system

Abstract

The system according to the embodiment aims to generate original lyrics and melodies based on keywords entered by the user. [Solution] A system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives keyword input from a user. The generation unit analyzes the keywords received by the reception unit and generates lyrics and a melody. The provision unit provides the lyrics and melody generated by the generation unit to the user.

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 technology has had the problem of making it difficult to generate original lyrics and melodies based on keywords desired by the user.

[0005] The system according to the embodiment aims to generate original lyrics and melodies based on keywords entered by the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives keyword input from a user. The generation unit analyzes the keywords received by the reception unit and generates lyrics and a melody. The provision unit provides the lyrics and melody generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can generate original lyrics and melodies based on keywords entered by the user. [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 uses a generation AI to create original lyrics and melodies based on keywords entered by a user. This music generation system allows a user to input keywords such as "poppy," analyzes the keywords, generates lyrics and melodies, and provides them to the user. For example, if a user inputs the keyword "poppy, summer memories," the generation AI analyzes the keywords and generates bright, cheerful lyrics and melodies. These generated lyrics and melodies are original to the user and unique to them, making them unique and different from other users. Users can enjoy their own original music, for example, by playing the generated music on their smartphones or computers or sharing it with friends. This system allows users to easily create original music, even without special musical knowledge or skills, by easily creating music that matches their desired atmosphere or theme, making it an appealing system for many people. This allows the music generation system to generate and provide original lyrics and melodies based on user keyword input.

[0029] A music generation system according to an embodiment includes a receiving unit, a generating unit, and a providing unit. The receiving unit receives keyword input from a user. The user can input a desired atmosphere or theme, such as a "poppy feel" or an "uplifting song." For example, if a user inputs the keyword "poppy feel, summer memories," this information is input to the generation AI. The generating unit uses the generating AI to analyze the keywords received by the receiving unit and generate lyrics and a melody. The generating AI understands the meaning of the keywords and related themes and generates lyrics and a melody based on them. For example, the generating AI generates bright and cheerful lyrics and a melody based on the keyword "poppy feel, summer memories." Some or all of the above-described processing in the generating unit is performed using the generating AI. The providing unit provides the lyrics and melody generated by the generating unit to a user. The providing unit can provide the generated music as a download link or as a streaming playback. For example, the providing unit provides the generated music so that it can be played on a smartphone or a PC. In this way, the music generation system according to an embodiment can generate and provide original lyrics and a melody based on the user's keyword input.

[0030] The generation unit can analyze keywords using a generation AI and generate lyrics and a melody. For example, the generation unit uses a generation AI to analyze keywords entered by a user. The generation AI understands the meaning of the keywords and related themes, and generates lyrics and a melody based on that. For example, the generation AI generates bright and cheerful lyrics and a melody based on the keywords "poppy, summer memories." The generation AI can also use natural language processing technology to analyze the meaning of keywords. For example, the generation AI performs grammatical and semantic analysis of keywords and generates lyrics and a melody based on that. This makes it possible to generate lyrics and a melody based on keywords using the generation AI. Some or all of the above-mentioned processing in the generation unit is performed using the generation AI.

[0031] The generation unit can use generation AI to understand the meaning of keywords and related themes and generate lyrics and melodies based on that. For example, the generation unit uses generation AI to understand the meaning of keywords entered by a user and related themes. The generation AI can use semantic networks and context analysis to analyze the meaning of keywords. For example, the generation AI can generate bright and cheerful lyrics and melodies based on the keywords "poppy, summer memories." The generation AI can also use topic modeling and relevance scores to identify related themes. For example, the generation AI can evaluate the relevance of keywords and generate lyrics and melodies based on that. This allows for understanding the meaning of keywords and related themes, making it possible to generate more appropriate lyrics and melodies. Some or all of the above-mentioned processing in the generation unit is performed using generation AI.

[0032] The providing unit can provide the generated lyrics and melody to a user. For example, the providing unit provides the lyrics and melody generated by the generating unit to a user. The providing unit can provide the generated music as a download link or as streaming playback. For example, the providing unit provides the generated music so that it can be played on a smartphone or a PC. The providing unit can also provide an interface for displaying the generated music to a user. For example, the providing unit provides visual effects and interface design for visually displaying the generated music. By providing the generated lyrics and melody to a user, the user can enjoy an original music piece. Some or all of the above-mentioned processing in the providing unit is performed using AI.

[0033] The providing unit can provide the generated song as a download link. For example, the providing unit provides the generated song as a download link. The providing unit generates a URL for downloading the generated song and provides the link to the user. For example, the providing unit can send the download link for the generated song by email. The providing unit can also display the download link for the generated song on a webpage. For example, the providing unit displays a link for the user to download the song on a webpage, allowing the user to download the song by clicking the link. In this way, by providing the generated song as a download link, the user can download and enjoy the song. Some or all of the above-mentioned processing in the providing unit is performed using AI.

[0034] The providing unit can provide the generated music as a streaming playback. For example, the providing unit provides the generated music as a streaming playback. The providing unit uses a streaming protocol for playing the generated music in real time. For example, the providing unit streams the generated music to a user's device, allowing the user to play the music in real time. The providing unit also has a function for adjusting the quality of the streaming playback. For example, the providing unit automatically adjusts the quality of the streaming playback according to the user's network environment to provide an optimal playback experience. As a result, by providing the generated music as a streaming playback, the user can enjoy the music in real time. Some or all of the above-mentioned processing in the providing unit is performed using AI.

[0035] The reception unit can analyze the user's past keyword input history and select the optimal input method. The reception unit, for example, uses data mining technology to analyze the user's past keyword input history. For example, the reception unit collects data on keywords previously input by the user and analyzes that data. The reception unit can also suggest the optimal input method based on the user's input history. For example, the reception unit automatically displays keywords frequently used by the user in the past as candidates. The reception unit can also prioritize suggesting input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest keywords that will be used in a specific time period based on the user's past input history. In this way, the optimal input method can be provided to the user by analyzing the user's past keyword input history. Some or all of the above-mentioned processing in the reception unit is performed using AI.

[0036] When a keyword is entered, the reception unit can filter the keywords based on the user's current mood and areas of interest. The reception unit, for example, uses a questionnaire or behavioral analysis to identify the user's mood. For example, the reception unit may conduct a simple questionnaire to identify the user's current mood. The reception unit can also identify the user's mood by analyzing the user's behavioral data. For example, the reception unit may estimate the user's current mood based on the user's past behavioral data. Furthermore, the reception unit can analyze the user's past search history and social media activity to identify the user's areas of interest. For example, the reception unit may identify the user's areas of interest based on keywords the user has previously searched for and activities on social media. This allows the reception unit to filter keywords based on the user's current mood and areas of interest. For example, if the user desires an uplifting song, the reception unit may prioritize positive keywords. Furthermore, if the user wants to relax, the reception unit may suggest keywords with a calming atmosphere. Furthermore, if the user is interested in a particular theme, the reception unit may display keywords related to that theme. This allows more appropriate keywords to be provided by filtering keywords based on the user's mood and areas of interest. Some or all of the above-described processing in the reception unit is performed using AI.

[0037] When a keyword is input, the reception unit can prioritize acquiring highly relevant keywords by taking into account the user's geographical location information. The reception unit uses, for example, GPS data or an IP address to acquire the user's geographical location information. For example, the reception unit acquires GPS data from the user's device to identify the user's current geographical location. The reception unit can also identify the user's geographical location by analyzing the user's IP address. This allows the reception unit to prioritize acquiring highly relevant keywords by taking into account the user's geographical location information. For example, if the user is at the beach, the reception unit prioritizes displaying keywords related to the sea. Furthermore, if the user is in an urban area, the reception unit suggests keywords with an urban theme. Furthermore, if the user is in a mountainous area, the reception unit prioritizes displaying keywords related to nature. This allows highly relevant keywords to be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the reception unit is performed using AI.

[0038] When a keyword is input, the reception unit can analyze the user's social media activity and acquire related keywords. The reception unit, for example, uses data mining technology to analyze the user's social media activity. For example, the reception unit can analyze content recently shared by the user on social media and suggest related keywords. The reception unit can also analyze the content posted by accounts the user follows and display related keywords. The reception unit can also suggest keywords based on topics in online communities in which the user participates. In this way, the reception unit can acquire related keywords by analyzing the user's social media activity. For example, the reception unit can suggest related keywords based on content recently shared by the user on social media. The reception unit can also analyze the content posted by accounts the user follows and display related keywords. The reception unit can also suggest keywords based on topics in online communities in which the user participates. In this way, the reception unit can acquire related keywords by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reception unit is performed using AI.

[0039] The generation unit can adjust the level of detail of the lyrics and melody based on the importance of the keywords during generation. The generation unit, for example, uses frequency analysis or the user's interest level to evaluate the importance of the keywords. For example, the generation unit analyzes the frequency of keywords entered by the user and evaluates their importance. The generation unit can also evaluate the importance of keywords based on the user's interest level. For example, the generation unit evaluates the importance of current keywords based on data of keywords entered by the user in the past. This allows the generation unit to adjust the level of detail of the lyrics and melody based on the importance of the keywords. For example, the generation unit generates detailed lyrics and melodies based on important keywords. The generation unit also generates simple lyrics and melodies based on keywords with low importance. Furthermore, the generation unit adjusts the length of the lyrics and the complexity of the melody according to the importance of the keywords. This allows the generation of more appropriate music by adjusting the level of detail of the lyrics and melody based on the importance of the keywords. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI.

[0040] During generation, the generation unit can apply different generation algorithms depending on the keyword category. The generation unit uses, for example, topic classification or genre classification to identify the keyword category. For example, the generation unit topically classifies keywords entered by a user and identifies their category. The generation unit can also perform genre classification of keywords and identify their category. This allows the generation unit to apply different generation algorithms depending on the keyword category. For example, for a pop keyword, the generation unit applies a generation algorithm for pop music. For a classical keyword, the generation unit applies a generation algorithm for classical music. For a rock keyword, the generation unit applies a generation algorithm for rock music. This allows more appropriate music to be generated by applying different generation algorithms depending on the keyword category. Some or all of the above-mentioned processing in the generation unit is performed using generation AI.

[0041] During generation, the generation unit can determine the generation priority based on the time when the keywords were input. The generation unit uses, for example, a timestamp or input history to identify the time when the keywords were input. For example, the generation unit records the timestamp of the keywords input by the user and identifies the time when the keywords were input. The generation unit can also identify the time when the keywords were input by analyzing the user's past input history. This allows the generation unit to determine the generation priority based on the time when the keywords were input. For example, recently input keywords can be processed with priority. Keywords input during a specific time period can also be processed with priority. Keywords frequently input by the user can also be processed with priority. This allows more appropriate music to be generated by determining the generation priority based on the time when the keywords were input. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI.

[0042] The generation unit can adjust the order of lyrics and melody based on keyword relevance during generation. The generation unit, for example, uses context analysis and relevance scores to evaluate keyword relevance. For example, the generation unit analyzes the context of keywords entered by the user and evaluates their relevance. The generation unit can also evaluate keyword relevance based on the relevance score. This allows the generation unit to adjust the order of lyrics and melody based on keyword relevance. For example, the generation unit determines the order of lyrics based on highly relevant keywords. The generation unit can also adjust the order of melodies based on less relevant keywords. Furthermore, the generation unit can adjust the overall composition of lyrics and melody according to keyword relevance. This allows a more appropriate song to be generated by adjusting the order of lyrics and melody based on keyword relevance. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI.

[0043] When providing music, the providing unit can select the optimal delivery method by referring to the user's past music usage history. The providing unit, for example, uses data mining technology to analyze the user's past music usage history. For example, the providing unit collects a history of songs the user has played in the past and analyzes that data. The providing unit can also suggest the optimal delivery method based on the user's past download history. This allows the providing unit to select the optimal delivery method by referring to the user's past music usage history. For example, the providing unit can prioritize the delivery method (downloading, streaming, etc.) that the user has preferred in the past. The providing unit can also suggest the optimal delivery method for a specific time period based on the user's past usage history. Furthermore, the providing unit can analyze the user's past usage history and suggest the most efficient delivery method. This allows the optimal delivery method to be selected by referring to the user's past music usage history. Some or all of the above-mentioned processing in the providing unit is performed using AI.

[0044] The providing unit can customize the means of provision based on the user's current device information at the time of provision. The providing unit, for example, identifies the device type and OS version to obtain the user's device information. For example, the providing unit identifies whether the user's device is a smartphone, a tablet, or a PC. The providing unit can also identify the OS version of the user's device. This allows the providing unit to customize the means of provision based on the user's current device information. For example, if the user is using a smartphone, the providing unit proposes a provision method optimized for smartphones. Also, if the user is using a tablet, the providing unit proposes a provision method optimized for tablets. Furthermore, if the user is using a PC, the providing unit proposes a provision method optimized for PCs. This enables more appropriate provision by customizing the means of provision based on the user's device information. Some or all of the above-mentioned processing in the providing unit is performed using AI.

[0045] The providing unit can select the optimal delivery method by taking into account the user's geographical location information when providing the content. The providing unit, for example, uses GPS data or an IP address to acquire the user's geographical location information. For example, the providing unit acquires GPS data from the user's device to identify the user's current geographical location. The providing unit can also identify the user's geographical location by analyzing the user's IP address. This allows the providing unit to select the optimal delivery method by taking into account the user's geographical location information. For example, if the user is at home, the providing unit suggests high-quality streaming using Wi-Fi. Furthermore, if the user is out, the providing unit suggests downloading to save data. Furthermore, if the user is in a specific location, the providing unit provides music related to that location. This allows the optimal delivery method to be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the providing unit is performed using AI.

[0046] The providing unit can analyze the user's social media activity and suggest a means of provision when providing the content. The providing unit, for example, uses data mining technology to analyze the user's social media activity. For example, the providing unit can suggest related songs based on songs recently shared by the user on social media. The providing unit can also prioritize providing new songs by artists the user follows. Furthermore, the providing unit can also suggest songs based on topics in online communities in which the user participates. This allows the providing unit to analyze the user's social media activity and suggest a means of provision. For example, the providing unit can suggest related songs based on songs recently shared by the user on social media. The providing unit can also prioritize providing new songs by artists the user follows. Furthermore, the providing unit can suggest songs based on topics in online communities in which the user participates. This allows the providing unit to suggest a more appropriate means of provision by analyzing the user's social media activity. Some or all of the above-mentioned processing in the providing unit is performed using AI.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The reception unit can automatically search for related images and videos based on keywords entered by the user and provide them along with the generated music. For example, if a user enters the keyword "summer memories," the reception unit can search the Internet for images and videos related to summer scenery and memories and display them along with the generated music. The reception unit can also suggest more personalized images and videos based on the user's past search history and preferences. This allows the user to enjoy the music visually and enriches the music experience. Furthermore, the reception unit can also provide a function for the user to edit the provided images and videos and add comments. This allows users to create and share their own original music and visual content.

[0049] The generation unit can adjust the tempo and rhythm of a piece of music based on keywords entered by the user. For example, if the user enters the keyword "I want to relax," the generation unit can generate music with a slow tempo. On the other hand, if the user enters the keyword "I feel energetic," the generation unit can generate music with a fast tempo. Furthermore, the generation unit can analyze the user's past music generation history and learn the user's preferred tempo and rhythm. This allows the generation unit to generate music that matches the user's preferences. For example, the generation unit can suggest optimal tempos and rhythms for the next music generation based on the tempos and rhythms of music previously generated by the user.

[0050] The providing unit can adjust the generated music in real time based on user feedback. For example, if the user provides feedback such as "I want it to be brighter" while the music is being played, the providing unit can adjust the melody and tempo of the music to make it brighter. Also, if the user provides feedback such as "I want it to be a little quieter," the providing unit can adjust the volume of the music or the selection of instruments to make it quieter. Furthermore, the providing unit can learn the user's feedback and reflect it in the next music generation. This makes it possible to provide music that suits the user's preferences.

[0051] The providing unit can adjust the method of providing the generated music based on the remaining battery level of the user's device. For example, if the remaining battery level of the user's device is low, the providing unit can suggest low-quality streaming to save data communication volume. Also, if the remaining battery level of the user's device is sufficient, the providing unit can suggest high-quality streaming. Furthermore, if the remaining battery level of the user's device is very low, the providing unit can suggest downloading the music and recommend playing it offline. In this way, the optimal providing method can be selected based on the remaining battery level of the user's device.

[0052] The providing unit can adjust the method of providing the generated music based on the user's current activity. For example, if the user is exercising, the providing unit can stream energetic music. If the user is relaxing, the providing unit can provide a download link for calm music. Furthermore, if the user is working, the providing unit can suggest music that will help improve concentration. This allows the optimal method of providing music to be selected based on the user's current activity.

[0053] The providing unit can adjust the method of providing the generated music piece based on the screen size of the user's device. For example, if the user's device is a smartphone, the providing unit can provide an interface optimized for the smartphone. Also, if the user's device is a tablet, the providing unit can provide an interface optimized for the tablet. Furthermore, if the user's device is a personal computer, the providing unit can also provide an interface optimized for the personal computer. This makes it possible to select the optimal method of providing the music piece based on the screen size of the user's device.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The reception unit receives keyword input from the user. The user can input the desired atmosphere or theme, such as "a pop feel" or "an uplifting song." Step 2: The generation unit uses the generation AI to analyze the keywords received by the reception unit and generate lyrics and a melody. The generation AI understands the meaning of the keywords and related themes, and generates lyrics and a melody based on that. For example, based on the keywords "poppy, summer memories," it generates bright and cheerful lyrics and a melody. Step 3: The providing unit provides the lyrics and melody generated by the generating unit to the user. The providing unit can provide the generated music as a download link or as a streaming playback. For example, the providing unit provides the generated music so that it can be played on a smartphone or a PC.

[0056] (Example 2) A music generation system according to an embodiment of the present invention uses a generation AI to create original lyrics and melodies based on keywords entered by a user. This music generation system allows a user to input keywords such as "poppy," analyzes the keywords, generates lyrics and melodies, and provides them to the user. For example, if a user inputs the keyword "poppy, summer memories," the generation AI analyzes the keywords and generates bright, cheerful lyrics and melodies. These generated lyrics and melodies are original to the user and unique to them, making them unique and different from other users. Users can enjoy their own original music, for example, by playing the generated music on their smartphones or computers or sharing it with friends. This system allows users to easily create original music, even without special musical knowledge or skills, by easily creating music that matches their desired atmosphere or theme, making it an appealing system for many people. This allows the music generation system to generate and provide original lyrics and melodies based on user keyword input.

[0057] A music generation system according to an embodiment includes a receiving unit, a generating unit, and a providing unit. The receiving unit receives keyword input from a user. The user can input a desired atmosphere or theme, such as a "poppy feel" or an "uplifting song." For example, if a user inputs the keyword "poppy feel, summer memories," this information is input to the generation AI. The generating unit uses the generating AI to analyze the keywords received by the receiving unit and generate lyrics and a melody. The generating AI understands the meaning of the keywords and related themes and generates lyrics and a melody based on them. For example, the generating AI generates bright and cheerful lyrics and a melody based on the keyword "poppy feel, summer memories." Some or all of the above-described processing in the generating unit is performed using the generating AI. The providing unit provides the lyrics and melody generated by the generating unit to a user. The providing unit can provide the generated music as a download link or as a streaming playback. For example, the providing unit provides the generated music so that it can be played on a smartphone or a PC. In this way, the music generation system according to an embodiment can generate and provide original lyrics and a melody based on the user's keyword input.

[0058] The generation unit can analyze keywords using a generation AI and generate lyrics and a melody. For example, the generation unit uses a generation AI to analyze keywords entered by a user. The generation AI understands the meaning of the keywords and related themes, and generates lyrics and a melody based on that. For example, the generation AI generates bright and cheerful lyrics and a melody based on the keywords "poppy, summer memories." The generation AI can also use natural language processing technology to analyze the meaning of keywords. For example, the generation AI performs grammatical and semantic analysis of keywords and generates lyrics and a melody based on that. This makes it possible to generate lyrics and a melody based on keywords using the generation AI. Some or all of the above-mentioned processing in the generation unit is performed using the generation AI.

[0059] The generation unit can use generation AI to understand the meaning of keywords and related themes and generate lyrics and melodies based on that. For example, the generation unit uses generation AI to understand the meaning of keywords entered by a user and related themes. The generation AI can use semantic networks and context analysis to analyze the meaning of keywords. For example, the generation AI can generate bright and cheerful lyrics and melodies based on the keywords "poppy, summer memories." The generation AI can also use topic modeling and relevance scores to identify related themes. For example, the generation AI can evaluate the relevance of keywords and generate lyrics and melodies based on that. This allows for understanding the meaning of keywords and related themes, making it possible to generate more appropriate lyrics and melodies. Some or all of the above-mentioned processing in the generation unit is performed using generation AI.

[0060] The providing unit can provide the generated lyrics and melody to a user. For example, the providing unit provides the lyrics and melody generated by the generating unit to a user. The providing unit can provide the generated music as a download link or as streaming playback. For example, the providing unit provides the generated music so that it can be played on a smartphone or a PC. The providing unit can also provide an interface for displaying the generated music to a user. For example, the providing unit provides visual effects and interface design for visually displaying the generated music. By providing the generated lyrics and melody to a user, the user can enjoy an original music piece. Some or all of the above-mentioned processing in the providing unit is performed using AI.

[0061] The providing unit can provide the generated song as a download link. For example, the providing unit provides the generated song as a download link. The providing unit generates a URL for downloading the generated song and provides the link to the user. For example, the providing unit can send the download link for the generated song by email. The providing unit can also display the download link for the generated song on a webpage. For example, the providing unit displays a link for the user to download the song on a webpage, allowing the user to download the song by clicking the link. In this way, by providing the generated song as a download link, the user can download and enjoy the song. Some or all of the above-mentioned processing in the providing unit is performed using AI.

[0062] The providing unit can provide the generated music as a streaming playback. For example, the providing unit provides the generated music as a streaming playback. The providing unit uses a streaming protocol for playing the generated music in real time. For example, the providing unit streams the generated music to a user's device, allowing the user to play the music in real time. The providing unit also has a function for adjusting the quality of the streaming playback. For example, the providing unit automatically adjusts the quality of the streaming playback according to the user's network environment to provide an optimal playback experience. As a result, by providing the generated music as a streaming playback, the user can enjoy the music in real time. Some or all of the above-mentioned processing in the providing unit is performed using AI.

[0063] The reception unit can estimate the user's emotion and adjust the timing of keyword input based on the estimated user's emotion. The reception unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The reception unit can also estimate the emotion by analyzing the user's voice. For example, the reception unit can analyze the tone and speed of the user's voice to estimate the emotion. The reception unit can also estimate the emotion by analyzing the user's text input. For example, the reception unit can analyze the content of the text input by the user to estimate the emotion. This allows the reception unit to adjust the timing of keyword input based on the user's emotion. For example, if the user is excited, the reception unit can quickly display the input screen to allow the user to input keywords quickly. On the other hand, if the user is relaxed, the reception unit can display the input screen at a slower pace to allow the user to input keywords calmly. Furthermore, if the user is feeling stressed, the reception unit can provide a simple and intuitive input screen to minimize the effort required for input. This allows the keyword input timing to be adjusted according to the user's emotion, allowing the keyword to be input at a more appropriate timing. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit is performed using AI.

[0064] The reception unit can analyze the user's past keyword input history and select the optimal input method. The reception unit, for example, uses data mining technology to analyze the user's past keyword input history. For example, the reception unit collects data on keywords previously input by the user and analyzes that data. The reception unit can also suggest the optimal input method based on the user's input history. For example, the reception unit automatically displays keywords frequently used by the user in the past as candidates. The reception unit can also prioritize suggesting input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest keywords that will be used in a specific time period based on the user's past input history. In this way, the optimal input method can be provided to the user by analyzing the user's past keyword input history. Some or all of the above-mentioned processing in the reception unit is performed using AI.

[0065] When a keyword is entered, the reception unit can filter the keywords based on the user's current mood and areas of interest. The reception unit, for example, uses a questionnaire or behavioral analysis to identify the user's mood. For example, the reception unit may conduct a simple questionnaire to identify the user's current mood. The reception unit can also identify the user's mood by analyzing the user's behavioral data. For example, the reception unit may estimate the user's current mood based on the user's past behavioral data. Furthermore, the reception unit can analyze the user's past search history and social media activity to identify the user's areas of interest. For example, the reception unit may identify the user's areas of interest based on keywords the user has previously searched for and activities on social media. This allows the reception unit to filter keywords based on the user's current mood and areas of interest. For example, if the user desires an uplifting song, the reception unit may prioritize positive keywords. Furthermore, if the user wants to relax, the reception unit may suggest keywords with a calming atmosphere. Furthermore, if the user is interested in a particular theme, the reception unit may display keywords related to that theme. This allows more appropriate keywords to be provided by filtering keywords based on the user's mood and areas of interest. Some or all of the above-described processing in the reception unit is performed using AI.

[0066] The reception unit can estimate the user's emotions and prioritize the input keywords based on the estimated user emotions. The reception unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the reception unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression recognition algorithm. The reception unit can also estimate the emotions by analyzing the user's voice. For example, the reception unit analyzes the tone and speed of the user's voice to estimate the emotions. The reception unit can also estimate the emotions by analyzing the user's text input. For example, the reception unit analyzes the content of the text input by the user to estimate the emotions. This allows the reception unit to prioritize the input keywords based on the user's emotions. For example, if the user is excited, the reception unit prioritizes energetic keywords. On the other hand, if the user is relaxed, the reception unit prioritizes calm keywords. Furthermore, if the user is stressed, the reception unit prioritizes keywords that are useful for stress relief. Thus, by prioritizing keywords based on the user's emotions, more appropriate keywords can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit is performed using AI.

[0067] When a keyword is input, the reception unit can prioritize acquiring highly relevant keywords by taking into account the user's geographical location information. The reception unit uses, for example, GPS data or an IP address to acquire the user's geographical location information. For example, the reception unit acquires GPS data from the user's device to identify the user's current geographical location. The reception unit can also identify the user's geographical location by analyzing the user's IP address. This allows the reception unit to prioritize acquiring highly relevant keywords by taking into account the user's geographical location information. For example, if the user is at the beach, the reception unit prioritizes displaying keywords related to the sea. Furthermore, if the user is in an urban area, the reception unit suggests keywords with an urban theme. Furthermore, if the user is in a mountainous area, the reception unit prioritizes displaying keywords related to nature. This allows highly relevant keywords to be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the reception unit is performed using AI.

[0068] When a keyword is input, the reception unit can analyze the user's social media activity and acquire related keywords. The reception unit, for example, uses data mining technology to analyze the user's social media activity. For example, the reception unit can analyze content recently shared by the user on social media and suggest related keywords. The reception unit can also analyze the content posted by accounts the user follows and display related keywords. The reception unit can also suggest keywords based on topics in online communities in which the user participates. In this way, the reception unit can acquire related keywords by analyzing the user's social media activity. For example, the reception unit can suggest related keywords based on content recently shared by the user on social media. The reception unit can also analyze the content posted by accounts the user follows and display related keywords. The reception unit can also suggest keywords based on topics in online communities in which the user participates. In this way, the reception unit can acquire related keywords by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reception unit is performed using AI.

[0069] The generation unit can estimate the user's emotions and adjust the way the lyrics and melody are expressed based on the estimated user's emotions. The generation unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the generation unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression recognition algorithm. The generation unit can also estimate the emotions by analyzing the user's voice. For example, the generation unit analyzes the tone and speed of the user's voice to estimate the emotions. The generation unit can also estimate the emotions by analyzing the user's text input. For example, the generation unit analyzes the content of the text input by the user to estimate the emotions. This allows the generation unit to adjust the way the lyrics and melody are expressed based on the user's emotions. For example, if the user is relaxed, the generation unit generates a calm melody and lyrics. If the user is excited, the generation unit generates an energetic melody and lyrics. If the user is sad, the generation unit generates a melody and lyrics that comforts the user's emotions. This allows a more appropriate song to be generated by adjusting the way the lyrics and melody are expressed based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the generation unit is performed using AI.

[0070] The generation unit can adjust the level of detail of the lyrics and melody based on the importance of the keywords during generation. The generation unit, for example, uses frequency analysis or the user's interest level to evaluate the importance of the keywords. For example, the generation unit analyzes the frequency of keywords entered by the user and evaluates their importance. The generation unit can also evaluate the importance of keywords based on the user's interest level. For example, the generation unit evaluates the importance of current keywords based on data of keywords entered by the user in the past. This allows the generation unit to adjust the level of detail of the lyrics and melody based on the importance of the keywords. For example, the generation unit generates detailed lyrics and melodies based on important keywords. The generation unit also generates simple lyrics and melodies based on keywords with low importance. Furthermore, the generation unit adjusts the length of the lyrics and the complexity of the melody according to the importance of the keywords. This allows the generation of more appropriate music by adjusting the level of detail of the lyrics and melody based on the importance of the keywords. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI.

[0071] During generation, the generation unit can apply different generation algorithms depending on the keyword category. The generation unit uses, for example, topic classification or genre classification to identify the keyword category. For example, the generation unit topically classifies keywords entered by a user and identifies their category. The generation unit can also perform genre classification of keywords and identify their category. This allows the generation unit to apply different generation algorithms depending on the keyword category. For example, for a pop keyword, the generation unit applies a generation algorithm for pop music. For a classical keyword, the generation unit applies a generation algorithm for classical music. For a rock keyword, the generation unit applies a generation algorithm for rock music. This allows more appropriate music to be generated by applying different generation algorithms depending on the keyword category. Some or all of the above-mentioned processing in the generation unit is performed using generation AI.

[0072] The generation unit can estimate the user's emotions and adjust the length of the lyrics and melody based on the estimated user's emotions. The generation unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the generation unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression recognition algorithm. The generation unit can also estimate the emotions by analyzing the user's voice. For example, the generation unit analyzes the tone and speed of the user's voice to estimate the emotions. The generation unit can also estimate the emotions by analyzing the user's text input. For example, the generation unit analyzes the content of the text input by the user to estimate the emotions. This allows the generation unit to adjust the length of the lyrics and melody based on the user's emotions. For example, if the user is in a hurry, the generation unit generates short lyrics and a melody. On the other hand, if the user is relaxed, the generation unit generates longer lyrics and a melody. Furthermore, if the user is emotional, the generation unit generates lyrics and a melody of an appropriate length to express the emotions. This allows a more appropriate song to be generated by adjusting the length of the lyrics and a melody based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the generation unit is performed using AI.

[0073] During generation, the generation unit can determine the generation priority based on the time when the keywords were input. The generation unit uses, for example, a timestamp or input history to identify the time when the keywords were input. For example, the generation unit records the timestamp of the keywords input by the user and identifies the time when the keywords were input. The generation unit can also identify the time when the keywords were input by analyzing the user's past input history. This allows the generation unit to determine the generation priority based on the time when the keywords were input. For example, recently input keywords can be processed with priority. Keywords input during a specific time period can also be processed with priority. Keywords frequently input by the user can also be processed with priority. This allows more appropriate music to be generated by determining the generation priority based on the time when the keywords were input. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI.

[0074] The generation unit can adjust the order of lyrics and melody based on keyword relevance during generation. The generation unit, for example, uses context analysis and relevance scores to evaluate keyword relevance. For example, the generation unit analyzes the context of keywords entered by the user and evaluates their relevance. The generation unit can also evaluate keyword relevance based on the relevance score. This allows the generation unit to adjust the order of lyrics and melody based on keyword relevance. For example, the generation unit determines the order of lyrics based on highly relevant keywords. The generation unit can also adjust the order of melodies based on less relevant keywords. Furthermore, the generation unit can adjust the overall composition of lyrics and melody according to keyword relevance. This allows a more appropriate song to be generated by adjusting the order of lyrics and melody based on keyword relevance. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI.

[0075] The providing unit can estimate the user's emotions and adjust the display method of the music to be provided based on the estimated user's emotions. The providing unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the providing unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression recognition algorithm. The providing unit can also estimate the emotions by analyzing the user's voice. For example, the providing unit analyzes the tone and speed of the user's voice to estimate the emotions. Furthermore, the providing unit can also estimate the emotions by analyzing the user's text input. For example, the providing unit analyzes the content of the text input by the user to estimate the emotions. This allows the providing unit to adjust the display method of the music to be provided based on the user's emotions. For example, if the user is relaxed, the providing unit provides a display method with calm colors. Also, if the user is excited, the providing unit provides a display method with bright and energetic colors. Furthermore, if the user is sad, the providing unit provides a display method with subdued colors. This allows the music to be displayed more appropriately by adjusting the display method of the music based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit is performed using AI.

[0076] When providing music, the providing unit can select the optimal delivery method by referring to the user's past music usage history. The providing unit, for example, uses data mining technology to analyze the user's past music usage history. For example, the providing unit collects a history of songs the user has played in the past and analyzes that data. The providing unit can also suggest the optimal delivery method based on the user's past download history. This allows the providing unit to select the optimal delivery method by referring to the user's past music usage history. For example, the providing unit can prioritize the delivery method (downloading, streaming, etc.) that the user has preferred in the past. The providing unit can also suggest the optimal delivery method for a specific time period based on the user's past usage history. Furthermore, the providing unit can analyze the user's past usage history and suggest the most efficient delivery method. This allows the optimal delivery method to be selected by referring to the user's past music usage history. Some or all of the above-mentioned processing in the providing unit is performed using AI.

[0077] The providing unit can customize the means of provision based on the user's current device information at the time of provision. The providing unit, for example, identifies the device type and OS version to obtain the user's device information. For example, the providing unit identifies whether the user's device is a smartphone, a tablet, or a PC. The providing unit can also identify the OS version of the user's device. This allows the providing unit to customize the means of provision based on the user's current device information. For example, if the user is using a smartphone, the providing unit proposes a provision method optimized for smartphones. Also, if the user is using a tablet, the providing unit proposes a provision method optimized for tablets. Furthermore, if the user is using a PC, the providing unit proposes a provision method optimized for PCs. This enables more appropriate provision by customizing the means of provision based on the user's device information. Some or all of the above-mentioned processing in the providing unit is performed using AI.

[0078] The providing unit can estimate the user's emotions and prioritize songs to provide based on the estimated user's emotions. The providing unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the providing unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression recognition algorithm. The providing unit can also estimate the emotions by analyzing the user's voice. For example, the providing unit analyzes the tone and speed of the user's voice to estimate the emotions. Furthermore, the providing unit can also estimate the emotions by analyzing the user's text input. For example, the providing unit analyzes the content of the text input by the user to estimate the emotions. This allows the providing unit to prioritize songs to provide based on the user's emotions. For example, if the user is relaxed, the providing unit prioritizes calm songs to provide. Furthermore, if the user is excited, the providing unit prioritizes energetic songs to provide. Furthermore, if the user is sad, the providing unit prioritizes songs that comfort the user. This allows more appropriate songs to be provided by prioritizing songs based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit is performed using AI.

[0079] The providing unit can select the optimal delivery method by taking into account the user's geographical location information when providing the content. The providing unit, for example, uses GPS data or an IP address to acquire the user's geographical location information. For example, the providing unit acquires GPS data from the user's device to identify the user's current geographical location. The providing unit can also identify the user's geographical location by analyzing the user's IP address. This allows the providing unit to select the optimal delivery method by taking into account the user's geographical location information. For example, if the user is at home, the providing unit suggests high-quality streaming using Wi-Fi. Furthermore, if the user is out, the providing unit suggests downloading to save data. Furthermore, if the user is in a specific location, the providing unit provides music related to that location. This allows the optimal delivery method to be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the providing unit is performed using AI.

[0080] The providing unit can analyze the user's social media activity and suggest a means of provision when providing the content. The providing unit, for example, uses data mining technology to analyze the user's social media activity. For example, the providing unit can suggest related songs based on songs recently shared by the user on social media. The providing unit can also prioritize providing new songs by artists the user follows. Furthermore, the providing unit can also suggest songs based on topics in online communities in which the user participates. This allows the providing unit to analyze the user's social media activity and suggest a means of provision. For example, the providing unit can suggest related songs based on songs recently shared by the user on social media. The providing unit can also prioritize providing new songs by artists the user follows. Furthermore, the providing unit can suggest songs based on topics in online communities in which the user participates. This allows the providing unit to suggest a more appropriate means of provision by analyzing the user's social media activity. Some or all of the above-mentioned processing in the providing unit is performed using AI. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, generation unit, and provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives a keyword input from a user using a touch panel 38A or a microphone 38B of the smart device 14. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the keyword using a generation AI to generate lyrics and a melody. The provision unit provides the generated music to the user using, for example, the output device 40 of the smart device 14. The provision unit can also provide the music generated by the specific processing unit 290 of the data processing device 12 as a download link or streaming playback. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives a user's keyword input using the microphone 238 of the smart glasses 214. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the keywords using a generation AI to generate lyrics and a melody. The provision unit provides the generated music to the user using, for example, the speaker 240 of the smart glasses 214. The provision unit can also provide the music generated by the specific processing unit 290 of the data processing device 12 as a download link or streaming playback. === Hard Collateral 1-3 === Each of the multiple elements including the above-described reception unit, generation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit receives a keyword input from a user using the microphone 238 of the headset-type terminal 314. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the keyword using a generation AI to generate lyrics and a melody. The provision unit provides the generated music to the user using, for example, the speaker 240 of the headset-type terminal 314. The provision unit can also provide the music generated by the specific processing unit 290 of the data processing device 12 as a download link or streaming playback. === Hard Collateral 1-4 === Each of the multiple elements including the above-described reception unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives a keyword input from a user using the microphone 238 of the robot 414. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the keyword using a generation AI to generate lyrics and a melody. The provision unit provides the generated music to the user using, for example, the speaker 240 of the robot 414. The provision unit can also provide the music generated by the specific processing unit 290 of the data processing device 12 as a download link or streaming playback.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The reception unit can automatically search for related images and videos based on keywords entered by the user and provide them along with the generated music. For example, if a user enters the keyword "summer memories," the reception unit can search the Internet for images and videos related to summer scenery and memories and display them along with the generated music. The reception unit can also suggest more personalized images and videos based on the user's past search history and preferences. This allows the user to enjoy the music visually and enriches the music experience. Furthermore, the reception unit can also provide a function for the user to edit the provided images and videos and add comments. This allows users to create and share their own original music and visual content.

[0083] The generation unit can adjust the tempo and rhythm of a piece of music based on keywords entered by the user. For example, if the user enters the keyword "I want to relax," the generation unit can generate music with a slow tempo. On the other hand, if the user enters the keyword "I feel energetic," the generation unit can generate music with a fast tempo. Furthermore, the generation unit can analyze the user's past music generation history and learn the user's preferred tempo and rhythm. This allows the generation unit to generate music that matches the user's preferences. For example, the generation unit can suggest optimal tempos and rhythms for the next music generation based on the tempos and rhythms of music previously generated by the user.

[0084] The generation unit can estimate the user's emotions and select instruments based on the estimated emotions. For example, if the user is relaxed, the generation unit can select calm instruments such as an acoustic guitar or piano. If the user is excited, the generation unit can select energetic instruments such as an electric guitar or drums. Furthermore, if the user is sad, the generation unit can select instruments that soothe emotions such as a violin or cello. In this way, by selecting instruments based on the user's emotions, more appropriate music can be generated. Emotions are estimated using facial expression recognition technology and voice analysis technology.

[0085] The providing unit can adjust the generated music in real time based on user feedback. For example, if the user provides feedback such as "I want it to be brighter" while the music is being played, the providing unit can adjust the melody and tempo of the music to make it brighter. Also, if the user provides feedback such as "I want it to be a little quieter," the providing unit can adjust the volume of the music or the selection of instruments to make it quieter. Furthermore, the providing unit can learn the user's feedback and reflect it in the next music generation. This makes it possible to provide music that suits the user's preferences.

[0086] The providing unit can adjust the method of providing the generated music based on the remaining battery level of the user's device. For example, if the remaining battery level of the user's device is low, the providing unit can suggest low-quality streaming to save data communication volume. Also, if the remaining battery level of the user's device is sufficient, the providing unit can suggest high-quality streaming. Furthermore, if the remaining battery level of the user's device is very low, the providing unit can suggest downloading the music and recommend playing it offline. In this way, the optimal providing method can be selected based on the remaining battery level of the user's device.

[0087] The reception unit can estimate the user's emotions and provide keyword input support based on the estimated emotions. For example, if the user is relaxed, the reception unit can suggest calm keywords. If the user is excited, the reception unit can suggest energetic keywords. Furthermore, if the user is sad, the reception unit can also suggest comforting keywords. In this way, keyword input support based on the user's emotions allows the user to enter more appropriate keywords. Emotions are estimated using facial expression recognition technology and voice analysis technology.

[0088] The generation unit can 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 lower key. If the user is excited, the generation unit can generate music in a higher key. Furthermore, if the user is sad, the generation unit can generate music in an intermediate key. In this way, by adjusting the key of the music based on the user's emotion, more appropriate music can be generated. Emotion estimation is performed using facial expression recognition technology and voice analysis technology.

[0089] The providing unit can adjust the method of providing the generated music based on the user's current activity. For example, if the user is exercising, the providing unit can stream energetic music. If the user is relaxing, the providing unit can provide a download link for calm music. Furthermore, if the user is working, the providing unit can suggest music that will help improve concentration. This allows the optimal method of providing music to be selected based on the user's current activity.

[0090] The providing unit can estimate the user's emotions and adjust the playback order of songs based on the estimated emotions. For example, if the user is relaxed, the providing unit can play calm songs first. If the user is excited, the providing unit can play energetic songs first. Furthermore, if the user is sad, the providing unit can play comforting songs first. In this way, by adjusting the playback order of songs based on the user's emotions, more appropriate songs can be provided. Emotion estimation is performed using facial expression recognition technology and voice analysis technology.

[0091] The providing unit can adjust the method of providing the generated music piece based on the screen size of the user's device. For example, if the user's device is a smartphone, the providing unit can provide an interface optimized for the smartphone. Also, if the user's device is a tablet, the providing unit can provide an interface optimized for the tablet. Furthermore, if the user's device is a personal computer, the providing unit can also provide an interface optimized for the personal computer. This makes it possible to select the optimal method of providing the music piece based on the screen size of the user's device.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The reception unit receives keyword input from the user. The user can input the desired atmosphere or theme, such as "a pop feel" or "an uplifting song." Step 2: The generation unit uses the generation AI to analyze the keywords received by the reception unit and generate lyrics and a melody. The generation AI understands the meaning of the keywords and related themes, and generates lyrics and a melody based on that. For example, based on the keywords "poppy, summer memories," it generates bright and cheerful lyrics and a melody. Step 3: The providing unit provides the lyrics and melody generated by the generating unit to the user. The providing unit can provide the generated music as a download link or as a streaming playback. For example, the providing unit provides the generated music so that it can be played on a smartphone or a PC.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] [Explanation of symbols]

[0166] 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 a keyword input from a user; a generating unit that analyzes the keywords received by the receiving unit and generates lyrics and a melody; a providing unit that provides the lyrics and melody generated by the generating unit to a user. A system characterized by:

2. The generation unit Generative AI analyzes keywords and generates lyrics and melodies 2. The system of claim 1.

3. The generation unit Generative AI understands the meaning of keywords and related themes, and generates lyrics and melodies based on them.

2. The system of claim 1.

4. The providing unit Providing the generated lyrics and melody to the user 2. The system of claim 1.

5. The providing unit Provide the generated song as a download link 2. The system of claim 1.

6. The providing unit Providing the generated music as streaming playback 2. The system of claim 1.

7. The reception unit Estimates the user's emotions and adjusts the timing of keyword input based on the estimated user emotions.

2. The system of claim 1.

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

Images