system

Abstract

The system according to the embodiment aims to generate and provide optimal music based on the preferences of users in order to improve the performance of the entire group. [Solution] A system according to an embodiment includes a receiving unit, an analysis unit, a generation unit, and a provision unit. The receiving unit inputs a user's musical preferences. The analysis unit analyzes the information input by the receiving unit. The generation unit generates music suitable for the entire group based on the information analyzed by the analysis unit. The provision unit provides the music generated by the generation unit.

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] With conventional technology, the music played in shared spaces such as coworking spaces may not be suited to the preferences of individual users, making it difficult to optimize the performance of the entire group.

[0005] The system according to the embodiment aims to generate and provide optimal music based on the preferences of users in order to improve the performance of the entire group. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analyzing unit, a generating unit, and a providing unit. The receiving unit inputs a user's musical preferences. The analyzing unit analyzes the information input by the receiving unit. The generating unit generates music suitable for the entire group based on the information analyzed by the analyzing unit. The providing unit provides the music generated by the generating unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate and provide optimal music based on user preferences to improve the performance of the entire group. [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 touch of 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 generation system according to an embodiment of the present invention identifies the musical preferences of each user and generates music optimal for the entire group. In this music generation system, users input their musical preferences, and a generation AI analyzes those preferences and generates music optimal for the entire group. For example, when users input their musical preferences, they provide information such as their favorite genre, artist, and tempo. If a user inputs, "I like classical music, but prefer slower tempos," that information is sent to the generation AI. The generation AI then analyzes the input information and identifies the preferences of the entire group. The generation AI then generates music optimal for the entire group, taking each user's preferences into account. For example, if multiple users input, "I like classical music," the generation AI generates music based on classical music. The generated music can be played in shared spaces such as coworking spaces, improving the performance of the entire group. For example, playing relaxing music can improve work efficiency and reduce stress. This system can improve group performance in shared spaces such as coworking spaces, potentially improving the performance of all humanity through the power of music. This allows the music generation system to understand users' musical preferences and generate and provide music that is optimal for the entire group, thereby improving group performance in shared spaces such as coworking spaces.

[0029] A music generation system according to an embodiment includes a receiving unit, an analysis unit, a generation unit, and a providing unit. The receiving unit inputs a user's music preferences. The user's music preferences include, but are not limited to, genre, artist, and tempo. The receiving unit can also understand the user's preferences by, for example, using a history of music played by the user in the past. The analysis unit analyzes the information input by the receiving unit. The analysis unit analyzes each user's preferences using, for example, statistical techniques or machine learning algorithms. The analysis unit takes each user's preferences into account and balances the entire group. The generation unit generates music that is optimal for the entire group based on the information analyzed by the analysis unit. The generation unit generates music using, for example, a generation AI. The generation AI generates music based on the user's preferences using technologies such as neural networks and generative models. The providing unit provides the music generated by the generation unit. The providing unit can evaluate the effectiveness of the generated music and reflect the evaluation in the next music generation. As a result, the music generation system according to the embodiment can understand the musical preferences of users and generate and provide music that is optimal for the entire group, thereby improving group performance in shared spaces such as coworking spaces.

[0030] The reception unit can grasp the user's preferences by using the user's past music playback history. The past music playback history includes, for example, the number of playbacks, playback time, a list of played songs, etc., but is not limited to these examples. For example, the reception unit automatically displays music genres that the user has frequently played in the past as candidates. The reception unit can also prioritize input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can analyze the user's preferences for specific time periods based on the user's past playback history and adjust the input method. In this way, by utilizing the user's past music playback history, the user's preferences can be more accurately grasped. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past music playback history into the generation AI and cause the generation AI to grasp the user's preferences.

[0031] The analysis unit can balance the entire group based on each user's preferences. For example, the analysis unit weights each user's preferences to create a balanced playlist. For example, the analysis unit weights the user's preferred genres and artists to balance the entire group. The analysis unit can also weight the user's preferred tempo and rhythm to generate balanced music. Furthermore, the analysis unit can generate balanced music based on the user's preferred time of day and activity. This allows the entire group to be balanced while taking each user's preferences into consideration, thereby providing optimal music for the entire group. Some or all of the above-described processing by the analysis unit may be performed using, or without, AI. For example, the analysis unit can input each user's preference data into the generation AI and have the generation AI perform an analysis to balance the entire group.

[0032] The generation unit can use a generation AI to generate music that is optimal for the entire group. Examples of the generation AI include, but are not limited to, neural networks and generative models. The generation unit can use a generation AI to generate music based on a user's preferences. For example, the generation unit can use a generation AI to generate music based on classical music. The generation unit can also use a generation AI to generate music that combines genres such as pop and rock. Furthermore, the generation unit can use a generation AI to generate music based on the user's preferred tempo and rhythm. In this way, optimal music can be generated for the entire group by using the generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input data about the user's preferences into the generation AI and cause the generation AI to generate optimal music.

[0033] The providing unit can evaluate the impact of the generated music. For example, the providing unit evaluates the effect of the generated music in real time and reflects it in the next music generation. For example, the providing unit evaluates the effect of the generated music based on user feedback. The providing unit can also evaluate the effect of the generated music based on physiological data of the user. Furthermore, the providing unit can evaluate the effect of the generated music based on performance data of the user. In this way, the effect of the generated music can be evaluated and reflected in the next music generation. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can use an AI model that evaluates the effect of the generated music and reflect it in the next music generation.

[0034] The reception unit can analyze the user's past music playback history and select the optimal input method. For example, the reception unit can automatically display as candidates music genres that the user has frequently played in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can analyze the user's music preferences during specific time periods from the user's past playback history and adjust the input method. In this way, the optimal input method can be selected by analyzing the user's past music playback history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past music playback history into a generation AI and cause the generation AI to select the optimal input method.

[0035] When inputting music preferences, the reception unit can perform filtering based on the user's current mood and activity details. For example, if the user is exercising, the reception unit can preferentially display energetic music candidates. Furthermore, if the user is relaxing, the reception unit can preferentially display calm music candidates. Furthermore, if the user is working, the reception unit can preferentially display music candidates that improve concentration. This allows for filtering based on the user's current mood and activity details, allowing for more appropriate music preferences to be input. Some or all of the above-described processing in the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input data on the user's current mood and activity details to the generation AI and have the generation AI perform filtering.

[0036] When inputting music preferences, the reception unit can prioritize inputting highly relevant preferences by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize inputting music that is popular in that area. Furthermore, if the user is traveling, the reception unit can prioritize inputting music related to the culture of the travel destination. Furthermore, if the user is at home, the reception unit can prioritize inputting music that the user often listened to at home in the past. In this way, highly relevant music preferences can be input by taking the user's geographical location information into account. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to input highly relevant music preferences.

[0037] When inputting music preferences, the reception unit can analyze the user's social media activity and input related preferences. For example, the reception unit can input preferences based on music shared by the user on social media. The reception unit can also preferentially input music by artists or bands the user follows. Furthermore, the reception unit can input preferences based on information about music events or concerts the user is attending. In this way, related music preferences can be input by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input data on the user's social media activity to the generation AI and cause the generation AI to input related music preferences.

[0038] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between musical preferences during analysis. For example, if a user likes multiple genres, the analysis unit performs analysis taking into account the interrelationships between those genres. Furthermore, if a user likes a particular artist, the analysis unit can also consider related artists of that artist in its analysis. Furthermore, if a user likes a particular tempo, the analysis unit can also consider music related to that tempo in its analysis. In this way, by taking into account the interrelationships between musical preferences, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the interrelationships between musical preferences into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0039] The analysis unit can perform analysis while taking into account the user's attribute information. For example, the analysis unit can analyze music appropriate for the user's age by taking into account the user's age. The analysis unit can also analyze music appropriate for the user's gender by taking into account the user's gender. Furthermore, the analysis unit can analyze music related to the user's occupation by taking into account the user's occupation. This allows for more appropriate analysis by taking into account the user's attribute information. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or without using AI. For example, the analysis unit can input data on the user's attribute information into the generation AI and have the generation AI perform the analysis.

[0040] The analysis unit can take the geographical distribution of music into consideration when performing the analysis. For example, the analysis unit analyzes music that is popular in a specific region. The analysis unit can also prioritize analyzing music from the region where the user is currently located. Furthermore, the analysis unit can analyze music from regions the user has previously visited. This allows for more appropriate analysis by taking the geographical distribution of music into consideration. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data on the geographical distribution of music into the generation AI and have the generation AI perform the analysis.

[0041] During analysis, the analysis unit can improve the accuracy of the analysis by referring to music-related literature. The analysis unit, for example, performs analysis by referring to research papers on the effects of music. The analysis unit can also perform analysis by referring to literature on the history and culture of music. Furthermore, the analysis unit can perform analysis by referring to literature on the psychological effects of music. By doing so, the accuracy of the analysis is improved by referring to music-related literature. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data from music-related literature into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0042] The generation unit can improve the accuracy of generation by taking into account the interrelationships between musical preferences during generation. For example, if a user likes multiple genres, the generation unit generates music by taking into account the interrelationships between those genres. Furthermore, if a user likes a particular artist, the generation unit can also generate music by taking into account artists related to that artist. Furthermore, if a user likes a particular tempo, the generation unit can also generate music by taking into account music related to that tempo. In this way, by taking into account the interrelationships between musical preferences, the accuracy of generation is improved. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the interrelationships between musical preferences into the generation AI and cause the generation AI to improve the accuracy of generation.

[0043] The generation unit can generate music taking into account the user's attribute information. For example, the generation unit can generate music appropriate for the user's age by taking into account the user's age. The generation unit can also generate music appropriate for the user's gender by taking into account the user's gender. Furthermore, the generation unit can generate music related to the user's occupation by taking into account the user's occupation. This allows more appropriate music to be generated by taking into account the user's attribute information. Some or all of the above-described processing in the generation unit can be performed using AI, for example, or can be performed without using AI. For example, the generation unit can input data of the user's attribute information into the generation AI and have the generation AI perform the generation.

[0044] The generation unit can generate music taking into consideration the geographical distribution of music. For example, the generation unit generates music that is popular in a specific region. The generation unit can also generate music that is in the region where the user is currently located with priority. Furthermore, the generation unit can generate music from regions that the user has previously visited. This allows more appropriate music to be generated by taking the geographical distribution of music into consideration. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input data on the geographical distribution of music into the generation AI and have the generation AI perform the generation.

[0045] The generation unit can improve the accuracy of generation by referring to literature related to music during generation. The generation unit generates the music by referring to, for example, research papers on the effects of music. The generation unit can also generate the music by referring to literature on the history and culture of music. Furthermore, the generation unit can generate the music by referring to literature on the psychological effects of music. By referring to literature related to music, the accuracy of generation is improved. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data from literature related to music into the generation AI and cause the generation AI to improve the accuracy of generation.

[0046] The providing unit can evaluate the effectiveness of the music at the time of providing it and select an appropriate providing method. For example, the providing unit can evaluate the effectiveness of the provided music in real time and adjust the next providing method. The providing unit can also evaluate the effectiveness of the provided music based on user feedback and select the optimal providing method. Furthermore, the providing unit can evaluate the effectiveness of the provided music based on physiological data and select the optimal providing method. In this way, the next providing method can be optimized by evaluating the effectiveness of the music. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can optimize the next providing method using an AI model that evaluates the effectiveness of the provided music.

[0047] The providing unit can provide music taking into consideration the user's attribute information when providing the music. For example, the providing unit can provide music appropriate for the user's age by taking into consideration the user's age. The providing unit can also provide music appropriate for the user's gender by taking into consideration the user's gender. Furthermore, the providing unit can provide music related to the user's occupation by taking into consideration the user's occupation. In this way, more appropriate music can be provided by taking into consideration the user's attribute information. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or can be performed without using AI. For example, the providing unit can input data of the user's attribute information into the generation AI and cause the generation AI to perform the provision.

[0048] The providing unit can provide music taking into consideration the geographical distribution of the music. For example, the providing unit can provide music that is popular in a specific region. The providing unit can also provide music from the region where the user is currently located with priority. Furthermore, the providing unit can provide music from regions that the user has previously visited. This allows more appropriate music to be provided by taking the geographical distribution of music into consideration. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or without using AI. For example, the providing unit can input data on the geographical distribution of music to a generation AI and cause the generation AI to perform the provision.

[0049] The providing unit can improve the accuracy of the provision by referring to literature related to music when providing the data. The providing unit, for example, refers to research papers on the effects of music when providing the data. The providing unit can also refer to literature on the history and culture of music when providing the data. Furthermore, the providing unit can also refer to literature on the psychological effects of music when providing the data. By referring to literature related to music, the accuracy of the provision is improved. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on literature related to music into the generating AI and cause the generating AI to improve the accuracy of the provision.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] When providing the generated music, the generation unit can generate different music for each region by taking into account the user's geographical location information. For example, if the user is in a specific region, music popular in that region can be generated. Also, if the user is traveling, music related to the culture of the travel destination can be generated. Furthermore, if the user is at home, music that the user often listened to at home in the past can be generated. In this way, by taking into account the user's geographical location information, more relevant music can be provided.

[0052] When providing the generated music, the providing unit can provide different music for each attribute by taking into account the user's attribute information. For example, by taking into account the user's age, music appropriate for the age group can be provided. Furthermore, by taking into account the user's gender, music appropriate for the gender can be provided. Furthermore, by taking into account the user's occupation, music related to the occupation can be provided. In this way, more appropriate music can be provided by taking into account the user's attribute information.

[0053] When providing the generated music, the providing unit can collect user feedback in real time and adjust the content of the music based on that feedback. For example, if a user gives positive feedback about the provided music, the providing unit can emphasize elements of that music. Also, if a user gives negative feedback, the providing unit can delete elements of that music. Furthermore, the providing unit can reflect user feedback in the next music generation. This allows the providing unit to provide more appropriate music by taking user feedback into consideration.

[0054] When inputting the user's music preferences, the reception unit can perform filtering based on the user's current mood and activity. For example, if the user is exercising, energetic music candidates can be displayed preferentially. If the user is relaxing, calm music candidates can be displayed preferentially. Furthermore, if the user is working, music candidates that improve concentration can be displayed preferentially. In this way, filtering based on the user's current mood and activity allows for more appropriate music preferences to be input.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The reception unit inputs the user's music preferences. The user's music preferences include genre, artist, tempo, etc. The reception unit can also understand the user's preferences by using the history of music played by the user in the past. Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis unit uses statistical methods and machine learning algorithms to analyze each user's preferences and balance the entire group. Step 3: The generator generates optimal music for the entire group based on the information analyzed by the analyzer. The generator uses generative AI to generate music and utilizes technologies such as neural networks and generative models. Step 4: The providing unit provides the music generated by the generating unit. The providing unit can evaluate the effectiveness of the generated music and reflect it in the next music generation.

[0057] (Example 2) A music generation system according to an embodiment of the present invention identifies the musical preferences of each user and generates music optimal for the entire group. In this music generation system, users input their musical preferences, and a generation AI analyzes those preferences and generates music optimal for the entire group. For example, when users input their musical preferences, they provide information such as their favorite genre, artist, and tempo. If a user inputs, "I like classical music, but prefer slower tempos," that information is sent to the generation AI. The generation AI then analyzes the input information and identifies the preferences of the entire group. The generation AI then generates music optimal for the entire group, taking each user's preferences into account. For example, if multiple users input, "I like classical music," the generation AI generates music based on classical music. The generated music can be played in shared spaces such as coworking spaces, improving the performance of the entire group. For example, playing relaxing music can improve work efficiency and reduce stress. This system can improve group performance in shared spaces such as coworking spaces, potentially improving the performance of all humanity through the power of music. This allows the music generation system to understand users' musical preferences and generate and provide music that is optimal for the entire group, thereby improving group performance in shared spaces such as coworking spaces.

[0058] A music generation system according to an embodiment includes a receiving unit, an analysis unit, a generation unit, and a providing unit. The receiving unit inputs a user's music preferences. The user's music preferences include, but are not limited to, genre, artist, and tempo. The receiving unit can also understand the user's preferences by, for example, using a history of music played by the user in the past. The analysis unit analyzes the information input by the receiving unit. The analysis unit analyzes each user's preferences using, for example, statistical techniques or machine learning algorithms. The analysis unit takes each user's preferences into account and balances the entire group. The generation unit generates music that is optimal for the entire group based on the information analyzed by the analysis unit. The generation unit generates music using, for example, a generation AI. The generation AI generates music based on the user's preferences using technologies such as neural networks and generative models. The providing unit provides the music generated by the generation unit. The providing unit can evaluate the effectiveness of the generated music and reflect the evaluation in the next music generation. As a result, the music generation system according to the embodiment can understand the musical preferences of users and generate and provide music that is optimal for the entire group, thereby improving group performance in shared spaces such as coworking spaces.

[0059] The reception unit can grasp the user's preferences by using the user's past music playback history. The past music playback history includes, for example, the number of playbacks, playback time, a list of played songs, etc., but is not limited to these examples. For example, the reception unit automatically displays music genres that the user has frequently played in the past as candidates. The reception unit can also prioritize input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can analyze the user's preferences for specific time periods based on the user's past playback history and adjust the input method. In this way, by utilizing the user's past music playback history, the user's preferences can be more accurately grasped. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past music playback history into the generation AI and cause the generation AI to grasp the user's preferences.

[0060] The analysis unit can balance the entire group based on each user's preferences. For example, the analysis unit weights each user's preferences to create a balanced playlist. For example, the analysis unit weights the user's preferred genres and artists to balance the entire group. The analysis unit can also weight the user's preferred tempo and rhythm to generate balanced music. Furthermore, the analysis unit can generate balanced music based on the user's preferred time of day and activity. This allows the entire group to be balanced while taking each user's preferences into consideration, thereby providing optimal music for the entire group. Some or all of the above-described processing by the analysis unit may be performed using, or without, AI. For example, the analysis unit can input each user's preference data into the generation AI and have the generation AI perform an analysis to balance the entire group.

[0061] The generation unit can use a generation AI to generate music that is optimal for the entire group. Examples of the generation AI include, but are not limited to, neural networks and generative models. The generation unit can use a generation AI to generate music based on a user's preferences. For example, the generation unit can use a generation AI to generate music based on classical music. The generation unit can also use a generation AI to generate music that combines genres such as pop and rock. Furthermore, the generation unit can use a generation AI to generate music based on the user's preferred tempo and rhythm. In this way, optimal music can be generated for the entire group by using the generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input data about the user's preferences into the generation AI and cause the generation AI to generate optimal music.

[0062] The providing unit can evaluate the impact of the generated music. For example, the providing unit evaluates the effect of the generated music in real time and reflects it in the next music generation. For example, the providing unit evaluates the effect of the generated music based on user feedback. The providing unit can also evaluate the effect of the generated music based on physiological data of the user. Furthermore, the providing unit can evaluate the effect of the generated music based on performance data of the user. In this way, the effect of the generated music can be evaluated and reflected in the next music generation. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can use an AI model that evaluates the effect of the generated music and reflect it in the next music generation.

[0063] The reception unit can estimate the user's emotions and adjust the timing of inputting the music preferences based on the estimated user emotions. For example, if the user is relaxed, the reception unit can delay the timing of inputting the music preferences and prompt the user to input them when they are concentrating. Furthermore, if the user is feeling stressed, the reception unit can prompt the user to input their music preferences immediately and provide relaxing music quickly. Furthermore, if the user is excited, the reception unit can adjust the input timing so that the user can input their music preferences when they are calm. This allows the user to input more appropriate music preferences by adjusting the timing of inputting the music preferences according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI. For example, the reception unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of inputting the music preferences.

[0064] The reception unit can analyze the user's past music playback history and select the optimal input method. For example, the reception unit can automatically display as candidates music genres that the user has frequently played in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can analyze the user's music preferences during specific time periods from the user's past playback history and adjust the input method. In this way, the optimal input method can be selected by analyzing the user's past music playback history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past music playback history into a generation AI and cause the generation AI to select the optimal input method.

[0065] When inputting music preferences, the reception unit can perform filtering based on the user's current mood and activity details. For example, if the user is exercising, the reception unit can preferentially display energetic music candidates. Furthermore, if the user is relaxing, the reception unit can preferentially display calm music candidates. Furthermore, if the user is working, the reception unit can preferentially display music candidates that improve concentration. This allows for filtering based on the user's current mood and activity details, allowing for more appropriate music preferences to be input. Some or all of the above-described processing in the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input data on the user's current mood and activity details to the generation AI and have the generation AI perform filtering.

[0066] The reception unit can estimate the user's emotions and determine the priority of music preferences to be input based on the estimated user emotions. For example, if the user is relaxed, the reception unit can prioritize music with a relaxing effect. Furthermore, if the user is stressed, the reception unit can prioritize music with a stress-reducing effect. Furthermore, if the user is concentrating, the reception unit can prioritize music that improves concentration. This allows more appropriate music preferences to be input by determining the priority of music preferences according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input the user's emotion data to the generation AI and have the generation AI determine the priority of music preferences.

[0067] When inputting music preferences, the reception unit can prioritize inputting highly relevant preferences by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize inputting music that is popular in that area. Furthermore, if the user is traveling, the reception unit can prioritize inputting music related to the culture of the travel destination. Furthermore, if the user is at home, the reception unit can prioritize inputting music that the user often listened to at home in the past. In this way, highly relevant music preferences can be input by taking the user's geographical location information into account. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to input highly relevant music preferences.

[0068] When inputting music preferences, the reception unit can analyze the user's social media activity and input related preferences. For example, the reception unit can input preferences based on music shared by the user on social media. The reception unit can also preferentially input music by artists or bands the user follows. Furthermore, the reception unit can input preferences based on information about music events or concerts the user is attending. In this way, related music preferences can be input by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input data on the user's social media activity to the generation AI and cause the generation AI to input related music preferences.

[0069] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can prioritize analyzing music with a relaxing effect. Furthermore, if the user is feeling stressed, the analysis unit can prioritize analyzing music with a stress-reducing effect. Furthermore, if the user is concentrating, the analysis unit can prioritize analyzing music that improves concentration. This allows for more appropriate analysis by adjusting the analysis criteria according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the analysis criteria.

[0070] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between musical preferences during analysis. For example, if a user likes multiple genres, the analysis unit performs analysis taking into account the interrelationships between those genres. Furthermore, if a user likes a particular artist, the analysis unit can also consider related artists of that artist in its analysis. Furthermore, if a user likes a particular tempo, the analysis unit can also consider music related to that tempo in its analysis. In this way, by taking into account the interrelationships between musical preferences, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the interrelationships between musical preferences into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0071] The analysis unit can perform analysis while taking into account the user's attribute information. For example, the analysis unit can analyze music appropriate for the user's age by taking into account the user's age. The analysis unit can also analyze music appropriate for the user's gender by taking into account the user's gender. Furthermore, the analysis unit can analyze music related to the user's occupation by taking into account the user's occupation. This allows for more appropriate analysis by taking into account the user's attribute information. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or without using AI. For example, the analysis unit can input data on the user's attribute information into the generation AI and have the generation AI perform the analysis.

[0072] The analysis unit can estimate the user's emotions and adjust the order in which the analysis results are displayed based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can first display music with a relaxing effect. Furthermore, if the user is feeling stressed, the analysis unit can first display music with a stress-reducing effect. Furthermore, if the user is concentrating, the analysis unit can first display music that improves concentration. This allows for adjusting the order in which the analysis results are displayed according to the user's emotions, thereby providing more appropriate analysis results. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display order of the analysis results.

[0073] The analysis unit can take the geographical distribution of music into consideration when performing the analysis. For example, the analysis unit analyzes music that is popular in a specific region. The analysis unit can also prioritize analyzing music from the region where the user is currently located. Furthermore, the analysis unit can analyze music from regions the user has previously visited. This allows for more appropriate analysis by taking the geographical distribution of music into consideration. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data on the geographical distribution of music into the generation AI and have the generation AI perform the analysis.

[0074] During analysis, the analysis unit can improve the accuracy of the analysis by referring to music-related literature. The analysis unit, for example, performs analysis by referring to research papers on the effects of music. The analysis unit can also perform analysis by referring to literature on the history and culture of music. Furthermore, the analysis unit can perform analysis by referring to literature on the psychological effects of music. By doing so, the accuracy of the analysis is improved by referring to music-related literature. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data from music-related literature into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0075] The generation unit can estimate the user's emotions and adjust the characteristics of the music to be generated based on the estimated user's emotions. For example, when the user is relaxed, the generation unit can generate music with a relaxing effect. Furthermore, when the user is stressed, the generation unit can generate music with a stress-reducing effect. Furthermore, when the user is concentrating, the generation unit can generate music that enhances concentration. This allows for adjusting the characteristics of the music to be generated according to the user's emotions, thereby generating more appropriate music. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the characteristics of the music.

[0076] The generation unit can improve the accuracy of generation by taking into account the interrelationships between musical preferences during generation. For example, if a user likes multiple genres, the generation unit generates music by taking into account the interrelationships between those genres. Furthermore, if a user likes a particular artist, the generation unit can also generate music by taking into account artists related to that artist. Furthermore, if a user likes a particular tempo, the generation unit can also generate music by taking into account music related to that tempo. In this way, by taking into account the interrelationships between musical preferences, the accuracy of generation is improved. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the interrelationships between musical preferences into the generation AI and cause the generation AI to improve the accuracy of generation.

[0077] The generation unit can generate music taking into account the user's attribute information. For example, the generation unit can generate music appropriate for the user's age by taking into account the user's age. The generation unit can also generate music appropriate for the user's gender by taking into account the user's gender. Furthermore, the generation unit can generate music related to the user's occupation by taking into account the user's occupation. This allows more appropriate music to be generated by taking into account the user's attribute information. Some or all of the above-described processing in the generation unit can be performed using AI, for example, or can be performed without using AI. For example, the generation unit can input data of the user's attribute information into the generation AI and have the generation AI perform the generation.

[0078] The generation unit can estimate the user's emotion and adjust the display method of the generated music based on the estimated user's emotion. For example, when the user is relaxed, the generation unit can visually display music with a relaxing effect. Furthermore, when the user is feeling stressed, the generation unit can visually display music with a stress-reducing effect. Furthermore, when the user is concentrating, the generation unit can visually display music that enhances concentration. This allows for adjusting the display method of the generated music according to the user's emotion, thereby providing more appropriate music. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method of the music.

[0079] The generation unit can generate music taking into consideration the geographical distribution of music. For example, the generation unit generates music that is popular in a specific region. The generation unit can also generate music that is in the region where the user is currently located with priority. Furthermore, the generation unit can generate music from regions that the user has previously visited. This allows more appropriate music to be generated by taking the geographical distribution of music into consideration. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input data on the geographical distribution of music into the generation AI and have the generation AI perform the generation.

[0080] The generation unit can improve the accuracy of generation by referring to literature related to music during generation. The generation unit generates the music by referring to, for example, research papers on the effects of music. The generation unit can also generate the music by referring to literature on the history and culture of music. Furthermore, the generation unit can generate the music by referring to literature on the psychological effects of music. By referring to literature related to music, the accuracy of generation is improved. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data from literature related to music into the generation AI and cause the generation AI to improve the accuracy of generation.

[0081] The providing unit can estimate the user's emotions and adjust the timing of music to be provided based on the estimated user's emotions. For example, when the user is relaxed, the providing unit can adjust the timing of providing music with a relaxing effect. Furthermore, when the user is feeling stressed, the providing unit can also adjust the timing of providing music that has a stress-reducing effect. Furthermore, when the user is concentrating, the providing unit can adjust the timing of providing music that enhances concentration. This allows for more appropriate music to be provided by adjusting the timing of music provision according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of music provision.

[0082] The providing unit can evaluate the effectiveness of the music at the time of providing it and select an appropriate providing method. For example, the providing unit can evaluate the effectiveness of the provided music in real time and adjust the next providing method. The providing unit can also evaluate the effectiveness of the provided music based on user feedback and select the optimal providing method. Furthermore, the providing unit can evaluate the effectiveness of the provided music based on physiological data and select the optimal providing method. In this way, the next providing method can be optimized by evaluating the effectiveness of the music. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can optimize the next providing method using an AI model that evaluates the effectiveness of the provided music.

[0083] The providing unit can provide music taking into consideration the user's attribute information when providing the music. For example, the providing unit can provide music appropriate for the user's age by taking into consideration the user's age. The providing unit can also provide music appropriate for the user's gender by taking into consideration the user's gender. Furthermore, the providing unit can provide music related to the user's occupation by taking into consideration the user's occupation. In this way, more appropriate music can be provided by taking into consideration the user's attribute information. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or can be performed without using AI. For example, the providing unit can input data of the user's attribute information into the generation AI and cause the generation AI to perform the provision.

[0084] The providing unit can estimate the user's emotions and determine the priority of music to be provided based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can prioritize music that has a relaxing effect. Furthermore, if the user is feeling stressed, the providing unit can prioritize music that has a stress-reducing effect. Furthermore, if the user is concentrating, the providing unit can prioritize music that improves concentration. This allows for more appropriate music to be provided by determining the priority of music according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI determine the music priority.

[0085] The providing unit can provide music taking into consideration the geographical distribution of the music. For example, the providing unit can provide music that is popular in a specific region. The providing unit can also provide music from the region where the user is currently located with priority. Furthermore, the providing unit can provide music from regions that the user has previously visited. This allows more appropriate music to be provided by taking the geographical distribution of music into consideration. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or without using AI. For example, the providing unit can input data on the geographical distribution of music to a generation AI and cause the generation AI to perform the provision.

[0086] The providing unit can improve the accuracy of the provision by referring to literature related to music when providing the data. The providing unit, for example, refers to research papers on the effects of music when providing the data. The providing unit can also refer to literature on the history and culture of music when providing the data. Furthermore, the providing unit can also refer to literature on the psychological effects of music when providing the data. By referring to literature related to music, the accuracy of the provision is improved. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on literature related to music into the generating AI and cause the generating AI to improve the accuracy of the provision. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and inputs the user's music preferences. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates music based on the analyzed information. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the generated music. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and inputs the user's music preferences. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates music based on the analyzed information. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the generated music. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and inputs the user's music preferences. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates music based on the analyzed information. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides the generated music. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and inputs the user's music preferences. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates music based on the analyzed information. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the generated music.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] When analyzing a user's music preferences, the analysis unit can monitor not only the user's past music playback history but also the user's current activity status in real time and reflect that information in the analysis. For example, if the user is exercising, the analysis unit can prioritize energetic music in the analysis. Also, if the user is relaxing, the analysis unit can prioritize music that has a relaxing effect in the analysis. Furthermore, if the user is concentrating, the analysis unit can prioritize music that improves concentration in the analysis. This allows the system to provide more appropriate music by taking the user's current activity status into consideration.

[0089] When providing the generated music, the providing unit can monitor the user's physiological data in real time and adjust the volume and tempo of the music based on that data. For example, if the user's heart rate is increasing, the providing unit can speed up the tempo of the music. Also, if the user's heart rate is decreasing, the providing unit can slow down the tempo of the music. Furthermore, if the user's stress level is high, the music volume can be lowered to enhance the relaxation effect. This makes it possible to provide more effective music by taking the user's physiological data into consideration.

[0090] When inputting the user's music preferences, the reception unit analyzes not only the user's past music playback history but also the user's social media activity, and can estimate the user's music preferences based on that information. For example, the reception unit can estimate the user's preferences based on information about music the user has shared on social media and artists the user follows. The reception unit can also estimate the user's preferences based on information about music events and concerts the user has attended. Furthermore, the reception unit can analyze the emotions the user has expressed on social media and estimate the user's music preferences based on those emotions. This allows the reception unit to understand the user's music preferences more accurately by taking into account the user's social media activity.

[0091] When providing the generated music, the generation unit can generate different music for each region by taking into account the user's geographical location information. For example, if the user is in a specific region, music popular in that region can be generated. Also, if the user is traveling, music related to the culture of the travel destination can be generated. Furthermore, if the user is at home, music that the user often listened to at home in the past can be generated. In this way, by taking into account the user's geographical location information, more relevant music can be provided.

[0092] When providing the generated music, the providing unit can provide different music for each attribute by taking into account the user's attribute information. For example, by taking into account the user's age, music appropriate for the age group can be provided. Furthermore, by taking into account the user's gender, music appropriate for the gender can be provided. Furthermore, by taking into account the user's occupation, music related to the occupation can be provided. In this way, more appropriate music can be provided by taking into account the user's attribute information.

[0093] When analyzing a user's music preferences, the analysis unit can estimate the user's emotions as well as the user's past music playback history, and adjust the analysis criteria based on those emotions. For example, if the user is relaxed, music with a relaxing effect can be prioritized in the analysis. Also, if the user is feeling stressed, music with a stress-reducing effect can be prioritized in the analysis. Furthermore, if the user is concentrating, music that improves concentration can be prioritized in the analysis. In this way, by adjusting the analysis criteria according to the user's emotions, more appropriate analysis can be performed.

[0094] When providing the generated music, the providing unit can collect user feedback in real time and adjust the content of the music based on that feedback. For example, if a user gives positive feedback about the provided music, the providing unit can emphasize elements of that music. Also, if a user gives negative feedback, the providing unit can delete elements of that music. Furthermore, the providing unit can reflect user feedback in the next music generation. This allows the providing unit to provide more appropriate music by taking user feedback into consideration.

[0095] When inputting the user's music preferences, the reception unit can perform filtering based on the user's current mood and activity. For example, if the user is exercising, energetic music candidates can be displayed preferentially. If the user is relaxing, calm music candidates can be displayed preferentially. Furthermore, if the user is working, music candidates that improve concentration can be displayed preferentially. In this way, filtering based on the user's current mood and activity allows for more appropriate music preferences to be input.

[0096] When providing the generated music, the generation unit can estimate the user's emotions and adjust the characteristics of the music based on those emotions. For example, if the user is relaxed, music with a relaxing effect can be generated. Also, if the user is feeling stressed, music with a stress-reducing effect can be generated. Furthermore, if the user is concentrating, music that enhances concentration can be generated. In this way, more appropriate music can be provided by adjusting the characteristics of the music according to the user's emotions.

[0097] When providing the generated music, the providing unit can monitor not only the user's past music playback history but also the user's current activity status in real time and adjust the timing of providing the music based on that information. For example, if the user is exercising, the timing of providing energetic music can be adjusted. Also, if the user is relaxing, the timing of providing music with a relaxing effect can be adjusted. Furthermore, if the user is concentrating, the timing of providing music that enhances concentration can be adjusted. In this way, more appropriate music can be provided by taking the user's current activity status into consideration.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The reception unit inputs the user's music preferences. The user's music preferences include genre, artist, tempo, etc. The reception unit can also understand the user's preferences by using the history of music played by the user in the past. Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis unit uses statistical methods and machine learning algorithms to analyze each user's preferences and balance the entire group. Step 3: The generator generates optimal music for the entire group based on the information analyzed by the analyzer. The generator uses generative AI to generate music and utilizes technologies such as neural networks and generative models. Step 4: The providing unit provides the music generated by the generating unit. The providing unit can evaluate the effectiveness of the generated music and reflect it in the next music generation.

[0100] 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.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0102] 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.

[0103] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0114] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] 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.

[0116] 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.

[0117] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0118] 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.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0131] 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.

[0132] 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.

[0133] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0134] 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.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0137] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[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 correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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 area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] [Explanation of symbols]

[0172] 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 reception unit for inputting a user's musical preferences; an analysis unit that analyzes the information input by the reception unit; a generation unit that generates music suitable for the entire group based on the information analyzed by the analysis unit; a providing unit that provides the music generated by the generating unit; Equipped with A system characterized by:

2. The reception unit Understand user preferences using the user's past music playback history 2. The system of claim 1.

3. The analysis unit Balancing the entire population based on each user's preferences 2. The system of claim 1.

4. The generation unit Using generative AI to generate optimal music for the entire group 2. The system of claim 1.

5. The providing unit Evaluating the impact of generated music 2. The system of claim 1.

6. The reception unit Estimate the user's emotions and adjust the timing of inputting music preferences based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's past music playback history and select the most appropriate input method 2. The system of claim 1.

8. The reception unit Filter your music preferences based on your current mood and activity 2. The system of claim 1.

9. The reception unit Estimating a user's emotions and prioritizing input music preferences based on the estimated user's emotions 2. The system of claim 1.

10. The reception unit When entering music preferences, consider the user's geographic location to prioritize relevant preferences.

2. The system of claim 1.

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