can analyze the music the user listens to during a specific activity based on the user's music listening history and recommend music suitable for that activity. For example, the listening history analysis unit can identify the music the user listens to while exercising and recommend songs suitable for exercise. It can also analyze the music the user listens to to concentrate while studying and recommend songs suitable for studying. This allows the user to enjoy the best music for each activity.

[0054] The listening history analysis unit can analyze the music that a user listens to at specific events or situations based on the user's music listening history and recommend music that is suitable for those events or situations. For example, the listening history analysis unit can identify the music that a user listens to at parties and recommend songs that are suitable for parties. It can also analyze the music that a user listens to to relax and recommend songs that are suitable for relaxation. This allows the user to enjoy the best music for each event or situation.

[0055] The listening history analysis unit can recommend music that matches a user's specific mood or state based on the user's music listening history. For example, the listening history analysis unit can identify the type of music a user tends to listen to when they want to relax and recommend songs that are suitable for relaxation. It can also analyze the type of music a user tends to listen to when they are in an energetic mood and recommend energetic songs. This allows the user to enjoy music that best suits their mood or state.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The listening history analysis unit analyzes the user's music listening history and preferences. Specifically, it collects data on the songs and artists the user has listened to in the past and analyzes their trends. It also analyzes data such as the number of plays, play time, and genre of songs played. For example, if the user mainly listens to rock, it will determine this tendency. Step 2: The recommendation unit recommends undiscovered artists and songs with low similarity based on the user's preferences analyzed by the listening history analysis unit. Specifically, if the user mainly listens to rock, it will recommend artists and songs from different genres, such as jazz or classical. Recommendations are also made based on the user's preference data and data on undiscovered artists and songs. For example, it will recommend undiscovered artists and songs with low similarity based on genres and artists that the user has listened to at least once in the past.

[0058] (Example 2) A music recommendation system according to an embodiment of the present invention analyzes a user's music listening history and preferences, and uses a generation AI to recommend undiscovered artists and songs with low similarity. This allows users to efficiently discover new music and artists, broadening their musical experience. It also provides emerging artists with more opportunities to promote their music.

[0059] A music recommendation system according to an embodiment includes a listening history analysis unit and a recommendation unit. The listening history analysis unit analyzes a user's music listening history and preferences. For example, the listening history analysis unit collects data on songs and artists the user has listened to in the past and analyzes the trends. The listening history analysis unit can also analyze data such as the user's playback count, playback time, and the genres of the songs played. For example, if the user primarily listens to rock, the listening history analysis unit identifies this tendency. The recommendation unit recommends undiscovered artists and songs with low similarity based on the user's preferences analyzed by the listening history analysis unit. For example, if the user primarily listens to rock, the recommendation unit recommends artists and songs from different genres, such as jazz or classical. The recommendation unit can also make recommendations based on data on the user's preferences and data on undiscovered artists and songs. For example, the recommendation unit recommends undiscovered artists and songs with low similarity based on genres and artists the user has listened to at least once in the past. As a result, the music recommendation system according to the embodiment allows users to efficiently discover new music and artists, broadening the range of music experiences, and also increasing opportunities for emerging artists to spread their music.

[0060] The listening history analysis unit analyzes the user's lifestyle data and can grasp the user's preference trends with greater accuracy. For example, the listening history analysis unit collects activity data when the user listens to music and analyzes the differences between music listened to while exercising and music listened to while relaxing. For example, the listening history analysis unit grasps the tendency of music listened to while exercising and the tendency of music listened to while relaxing. Furthermore, the listening history analysis unit can grasp the user's preference trends with greater accuracy based on the user's lifestyle data. For example, the listening history analysis unit grasps the tendency of music listened to while commuting and analyzes the user's preferences. This makes it possible to grasp the user's music preferences according to their lifestyle.

[0061] The listening history analysis unit can estimate a user's past emotional state based on the user's music listening history and recommend music according to changes in emotion. The listening history analysis unit, for example, analyzes the user's listening history and estimates the emotional state when listening to a particular song or artist. For example, the listening history analysis unit identifies a period when the user listened to sad songs and estimates the emotional state at that period. The listening history analysis unit can also recommend music according to the user's emotional state. For example, the listening history analysis unit recommends music according to changes in emotion based on songs the user listened to when they were sad in the past. This makes it possible to recommend music according to the user's emotional state.

[0062] The listening history analysis unit uses the emotion estimation function to analyze the emotion of the user while listening to music in real time and recommend the next song to listen to based on that emotion. For example, the listening history analysis unit uses a camera to analyze the facial expression of the user while listening to music and estimate the emotion in real time. For example, the listening history analysis unit recommends positive songs when the user is smiling. The listening history analysis unit also analyzes the voice of the user while listening to music and estimates the emotion in real time. For example, the listening history analysis unit analyzes the tone and speed of the user's voice to estimate the emotion. The listening history analysis unit also collects the user's biometric data (heart rate and electrodermal activity) using a sensor and analyzes the emotion in real time. For example, the listening history analysis unit estimates the emotion based on fluctuations in the user's heart rate. This makes it possible to recommend music based on the user's real-time emotion.

[0063] The listening history analysis unit can analyze a user's preferences for other entertainment based on their music listening history and build a comprehensive entertainment recommendation system. The listening history analysis unit, for example, analyzes a user's music listening history and movie viewing history to build a comprehensive entertainment recommendation system. For example, the listening history analysis unit recommends movies related to a user who likes a particular music genre. The listening history analysis unit can also analyze a user's game preferences based on their music listening history. For example, the listening history analysis unit recommends games related to a user who likes a particular music genre. This can improve the user's overall entertainment experience.

[0064] The listening history analysis unit can match a user with other users who share the same tastes based on the user's listening history, thereby forming a music community. The listening history analysis unit, for example, analyzes the user's music listening history and identifies other users who share the same tastes. For example, the listening history analysis unit matches users who frequently listen to the same artists. The listening history analysis unit can also form a music community based on user preference data. For example, the listening history analysis unit connects users who like the same genre of music. This promotes interaction between users and forms a music community.

[0065] The listening history analysis unit can use the emotion estimation function to analyze the emotions a user feels while listening to music and provide a function for sharing emotions with other users based on those emotions. The listening history analysis unit, for example, analyzes the emotions a user feels while listening to music in real time and connects the user with other users who share the same emotions. For example, the listening history analysis unit matches users who have been moved by listening to the same song. The listening history analysis unit can also provide a function for sharing emotions based on the user's emotion data. For example, the listening history analysis unit shares emotions based on the user's emotion score. This allows users to share emotions and empathize with each other.

[0066] The recommendation unit can make recommendations based on genres and artists that are different from the user's preferences but that the user has shown interest in at least once in the past. For example, the recommendation unit recommends undiscovered artists or songs that are less similar to genres and artists the user has listened to at least once in the past. For example, the recommendation unit recommends a new jazz artist based on a jazz artist the user has listened to only once in the past. The recommendation unit can also recommend genres and artists the user has shown interest in at least once in the past based on the user's preference data. For example, the recommendation unit recommends a new classical artist based on a classical artist the user has listened to only once in the past. This makes it possible to recommend new music and artists that will interest the user.

[0067] The recommendation unit can recommend music from countries or regions that the user has not listened to before, based on the user's music listening history. The recommendation unit, for example, analyzes the user's listening history and recommends music from countries or regions that the user has not listened to before. For example, the recommendation unit recommends African music to a user who only listens to American music. The recommendation unit can also recommend music from regions that the user has not listened to before, based on the user's listening history. For example, the recommendation unit recommends Asian music to a user who only listens to European music. This makes it possible to introduce music from new cultural spheres to the user.

[0068] The recommendation unit can use the emotion estimation function to recommend undiscovered artists and songs that resonate most with the user when the user is in a specific emotional state. For example, the recommendation unit uses the emotion estimation function to recommend undiscovered artists and songs that resonate most with the user when the user is in a specific emotional state. For example, the recommendation unit recommends songs that are soothing to listen to when the user is sad. The recommendation unit can also recommend undiscovered artists and songs that resonate most with the user based on the user's emotional state. For example, the recommendation unit recommends songs that are relaxing when the user is feeling stressed. This makes it possible to recommend new music and artists that match the user's emotional state.

[0069] The recommendation unit can recommend undiscovered artists and songs by referring to the listening history of the user's friends and family. The recommendation unit, for example, analyzes the listening history of the user's friends and family and recommends undiscovered artists and songs based on that data. For example, the recommendation unit recommends undiscovered artists that friends listen to. The recommendation unit can also recommend undiscovered artists and songs based on the listening history of the user's friends and family. For example, the recommendation unit recommends undiscovered artists that family members listen to. This makes it possible to recommend new music and artists based on the preferences of the user's friends and family.

[0070] The recommendation unit can recommend undiscovered artists and songs by utilizing data from live events and concerts that the user has attended in the past. For example, the recommendation unit analyzes data from live events and concerts that the user has attended in the past and recommends undiscovered artists and songs based on that data. For example, the recommendation unit recommends other artists who performed at the same event. The recommendation unit can also recommend undiscovered artists and songs based on the user's history of attending live events and concerts. For example, the recommendation unit recommends new artists based on songs played at concerts that the user has attended in the past. This makes it possible to recommend new music and artists based on the user's history of attending live events and concerts.

[0071] The recommendation unit can use the emotion estimation function to display undiscovered artists and songs recommended by friends and family when the user is in a specific emotional state. For example, the recommendation unit can use the emotion estimation function to display undiscovered artists and songs recommended by friends and family when the user is in a specific emotional state. For example, the recommendation unit can display soothing songs recommended by friends when the user is sad. The recommendation unit can also display undiscovered artists and songs recommended by friends and family based on the user's emotional state. For example, the recommendation unit can display relaxing songs recommended by family when the user is feeling stressed. This makes it possible to display music recommended by friends and family according to the user's emotional state.

[0072] The recommendation unit can predict music and artists that the user is likely to discover next based on the user's past discovery history. The recommendation unit, for example, analyzes the user's past discovery history and predicts music and artists that the user is likely to discover next. For example, the recommendation unit recommends new artists based on trends in artists discovered in the past. The recommendation unit can also predict music and artists that the user is likely to discover next based on the user's discovery history. For example, the recommendation unit recommends new songs based on trends in songs discovered in the past. This makes it possible to predict music and artists that the user will discover next based on the user's past discovery history.

[0073] When a user discovers new music or an artist, the recommendation unit can provide the user with a backstory or an interview with the artist related to the discovery. For example, when a user discovers new music or an artist, the recommendation unit can provide the user with a backstory of the artist. For example, the recommendation unit can introduce the artist's background and musical career. Furthermore, when a user discovers new music or an artist, the recommendation unit can provide the user with an interview with the artist. For example, the recommendation unit can display the contents of the artist's interview. This can provide the user with relevant information when they discover new music or an artist.

[0074] The recommendation unit can use the emotion estimation function to analyze the emotions of the user when discovering new music or artists, and suggest the next discovery based on those emotions. For example, the recommendation unit can analyze the emotions of the user when discovering new music or artists in real time, and suggest the next music or artist to discover based on those emotions. For example, the recommendation unit can recommend an emotional song when the user is emotional. The recommendation unit can also suggest the next music or artist to discover based on the user's emotional state. For example, the recommendation unit can recommend a relaxing song when the user is relaxing. This makes it possible to suggest the next music or artist to discover based on the user's emotions.

[0075] The recommendation unit can add a function for discovering new music and artists based on recommendations from the user's friends and family. The recommendation unit can add a function for discovering new music and artists based on recommendations from the user's friends and family, for example. For example, the recommendation unit displays new artists recommended by friends. The recommendation unit can also discover new music and artists based on recommendations from the user's friends and family. For example, the recommendation unit displays new artists recommended by family members. This allows the user to discover new music and artists based on recommendations from the user's friends and family.

[0076] The recommendation unit can add a function to discover new music and artists based on music from places the user has visited in the past or at travel destinations. The recommendation unit can add a function to discover new music and artists based on music from places the user has visited in the past or at travel destinations. For example, the recommendation unit recommends new artists based on music listened to at travel destinations. The recommendation unit can also discover new music and artists based on the user's past visit history. For example, the recommendation unit recommends new artists based on music listened to at places the user has visited in the past. This makes it possible to discover new music and artists based on music from travel destinations the user has visited in the past.

[0077] The recommendation unit can use the emotion estimation function to provide a function that allows a user to share emotions when discovering new music or artists and gain empathy with other users. The recommendation unit, for example, analyzes emotions in real time when a user discovers new music or artists and provides a function that allows the user to share those emotions with other users. For example, the recommendation unit shares emotions when a user is moved. The recommendation unit can also provide a function that allows the user to gain empathy with other users based on the user's emotion data. For example, the recommendation unit performs emotion sharing based on the user's emotion score. This allows the user to share emotions when discovering new music or artists with other users and gain empathy.

[0078] The recommendation unit can introduce targeted advertisements based on the user's listening history and promote emerging artists. The recommendation unit, for example, analyzes the user's listening history and displays targeted advertisements for emerging artists based on that data. For example, the recommendation unit advertises emerging artists of a particular genre to a user who likes that genre. The recommendation unit can also introduce targeted advertisements based on the user's listening history. For example, the recommendation unit advertises emerging artists of a genre that the user frequently listens to. This makes it possible to display targeted advertisements for emerging artists based on the user's listening history.

[0079] The recommendation unit can analyze the user's past promotion response data and suggest the optimal promotion method. The recommendation unit, for example, analyzes the user's past promotion response data and suggests the optimal promotion method for an up-and-coming artist based on that data. For example, the recommendation unit reuses promotion methods that have received good responses in the past. The recommendation unit can also suggest the optimal promotion method based on the user's promotion response data. For example, the recommendation unit suggests a new promotion method based on the tendency of advertisements that the user has clicked in the past. This makes it possible to suggest the optimal promotion method based on the user's past promotion response data.

[0080] The recommendation unit can use the emotion estimation function to analyze the emotion a user feels when listening to a song by an emerging artist, and optimize promotions based on the emotion. The recommendation unit can, for example, analyze the emotion a user feels when listening to a song by an emerging artist in real time, and optimize promotions based on the emotion. For example, the recommendation unit displays an advertisement that elicits positive emotions when the user feels those emotions. The recommendation unit can also optimize promotions based on the emotional state of the user. For example, the recommendation unit displays an emotional advertisement when the user is moved. This makes it possible to optimize promotions for emerging artists based on the user's emotions.

[0081] The recommendation unit can promote emerging artists by referring to the listening history of the user's friends and family. The recommendation unit, for example, analyzes the listening history of the user's friends and family and promotes emerging artists based on that data. For example, the recommendation unit recommends emerging artists that friends are listening to to the user. The recommendation unit can also promote emerging artists based on the listening history of the user's friends and family. For example, the recommendation unit recommends emerging artists that family members are listening to to the user. This makes it possible to promote emerging artists based on the listening history of the user's friends and family.

[0082] The recommendation unit can promote emerging artists by utilizing data on live events and concerts that the user has previously attended. For example, the recommendation unit analyzes data on live events and concerts that the user has previously attended and promotes emerging artists based on that data. For example, the recommendation unit recommends other emerging artists who performed at the same event. The recommendation unit can also promote emerging artists based on the user's history of attending live events and concerts. For example, the recommendation unit recommends new artists based on songs that were played at concerts that the user has previously attended. This makes it possible to promote emerging artists based on the user's history of attending live events and concerts.

[0083] The recommendation unit can use the emotion estimation function to provide a function that allows a user to share emotions felt when listening to a song by an emerging artist and gain empathy with other users. The recommendation unit, for example, analyzes emotions felt by a user when listening to a song by an emerging artist in real time and provides a function that allows the user to share those emotions with other users. For example, the recommendation unit shares emotions felt when a user is moved. The recommendation unit can also provide a function that allows the user to gain empathy with other users based on the user's emotion data. For example, the recommendation unit performs emotion sharing based on the user's emotion score. This allows the user to share emotions felt when listening to a song by an emerging artist with other users and gain empathy.

[0084] The recommendation unit can add a function to provide users' feedback to artists in real time. The recommendation unit builds a system that provides users with feedback on songs they have listened to in real time to artists. For example, the recommendation unit immediately notifies artists of users' comments and ratings. The recommendation unit can also provide users' feedback to artists in real time based on the user's feedback. For example, if a user gives positive feedback on a song, the recommendation unit communicates that information to the artist. This allows users' feedback to be provided to artists in real time.

[0085] The recommendation unit can provide information about live events of artists based on the user's listening history. For example, the recommendation unit analyzes the user's listening history and provides information about live events of artists based on that data. For example, the recommendation unit notifies the user of live performance information of artists that the user frequently listens to. The recommendation unit can also provide information about live events of artists based on the user's listening history. For example, the recommendation unit provides information about live events of artists that the user has listened to in the past. This makes it possible to provide information about live events of artists based on the user's listening history.

[0086] The recommendation unit uses the emotion estimation function to provide feedback to the artist about the emotions felt by the user when listening to the artist's songs, and can support the artist in creating new songs based on those emotions. The recommendation unit, for example, analyzes the emotions felt by the user when listening to the artist's songs in real time and provides feedback on those emotions to the artist. For example, the recommendation unit conveys to the artist the emotions felt when the user is moved. The recommendation unit can also support the artist in creating new songs based on the user's emotion data. For example, the recommendation unit provides inspiration to the artist based on the user's emotion score. This can support the artist in creating new songs based on the user's emotions.

[0087] The recommendation unit can add a function to provide feedback from the user's friends and family to the artist. The recommendation unit, for example, analyzes the feedback from the user's friends and family and provides the artist with the data based on that. For example, if a friend gives positive feedback about an artist's song, the recommendation unit communicates that information to the artist. The recommendation unit can also provide the artist with feedback from the user's friends and family. For example, if a family member gives positive feedback about an artist's song, the recommendation unit communicates that information to the artist. This makes it possible to provide the artist with feedback from the user's friends and family.

[0088] The recommendation unit can provide artists with data on live events and concerts that the user has attended in the past. For example, the recommendation unit analyzes data on live events and concerts that the user has attended in the past and provides the artists with information based on that data. For example, the recommendation unit provides the artists with feedback from users who attended the same event. The recommendation unit can also provide artists with information based on the user's participation history in live events and concerts. For example, the recommendation unit provides the artists with feedback from concerts that the user has attended in the past. This makes it possible to provide artists with data based on the user's participation history in live events and concerts.

[0089] The recommendation unit can use the emotion estimation function to provide a function that allows a user to share the emotions felt when listening to an artist's song and gain empathy with other users. The recommendation unit, for example, analyzes the emotions felt when a user listens to an artist's song in real time and provides a function that allows the user to share the emotions with other users. For example, the recommendation unit shares the emotions felt when a user is moved. The recommendation unit can also provide a function that allows the user to gain empathy with other users based on the user's emotion data. For example, the recommendation unit performs emotion sharing based on the user's emotion score. This allows the user to share the emotions felt when listening to an artist's song with other users and gain empathy.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The listening history analysis unit can recommend music from places or travel destinations visited by the user in the past based on the user's music listening history. For example, the listening history analysis unit can analyze music from countries or regions visited by the user in the past and recommend undiscovered artists and songs from those regions. The listening history analysis unit can also grasp trends in the music the user listened to while traveling and recommend new music based on those trends. This allows the user to enjoy the culture and music of the travel destination more deeply.

[0092] The listening history analysis unit can analyze the music that a user listens to at a particular time period based on the user's music listening history and recommend music that is suitable for that time period. For example, the listening history analysis unit can identify the music that a user listens to in the morning and recommend music that is suitable for that time period. The listening history analysis unit can also analyze the music that a user listens to to relax in the evening and recommend music that is suitable for the evening. This allows the user to enjoy music that is best suited to each time period of the day.

[0093] The listening history analysis unit uses the emotion estimation function to analyze the emotions of the user while listening to music and can recommend the next song to listen to based on those emotions. For example, the listening history analysis unit uses a camera to analyze the facial expressions of the user while listening to music and estimate the emotions in real time. If the user is smiling, it can recommend positive songs. It can also analyze the voice of the user while listening to music and estimate the emotions in real time. This makes it possible to recommend music based on the user's real-time emotions.

[0094] The listening history analysis unit can analyze the music the user listens to in a particular season based on the user's music listening history and recommend music suitable for that season. For example, the listening history analysis unit can identify the music the user listens to in the summer and recommend songs suitable for summer. The listening history analysis unit can also analyze the music the user listens to to relax in the winter and recommend songs suitable for winter. This allows the user to enjoy the best music for each season.

[0095] The listening history analysis unit can use the emotion estimation function to analyze the emotions a user feels while listening to music and provide a function for sharing emotions with other users based on those emotions. For example, the listening history analysis unit can analyze the emotions a user feels while listening to music in real time and connect the user with other users who share the same emotions. When a user is moved, it can match users who have been moved by listening to the same song with other users. This allows users to share emotions and empathize with each other.

[0096] The listening history analysis unit can analyze the music the user listens to during a specific activity based on the user's music listening history and recommend music suitable for that activity. For example, the listening history analysis unit can identify the music the user listens to while exercising and recommend songs suitable for exercise. It can also analyze the music the user listens to to concentrate while studying and recommend songs suitable for studying. This allows the user to enjoy the best music for each activity.

[0097] The listening history analysis unit uses the emotion estimation function to analyze the user's emotions while listening to music and can recommend the next song to listen to based on those emotions. For example, the listening history analysis unit uses sensors to collect biometric data (heart rate and electrodermal activity) while the user is listening to music and analyzes emotions in real time. It can estimate emotions based on fluctuations in the user's heart rate and recommend relaxing songs when the user is relaxing. This makes it possible to recommend music based on the user's real-time emotions.

[0098] The listening history analysis unit can analyze the music that a user listens to at specific events or situations based on the user's music listening history and recommend music that is suitable for those events or situations. For example, the listening history analysis unit can identify the music that a user listens to at parties and recommend songs that are suitable for parties. It can also analyze the music that a user listens to to relax and recommend songs that are suitable for relaxation. This allows the user to enjoy the best music for each event or situation.

[0099] The listening history analysis unit uses the emotion estimation function to analyze the emotions of the user while listening to music and can recommend the next song to listen to based on those emotions. For example, the listening history analysis unit analyzes the voice of the user while listening to music and estimates emotions in real time. It can analyze the tone and speed of the user's voice to estimate emotions. When the user is feeling stressed, it can recommend relaxing songs. This makes it possible to recommend music based on the user's real-time emotions.

[0100] The listening history analysis unit can recommend music that matches a user's specific mood or state based on the user's music listening history. For example, the listening history analysis unit can identify the type of music a user tends to listen to when they want to relax and recommend songs that are suitable for relaxation. It can also analyze the type of music a user tends to listen to when they are in an energetic mood and recommend energetic songs. This allows the user to enjoy music that best suits their mood or state.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The listening history analysis unit analyzes the user's music listening history and preferences. Specifically, it collects data on the songs and artists the user has listened to in the past and analyzes their trends. It also analyzes data such as the number of plays, play time, and genre of songs played. For example, if the user mainly listens to rock, it will determine this tendency. Step 2: The recommendation unit recommends undiscovered artists and songs with low similarity based on the user's preferences analyzed by the listening history analysis unit. Specifically, if the user mainly listens to rock, it will recommend artists and songs from different genres, such as jazz or classical. Recommendations are also made based on the user's preference data and data on undiscovered artists and songs. For example, it will recommend undiscovered artists and songs with low similarity based on genres and artists that the user has listened to at least once in the past.

[0103] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, 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. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. 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. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a 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.

[0138] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. 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. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0162] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a listening history analysis unit that analyzes a user's music listening history and preferences; a recommendation unit that recommends undiscovered artists and songs with low similarity based on the user's preferences analyzed by the listening history analysis unit. A system characterized by:

2. The listening history analysis unit The past emotional state of the user is estimated based on the user's music listening history, and music is recommended according to changes in the user's emotions.

2. The system of claim 1.

3. The listening history analysis unit Analyzing the user's preferences for other entertainment based on their music listening history and building a comprehensive entertainment recommendation system 2. The system of claim 1.

4. The recommendation unit The recommendation is made based on genres or artists that are different from the user's preferences but that the user has shown interest in at least once in the past.

2. The system of claim 1.

5. The recommendation unit Using an emotion estimation function, the undiscovered artists and songs recommended by friends and family are displayed when the user is in a specific emotional state.

2. The system of claim 1.

6. The recommendation unit Using emotion estimation to analyze the emotions of the user when discovering new music or artists and suggest the next discovery based on the emotions 2. The system of claim 1.

7. The recommendation unit Using an emotion estimation function, the emotion of the user when listening to the song by the emerging artist is analyzed, and promotion is optimized based on the emotion.

2. The system of claim 1.

8. The recommendation unit Using an emotion estimation function, the emotion felt by the user when listening to the song by the artist is fed back to the artist, and support is provided to the artist in creating the new song based on the emotion.

2. The system of claim 1.

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