system

Abstract

The system according to the embodiment aims to enable users to easily generate and provide music. [Solution] A system according to an embodiment includes a receiving unit, an analysis unit, a generation unit, and a providing unit. The receiving unit receives input from a user. The analysis unit analyzes the information received by the receiving unit. The generation unit generates music based on the information analyzed by the analysis unit. The providing unit provides the music generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it is difficult for users to easily generate and provide music.

[0005] The system according to the embodiment aims to enable users to easily generate and provide music. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analyzing unit, a generating unit, and a providing unit. The receiving unit receives input from a user. The analyzing unit analyzes the information received by the receiving unit. The generating unit generates music based on the information analyzed by the analyzing unit. The providing unit provides the music generated by the generating unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to easily create and provide music. [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 allows anyone to easily generate music and record their life through that music. In this music generation system, a user inputs daily events and emotions, and a generation AI generates music based on the input. The generated music reflects the user's emotions and events, allowing the user to record their life through the music. This system allows users to reflect on their life through music and share it with others. For example, a user inputs daily events and emotions, such as "Today was fun" or "Something sad happened." This input is sent to the generation AI. The generation AI then analyzes the input emotions and events and generates music based on them. The generation AI selects appropriate melodies, rhythms, and instruments based on the input information to generate music. For example, an input such as "I had fun" generates a bright and lively melody, while an input such as "Something sad happened" generates a melancholic melody. The generated music reflects the user's emotions and events, allowing the user to record their life through the music. For example, a user can recall the events and emotions of that day by listening to music generated on a particular day. Furthermore, the generated music can be shared with others. Users can share the music they have created with family and friends to convey their feelings and events. This allows them to share their feelings with others through music and gain sympathy. This system allows anyone to easily generate music and record their life through that music. By recording everyday events and emotions as music, users can look back on their lives, and by sharing it with others, they can share their feelings and gain sympathy. This allows the music generation system to generate and provide music based on user input.

[0029] A music generation system according to an embodiment includes a receiving unit, an analysis unit, a generation unit, and a provision unit. The receiving unit receives input from a user. The input from the user includes, but is not limited to, text input, voice input, and image input. For example, the receiving unit receives input of daily events and emotions from the user in text format. The receiving unit can also receive voice input of emotions and events from the user. The receiving unit can also receive input of emotions and events from the user using images. For example, the receiving unit can receive input of images taken by the user using a smartphone camera. The analysis unit analyzes the information received by the receiving unit. For example, the analysis unit analyzes the text information input by the user using text analysis technology. The analysis unit can also analyze the voice information input by the user using voice analysis technology. The analysis unit can also analyze the image information input by the user using image analysis technology. For example, the analysis unit analyzes the text information input by the user using natural language processing technology to extract emotions and events. Speech information is converted into text using speech recognition technology and then analyzed using natural language processing technology. Image information is extracted from the image using image recognition technology. The generation unit generates music based on the information analyzed by the analysis unit. The generation unit generates music based on the user's emotions and events using a generation AI. The generation AI, for example, uses a text generation AI (e.g., LLM) to select a melody, rhythm, and instruments based on the user's emotions and events, and generates music. The generation unit can also generate music based on the user's emotions and events using a multimodal generation AI. For example, the generation unit generates a bright and lively melody based on the user's input emotion of "I had fun." Conversely, it generates a melancholic melody based on the user's input emotion of "Something sad happened." The provision unit provides the music generated by the generation unit. The provision unit provides the generated music to the user in streaming format, for example. The provision unit can also provide the generated music to the user in download format.Furthermore, the providing unit may provide a function for sharing the generated music with other people. For example, the providing unit may provide a function for sharing the generated music on social media. This allows the music generation system according to the embodiment to generate and provide music based on user input.

[0030] The music generation system includes a sharing unit for sharing the generated music with others. The sharing unit provides a function for sharing the generated music with others. For example, the sharing unit provides a function for sharing the generated music on social media. For example, a user can share the generated music on a social media platform to share emotions or events with others. The sharing unit can also provide a function for sharing the generated music via email. For example, a user can send the generated music to family or friends via email to convey their emotions or events. Furthermore, the sharing unit can also provide a function for saving the generated music in cloud storage and sharing it with others. For example, a user can upload the generated music to cloud storage and share it with others via a sharing link. In this way, the music generation system enables the generated music to be shared with others.

[0031] The music generation system includes a storage unit that stores the generated music. The storage unit provides a function for storing the generated music. For example, the storage unit provides a function for storing the generated music in cloud storage. For example, a user can upload the generated music to the cloud storage to make it accessible at any time. The storage unit can also provide a function for storing the generated music in local storage. For example, a user can download the generated music to their device to make it playable offline. Furthermore, the storage unit can also provide a function for backing up the generated music. For example, a user can regularly back up the generated music to prevent data loss. In this way, the music generation system can store the generated music.

[0032] The generation unit can select a melody, rhythm, and instrument based on the input emotion or event, and generate music. The generation unit selects an appropriate melody, rhythm, and instrument based on, for example, the emotion or event input by the user, and generates music. For example, if the user inputs the emotion "I had fun," the generation unit generates a bright and lively melody. Also, if the user inputs the emotion "Something sad happened," the generation unit can generate a melancholic melody. Furthermore, the generation unit can select an appropriate instrument based on the event input by the user. For example, if the user inputs the event "I attended a fun party," the generation unit can select an instrument such as a guitar or drums and generate music. In this way, the generation unit can generate music based on the input emotion or event.

[0033] The providing unit can provide the generated music to a user. For example, the providing unit can provide the generated music to a user in a streaming format. For example, the user can play the generated music in real time via the Internet. The providing unit can also provide the generated music to a user in a download format. For example, the user can download the generated music to their own device and play it offline. Furthermore, the providing unit can also provide a function for sharing the generated music with others. For example, the providing unit can provide a function for sharing the generated music on social media. In this way, the providing unit can provide the generated music to a user.

[0034] The reception unit can analyze the user's past input history and select an input method. For example, the reception unit preferentially suggests an input method (such as voice or text) that the user has frequently used in the past. For example, if the user has frequently used voice input in the past, the reception unit preferentially suggests voice input. The reception unit can also predict and suggest an input method to be used in a specific time period based on the user's past input history. For example, if the user has frequently used text input in a specific time period, the reception unit suggests text input for that time period. Furthermore, the reception unit can also suggest a similar input method by referring to content that the user has input in the past. For example, if the user has input information related to a specific topic in the past, the reception unit suggests an input method related to that topic. In this way, the reception unit can select the optimal input method based on the user's past input history.

[0035] The reception unit can perform filtering based on the user's current situation and areas of interest when receiving input. For example, when the user inputs their current situation, the reception unit prioritizes displaying related information. For example, if the user inputs "currently traveling," the reception unit prioritizes displaying information related to travel. The reception unit can also filter the input content based on the user's areas of interest and provide appropriate information. For example, if the user inputs that they are interested in "music," the reception unit prioritizes displaying information related to music. Furthermore, the reception unit can automatically adjust the input content according to the user's current situation. For example, if the user inputs "at work," the reception unit prioritizes displaying information related to work. This allows the reception unit to filter the input content based on the user's current situation and areas of interest.

[0036] When receiving an input, the reception unit can prioritize receiving highly relevant input taking into account the user's geographical location information. For example, when the user is in a specific location, the reception unit prioritizes receiving input related to that location. For example, when the user is in a tourist spot, the reception unit prioritizes receiving information related to that tourist spot. Furthermore, when the user is traveling, the reception unit can also prioritize receiving appropriate input based on the user's current location. For example, when the user searches for "nearby restaurants" while traveling, the reception unit prioritizes receiving nearby restaurant information based on the user's current location. Furthermore, when the user is in a specific region, the reception unit can also prioritize receiving information related to that region. For example, when the user is in a specific city, the reception unit prioritizes receiving information related to that city. This allows the reception unit to prioritize receiving highly relevant input based on the user's geographical location information.

[0037] The reception unit can analyze the user's social media activity when receiving input and receive related input. For example, the reception unit preferentially receives related input based on information shared by the user on social media. For example, if the user shares on social media that they "listened to a new music album," the reception unit preferentially receives information related to that album. The reception unit can also analyze topics of interest from the user's social media activity and receive appropriate input. For example, if the user frequently posts about "travel" on social media, the reception unit preferentially receives information related to travel. Furthermore, the reception unit can also receive related input based on the content of the user's social media posts. For example, if the user posts about "cooking" on social media, the reception unit preferentially receives information related to cooking. This allows the reception unit to receive related input based on the user's social media activity.

[0038] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the input information. For example, the analysis unit performs a detailed analysis on important information. For example, if the information input by the user is important, the analysis unit performs a detailed analysis to increase the accuracy of the information. The analysis unit can also perform a normal analysis on general information. For example, if the information input by the user is general, the analysis unit performs a normal analysis to quickly provide a result. Furthermore, the analysis unit can also perform a quick and detailed analysis on urgent information. For example, if the information input by the user is urgent, the analysis unit performs a quick and detailed analysis to quickly respond. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the input information.

[0039] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the input information. For example, the analysis unit applies an emotion analysis algorithm to information regarding emotions. For example, if the information input by the user is related to emotions, the analysis unit applies an emotion analysis algorithm to accurately analyze the emotions. The analysis unit can also apply an event analysis algorithm to information regarding events. For example, if the information input by the user is related to events, the analysis unit applies an event analysis algorithm to accurately analyze the events. Furthermore, the analysis unit can select an appropriate analysis algorithm for other information. For example, if the information input by the user is other, the analysis unit selects an appropriate analysis algorithm to accurately analyze the information. This allows the analysis unit to apply an appropriate analysis algorithm depending on the category of the input information.

[0040] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the input information. The analysis unit, for example, prioritizes analysis of recently submitted information. For example, it prioritizes analysis of information recently input by the user and provides results quickly. The analysis unit can also prioritize analysis of urgent information. For example, when a user inputs urgent information, the analysis unit prioritizes analysis of that information and responds quickly. Furthermore, the analysis unit can also postpone analysis of information submitted earlier. For example, it prioritizes analysis of recent information, leaving information input by the user in the past as the latter. This allows the analysis unit to determine the priority of analysis based on the time of submission of the input information.

[0041] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the input information. The analysis unit, for example, prioritizes analysis of highly relevant information. For example, if information input by a user is highly relevant to other information, the analysis unit prioritizes analysis of that information. The analysis unit can also postpone analysis of less relevant information. For example, if information input by a user is less relevant to other information, the analysis unit postpones analysis of that information. Furthermore, the analysis unit can group and analyze relevant information. For example, if information input by a user includes multiple pieces of relevant information, the analysis unit groups and analyzes the pieces of information. This allows the analysis unit to adjust the order of analysis based on the relevance of the input information.

[0042] The generation unit can adjust the level of detail of the music based on the input emotion or importance of the event during generation. For example, the generation unit generates detailed music for an important event. For example, if the event input by the user is important, the generation unit generates detailed music to reflect the importance of the event. The generation unit can also generate normal music for a general event. For example, if the event input by the user is general, the generation unit generates normal music to quickly provide a result. Furthermore, the generation unit can also generate fast and detailed music for an urgent event. For example, if the event input by the user is urgent, the generation unit generates fast and detailed music to quickly respond. In this way, the generation unit can adjust the level of detail of the music based on the input emotion or importance of the event.

[0043] During generation, the generation unit can apply different generation algorithms depending on the category of the input emotion or event. For example, the generation unit applies an emotion generation algorithm to information about emotions. For example, if the information input by the user is related to emotions, the generation unit applies an emotion generation algorithm to generate music that accurately reflects the emotion. The generation unit can also apply an event generation algorithm to information about events. For example, if the information input by the user is related to events, the generation unit applies an event generation algorithm to generate music that accurately reflects the event. Furthermore, the generation unit can select an appropriate generation algorithm for other information. For example, if the information input by the user is other information, the generation unit selects an appropriate generation algorithm to generate music that accurately reflects the information. This allows the generation unit to apply an appropriate generation algorithm depending on the category of the input emotion or event.

[0044] During generation, the generation unit can determine the priority of music based on the submission date of the input emotions and events. The generation unit, for example, preferentially generates music based on recent emotions and events. For example, the generation unit preferentially generates music based on emotions and events recently input by the user, and quickly provides results. The generation unit can also preferentially generate music based on urgent emotions and events. For example, if the user inputs urgent emotions or events, the generation unit preferentially generates music based on that information and responds quickly. Furthermore, the generation unit can also generate music by putting emotions and events that were submitted earlier later. For example, emotions and events input by the user in the past are put later, and recent information is preferentially generated. This allows the generation unit to determine the priority of music based on the submission date of the input emotions and events.

[0045] The generation unit can adjust the order of music based on the relevance of the input emotions and events during generation. The generation unit, for example, preferentially generates music based on highly related emotions and events. For example, if the emotions or events input by the user are highly related to other information, the generation unit preferentially generates music based on that information. The generation unit can also postpone generating music for emotions or events with low relevance. For example, if the emotions or events input by the user are low related to other information, the generation unit postpones generating music for that information. Furthermore, the generation unit can also generate music by grouping related emotions or events. For example, if the emotions or events input by the user include multiple pieces of related information, the generation unit groups the information and generates music. This allows the generation unit to adjust the order of music based on the relevance of the input emotions and events.

[0046] The providing unit can adjust the level of detail of the provided information based on the importance of the generated music when providing the information. For example, the providing unit provides detailed information for important music. For example, if music generated by a user is important, the providing unit provides detailed information about the music so that the user can understand the background and intent of the music. The providing unit can also provide normal information for general music. For example, if the music generated by a user is general, the providing unit provides normal information about the music and quickly provides a result. Furthermore, the providing unit can also quickly provide detailed information for urgent music. For example, if the music generated by a user is urgent, the providing unit quickly provides detailed information about the music and quickly responds. This allows the providing unit to adjust the level of detail of the provided information based on the importance of the generated music.

[0047] The providing unit can apply different providing algorithms depending on the category of the generated music when providing the music. For example, the providing unit applies an emotion providing algorithm to music related to emotions. For example, if the music generated by the user is related to emotions, the providing unit applies an emotion providing algorithm to provide music that accurately reflects the emotions. The providing unit can also apply an event providing algorithm to music related to events. For example, if the music generated by the user is related to events, the providing unit applies an event providing algorithm to provide music that accurately reflects the events. Furthermore, the providing unit can select an appropriate providing algorithm for other music. For example, if the music generated by the user is other, the providing unit selects an appropriate providing algorithm to provide music that accurately reflects the information. This allows the providing unit to apply an appropriate providing algorithm depending on the category of the generated music.

[0048] The providing unit can determine the priority of providing the generated music at the time of providing the music based on the time of submission of the generated music. The providing unit, for example, prioritizes providing recently generated music. For example, music recently generated by a user is prioritized and results are provided quickly. The providing unit can also prioritize providing urgent music. For example, if music generated by a user is urgent, the providing unit prioritizes providing the music and responds quickly. Furthermore, the providing unit can also postpone providing music that was submitted earlier. For example, music generated by a user in the past is postponed and recent music is prioritized. This allows the providing unit to determine the priority of providing the music based on the time of submission of the generated music.

[0049] The providing unit can adjust the order of providing the generated music based on the relevance of the music when providing the music. The providing unit, for example, prioritizes providing highly relevant music. For example, if music generated by a user is highly relevant to other information, the providing unit prioritizes providing the music. The providing unit can also provide less relevant music later. For example, if music generated by a user is less relevant to other information, the providing unit provides the music later. Furthermore, the providing unit can group related music and provide it. For example, if music generated by a user includes multiple pieces of related information, the providing unit groups the music and provides it. This allows the providing unit to adjust the order of providing the generated music based on the relevance of the music.

[0050] The sharing unit can adjust the level of detail of sharing based on the importance of the generated music when sharing. For example, the sharing unit shares detailed information for important music. For example, if music generated by a user is important, the sharing unit shares detailed information about the music so that others can understand the background and intent of the music. The sharing unit can also share ordinary information for general music. For example, if music generated by a user is general, the sharing unit shares ordinary information about the music to provide a quick result. Furthermore, the sharing unit can quickly share detailed information for urgent music. For example, if music generated by a user is urgent, the sharing unit quickly shares detailed information about the music to provide a quick response. In this way, the sharing unit can adjust the level of detail of sharing based on the importance of the generated music.

[0051] The sharing unit can determine a priority for sharing based on the time of submission of the generated music when sharing. For example, the sharing unit prioritizes sharing of recently generated music. For example, music recently generated by a user is prioritized and shared to provide a quick result. The sharing unit can also prioritize sharing of urgent music. For example, if music generated by a user is urgent, the sharing unit prioritizes sharing of that music to respond quickly. Furthermore, the sharing unit can also share music that was submitted earlier later. For example, music generated by a user in the past is prioritized and recent music is shared preferentially. In this way, the sharing unit can determine a priority for sharing based on the time of submission of the generated music.

[0052] The storage unit may adjust the level of detail of the stored data based on the importance of the generated music when storing the data. For example, the storage unit stores detailed information for important music. For example, if music generated by a user is important, the storage unit stores detailed information about the music to allow the user to understand the background and intent of the music. The storage unit may also store general information for general music. For example, if music generated by a user is general, the storage unit stores general information about the music to provide a quick result. The storage unit may also store quick and detailed information for urgent music. For example, if music generated by a user is urgent, the storage unit stores quick and detailed information about the music to provide a quick response. In this way, the storage unit may adjust the level of detail of the stored data based on the importance of the generated music.

[0053] When saving, the storage unit can determine a priority for saving based on the time of submission of the created music. For example, the storage unit prioritizes saving recently created music. For example, the storage unit prioritizes saving music recently created by a user to provide results quickly. The storage unit can also prioritize saving urgent music. For example, if music created by a user is urgent, the storage unit prioritizes saving that music to respond quickly. Furthermore, the storage unit can also postpone saving music that was submitted earlier. For example, the storage unit prioritizes saving recent music over music created in the past. This allows the storage unit to determine a priority for saving based on the time of submission of the created music.

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

[0055] The music generation system may further include a hobby learning unit that learns the user's hobbies and interests. The hobby learning unit analyzes information previously input by the user and music generated by the user to identify the user's hobbies and interests. For example, if the user has generated a lot of rock music in the past, the hobby learning unit may determine that the user is interested in rock music and prioritize generating rock music. Also, if the user is interested in a particular instrument, the hobby learning unit may generate music using that instrument. Furthermore, the hobby learning unit may respond to changes in the user's interests and generate music based on the user's new hobbies and interests. This allows the music generation system to generate and provide music based on the user's hobbies and interests.

[0056] The music generation system may further include a social relationship analysis unit that considers the user's social relationships. The social relationship analysis unit analyzes the user's social media activity and contact information to identify the user's social relationships. For example, if the user frequently interacts with a particular friend, music related to that friend may be generated. Also, if the user values ​​relationships with family, music related to family may be generated. Furthermore, the social relationship analysis unit may respond to changes in the user's social relationships and generate music based on the new relationships. This allows the music generation system to generate and provide music based on the user's social relationships.

[0057] The music generation system may further include an environmental sound acquisition unit that captures the user's environmental sounds. The environmental sound acquisition unit acquires sounds around the user through a microphone and provides the sounds to the analysis unit. For example, if the user is in nature, the environmental sound acquisition unit can acquire the chirping of birds and the sound of the wind and generate music that evokes a sense of nature based on the sounds. If the user is in an urban area, the environmental sound acquisition unit can acquire the sounds of cars and people talking and generate music that reflects the urban atmosphere based on the sounds. Furthermore, the environmental sound acquisition unit can respond to changes in the user's environment and generate music based on the new environment. This allows the music generation system to generate and provide music based on the user's environmental sounds.

[0058] The music generation system may further include an activity monitoring unit that monitors the user's activity level. The activity monitoring unit measures the user's number of steps and amount of exercise and provides the data to the analysis unit. For example, if the user has recorded a large number of steps, the analysis unit may determine that the user is active and generate energetic music. Alternatively, if the user is exercising, the analysis unit may determine that the user is exercising and generate music with a rhythm suitable for exercise. Furthermore, the activity monitoring unit may respond to changes in the user's activity level and generate music based on the new activity level. This allows the music generation system to generate and provide music based on the user's activity level.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The reception unit receives input from the user. The input from the user includes text input, voice input, image input, and the like. For example, the user can input daily events and emotions in text format, input emotions and events by voice, or even input emotions and events using images. Images taken with a smartphone camera can also be received as input. Step 2: The analysis unit analyzes the information received by the reception unit. It uses text analysis technology, voice analysis technology, and image analysis technology to analyze the information entered by the user. For example, text information is analyzed using natural language processing technology to extract emotions and events. Voice information is converted into text using voice recognition technology and then analyzed using natural language processing technology. Image information is analyzed using image recognition technology to extract emotions and events from images. Step 3: The generation unit generates music based on the information analyzed by the analysis unit. Using the generation AI, it selects melodies, rhythms, and instruments based on the user's emotions and events, and generates the music. For example, a bright and lively melody can be generated based on the user's input emotion of "I had fun," while a melancholic melody can be generated based on the user's input emotion of "Something sad happened." Step 4: The providing unit provides the music generated by the generating unit. The generated music can be provided to the user in streaming or download format. The providing unit also provides a function for sharing the generated music with others. For example, the provided function provides a function for sharing the generated music on social media.

[0061] (Example 2) A music generation system according to an embodiment of the present invention allows anyone to easily generate music and record their life through that music. In this music generation system, a user inputs daily events and emotions, and a generation AI generates music based on the input. The generated music reflects the user's emotions and events, allowing the user to record their life through the music. This system allows users to reflect on their life through music and share it with others. For example, a user inputs daily events and emotions, such as "Today was fun" or "Something sad happened." This input is sent to the generation AI. The generation AI then analyzes the input emotions and events and generates music based on them. The generation AI selects appropriate melodies, rhythms, and instruments based on the input information to generate music. For example, an input such as "I had fun" generates a bright and lively melody, while an input such as "Something sad happened" generates a melancholic melody. The generated music reflects the user's emotions and events, allowing the user to record their life through the music. For example, a user can recall the events and emotions of that day by listening to music generated on a particular day. Furthermore, the generated music can be shared with others. Users can share the music they have created with family and friends to convey their feelings and events. This allows them to share their feelings with others through music and gain sympathy. This system allows anyone to easily generate music and record their life through that music. By recording everyday events and emotions as music, users can look back on their lives, and by sharing it with others, they can share their feelings and gain sympathy. This allows the music generation system to generate and provide music based on user input.

[0062] A music generation system according to an embodiment includes a receiving unit, an analysis unit, a generation unit, and a provision unit. The receiving unit receives input from a user. The input from the user includes, but is not limited to, text input, voice input, and image input. For example, the receiving unit receives input of daily events and emotions from the user in text format. The receiving unit can also receive voice input of emotions and events from the user. The receiving unit can also receive input of emotions and events from the user using images. For example, the receiving unit can receive input of images taken by the user using a smartphone camera. The analysis unit analyzes the information received by the receiving unit. For example, the analysis unit analyzes the text information input by the user using text analysis technology. The analysis unit can also analyze the voice information input by the user using voice analysis technology. The analysis unit can also analyze the image information input by the user using image analysis technology. For example, the analysis unit analyzes the text information input by the user using natural language processing technology to extract emotions and events. Speech information is converted into text using speech recognition technology and then analyzed using natural language processing technology. Image information is extracted from the image using image recognition technology. The generation unit generates music based on the information analyzed by the analysis unit. The generation unit generates music based on the user's emotions and events using a generation AI. The generation AI, for example, uses a text generation AI (e.g., LLM) to select a melody, rhythm, and instruments based on the user's emotions and events, and generates music. The generation unit can also generate music based on the user's emotions and events using a multimodal generation AI. For example, the generation unit generates a bright and lively melody based on the user's input emotion of "I had fun." Conversely, it generates a melancholic melody based on the user's input emotion of "Something sad happened." The provision unit provides the music generated by the generation unit. The provision unit provides the generated music to the user in streaming format, for example. The provision unit can also provide the generated music to the user in download format.Furthermore, the providing unit may provide a function for sharing the generated music with other people. For example, the providing unit may provide a function for sharing the generated music on social media. This allows the music generation system according to the embodiment to generate and provide music based on user input.

[0063] The music generation system includes a sharing unit for sharing the generated music with others. The sharing unit provides a function for sharing the generated music with others. For example, the sharing unit provides a function for sharing the generated music on social media. For example, a user can share the generated music on a social media platform to share emotions or events with others. The sharing unit can also provide a function for sharing the generated music via email. For example, a user can send the generated music to family or friends via email to convey their emotions or events. Furthermore, the sharing unit can also provide a function for saving the generated music in cloud storage and sharing it with others. For example, a user can upload the generated music to cloud storage and share it with others via a sharing link. In this way, the music generation system enables the generated music to be shared with others.

[0064] The music generation system includes a storage unit that stores the generated music. The storage unit provides a function for storing the generated music. For example, the storage unit provides a function for storing the generated music in cloud storage. For example, a user can upload the generated music to the cloud storage to make it accessible at any time. The storage unit can also provide a function for storing the generated music in local storage. For example, a user can download the generated music to their device to make it playable offline. Furthermore, the storage unit can also provide a function for backing up the generated music. For example, a user can regularly back up the generated music to prevent data loss. In this way, the music generation system can store the generated music.

[0065] The generation unit can select a melody, rhythm, and instrument based on the input emotion or event, and generate music. The generation unit selects an appropriate melody, rhythm, and instrument based on, for example, the emotion or event input by the user, and generates music. For example, if the user inputs the emotion "I had fun," the generation unit generates a bright and lively melody. Also, if the user inputs the emotion "Something sad happened," the generation unit can generate a melancholic melody. Furthermore, the generation unit can select an appropriate instrument based on the event input by the user. For example, if the user inputs the event "I attended a fun party," the generation unit can select an instrument such as a guitar or drums and generate music. In this way, the generation unit can generate music based on the input emotion or event.

[0066] The providing unit can provide the generated music to a user. For example, the providing unit can provide the generated music to a user in a streaming format. For example, the user can play the generated music in real time via the Internet. The providing unit can also provide the generated music to a user in a download format. For example, the user can download the generated music to their own device and play it offline. Furthermore, the providing unit can also provide a function for sharing the generated music with others. For example, the providing unit can provide a function for sharing the generated music on social media. In this way, the providing unit can provide the generated music to a user.

[0067] The reception unit can estimate the user's emotions and adjust the timing of input reception based on the estimated user's emotions. For example, when the user is feeling stressed, the reception unit delays the timing of input reception to provide the user with time to relax. For example, when the user is feeling stressed, the reception unit temporarily stops receiving input to allow the user to relax. Furthermore, when the user is relaxed, the reception unit can immediately accept input to enable smooth operation. For example, when the user is relaxed, the reception unit quickly accepts input to allow the user to operate smoothly. Furthermore, when the user is in a hurry, the reception unit can accelerate the timing of input reception to respond quickly. For example, when the user is in a hurry, the reception unit quickly accepts input to allow the user to operate quickly. This allows the reception unit to adjust the timing of input reception based on the user's emotions.

[0068] The reception unit can analyze the user's past input history and select an input method. For example, the reception unit preferentially suggests an input method (such as voice or text) that the user has frequently used in the past. For example, if the user has frequently used voice input in the past, the reception unit preferentially suggests voice input. The reception unit can also predict and suggest an input method to be used in a specific time period based on the user's past input history. For example, if the user has frequently used text input in a specific time period, the reception unit suggests text input for that time period. Furthermore, the reception unit can also suggest a similar input method by referring to content that the user has input in the past. For example, if the user has input information related to a specific topic in the past, the reception unit suggests an input method related to that topic. In this way, the reception unit can select the optimal input method based on the user's past input history.

[0069] The reception unit can perform filtering based on the user's current situation and areas of interest when receiving input. For example, when the user inputs their current situation, the reception unit prioritizes displaying related information. For example, if the user inputs "currently traveling," the reception unit prioritizes displaying information related to travel. The reception unit can also filter the input content based on the user's areas of interest and provide appropriate information. For example, if the user inputs that they are interested in "music," the reception unit prioritizes displaying information related to music. Furthermore, the reception unit can automatically adjust the input content according to the user's current situation. For example, if the user inputs "at work," the reception unit prioritizes displaying information related to work. This allows the reception unit to filter the input content based on the user's current situation and areas of interest.

[0070] The reception unit can estimate the user's emotions and determine the priority of inputs to be received based on the estimated user's emotions. For example, when the user is feeling stressed, the reception unit prioritizes receiving important inputs. For example, when the user is feeling stressed, the reception unit prioritizes receiving important inputs, allowing the user to respond quickly. The reception unit can also prioritize receiving normal inputs when the user is relaxed. For example, when the user is relaxed, the reception unit prioritizes receiving normal inputs, allowing the user to operate smoothly. Furthermore, when the user is in a hurry, the reception unit can also prioritize receiving urgent inputs. For example, when the user is in a hurry, the reception unit prioritizes receiving urgent inputs, allowing the user to respond quickly. This allows the reception unit to determine the priority of inputs based on the user's emotions.

[0071] When receiving an input, the reception unit can prioritize receiving highly relevant input taking into account the user's geographical location information. For example, when the user is in a specific location, the reception unit prioritizes receiving input related to that location. For example, when the user is in a tourist spot, the reception unit prioritizes receiving information related to that tourist spot. Furthermore, when the user is traveling, the reception unit can also prioritize receiving appropriate input based on the user's current location. For example, when the user searches for "nearby restaurants" while traveling, the reception unit prioritizes receiving nearby restaurant information based on the user's current location. Furthermore, when the user is in a specific region, the reception unit can also prioritize receiving information related to that region. For example, when the user is in a specific city, the reception unit prioritizes receiving information related to that city. This allows the reception unit to prioritize receiving highly relevant input based on the user's geographical location information.

[0072] The reception unit can analyze the user's social media activity when receiving input and receive related input. For example, the reception unit preferentially receives related input based on information shared by the user on social media. For example, if the user shares on social media that they "listened to a new music album," the reception unit preferentially receives information related to that album. The reception unit can also analyze topics of interest from the user's social media activity and receive appropriate input. For example, if the user frequently posts about "travel" on social media, the reception unit preferentially receives information related to travel. Furthermore, the reception unit can also receive related input based on the content of the user's social media posts. For example, if the user posts about "cooking" on social media, the reception unit preferentially receives information related to cooking. This allows the reception unit to receive related input based on the user's social media activity.

[0073] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user's emotions. For example, when the user is relaxed, the analysis unit performs a detailed analysis. For example, when the user is relaxed, the analysis unit performs a detailed analysis to gain a deep understanding of the user's emotions and events. The analysis unit can also perform a simplified analysis when the user is in a hurry. For example, when the user is in a hurry, the analysis unit performs a simplified analysis to quickly provide results. Furthermore, when the user is feeling stressed, the analysis unit can select an analysis method to reduce stress. For example, when the user is feeling stressed, the analysis unit selects an analysis method to reduce stress and stabilize the user's emotions. This allows the analysis unit to adjust the analysis method based on the user's emotions.

[0074] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the input information. For example, the analysis unit performs a detailed analysis on important information. For example, if the information input by the user is important, the analysis unit performs a detailed analysis to increase the accuracy of the information. The analysis unit can also perform a normal analysis on general information. For example, if the information input by the user is general, the analysis unit performs a normal analysis to quickly provide a result. Furthermore, the analysis unit can also perform a quick and detailed analysis on urgent information. For example, if the information input by the user is urgent, the analysis unit performs a quick and detailed analysis to quickly respond. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the input information.

[0075] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the input information. For example, the analysis unit applies an emotion analysis algorithm to information regarding emotions. For example, if the information input by the user is related to emotions, the analysis unit applies an emotion analysis algorithm to accurately analyze the emotions. The analysis unit can also apply an event analysis algorithm to information regarding events. For example, if the information input by the user is related to events, the analysis unit applies an event analysis algorithm to accurately analyze the events. Furthermore, the analysis unit can select an appropriate analysis algorithm for other information. For example, if the information input by the user is other, the analysis unit selects an appropriate analysis algorithm to accurately analyze the information. This allows the analysis unit to apply an appropriate analysis algorithm depending on the category of the input information.

[0076] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, when the user is feeling stressed, the analysis unit prioritizes the analysis of important information. For example, when the user is feeling stressed, the analysis unit prioritizes the analysis of important information, allowing the user to respond quickly. The analysis unit can also prioritize the analysis of normal information when the user is relaxed. For example, when the user is relaxed, the analysis unit prioritizes the analysis of normal information, allowing the user to operate smoothly. Furthermore, the analysis unit can also prioritize the analysis of urgent information when the user is in a hurry. For example, when the user is in a hurry, the analysis unit prioritizes the analysis of urgent information, allowing the user to respond quickly. This allows the analysis unit to determine the priority of analysis based on the user's emotions.

[0077] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the input information. The analysis unit, for example, prioritizes analysis of recently submitted information. For example, it prioritizes analysis of information recently input by the user and provides results quickly. The analysis unit can also prioritize analysis of urgent information. For example, when a user inputs urgent information, the analysis unit prioritizes analysis of that information and responds quickly. Furthermore, the analysis unit can also postpone analysis of information submitted earlier. For example, it prioritizes analysis of recent information, leaving information input by the user in the past as the latter. This allows the analysis unit to determine the priority of analysis based on the time of submission of the input information.

[0078] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the input information. The analysis unit, for example, prioritizes analysis of highly relevant information. For example, if information input by a user is highly relevant to other information, the analysis unit prioritizes analysis of that information. The analysis unit can also postpone analysis of less relevant information. For example, if information input by a user is less relevant to other information, the analysis unit postpones analysis of that information. Furthermore, the analysis unit can group and analyze relevant information. For example, if information input by a user includes multiple pieces of relevant information, the analysis unit groups and analyzes the pieces of information. This allows the analysis unit to adjust the order of analysis based on the relevance of the input information.

[0079] The generation unit can estimate the user's emotions and adjust the expression method of the generated music based on the estimated user's emotions. For example, when the user is relaxed, the generation unit generates a calm melody. For example, when the user is relaxed, the generation unit generates a calm melody to stabilize the user's emotions. Furthermore, when the user is excited, the generation unit can generate an up-tempo melody. For example, when the user is excited, the generation unit generates an up-tempo melody to uplift the user's emotions. Furthermore, when the user is sad, the generation unit can generate a melancholic melody. For example, when the user is sad, the generation unit generates a melancholic melody to soothe the user's emotions. This allows the generation unit to adjust the expression method of the generated music based on the user's emotions.

[0080] The generation unit can adjust the level of detail of the music based on the input emotion or importance of the event during generation. For example, the generation unit generates detailed music for an important event. For example, if the event input by the user is important, the generation unit generates detailed music to reflect the importance of the event. The generation unit can also generate normal music for a general event. For example, if the event input by the user is general, the generation unit generates normal music to quickly provide a result. Furthermore, the generation unit can also generate fast and detailed music for an urgent event. For example, if the event input by the user is urgent, the generation unit generates fast and detailed music to quickly respond. In this way, the generation unit can adjust the level of detail of the music based on the input emotion or importance of the event.

[0081] During generation, the generation unit can apply different generation algorithms depending on the category of the input emotion or event. For example, the generation unit applies an emotion generation algorithm to information about emotions. For example, if the information input by the user is related to emotions, the generation unit applies an emotion generation algorithm to generate music that accurately reflects the emotion. The generation unit can also apply an event generation algorithm to information about events. For example, if the information input by the user is related to events, the generation unit applies an event generation algorithm to generate music that accurately reflects the event. Furthermore, the generation unit can select an appropriate generation algorithm for other information. For example, if the information input by the user is other information, the generation unit selects an appropriate generation algorithm to generate music that accurately reflects the information. This allows the generation unit to apply an appropriate generation algorithm depending on the category of the input emotion or event.

[0082] The generation unit can estimate the user's emotions and adjust the length of the music to be generated based on the estimated user's emotions. For example, when the user is relaxed, the generation unit generates longer music. For example, when the user is relaxed, the generation unit generates longer music to stabilize the user's emotions. Furthermore, when the user is in a hurry, the generation unit can generate shorter music. For example, when the user is in a hurry, the generation unit generates shorter music to quickly reflect the user's emotions. Furthermore, when the user is excited, the generation unit can generate music of an appropriate length. For example, when the user is excited, the generation unit generates music of an appropriate length to elevate the user's emotions. This allows the generation unit to adjust the length of the music to be generated based on the user's emotions.

[0083] During generation, the generation unit can determine the priority of music based on the submission date of the input emotions and events. The generation unit, for example, preferentially generates music based on recent emotions and events. For example, the generation unit preferentially generates music based on emotions and events recently input by the user, and quickly provides results. The generation unit can also preferentially generate music based on urgent emotions and events. For example, if the user inputs urgent emotions or events, the generation unit preferentially generates music based on that information and responds quickly. Furthermore, the generation unit can also generate music by putting emotions and events that were submitted earlier later. For example, emotions and events input by the user in the past are put later, and recent information is preferentially generated. This allows the generation unit to determine the priority of music based on the submission date of the input emotions and events.

[0084] The generation unit can adjust the order of music based on the relevance of the input emotions and events during generation. The generation unit, for example, preferentially generates music based on highly related emotions and events. For example, if the emotions or events input by the user are highly related to other information, the generation unit preferentially generates music based on that information. The generation unit can also postpone generating music for emotions or events with low relevance. For example, if the emotions or events input by the user are low related to other information, the generation unit postpones generating music for that information. Furthermore, the generation unit can also generate music by grouping related emotions or events. For example, if the emotions or events input by the user include multiple pieces of related information, the generation unit groups the information and generates music. This allows the generation unit to adjust the order of music based on the relevance of the input emotions and events.

[0085] The providing unit can estimate the user's emotions and adjust the music providing method based on the estimated user's emotions. For example, when the user is relaxed, the providing unit provides calm music. For example, when the user is relaxed, the providing unit provides calm music to stabilize the user's emotions. Furthermore, when the user is excited, the providing unit can also provide up-tempo music. For example, when the user is excited, the providing unit provides up-tempo music to uplift the user's emotions. Furthermore, when the user is sad, the providing unit can also provide melancholic music. For example, when the user is sad, the providing unit provides melancholic music to soothe the user's emotions. This allows the providing unit to adjust the music providing method based on the user's emotions.

[0086] The providing unit can adjust the level of detail of the provided information based on the importance of the generated music when providing the information. For example, the providing unit provides detailed information for important music. For example, if music generated by a user is important, the providing unit provides detailed information about the music so that the user can understand the background and intent of the music. The providing unit can also provide normal information for general music. For example, if the music generated by a user is general, the providing unit provides normal information about the music and quickly provides a result. Furthermore, the providing unit can also quickly provide detailed information for urgent music. For example, if the music generated by a user is urgent, the providing unit quickly provides detailed information about the music and quickly responds. This allows the providing unit to adjust the level of detail of the provided information based on the importance of the generated music.

[0087] The providing unit can apply different providing algorithms depending on the category of the generated music when providing the music. For example, the providing unit applies an emotion providing algorithm to music related to emotions. For example, if the music generated by the user is related to emotions, the providing unit applies an emotion providing algorithm to provide music that accurately reflects the emotions. The providing unit can also apply an event providing algorithm to music related to events. For example, if the music generated by the user is related to events, the providing unit applies an event providing algorithm to provide music that accurately reflects the events. Furthermore, the providing unit can select an appropriate providing algorithm for other music. For example, if the music generated by the user is other, the providing unit selects an appropriate providing algorithm to provide music that accurately reflects the information. This allows the providing unit to apply an appropriate providing algorithm depending on the category of the generated music.

[0088] The providing unit can estimate the user's emotions and adjust the order in which music is provided based on the estimated user's emotions. For example, when the user is relaxed, the providing unit preferentially provides calm music. For example, when the user is relaxed, the providing unit preferentially provides calm music to stabilize the user's emotions. Furthermore, when the user is excited, the providing unit can preferentially provide up-tempo music. For example, when the user is excited, the providing unit preferentially provides up-tempo music to uplift the user's emotions. Furthermore, when the user is sad, the providing unit can preferentially provide melancholic music. For example, when the user is sad, the providing unit preferentially provides melancholic music to soothe the user's emotions. In this way, the providing unit can adjust the order in which music is provided based on the user's emotions.

[0089] The providing unit can determine the priority of providing the generated music at the time of providing the music based on the time of submission of the generated music. The providing unit, for example, prioritizes providing recently generated music. For example, music recently generated by a user is prioritized and results are provided quickly. The providing unit can also prioritize providing urgent music. For example, if music generated by a user is urgent, the providing unit prioritizes providing the music and responds quickly. Furthermore, the providing unit can also postpone providing music that was submitted earlier. For example, music generated by a user in the past is postponed and recent music is prioritized. This allows the providing unit to determine the priority of providing the music based on the time of submission of the generated music.

[0090] The providing unit can adjust the order of providing the generated music based on the relevance of the music when providing the music. The providing unit, for example, prioritizes providing highly relevant music. For example, if music generated by a user is highly relevant to other information, the providing unit prioritizes providing the music. The providing unit can also provide less relevant music later. For example, if music generated by a user is less relevant to other information, the providing unit provides the music later. Furthermore, the providing unit can group related music and provide it. For example, if music generated by a user includes multiple pieces of related information, the providing unit groups the music and provides it. This allows the providing unit to adjust the order of providing the generated music based on the relevance of the music.

[0091] The sharing unit can estimate the user's emotions and select music to share based on the estimated user's emotions. For example, when the user is relaxed, the sharing unit shares calm music. For example, when the user is relaxed, the sharing unit shares calm music to stabilize the user's emotions. Furthermore, when the user is excited, the sharing unit can also share upbeat music. For example, when the user is excited, the sharing unit shares upbeat music to uplift the user's emotions. Furthermore, when the user is sad, the sharing unit can also share melancholic music. For example, when the user is sad, the sharing unit shares melancholic music to soothe the user's emotions. In this way, the sharing unit can select music to share based on the user's emotions.

[0092] The sharing unit can adjust the level of detail of sharing based on the importance of the generated music when sharing. For example, the sharing unit shares detailed information for important music. For example, if music generated by a user is important, the sharing unit shares detailed information about the music so that others can understand the background and intent of the music. The sharing unit can also share ordinary information for general music. For example, if music generated by a user is general, the sharing unit shares ordinary information about the music to provide a quick result. Furthermore, the sharing unit can quickly share detailed information for urgent music. For example, if music generated by a user is urgent, the sharing unit quickly shares detailed information about the music to provide a quick response. In this way, the sharing unit can adjust the level of detail of sharing based on the importance of the generated music.

[0093] The sharing unit can estimate the user's emotions and determine a sharing priority based on the estimated user's emotions. For example, when the user is relaxed, the sharing unit prioritizes sharing calm music. For example, when the user is relaxed, the sharing unit prioritizes sharing calm music to stabilize the user's emotions. Furthermore, when the user is excited, the sharing unit can also prioritize sharing upbeat music. For example, when the user is excited, the sharing unit prioritizes sharing upbeat music to uplift the user's emotions. Furthermore, when the user is sad, the sharing unit can also prioritize sharing melancholic music. For example, when the user is sad, the sharing unit prioritizes sharing melancholic music to soothe the user's emotions. In this way, the sharing unit can determine a sharing priority based on the user's emotions.

[0094] The sharing unit can determine a priority for sharing based on the time of submission of the generated music when sharing. For example, the sharing unit prioritizes sharing of recently generated music. For example, music recently generated by a user is prioritized and shared to provide a quick result. The sharing unit can also prioritize sharing of urgent music. For example, if music generated by a user is urgent, the sharing unit prioritizes sharing of that music to respond quickly. Furthermore, the sharing unit can also share music that was submitted earlier later. For example, music generated by a user in the past is prioritized and recent music is shared preferentially. In this way, the sharing unit can determine a priority for sharing based on the time of submission of the generated music.

[0095] The storage unit can estimate the user's emotions and select music to be stored based on the estimated user's emotions. For example, when the user is relaxed, the storage unit stores calm music. For example, when the user is relaxed, the storage unit stores calm music to stabilize the user's emotions. Furthermore, when the user is excited, the storage unit can store upbeat music. For example, when the user is excited, the storage unit stores upbeat music to uplift the user's emotions. Furthermore, when the user is sad, the storage unit can store melancholic music. For example, when the user is sad, the storage unit stores melancholic music to soothe the user's emotions. In this way, the storage unit can select music to be stored based on the user's emotions.

[0096] The storage unit may adjust the level of detail of the stored data based on the importance of the generated music when storing the data. For example, the storage unit stores detailed information for important music. For example, if music generated by a user is important, the storage unit stores detailed information about the music to allow the user to understand the background and intent of the music. The storage unit may also store general information for general music. For example, if music generated by a user is general, the storage unit stores general information about the music to provide a quick result. The storage unit may also store quick and detailed information for urgent music. For example, if music generated by a user is urgent, the storage unit stores quick and detailed information about the music to provide a quick response. In this way, the storage unit may adjust the level of detail of the stored data based on the importance of the generated music.

[0097] The storage unit can estimate the user's emotions and determine a storage priority based on the estimated user's emotions. For example, when the user is relaxed, the storage unit preferentially stores calm music. For example, when the user is relaxed, the storage unit preferentially stores calm music to stabilize the user's emotions. Furthermore, when the user is excited, the storage unit can preferentially store upbeat music. For example, when the user is excited, the storage unit preferentially stores upbeat music to uplift the user's emotions. Furthermore, when the user is sad, the storage unit can preferentially store melancholic music. For example, when the user is sad, the storage unit preferentially stores melancholic music to soothe the user's emotions. In this way, the storage unit can determine a storage priority based on the user's emotions.

[0098] When saving, the storage unit can determine a priority for saving based on the time of submission of the created music. For example, the storage unit prioritizes saving recently created music. For example, the storage unit prioritizes saving music recently created by a user to provide results quickly. The storage unit can also prioritize saving urgent music. For example, if music created by a user is urgent, the storage unit prioritizes saving that music to respond quickly. Furthermore, the storage unit can also postpone saving music that was submitted earlier. For example, the storage unit prioritizes saving recent music over music created in the past. This allows the storage unit to determine a priority for saving based on the time of submission of the created music. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, provision unit, sharing unit, and storage unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives input from a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the received information. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates music based on the analyzed information. The provision unit is realized by the control unit 46A of the smart device 14 and provides the generated music to the user. The sharing unit is realized by the control unit 46A of the smart device 14 and shares the generated music with others. The storage unit is realized by the specific processing unit 290 of the data processing device 12 and stores the generated music. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, provision unit, sharing unit, and storage unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives input from a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the received information. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates music based on the analyzed information. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides the generated music to the user. The sharing unit is realized by the control unit 46A of the smart glasses 214 and shares the generated music with others. The storage unit is realized by the specific processing unit 290 of the data processing device 12 and stores the generated music. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, provision unit, sharing unit, and storage unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives input from a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the received information. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates music based on the analyzed information. The provision unit is realized by the control unit 46A of the headset type terminal 314 and provides the generated music to the user. The sharing unit is realized by the control unit 46A of the headset type terminal 314 and shares the generated music with others. The storage unit is realized by the specific processing unit 290 of the data processing device 12 and stores the generated music. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, provision unit, sharing unit, and storage unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives input from a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the received information. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates music based on the analyzed information. The provision unit is realized by the control unit 46A of the robot 414 and provides the generated music to the user. The sharing unit is realized by the control unit 46A of the robot 414 and shares the generated music with others. The storage unit is realized by the specific processing unit 290 of the data processing device 12 and stores the generated music.

[0099] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0100] The music generation system may further include a health monitoring unit that monitors the user's health condition. The health monitoring unit measures the user's heart rate and stress level and provides the data to the analysis unit. For example, if the user's heart rate is high, the analysis unit may determine that the user is feeling stressed and generate relaxing music. Alternatively, if the user's stress level is low, the analysis unit may determine that the user is relaxed and generate lively music. Furthermore, the health monitoring unit may analyze the user's sleep patterns and provide appropriate music. This allows the music generation system to generate and provide music based on the user's health condition.

[0101] The music generation system may further include a hobby learning unit that learns the user's hobbies and interests. The hobby learning unit analyzes information previously input by the user and music generated by the user to identify the user's hobbies and interests. For example, if the user has generated a lot of rock music in the past, the hobby learning unit may determine that the user is interested in rock music and prioritize generating rock music. Also, if the user is interested in a particular instrument, the hobby learning unit may generate music using that instrument. Furthermore, the hobby learning unit may respond to changes in the user's interests and generate music based on the user's new hobbies and interests. This allows the music generation system to generate and provide music based on the user's hobbies and interests.

[0102] The music generation system may further include a social relationship analysis unit that considers the user's social relationships. The social relationship analysis unit analyzes the user's social media activity and contact information to identify the user's social relationships. For example, if the user frequently interacts with a particular friend, music related to that friend may be generated. Also, if the user values ​​relationships with family, music related to family may be generated. Furthermore, the social relationship analysis unit may respond to changes in the user's social relationships and generate music based on the new relationships. This allows the music generation system to generate and provide music based on the user's social relationships.

[0103] The music generation system may further include an environmental sound acquisition unit that captures the user's environmental sounds. The environmental sound acquisition unit acquires sounds around the user through a microphone and provides the sounds to the analysis unit. For example, if the user is in nature, the environmental sound acquisition unit can acquire the chirping of birds and the sound of the wind and generate music that evokes a sense of nature based on the sounds. If the user is in an urban area, the environmental sound acquisition unit can acquire the sounds of cars and people talking and generate music that reflects the urban atmosphere based on the sounds. Furthermore, the environmental sound acquisition unit can respond to changes in the user's environment and generate music based on the new environment. This allows the music generation system to generate and provide music based on the user's environmental sounds.

[0104] The music generation system may further include an activity monitoring unit that monitors the user's activity level. The activity monitoring unit measures the user's number of steps and amount of exercise and provides the data to the analysis unit. For example, if the user has recorded a large number of steps, the analysis unit may determine that the user is active and generate energetic music. Alternatively, if the user is exercising, the analysis unit may determine that the user is exercising and generate music with a rhythm suitable for exercise. Furthermore, the activity monitoring unit may respond to changes in the user's activity level and generate music based on the new activity level. This allows the music generation system to generate and provide music based on the user's activity level.

[0105] The music generation system can further estimate the user's emotion and adjust the tempo of the music based on the estimated emotion. For example, if the user is relaxed, the generation unit can generate music with a slow tempo. If the user is excited, the generation unit can generate music with a fast tempo. If the user is sad, the generation unit can generate music with a moderate tempo. In this way, the music generation system can adjust and provide the tempo of the music based on the user's emotion.

[0106] The music generation system can further estimate the user's emotion and adjust the volume of the music based on the estimated emotion. For example, if the user is relaxed, the generation unit can generate music at a lower volume. If the user is excited, the generation unit can generate music at a higher volume. If the user is sad, the generation unit can generate music at a moderate volume. In this way, the music generation system can adjust and provide the volume of music based on the user's emotion.

[0107] The music generation system can further estimate the user's emotion and select a music genre based on the estimated emotion. For example, if the user is relaxed, the generation unit can generate classical music. If the user is excited, the generation unit can generate rock music. If the user is sad, the generation unit can generate a ballad. In this way, the music generation system can select and provide a music genre based on the user's emotion.

[0108] The music generation system can further estimate the user's emotion and adjust the instrumental composition of the music based on the estimated emotion. For example, if the user is relaxed, the generation unit can generate music mainly composed of piano and string instruments. If the user is excited, the generation unit can generate music mainly composed of guitar and drums. If the user is sad, the generation unit can generate music mainly composed of flute and cello. In this way, the music generation system can adjust and provide the instrumental composition of the music based on the user's emotion.

[0109] The music generation system can further estimate the user's emotion and adjust the key of the music based on the estimated emotion. For example, if the user is relaxed, the generation unit can generate music in a major key. If the user is excited, the generation unit can generate music in a minor key. If the user is sad, the generation unit can generate music in a diminished key. This allows the music generation system to adjust and provide the key of the music based on the user's emotion.

[0110] The processing flow of the second embodiment will be briefly explained below.

[0111] Step 1: The reception unit receives input from the user. The input from the user includes text input, voice input, image input, and the like. For example, the user can input daily events and emotions in text format, input emotions and events by voice, or even input emotions and events using images. Images taken with a smartphone camera can also be received as input. Step 2: The analysis unit analyzes the information received by the reception unit. It uses text analysis technology, voice analysis technology, and image analysis technology to analyze the information entered by the user. For example, text information is analyzed using natural language processing technology to extract emotions and events. Voice information is converted into text using voice recognition technology and then analyzed using natural language processing technology. Image information is analyzed using image recognition technology to extract emotions and events from images. Step 3: The generation unit generates music based on the information analyzed by the analysis unit. Using the generation AI, it selects melodies, rhythms, and instruments based on the user's emotions and events, and generates the music. For example, a bright and lively melody can be generated based on the user's input emotion of "I had fun," while a melancholic melody can be generated based on the user's input emotion of "Something sad happened." Step 4: The providing unit provides the music generated by the generating unit. The generated music can be provided to the user in streaming or download format. The providing unit also provides a function for sharing the generated music with others. For example, the provided function provides a function for sharing the generated music on social media.

[0112] 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.

[0113] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0114] 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.

[0115] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0130] 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.

[0131] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0132] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0133] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0146] 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.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0163] 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.

[0164] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] [Explanation of symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input from a user; an analysis unit that analyzes the information received by the reception unit; a generation unit that generates music based on the information analyzed by the analysis unit; a providing unit that provides the music generated by the generating unit; Equipped with A system characterized by:

2. It has a sharing section for sharing the created music with others.

2. The system of claim 1.

3. Equipped with a storage unit for storing the generated music 2. The system of claim 1.

4. The generation unit Generate music by selecting melodies, rhythms, and instruments based on input emotions and events 2. The system of claim 1.

5. The providing unit Providing the generated music to the user 2. The system of claim 1.

6. The reception unit Estimate the user's emotions and adjust the timing of input acceptance based on the estimated user emotions.

2. The system of claim 1.

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

8. The reception unit As input is received, it filters it based on the user's current situation and interests.

2. The system of claim 1.

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