System

Abstract

An object of a system according to an embodiment is to automatically provide music according to a situation or environment of a user.SOLUTION: A system according to an embodiment includes an acquisition unit, an estimation unit, a generation unit, and a provision unit. The acquisition unit acquires position information of the user. The estimation unit estimates a situation of the user on the basis of the position information acquired by the acquisition unit. The generation unit generates music based on the situation estimated by the estimation unit. The providing unit provides the music generated by the generating unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have not adequately provided music that is suited to the user's situation and environment, and there is room for improvement.

[0005] The system according to the embodiment aims to automatically provide music that matches the user's situation and environment. [Means for solving the problem]

[0006] A system according to an embodiment includes an acquisition unit, an estimation unit, a generation unit, and a provision unit. The acquisition unit acquires location information of a user. The estimation unit estimates the user's situation based on the location information acquired by the acquisition unit. The generation unit generates music based on the situation estimated by the estimation unit. The provision unit provides the music generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can automatically provide music that matches the user's situation and environment. [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 generates music tailored to a user's situation and environment. The music generation system acquires user location information, infers the user's situation based on the location information, and generates and provides music tailored to the situation to the user. For example, the music generation system acquires user location information. For example, the music generation system can acquire user location information using GPS information. Next, the music generation system infers the user's situation based on the acquired location information. For example, if the user is at the beach, the music generation system infers the user's situation based on the information. Next, the music generation system generates music based on the inferred situation. For example, the music generation system generates music that suits the beach. Next, the music generation system provides the generated music to the user. For example, the music generation system streams the generated music to the user's device. In this way, the music generation system can generate and provide music tailored to the user's situation and environment. In this way, the music generation system can generate and provide music tailored to the user's situation and environment. For example, if the user wants to relax, the music generation system generates relaxing music. Also, if the user is exercising, the music generation system generates music with a tempo suitable for exercise. In this way, the music generation system can improve the user's experience.

[0029] A music generation system according to an embodiment includes an acquisition unit, an estimation unit, a generation unit, and a provision unit. The acquisition unit acquires user location information. For example, the acquisition unit acquires the user location information using GPS information. The acquisition unit can also acquire location information using Wi-Fi location information or cell tower location information. The acquisition unit can also acquire sounds around the user. For example, the acquisition unit acquires ambient sounds using a smartphone microphone. The acquisition unit can also acquire weather information. For example, the acquisition unit acquires weather information from the Internet. The estimation unit estimates the user's situation based on the location information acquired by the acquisition unit. For example, if the user is at the beach, the estimation unit estimates the user's situation based on that information. The estimation unit can also estimate the user's situation based on the acquired ambient sounds and weather information. For example, if the ambient sound is the sound of waves, the estimation unit estimates that the user is at the beach. The generation unit generates music based on the situation estimated by the estimation unit. For example, the generation unit generates music that suits the beach. The generation unit generates music using a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation unit generates music based on past music data and user preferences. The providing unit provides the music generated by the generation unit to the user. For example, the providing unit streams the generated music to the user's device. The providing unit can also obtain user feedback and reflect it in subsequent music generation. For example, the providing unit learns from the user's evaluation of the generated music and reflects the evaluation in subsequent music generation. In this way, the music generation system according to the embodiment can generate and provide music that is tailored to the user's situation and environment.

[0030] The acquisition unit can acquire sounds around the user. Examples of ambient sounds include, but are not limited to, environmental sounds, conversation sounds, and traffic sounds. The acquisition unit can acquire ambient sounds using, for example, a smartphone microphone. The acquisition unit can also acquire ambient sounds using a dedicated microphone device. For example, the acquisition unit can use a microphone with a noise-canceling function to clearly acquire ambient sounds. The acquisition unit can also acquire ambient sounds in three dimensions using multiple microphones. For example, the acquisition unit can use a stereo microphone to acquire ambient sounds in left and right channels. This enables more accurate situation estimation by acquiring sounds around the user. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the acquisition unit can input the acquired ambient sound data to a generation AI and have the generation AI analyze the sound data.

[0031] The acquisition unit can acquire weather information. Weather information includes, but is not limited to, temperature, humidity, and precipitation, for example. The acquisition unit can acquire weather information from the Internet, for example. The acquisition unit can also acquire weather information using a meteorological sensor. For example, the acquisition unit can acquire temperature using a temperature sensor. The acquisition unit can also acquire humidity using a humidity sensor. The acquisition unit can also acquire precipitation using a rain gauge. For example, the acquisition unit acquires weather information in real time and reflects it in estimating the user's situation. In this way, acquiring the weather information improves the accuracy of estimating the user's situation. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input weather information acquired from the Internet to the generation AI and cause the generation AI to analyze the weather information.

[0032] The estimation unit can estimate the user's situation based on the acquired location information, ambient sounds, and weather information. The estimation unit estimates the user's situation based on, for example, the acquired location information. For example, if the user is at the beach, the estimation unit estimates the user's situation based on that information. The estimation unit can also estimate the user's situation based on the acquired ambient sounds. For example, if the ambient sounds are the sound of waves, the estimation unit estimates that the user is at the beach. The estimation unit can also estimate the user's situation based on the acquired weather information. For example, if the weather information is sunny, the estimation unit estimates that the user is outdoors. Furthermore, the estimation unit can comprehensively analyze the acquired location information, ambient sounds, and weather information to more accurately estimate the user's situation. For example, if the location information is the beach, the ambient sounds are the sound of waves, and the weather information is sunny, the estimation unit estimates that the user is relaxing on the beach. This allows the user's situation to be more accurately estimated based on multiple pieces of information. Some or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input the acquired location information, ambient sounds, and weather information into the generation AI and have the generation AI estimate the user's situation.

[0033] The generation unit can generate music based on past music data and user preferences. Past music data includes, but is not limited to, playback history and rating data. The generation unit can generate music based on, for example, the user's playback history. The generation unit can also generate music based on the user's rating data. For example, the generation unit can generate new music based on the characteristics of music that the user has given high ratings to. The generation unit can also generate music based on the user's preferences. For example, the generation unit can generate music in a genre and tempo that the user prefers. Furthermore, the generation unit can comprehensively analyze past music data and the user's preferences to generate optimal music. For example, the generation unit can extract characteristics of music that the user prefers based on the user's playback history and rating data, and generate new music based on those characteristics. This makes it possible to generate music tailored to the user's preferences. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation unit can input the user's playback history and evaluation data into the generation AI and have the generation AI generate music.

[0034] The providing unit can acquire user feedback and reflect it in subsequent music generation. Examples of feedback include, but are not limited to, user ratings, comments, and usage history. For example, the providing unit acquires ratings by users rating the generated music. The providing unit can also acquire user comments. For example, the providing unit acquires comments by users inputting comments about the music. The providing unit can also acquire user usage history. For example, the providing unit records what kind of music the user listens to and how often. Furthermore, the providing unit can reflect the acquired feedback in subsequent music generation. For example, the providing unit learns the characteristics of music that users have given high ratings to and generates subsequent music based on those characteristics. By incorporating user feedback, the accuracy of music generation is improved. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the acquired feedback data to a generation AI and cause the generation AI to analyze the feedback.

[0035] The acquisition unit can analyze the user's past location information history and select the optimal acquisition method. Examples of past location information history include, but are not limited to, GPS logs and movement history. The acquisition unit can adjust the frequency of location information acquisition based on, for example, places the user has frequently visited in the past. The acquisition unit can also analyze the user's past movement patterns and acquire location information at the optimal timing. For example, the acquisition unit can predict places the user will visit during a specific time period and prioritize acquiring location information during that time period. This enables efficient information acquisition by selecting the optimal acquisition method based on the past location information history. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input the past location information history into a generation AI and have the generation AI select the optimal acquisition method.

[0036] When acquiring location information, the acquisition unit can perform filtering based on the user's current activity. Current activities include, but are not limited to, for example, exercise, rest, and movement. For example, when the user is exercising, the acquisition unit can set the frequency of acquiring location information high and collect detailed movement data. Furthermore, when the user is resting, the acquisition unit can also set the frequency of acquiring location information low to reduce battery consumption. Furthermore, when the user is moving, the acquisition unit can set the frequency of acquiring location information to medium to perform balanced data collection. This enables acquisition of location information according to the user's activity. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input the user's current activity data to the generation AI and have the generation AI perform filtering.

[0037] When acquiring location information, the acquisition unit can adjust the acquisition frequency taking into account the battery status of the user's device. Examples of battery status include, but are not limited to, the remaining battery charge and charging status. For example, when the device's battery is low, the acquisition unit can reduce the acquisition frequency of location information to reduce battery consumption. Furthermore, when the device's battery is sufficient, the acquisition unit can increase the acquisition frequency of location information to collect detailed data. Furthermore, the acquisition unit can dynamically adjust the acquisition frequency of location information according to the device's battery status. This enables acquisition of location information according to the device's battery status. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input device battery status data to the generation AI and cause the generation AI to adjust the acquisition frequency.

[0038] When acquiring location information, the acquisition unit can prioritize acquiring highly relevant information taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS information, address information, etc. For example, when the user is in an urban area, the acquisition unit prioritizes acquiring traffic information and store information. Furthermore, when the user is in a natural environment, the acquisition unit can prioritize acquiring information about tourist spots and the natural environment. Furthermore, when the user is participating in a specific event, the acquisition unit can prioritize acquiring information related to the event. In this way, by acquiring highly relevant information based on the geographical location information, it is possible to provide useful information to the user. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input geographical location information data to a generation AI and cause the generation AI to acquire highly relevant information.

[0039] When acquiring location information, the acquisition unit can analyze the user's social media activity and acquire related information. Social media activity includes, but is not limited to, for example, posted content, number of likes, and comments. For example, the acquisition unit prioritizes acquiring information about locations where the user has checked in on social media. The acquisition unit can also analyze the content of the user's social media posts to acquire related location information. The acquisition unit can also acquire related location information by referring to the activities of the user's friends on social media. This makes it possible to acquire related information based on social media activity. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input social media activity data to a generation AI and cause the generation AI to acquire related information.

[0040] When acquiring location information, the acquisition unit can customize the acquisition method by reflecting the user's past feedback. Feedback includes, but is not limited to, user ratings, comments, and usage history. For example, the acquisition unit prioritizes acquiring information about places that the user has previously rated highly. The acquisition unit can also avoid acquiring information about places that the user has previously rated poorly. The acquisition unit can also customize the frequency and method of acquiring location information based on the user's past feedback. This enables customization based on past feedback. Some or all of the above-described processing in the acquisition unit may be performed using, or without, AI. For example, the acquisition unit can input past feedback data into a generation AI and cause the generation AI to customize the acquisition method.

[0041] The estimation unit can improve the accuracy of estimation by referring to the user's past behavioral patterns when making estimations. Past behavioral patterns include, but are not limited to, travel history and usage history. The estimation unit estimates the current situation based on data on places the user has visited in the past. The estimation unit can also analyze the user's past behavioral patterns to estimate the current situation. For example, the estimation unit estimates the current situation based on the user's behavioral patterns during a specific time period. This enables highly accurate estimation based on past behavioral patterns. Some or all of the above-described processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input past behavioral pattern data into the generation AI and cause the generation AI to improve the accuracy of the estimation.

[0042] During estimation, the estimation unit can adjust the estimation algorithm based on the user's current activity. Current activities include, but are not limited to, exercise, rest, and travel. For example, when the user is exercising, the estimation unit uses an algorithm that estimates a situation related to exercise. Furthermore, when the user is resting, the estimation unit can use an algorithm that estimates a situation related to relaxation. Furthermore, when the user is traveling, the estimation unit can use an algorithm that estimates a situation related to travel. This enables estimation based on the user's activity. Some or all of the above-described processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input the user's current activity data into the generation AI and cause the generation AI to adjust the estimation algorithm.

[0043] During estimation, the estimation unit can integrate sensor data from the user's device to improve the accuracy of the estimation. Sensor data includes, but is not limited to, an acceleration sensor, a gyro sensor, and a temperature sensor. For example, the estimation unit estimates the movement speed using the acceleration sensor of the user's smartphone. The estimation unit can also estimate the direction using the gyro sensor of the user's smartphone. The estimation unit can also estimate the altitude using the barometric pressure sensor of the user's smartphone. By integrating the sensor data, highly accurate estimation becomes possible. Some or all of the above-described processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input sensor data into the generation AI and cause the generation AI to improve the accuracy of the estimation.

[0044] The estimation unit can improve the accuracy of the estimation by taking into account the user's geographical location information during estimation. Examples of geographical location information include, but are not limited to, GPS information and address information. For example, if the user is in an urban area, the estimation unit can make estimations by taking into account surrounding buildings and traffic information. Furthermore, if the user is in a natural environment, the estimation unit can make estimations by taking into account information about the surrounding natural environment. Furthermore, if the user is participating in a specific event, the estimation unit can also make estimations by taking into account information related to the event. This enables highly accurate estimations that take into account the geographical location information. Some or all of the above-described processing in the estimation unit may be performed, for example, using AI, or may be performed without using AI. For example, the estimation unit can input geographical location information data into the generation AI and cause the generation AI to improve the accuracy of the estimation.

[0045] The estimation unit can analyze the user's social media activity during estimation to improve the accuracy of the estimation. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and comments. The estimation unit can make estimations based on, for example, information about the location where the user checked in on social media. The estimation unit can also analyze the content of the user's social media posts to estimate the current situation. The estimation unit can also estimate the current situation by referring to the activities of the user's friends on social media. This enables highly accurate estimation based on social media activity. Some or all of the above-described processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input social media activity data into a generation AI and cause the generation AI to improve the accuracy of the estimation.

[0046] The estimation unit can customize the estimation algorithm by reflecting the user's past feedback when making estimations. Examples of feedback include, but are not limited to, user ratings, comments, and usage history. For example, the estimation unit can adjust the algorithm based on estimation results that the user previously rated highly. The estimation unit can also improve the algorithm based on estimation results that the user previously rated poorly. The estimation unit can also customize the estimation algorithm based on the user's past feedback. This enables customization based on past feedback. Some or all of the above-described processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input past feedback data into the generation AI and cause the generation AI to customize the estimation algorithm.

[0047] When generating music, the generation unit can improve the accuracy of the generation by referring to the user's past music history. Past music history includes, for example, playback history and rating data, but is not limited to these examples. The generation unit generates music based on, for example, data on music the user liked to listen to in the past. The generation unit can also analyze the user's past music history and generate music that suits the current situation. The generation unit can also generate music based on patterns of music the user listens to during a specific time period. This enables highly accurate music generation based on the past music history. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. For example, the generation unit can input the user's past music history data into the generation AI and have the generation AI generate music.

[0048] When generating music, the generation unit can adjust the generation algorithm based on the user's current activity. Current activities include, but are not limited to, for example, exercise, rest, and travel. For example, when the user is exercising, the generation unit can generate music with a tempo suitable for exercise. Furthermore, when the user is resting, the generation unit can generate relaxing music. Furthermore, when the user is traveling, the generation unit can generate music with a tempo suitable for traveling. This enables music to be generated according to the user's activity. Some or all of the above-mentioned processing in the generation unit is performed using 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. For example, the generation unit can input the user's current activity data into the generation AI and cause the generation AI to adjust the generation algorithm.

[0049] When generating music, the generation unit can improve the accuracy of generation by taking into account the sound quality settings of the user's device. Sound quality settings include, but are not limited to, equalizer settings and volume settings. For example, if the user's device is set to high sound quality, the generation unit can generate music that emphasizes sound quality. Furthermore, if the user's device is set to low sound quality, the generation unit can generate music with a reduced amount of data. Furthermore, the generation unit can use an optimal music generation algorithm depending on the sound quality settings of the user's device. This enables music to be generated according to the sound quality settings of the device. Some or all of the above-mentioned processing in the generation unit is performed using 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. For example, the generation unit may input sound quality setting data of the user's device into the generation AI and cause the generation AI to generate music.

[0050] The generation unit can improve the accuracy of music generation by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS information and address information. For example, if the user is in an urban area, the generation unit can generate music with an urban atmosphere. Furthermore, if the user is in a natural environment, the generation unit can generate music incorporating natural sounds. Furthermore, if the user is participating in a specific event, the generation unit can generate music related to the event. This enables highly accurate music generation that takes into account the geographical location information. Some or all of the above-described processing in the generation unit is performed using 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. For example, the generation unit can input geographical location information data into the generation AI and cause the generation AI to generate music.

[0051] When generating music, the generation unit can analyze the user's social media activity to improve the accuracy of the generation. Social media activity includes, for example, but is not limited to, the content of posts, the number of likes, and comments. For example, the generation unit generates music based on data of music shared by the user on social media. The generation unit can also analyze the content of the user's social media posts to generate music that suits the current situation. The generation unit can also generate related music by referring to the activities of the user's friends on social media. This enables highly accurate music generation based on social media activity. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. For example, the generation unit can input social media activity data into the generation AI and cause the generation AI to generate music.

[0052] When generating music, the generation unit can customize the generation algorithm by reflecting past user feedback. Feedback includes, but is not limited to, user ratings, comments, and usage history. For example, the generation unit can adjust the generation algorithm based on data of music that the user has previously rated highly. The generation unit can also improve the generation algorithm based on data of music that the user has previously rated poorly. The generation unit can also customize the generation algorithm based on past user feedback. This enables customization based on past feedback. Some or all of the above-described processing in the generation unit is performed using 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. For example, the generation unit can input past feedback data into the generation AI and have the generation AI customize the generation algorithm.

[0053] When providing music, the providing unit can improve the accuracy of the music provision by referring to the user's past feedback. Feedback includes, for example, the user's ratings, comments, and usage history, but is not limited to these examples. For example, the providing unit can preferentially provide music that the user has previously rated highly. The providing unit can also avoid providing music that the user has previously rated poorly. The providing unit can also customize the providing algorithm based on the user's past feedback. This enables music to be provided with high accuracy based on the past feedback. 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 past feedback data into the generating AI and cause the generating AI to customize the providing algorithm.

[0054] When providing music, the providing unit can adjust the providing algorithm based on the user's current activity. Current activities include, but are not limited to, for example, exercise, rest, and travel. For example, when the user is exercising, the providing unit can provide music with a tempo suitable for exercise. Furthermore, when the user is resting, the providing unit can provide relaxing music. Furthermore, when the user is traveling, the providing unit can also provide music with a tempo suitable for traveling. This makes it possible to provide music according to the user's activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current activity data into the generation AI and cause the generation AI to adjust the providing algorithm.

[0055] When providing music, the providing unit can improve the accuracy of the music provided by taking into account the sound quality settings of the user's device. Sound quality settings include, but are not limited to, equalizer settings and volume settings. For example, if the user's device is set to high sound quality, the providing unit can provide music that emphasizes sound quality. Furthermore, if the user's device is set to low sound quality, the providing unit can provide music with a reduced data volume. Furthermore, the providing unit can use an optimal music providing algorithm depending on the sound quality settings of the user's device. This enables music to be provided according to the sound quality settings of the device. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input sound quality setting data of the user's device to the generation AI and cause the generation AI to provide music.

[0056] When providing music, the providing unit can improve the accuracy of the music provision by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS information and address information. For example, if the user is in an urban area, the providing unit can provide music with an urban atmosphere. Furthermore, if the user is in a natural environment, the providing unit can provide music incorporating natural sounds. Furthermore, if the user is participating in a specific event, the providing unit can also provide music related to the event. This enables music provision with high accuracy that takes into account the geographical location information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input geographical location information data to a generation AI and cause the generation AI to provide music.

[0057] When providing music, the providing unit can analyze the user's social media activity to improve the accuracy of the music provided. Social media activity includes, but is not limited to, the content of posts, the number of likes, and comments. For example, the providing unit provides music based on data on music shared by the user on social media. The providing unit can also analyze the content of the user's social media posts to provide music that suits the user's current situation. The providing unit can also provide related music by referring to the activities of the user's friends on social media. This enables highly accurate music provision based on social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input social media activity data into a generation AI and cause the generation AI to provide music.

[0058] The providing unit can customize the providing algorithm by reflecting the user's past feedback when providing music. Feedback includes, but is not limited to, user ratings, comments, and usage history, for example. The providing unit, for example, preferentially provides music that the user has previously rated highly. The providing unit can also prevent the provision of music that the user has previously rated poorly. The providing unit can also customize the providing algorithm based on the user's past feedback. This enables customization based on past feedback. 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 past feedback data into the generating AI and cause the generating AI to customize the providing algorithm.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The music generation system may also include an acquisition unit that acquires a user's heart rate and adjusts the tempo of the music based on the heart rate. For example, the acquisition unit may acquire heart rate data from a smartwatch or fitness tracker. If the user is exercising and their heart rate is high, the acquisition unit may instruct the generation unit to generate music with a faster tempo. Alternatively, if the user is relaxing, the acquisition unit may detect that their heart rate is low and instruct the generation unit to generate music with a slower tempo. This allows the system to provide music tailored to the user's physiological state.

[0061] The music generation system may also include an acquisition unit that acquires a user's schedule information and generates music based on the schedule. For example, the acquisition unit may acquire schedule information from a user's calendar app. If the user is in a meeting, the acquisition unit may instruct the generation unit to generate quiet background music. If the user is ready to exercise, the acquisition unit may instruct the generation unit to generate energetic music. This allows music to be provided that matches the user's schedule.

[0062] The music generation system may also include a generation unit that analyzes users' past music evaluation data and generates music based on those evaluations. For example, the system may extract features of music that users have previously rated highly and generate new music based on those features. The generation algorithm may also be adjusted to avoid features of music that users have rated poorly. This allows the system to provide music tailored to the user's preferences.

[0063] The music generation system may also include a provider that adjusts the amount of music data in consideration of the battery status of the user's device. For example, if the device's battery is low, the provider provides music with a reduced amount of data. Alternatively, if the device's battery is sufficient, the provider provides music with high sound quality. This allows music to be provided according to the device's battery status.

[0064] The music generation system may also include a generator that adjusts the tempo of the music based on the user's current activity. For example, if the user is exercising, the generator generates music with a fast tempo. If the user is resting, the generator generates music with a slower tempo. This allows the tempo of the music to be adjusted according to the user's activity.

[0065] The music generation system may also include an acquisition unit that analyzes a user's social media activity and generates music based on that activity. For example, the acquisition unit generates music based on music data shared by the user on social media. The acquisition unit may also analyze the user's social media posts and generate music that matches the current situation. This allows music to be generated based on social media activity.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The acquisition unit acquires the user's location information. For example, the acquisition unit acquires the user's location information using GPS information, Wi-Fi location information, and cell tower location information. The acquisition unit can also acquire ambient sounds using the smartphone's microphone and obtain weather information from the Internet. Step 2: The estimation unit estimates the user's situation based on the location information acquired by the acquisition unit. For example, if the user is at the beach, the estimation unit estimates the user's situation based on that information. The estimation unit can also estimate the user's situation based on acquired ambient sounds and weather information. For example, if the ambient sound is the sound of waves, the estimation unit estimates that the user is at the beach. Step 3: The generator generates music based on the situation inferred by the inferring unit. For example, the generator generates music that suits the beach. The generator uses a generative AI to generate music based on past music data and the user's preferences. Step 4: The providing unit provides the music generated by the generating unit to the user. For example, the providing unit streams the generated music to the user's device and obtains user feedback to reflect in subsequent music generation.

[0068] (Example 2) A music generation system according to an embodiment of the present invention generates music tailored to a user's situation and environment. The music generation system acquires user location information, infers the user's situation based on the location information, and generates and provides music tailored to the situation to the user. For example, the music generation system acquires user location information. For example, the music generation system can acquire user location information using GPS information. Next, the music generation system infers the user's situation based on the acquired location information. For example, if the user is at the beach, the music generation system infers the user's situation based on the information. Next, the music generation system generates music based on the inferred situation. For example, the music generation system generates music that suits the beach. Next, the music generation system provides the generated music to the user. For example, the music generation system streams the generated music to the user's device. In this way, the music generation system can generate and provide music tailored to the user's situation and environment. In this way, the music generation system can generate and provide music tailored to the user's situation and environment. For example, if the user wants to relax, the music generation system generates relaxing music. Also, if the user is exercising, the music generation system generates music with a tempo suitable for exercise. In this way, the music generation system can improve the user's experience.

[0069] A music generation system according to an embodiment includes an acquisition unit, an estimation unit, a generation unit, and a provision unit. The acquisition unit acquires user location information. For example, the acquisition unit acquires the user location information using GPS information. The acquisition unit can also acquire location information using Wi-Fi location information or cell tower location information. The acquisition unit can also acquire sounds around the user. For example, the acquisition unit acquires ambient sounds using a smartphone microphone. The acquisition unit can also acquire weather information. For example, the acquisition unit acquires weather information from the Internet. The estimation unit estimates the user's situation based on the location information acquired by the acquisition unit. For example, if the user is at the beach, the estimation unit estimates the user's situation based on that information. The estimation unit can also estimate the user's situation based on the acquired ambient sounds and weather information. For example, if the ambient sound is the sound of waves, the estimation unit estimates that the user is at the beach. The generation unit generates music based on the situation estimated by the estimation unit. For example, the generation unit generates music that suits the beach. The generation unit generates music using a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation unit generates music based on past music data and user preferences. The providing unit provides the music generated by the generation unit to the user. For example, the providing unit streams the generated music to the user's device. The providing unit can also obtain user feedback and reflect it in subsequent music generation. For example, the providing unit learns from the user's evaluation of the generated music and reflects the evaluation in subsequent music generation. In this way, the music generation system according to the embodiment can generate and provide music that is tailored to the user's situation and environment.

[0070] The acquisition unit can acquire sounds around the user. Examples of ambient sounds include, but are not limited to, environmental sounds, conversation sounds, and traffic sounds. The acquisition unit can acquire ambient sounds using, for example, a smartphone microphone. The acquisition unit can also acquire ambient sounds using a dedicated microphone device. For example, the acquisition unit can use a microphone with a noise-canceling function to clearly acquire ambient sounds. The acquisition unit can also acquire ambient sounds in three dimensions using multiple microphones. For example, the acquisition unit can use a stereo microphone to acquire ambient sounds in left and right channels. This enables more accurate situation estimation by acquiring sounds around the user. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the acquisition unit can input the acquired ambient sound data to a generation AI and have the generation AI analyze the sound data.

[0071] The acquisition unit can acquire weather information. Weather information includes, but is not limited to, temperature, humidity, and precipitation, for example. The acquisition unit can acquire weather information from the Internet, for example. The acquisition unit can also acquire weather information using a meteorological sensor. For example, the acquisition unit can acquire temperature using a temperature sensor. The acquisition unit can also acquire humidity using a humidity sensor. The acquisition unit can also acquire precipitation using a rain gauge. For example, the acquisition unit acquires weather information in real time and reflects it in estimating the user's situation. In this way, acquiring the weather information improves the accuracy of estimating the user's situation. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input weather information acquired from the Internet to the generation AI and cause the generation AI to analyze the weather information.

[0072] The estimation unit can estimate the user's situation based on the acquired location information, ambient sounds, and weather information. The estimation unit estimates the user's situation based on, for example, the acquired location information. For example, if the user is at the beach, the estimation unit estimates the user's situation based on that information. The estimation unit can also estimate the user's situation based on the acquired ambient sounds. For example, if the ambient sounds are the sound of waves, the estimation unit estimates that the user is at the beach. The estimation unit can also estimate the user's situation based on the acquired weather information. For example, if the weather information is sunny, the estimation unit estimates that the user is outdoors. Furthermore, the estimation unit can comprehensively analyze the acquired location information, ambient sounds, and weather information to more accurately estimate the user's situation. For example, if the location information is the beach, the ambient sounds are the sound of waves, and the weather information is sunny, the estimation unit estimates that the user is relaxing on the beach. This allows the user's situation to be more accurately estimated based on multiple pieces of information. Some or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input the acquired location information, ambient sounds, and weather information into the generation AI and have the generation AI estimate the user's situation.

[0073] The generation unit can generate music based on past music data and user preferences. Past music data includes, but is not limited to, playback history and rating data. The generation unit can generate music based on, for example, the user's playback history. The generation unit can also generate music based on the user's rating data. For example, the generation unit can generate new music based on the characteristics of music that the user has given high ratings to. The generation unit can also generate music based on the user's preferences. For example, the generation unit can generate music in a genre and tempo that the user prefers. Furthermore, the generation unit can comprehensively analyze past music data and the user's preferences to generate optimal music. For example, the generation unit can extract characteristics of music that the user prefers based on the user's playback history and rating data, and generate new music based on those characteristics. This makes it possible to generate music tailored to the user's preferences. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation unit can input the user's playback history and evaluation data into the generation AI and have the generation AI generate music.

[0074] The providing unit can acquire user feedback and reflect it in subsequent music generation. Examples of feedback include, but are not limited to, user ratings, comments, and usage history. For example, the providing unit acquires ratings by users rating the generated music. The providing unit can also acquire user comments. For example, the providing unit acquires comments by users inputting comments about the music. The providing unit can also acquire user usage history. For example, the providing unit records what kind of music the user listens to and how often. Furthermore, the providing unit can reflect the acquired feedback in subsequent music generation. For example, the providing unit learns the characteristics of music that users have given high ratings to and generates subsequent music based on those characteristics. By incorporating user feedback, the accuracy of music generation is improved. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the acquired feedback data to a generation AI and cause the generation AI to analyze the feedback.

[0075] The music generation system includes an acquisition unit that estimates a user's emotion and adjusts the timing of acquiring location information based on the estimated user emotion. The acquisition unit estimates the user's emotion and adjusts the timing of acquiring location information based on the estimated user emotion. Emotions include, but are not limited to, relaxation, excitement, stress, etc. For example, when the user is relaxed, the acquisition unit reduces the frequency of acquiring location information to reduce battery consumption. Furthermore, when the user is excited, the acquisition unit increases the frequency of acquiring location information to grasp the situation in real time. Furthermore, when the user is stressed, the acquisition unit can temporarily stop acquiring location information to reduce the burden on the user. In this way, by adjusting the timing of acquiring location information according to the user's emotion, appropriate information can be acquired while reducing battery consumption. Emotion estimation is realized 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 acquisition unit may be performed using, for example, an AI, or without using an AI. For example, the acquisition unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0076] The acquisition unit can analyze the user's past location information history and select the optimal acquisition method. Examples of past location information history include, but are not limited to, GPS logs and movement history. The acquisition unit can adjust the frequency of location information acquisition based on, for example, places the user has frequently visited in the past. The acquisition unit can also analyze the user's past movement patterns and acquire location information at the optimal timing. For example, the acquisition unit can predict places the user will visit during a specific time period and prioritize acquiring location information during that time period. This enables efficient information acquisition by selecting the optimal acquisition method based on the past location information history. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input the past location information history into a generation AI and have the generation AI select the optimal acquisition method.

[0077] When acquiring location information, the acquisition unit can perform filtering based on the user's current activity. Current activities include, but are not limited to, for example, exercise, rest, and movement. For example, when the user is exercising, the acquisition unit can set the frequency of acquiring location information high and collect detailed movement data. Furthermore, when the user is resting, the acquisition unit can also set the frequency of acquiring location information low to reduce battery consumption. Furthermore, when the user is moving, the acquisition unit can set the frequency of acquiring location information to medium to perform balanced data collection. This enables acquisition of location information according to the user's activity. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input the user's current activity data to the generation AI and have the generation AI perform filtering.

[0078] When acquiring location information, the acquisition unit can adjust the acquisition frequency taking into account the battery status of the user's device. Examples of battery status include, but are not limited to, the remaining battery charge and charging status. For example, when the device's battery is low, the acquisition unit can reduce the acquisition frequency of location information to reduce battery consumption. Furthermore, when the device's battery is sufficient, the acquisition unit can increase the acquisition frequency of location information to collect detailed data. Furthermore, the acquisition unit can dynamically adjust the acquisition frequency of location information according to the device's battery status. This enables acquisition of location information according to the device's battery status. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input device battery status data to the generation AI and cause the generation AI to adjust the acquisition frequency.

[0079] The acquisition unit can estimate the user's emotions and determine the priority of location information to be acquired based on the estimated user emotions. Examples of emotions include, but are not limited to, relaxation, excitement, and stress. For example, when the user is relaxed, the acquisition unit prioritizes acquiring location information of tourist spots and natural environments. Furthermore, when the user is in a hurry, the acquisition unit can prioritize acquiring location information of traffic information and the shortest route. Furthermore, when the user is stressed, the acquisition unit can prioritize acquiring location information of quiet places and places where the user can relax. This allows for more appropriate information to be acquired by determining the priority of location information according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as 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 acquisition unit may be performed using, for example, an AI. For example, the acquisition unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0080] When acquiring location information, the acquisition unit can prioritize acquiring highly relevant information taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS information, address information, etc. For example, when the user is in an urban area, the acquisition unit prioritizes acquiring traffic information and store information. Furthermore, when the user is in a natural environment, the acquisition unit can prioritize acquiring information about tourist spots and the natural environment. Furthermore, when the user is participating in a specific event, the acquisition unit can prioritize acquiring information related to the event. In this way, by acquiring highly relevant information based on the geographical location information, it is possible to provide useful information to the user. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input geographical location information data to a generation AI and cause the generation AI to acquire highly relevant information.

[0081] When acquiring location information, the acquisition unit can analyze the user's social media activity and acquire related information. Social media activity includes, but is not limited to, for example, posted content, number of likes, and comments. For example, the acquisition unit prioritizes acquiring information about locations where the user has checked in on social media. The acquisition unit can also analyze the content of the user's social media posts to acquire related location information. The acquisition unit can also acquire related location information by referring to the activities of the user's friends on social media. This makes it possible to acquire related information based on social media activity. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input social media activity data to a generation AI and cause the generation AI to acquire related information.

[0082] When acquiring location information, the acquisition unit can customize the acquisition method by reflecting the user's past feedback. Feedback includes, but is not limited to, user ratings, comments, and usage history. For example, the acquisition unit prioritizes acquiring information about places that the user has previously rated highly. The acquisition unit can also avoid acquiring information about places that the user has previously rated poorly. The acquisition unit can also customize the frequency and method of acquiring location information based on the user's past feedback. This enables customization based on past feedback. Some or all of the above-described processing in the acquisition unit may be performed using, or without, AI. For example, the acquisition unit can input past feedback data into a generation AI and cause the generation AI to customize the acquisition method.

[0083] The estimation unit can estimate the user's emotion and adjust the method of estimating the situation based on the estimated user emotion. Emotions include, but are not limited to, relaxation, excitement, stress, etc. For example, if the user is relaxed, the estimation unit can estimate the situation by focusing on ambient sounds and weather information. Furthermore, if the user is in a hurry, the estimation unit can estimate the situation by focusing on location information. Furthermore, if the user is stressed, the estimation unit can estimate the situation by focusing on ambient sounds and weather information. This enables situation estimation based on the user's emotion. 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 estimation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the estimation unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0084] The estimation unit can improve the accuracy of estimation by referring to the user's past behavioral patterns when making estimations. Past behavioral patterns include, but are not limited to, travel history and usage history. The estimation unit estimates the current situation based on data on places the user has visited in the past. The estimation unit can also analyze the user's past behavioral patterns to estimate the current situation. For example, the estimation unit estimates the current situation based on the user's behavioral patterns during a specific time period. This enables highly accurate estimation based on past behavioral patterns. Some or all of the above-described processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input past behavioral pattern data into the generation AI and cause the generation AI to improve the accuracy of the estimation.

[0085] During estimation, the estimation unit can adjust the estimation algorithm based on the user's current activity. Current activities include, but are not limited to, exercise, rest, and travel. For example, when the user is exercising, the estimation unit uses an algorithm that estimates a situation related to exercise. Furthermore, when the user is resting, the estimation unit can use an algorithm that estimates a situation related to relaxation. Furthermore, when the user is traveling, the estimation unit can use an algorithm that estimates a situation related to travel. This enables estimation based on the user's activity. Some or all of the above-described processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input the user's current activity data into the generation AI and cause the generation AI to adjust the estimation algorithm.

[0086] During estimation, the estimation unit can integrate sensor data from the user's device to improve the accuracy of the estimation. Sensor data includes, but is not limited to, an acceleration sensor, a gyro sensor, and a temperature sensor. For example, the estimation unit estimates the movement speed using the acceleration sensor of the user's smartphone. The estimation unit can also estimate the direction using the gyro sensor of the user's smartphone. The estimation unit can also estimate the altitude using the barometric pressure sensor of the user's smartphone. By integrating the sensor data, highly accurate estimation becomes possible. Some or all of the above-described processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input sensor data into the generation AI and cause the generation AI to improve the accuracy of the estimation.

[0087] The estimation unit estimates the user's emotion and adjusts the display method of the estimation result based on the estimated user emotion. Emotions include, but are not limited to, relaxation, excitement, stress, etc. For example, if the user is relaxed, the estimation unit provides a visually calming display method. Furthermore, if the user is in a hurry, the estimation unit can provide a concise and to-the-point display method. Furthermore, if the user is excited, the estimation unit can provide a visually stimulating display method. This enables adjustment of the display method according to the user's emotion. The emotion estimation is realized 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 estimation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the estimation unit may input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0088] The estimation unit can improve the accuracy of the estimation by taking into account the user's geographical location information during estimation. Examples of geographical location information include, but are not limited to, GPS information and address information. For example, if the user is in an urban area, the estimation unit can make estimations by taking into account surrounding buildings and traffic information. Furthermore, if the user is in a natural environment, the estimation unit can make estimations by taking into account information about the surrounding natural environment. Furthermore, if the user is participating in a specific event, the estimation unit can also make estimations by taking into account information related to the event. This enables highly accurate estimations that take into account the geographical location information. Some or all of the above-described processing in the estimation unit may be performed, for example, using AI, or may be performed without using AI. For example, the estimation unit can input geographical location information data into the generation AI and cause the generation AI to improve the accuracy of the estimation.

[0089] The estimation unit can analyze the user's social media activity during estimation to improve the accuracy of the estimation. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and comments. The estimation unit can make estimations based on, for example, information about the location where the user checked in on social media. The estimation unit can also analyze the content of the user's social media posts to estimate the current situation. The estimation unit can also estimate the current situation by referring to the activities of the user's friends on social media. This enables highly accurate estimation based on social media activity. Some or all of the above-described processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input social media activity data into a generation AI and cause the generation AI to improve the accuracy of the estimation.

[0090] The estimation unit can customize the estimation algorithm by reflecting the user's past feedback when making estimations. Examples of feedback include, but are not limited to, user ratings, comments, and usage history. For example, the estimation unit can adjust the algorithm based on estimation results that the user previously rated highly. The estimation unit can also improve the algorithm based on estimation results that the user previously rated poorly. The estimation unit can also customize the estimation algorithm based on the user's past feedback. This enables customization based on past feedback. Some or all of the above-described processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input past feedback data into the generation AI and cause the generation AI to customize the estimation algorithm.

[0091] The generation unit can estimate the user's emotion and adjust the music generation method based on the estimated user's emotion. Emotions include, but are not limited to, relaxation, excitement, stress, etc. For example, if the user is relaxed, the generation unit can generate music with a slow tempo. If the user is in a hurry, the generation unit can generate music with a fast tempo. If the user is excited, the generation unit can generate energetic music. This enables music generation according to the user's emotion. The emotion estimation is realized using an emotion estimation function, such as 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 generation unit is performed using the generation 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 music generation method.

[0092] When generating music, the generation unit can improve the accuracy of the generation by referring to the user's past music history. Past music history includes, for example, playback history and rating data, but is not limited to these examples. The generation unit generates music based on, for example, data on music the user liked to listen to in the past. The generation unit can also analyze the user's past music history and generate music that suits the current situation. The generation unit can also generate music based on patterns of music the user listens to during a specific time period. This enables highly accurate music generation based on the past music history. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. For example, the generation unit can input the user's past music history data into the generation AI and have the generation AI generate music.

[0093] When generating music, the generation unit can adjust the generation algorithm based on the user's current activity. Current activities include, but are not limited to, for example, exercise, rest, and travel. For example, when the user is exercising, the generation unit can generate music with a tempo suitable for exercise. Furthermore, when the user is resting, the generation unit can generate relaxing music. Furthermore, when the user is traveling, the generation unit can generate music with a tempo suitable for traveling. This enables music to be generated according to the user's activity. Some or all of the above-mentioned processing in the generation unit is performed using 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. For example, the generation unit can input the user's current activity data into the generation AI and cause the generation AI to adjust the generation algorithm.

[0094] When generating music, the generation unit can improve the accuracy of generation by taking into account the sound quality settings of the user's device. Sound quality settings include, but are not limited to, equalizer settings and volume settings. For example, if the user's device is set to high sound quality, the generation unit can generate music that emphasizes sound quality. Furthermore, if the user's device is set to low sound quality, the generation unit can generate music with a reduced amount of data. Furthermore, the generation unit can use an optimal music generation algorithm depending on the sound quality settings of the user's device. This enables music to be generated according to the sound quality settings of the device. Some or all of the above-mentioned processing in the generation unit is performed using 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. For example, the generation unit may input sound quality setting data of the user's device into the generation AI and cause the generation AI to generate music.

[0095] The generation unit can estimate the user's emotion and determine the genre of music to be generated based on the estimated user emotion. Emotions include, but are not limited to, relaxation, excitement, stress, etc. For example, if the user is relaxed, the generation unit can generate a genre such as classical or jazz. If the user is in a hurry, the generation unit can generate a genre such as rock or electronica. If the user is excited, the generation unit can generate a genre such as pop or dance music. This enables the generation of music genres according to the user's emotion. 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 generation unit can be performed using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI determine the music genre.

[0096] The generation unit can improve the accuracy of music generation by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS information and address information. For example, if the user is in an urban area, the generation unit can generate music with an urban atmosphere. Furthermore, if the user is in a natural environment, the generation unit can generate music incorporating natural sounds. Furthermore, if the user is participating in a specific event, the generation unit can generate music related to the event. This enables highly accurate music generation that takes into account the geographical location information. Some or all of the above-described processing in the generation unit is performed using 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. For example, the generation unit can input geographical location information data into the generation AI and cause the generation AI to generate music.

[0097] When generating music, the generation unit can analyze the user's social media activity to improve the accuracy of the generation. Social media activity includes, for example, but is not limited to, the content of posts, the number of likes, and comments. For example, the generation unit generates music based on data of music shared by the user on social media. The generation unit can also analyze the content of the user's social media posts to generate music that suits the current situation. The generation unit can also generate related music by referring to the activities of the user's friends on social media. This enables highly accurate music generation based on social media activity. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. For example, the generation unit can input social media activity data into the generation AI and cause the generation AI to generate music.

[0098] When generating music, the generation unit can customize the generation algorithm by reflecting past user feedback. Feedback includes, but is not limited to, user ratings, comments, and usage history. For example, the generation unit can adjust the generation algorithm based on data of music that the user has previously rated highly. The generation unit can also improve the generation algorithm based on data of music that the user has previously rated poorly. The generation unit can also customize the generation algorithm based on past user feedback. This enables customization based on past feedback. Some or all of the above-described processing in the generation unit is performed using 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. For example, the generation unit can input past feedback data into the generation AI and have the generation AI customize the generation algorithm.

[0099] The providing unit can estimate the user's emotion and adjust the music presentation method based on the estimated user's emotion. Emotions include, but are not limited to, relaxation, excitement, stress, etc. For example, the providing unit can provide slow music when the user is relaxed. Furthermore, the providing unit can provide fast-paced music when the user is in a hurry. Furthermore, the providing unit can provide energetic music when the user is excited. This enables music to be provided according to the user's emotion. 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 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 music presentation method.

[0100] When providing music, the providing unit can improve the accuracy of the music provision by referring to the user's past feedback. Feedback includes, for example, the user's ratings, comments, and usage history, but is not limited to these examples. For example, the providing unit can preferentially provide music that the user has previously rated highly. The providing unit can also avoid providing music that the user has previously rated poorly. The providing unit can also customize the providing algorithm based on the user's past feedback. This enables music to be provided with high accuracy based on the past feedback. 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 past feedback data into the generating AI and cause the generating AI to customize the providing algorithm.

[0101] When providing music, the providing unit can adjust the providing algorithm based on the user's current activity. Current activities include, but are not limited to, for example, exercise, rest, and travel. For example, when the user is exercising, the providing unit can provide music with a tempo suitable for exercise. Furthermore, when the user is resting, the providing unit can provide relaxing music. Furthermore, when the user is traveling, the providing unit can also provide music with a tempo suitable for traveling. This makes it possible to provide music according to the user's activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current activity data into the generation AI and cause the generation AI to adjust the providing algorithm.

[0102] When providing music, the providing unit can improve the accuracy of the music provided by taking into account the sound quality settings of the user's device. Sound quality settings include, but are not limited to, equalizer settings and volume settings. For example, if the user's device is set to high sound quality, the providing unit can provide music that emphasizes sound quality. Furthermore, if the user's device is set to low sound quality, the providing unit can provide music with a reduced data volume. Furthermore, the providing unit can use an optimal music providing algorithm depending on the sound quality settings of the user's device. This enables music to be provided according to the sound quality settings of the device. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input sound quality setting data of the user's device to the generation AI and cause the generation AI to provide music.

[0103] The providing unit can estimate the user's emotion and determine the order of music to be provided based on the estimated user's emotion. Emotions include, but are not limited to, relaxation, excitement, stress, etc. For example, if the user is relaxed, the providing unit can provide slow music first. Furthermore, if the user is in a hurry, the providing unit can provide fast-paced music first. Furthermore, if the user is excited, the providing unit can provide energetic music first. This enables the order of music to be provided according to the user's emotion. 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 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 order of music.

[0104] When providing music, the providing unit can improve the accuracy of the music provision by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS information and address information. For example, if the user is in an urban area, the providing unit can provide music with an urban atmosphere. Furthermore, if the user is in a natural environment, the providing unit can provide music incorporating natural sounds. Furthermore, if the user is participating in a specific event, the providing unit can also provide music related to the event. This enables music provision with high accuracy that takes into account the geographical location information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input geographical location information data to a generation AI and cause the generation AI to provide music.

[0105] When providing music, the providing unit can analyze the user's social media activity to improve the accuracy of the music provided. Social media activity includes, but is not limited to, the content of posts, the number of likes, and comments. For example, the providing unit provides music based on data on music shared by the user on social media. The providing unit can also analyze the content of the user's social media posts to provide music that suits the user's current situation. The providing unit can also provide related music by referring to the activities of the user's friends on social media. This enables highly accurate music provision based on social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input social media activity data into a generation AI and cause the generation AI to provide music.

[0106] The providing unit can customize the providing algorithm by reflecting the user's past feedback when providing music. Feedback includes, but is not limited to, user ratings, comments, and usage history, for example. The providing unit, for example, preferentially provides music that the user has previously rated highly. The providing unit can also prevent the provision of music that the user has previously rated poorly. The providing unit can also customize the providing algorithm based on the user's past feedback. This enables customization based on past feedback. 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 past feedback data into the generating AI and cause the generating AI to customize the providing algorithm. === Hard Collateral 1-1 === Each of the multiple elements, including the acquisition unit, estimation unit, generation unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires the user's location information and surrounding sounds using the GPS function or microphone of the smart device 14. The acquisition unit can also acquire weather information from the Internet via the data processing device 12. The estimation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and estimates the user's situation based on the acquired location information, surrounding sounds, and weather information. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates music using a generation AI based on the estimated situation. The provision unit is implemented, for example, by the control unit 46A of the smart device 14 and streams the generated music to the user's device. The acquisition unit has a function, for example, of estimating the user's emotions and adjusting the timing of acquiring location information based on the estimated emotions. === Hard Collateral 1-2 === Each of the multiple elements, including the acquisition unit, estimation 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 acquisition unit acquires the user's location information and ambient sounds using the GPS function or microphone of the smart glasses 214. The acquisition unit can also acquire weather information from the Internet via the data processing device 12. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and estimates the user's situation based on the acquired location information, ambient sounds, and weather information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates music using a generation AI based on the estimated situation. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and streams the generated music to the user's device. The acquisition unit has a function, for example, of estimating the user's emotions and adjusting the timing of acquiring location information based on the estimated emotions. === Hard Collateral 1-3 === Each of the multiple elements, including the acquisition unit, estimation unit, generation unit, and provision unit, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the acquisition unit acquires the user's location information and ambient sounds using the GPS function or microphone of the headset-type terminal 314. The acquisition unit can also acquire weather information from the Internet via the data processing device 12. The estimation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and estimates the user's situation based on the acquired location information, ambient sounds, and weather information. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates music using a generation AI based on the estimated situation. The provision unit is implemented, for example, by the control unit 46A of the headset-type terminal 314 and streams the generated music to the user's device. The acquisition unit has a function, for example, of estimating the user's emotions and adjusting the timing of acquiring location information based on the estimated emotions. === Hard Collateral 1-4 === Each of the multiple elements, including the acquisition unit, estimation 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 acquisition unit acquires the user's location information and surrounding sounds using the robot 414's GPS function or microphone. The acquisition unit can also acquire weather information from the Internet via the data processing device 12. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and estimates the user's situation based on the acquired location information, surrounding sounds, and weather information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates music using a generation AI based on the estimated situation. The provision unit is realized, for example, by the control unit 46A of the robot 414 and streams the generated music to the user's device. The acquisition unit has a function, for example, of estimating the user's emotions and adjusting the timing of acquiring location information based on the estimated emotions.

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

[0108] The music generation system may also include an acquisition unit that acquires a user's heart rate and adjusts the tempo of the music based on the heart rate. For example, the acquisition unit may acquire heart rate data from a smartwatch or fitness tracker. If the user is exercising and their heart rate is high, the acquisition unit may instruct the generation unit to generate music with a faster tempo. Alternatively, if the user is relaxing, the acquisition unit may detect that their heart rate is low and instruct the generation unit to generate music with a slower tempo. This allows the system to provide music tailored to the user's physiological state.

[0109] The music generation system may also include an acquisition unit that acquires a user's schedule information and generates music based on the schedule. For example, the acquisition unit may acquire schedule information from a user's calendar app. If the user is in a meeting, the acquisition unit may instruct the generation unit to generate quiet background music. If the user is ready to exercise, the acquisition unit may instruct the generation unit to generate energetic music. This allows music to be provided that matches the user's schedule.

[0110] The music generation system may also include a generation unit that estimates the user's emotions and selects a music genre based on the estimated emotions. For example, if the user is relaxed, the generation unit generates music in a relaxing genre such as classical or jazz. On the other hand, if the user is excited, the generation unit generates music in an energetic genre such as rock or electronica. This allows the system to provide a music genre that matches the user's emotions.

[0111] The music generation system may also include a generation unit that analyzes users' past music evaluation data and generates music based on those evaluations. For example, the system may extract features of music that users have previously rated highly and generate new music based on those features. The generation algorithm may also be adjusted to avoid features of music that users have rated poorly. This allows the system to provide music tailored to the user's preferences.

[0112] The music generation system may also include a providing unit that estimates the user's emotion and adjusts the volume of the music based on the estimated emotion. For example, if the user is relaxed, the providing unit sets the volume of the music low. On the other hand, if the user is excited, the providing unit sets the volume of the music high. This makes it possible to adjust the volume of the music according to the user's emotion.

[0113] The music generation system may also include a provider that adjusts the amount of music data in consideration of the battery status of the user's device. For example, if the device's battery is low, the provider provides music with a reduced amount of data. Alternatively, if the device's battery is sufficient, the provider provides music with high sound quality. This allows music to be provided according to the device's battery status.

[0114] The music generation system may also include a providing unit that estimates the user's emotion and determines the music playback order based on the estimated emotion. For example, if the user is relaxed, the providing unit may play slow music first. On the other hand, if the user is excited, the providing unit may play energetic music first. In this way, the music playback order can be provided according to the user's emotion.

[0115] The music generation system may also include a generator that adjusts the tempo of the music based on the user's current activity. For example, if the user is exercising, the generator generates music with a fast tempo. If the user is resting, the generator generates music with a slower tempo. This allows the tempo of the music to be adjusted according to the user's activity.

[0116] The music generation system may also include a generation unit that estimates the user's emotions and adjusts the music effects based on the estimated emotions. For example, if the user is relaxed, the generation unit generates music by adding effects such as reverb and echo. On the other hand, if the user is excited, the generation unit generates music by adding effects such as distortion and filters. This makes it possible to adjust the music effects according to the user's emotions.

[0117] The music generation system may also include an acquisition unit that analyzes a user's social media activity and generates music based on that activity. For example, the acquisition unit generates music based on music data shared by the user on social media. The acquisition unit may also analyze the user's social media posts and generate music that matches the current situation. This allows music to be generated based on social media activity.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The acquisition unit acquires the user's location information. For example, the acquisition unit acquires the user's location information using GPS information, Wi-Fi location information, and cell tower location information. The acquisition unit can also acquire ambient sounds using the smartphone's microphone and obtain weather information from the Internet. Step 2: The estimation unit estimates the user's situation based on the location information acquired by the acquisition unit. For example, if the user is at the beach, the estimation unit estimates the user's situation based on that information. The estimation unit can also estimate the user's situation based on acquired ambient sounds and weather information. For example, if the ambient sound is the sound of waves, the estimation unit estimates that the user is at the beach. Step 3: The generator generates music based on the situation inferred by the inferring unit. For example, the generator generates music that suits the beach. The generator uses a generative AI to generate music based on past music data and the user's preferences. Step 4: The providing unit provides the music generated by the generating unit to the user. For example, the providing unit streams the generated music to the user's device and obtains user feedback to reflect in subsequent music generation.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0121] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0125] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] The data processing device 12 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0157] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] [Explanation of symbols]

[0192] 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. an acquisition unit that acquires user location information; an estimation unit that estimates a situation of a user based on the location information acquired by the acquisition unit; a generation unit that generates music based on the situation inferred by the estimation unit; a providing unit that provides the music generated by the generating unit to a user; Equipped with A system characterized by:

2. The acquisition unit Capture sounds around the user 2. The system of claim 1.

3. The acquisition unit Get weather information 2. The system of claim 1.

4. The estimation unit Inferring the user's situation based on acquired location information, ambient sounds, and weather information 2. The system of claim 1.

5. The generation unit Generate music based on past music data and user preferences 2. The system of claim 1.

6. The providing unit Obtain user feedback and reflect it in future music generation 2. The system of claim 1.

7. The acquisition unit Estimates the user's emotions and adjusts the timing of acquiring location information based on the estimated user emotions.

2. The system of claim 1.

8. The acquisition unit Analyze the user's past location history and select the optimal acquisition method 2. The system of claim 1.

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