System

Abstract

An object of a system according to an embodiment is to automatically generate a musical piece based on an atmosphere or an emotion of a photograph and enable a user to easily share or save the musical piece.SOLUTION: A system includes a photograph upload unit, an analysis unit, a music generation unit, and a sharing unit. The photograph upload unit uploads a photograph stored in the user's smartphone. The analysis unit analyzes an atmosphere and a hue of the photograph uploaded by the photograph upload unit, and an emotion and a scene felt from the subject. The musical piece generation unit generates a musical piece on the basis of a result analyzed by the analysis unit. The sharing unit allows the user to share the music generated by the music generation unit on social media or stores the music as a personal commemoration.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is not possible to automatically generate music based on the atmosphere and emotion of a photo, and users must manually select music.

[0005] The system according to the embodiment aims to automatically generate music based on the atmosphere and emotion of a photo, allowing users to easily share or save the music. [Means for solving the problem]

[0006] The system according to the embodiment includes a photo upload unit, an analysis unit, a music generation unit, and a sharing unit. The photo upload unit uploads photos stored on a user's smartphone. The analysis unit analyzes the mood, color, and emotions and scenes perceived from the subject of the photos uploaded by the photo upload unit. The music generation unit generates music based on the results of the analysis by the analysis unit. The sharing unit allows the user to share the music generated by the music generation unit on social media or save it as a personal keepsake. [Effects of the Invention]

[0007] The system according to the embodiment automatically generates music based on the atmosphere and emotion of a photo, allowing users to easily share or save the music. [Brief explanation of the drawings]

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

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

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

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

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

[0013] In the following embodiments, the coded storage is one or more nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) A music generation system according to an embodiment of the present invention is a system that, when a user uploads a photo stored on their smartphone, analyzes the atmosphere, color, and emotions and scenes perceived from the subject of the photo and automatically generates original music that matches the uploaded photo. This allows the music generation system to generate original music based on the user's photos, which can then be shared and saved.

[0029] A music generation system according to an embodiment includes a photo upload unit, an analysis unit, a music generation unit, and a sharing unit. The photo upload unit uploads photos stored on a user's smartphone. For example, the user can select and upload photos from the smartphone's gallery. The photo upload unit can also select and upload photos from cloud storage. The photo upload unit can also upload photos taken in real time using a camera function. The analysis unit analyzes the atmosphere, color, and emotions and scenes perceived from the subjects of the uploaded photos. For example, the analysis unit uses image recognition technology to analyze the color tone and brightness of the photos to infer emotions. The analysis unit can also analyze the shape and placement of the subjects to determine the scene. The analysis unit can also analyze the emotions of the subjects using facial expression analysis technology. The music generation unit generates music based on the results of the analysis by the analysis unit. For example, the music generation unit uses a generation AI to create a genre, melody, harmony, and rhythm that match the analysis results. The music generation unit can also use a generation AI to select instruments that match the atmosphere of the photo. Furthermore, the music generation unit can use the generation AI to set a tempo and key based on the emotion of the photo. The sharing unit allows the user to share the generated music on social media or save it as a personal keepsake. For example, the sharing unit provides a function for posting the generated music to a social networking site. The sharing unit can also provide a function for saving the generated music in cloud storage. The sharing unit can also provide a function for downloading the generated music and saving it locally. This enables the music generation system according to the embodiment to generate, share, and save original music based on a user's photos. For example, the user can post the generated music to a social networking site to share it with friends. The user can also save the generated music as a keepsake of a special event. The user can also download the generated music and enjoy it offline.

[0030] The analysis unit can automatically acquire location information or time information and reflect it in the analysis when analyzing the atmosphere, color, and emotions and scenes perceived from the subject of a photo. For example, the analysis unit can automatically acquire location information using the smartphone's GPS function when uploading a photo and reflect that information in the analysis. For example, music can be generated taking into account the geographical features and cultural background of the location where the photo was taken. The analysis unit can also automatically acquire the date and time the photo was taken and reflect that information in the analysis. For example, the atmosphere of the music can be adjusted based on the time of day or season when the photo was taken. In this way, by reflecting location information and time information in the analysis, more accurate music generation is possible.

[0031] The analysis unit can estimate the background sound of a photo and determine the elements of a song based on that sound. For example, the analysis unit uses an algorithm that allows the generation AI to estimate background sounds when analyzing a photo, generating sounds that match the scene in the photo. For example, the analysis unit can estimate the sound of waves for a photo of the ocean, and birdsong for a photo of a forest. The analysis unit can also analyze the background sound of a photo and determine the elements of a song based on that sound. For example, if the background sound of a photo is quiet, it can generate a calm melody, and if the background sound is lively, it can generate a lively rhythm. This makes it possible to determine the elements of a song based on the background sound, creating a more immersive song.

[0032] The analysis unit can analyze videos taken by the user at the same time as uploading photos and generate music that matches the scenes in the video. For example, the analysis unit can analyze videos taken at the same time as uploading photos and generate music based on the scenes in the video. For example, the analysis unit can analyze the movement and sound of the video to generate dynamic music. The analysis unit can also analyze scenes in the video and generate music that matches the scene. For example, if the video shows an ocean scene, it can generate music that incorporates the sound of waves. By analyzing both photos and videos, a wider variety of music can be generated.

[0033] The analysis unit can automatically generate poems and lyrics relevant to the user based on the results of photo analysis and incorporate them into a song. For example, the analysis unit uses a generation AI to automatically generate poems and lyrics relevant to the user based on the results of photo analysis. For example, for a photo of a sunset, it can generate a poem such as "A beautiful sunset is setting" and incorporate it into a song. The analysis unit can also generate poems and lyrics based on the emotion of the photo. For example, for a photo with a relaxing atmosphere, it can generate calm lyrics and incorporate them into a song. In this way, by automatically generating poems and lyrics based on the results of photo analysis and incorporating them into a song, it is possible to create a song with a more unified feel.

[0034] The music generation unit can dynamically change the tempo or key of the music based on the results of photo analysis, thereby generating a wider variety of music. For example, the music generation unit uses a generation AI to dynamically change the tempo of the music based on the results of photo analysis. For example, it can generate music with a faster tempo for photos with movement and a slower tempo for quiet photos. The music generation unit can also dynamically change the key of the music based on the results of photo analysis. For example, it can generate music with a higher key for photos with a bright atmosphere and a lower key for photos with a calm atmosphere. This makes it possible to dynamically change the tempo and key of the music, thereby generating a wider variety of music.

[0035] The music generation unit can specially design a song's intro or outro based on the results of photo analysis. For example, the music generation unit uses a generative AI to specially design a song's intro based on the results of photo analysis. For example, it generates a quiet intro or a dramatic intro to match the atmosphere of the photo. The music generation unit can also specially design a song's outro based on the results of photo analysis. For example, it can generate an emotional outro or a calm outro based on the emotion of the photo. This allows for the creation of more unique songs by specially designing song intros and outros.

[0036] The music generation unit generates not only music but also environmental sounds or sound effects based on the results of photo analysis, providing a more realistic audio experience. For example, the music generation unit uses a generation AI to generate environmental sounds in addition to music based on the results of photo analysis. For example, birdsong and wind sounds can be added to photos of natural landscapes. The music generation unit can also generate sound effects based on the results of photo analysis. For example, the sounds of cars and people talking can be added to photos of cities. This allows for the generation of environmental sounds and sound effects, providing a more realistic audio experience.

[0037] The music generation unit can automatically generate visual effects or animations for the music based on the results of photo analysis, providing visual enjoyment as well. For example, the music generation unit uses a generation AI to automatically generate visual effects that match the music based on the results of photo analysis. For example, it can display light effects in time with the rhythm of the music. The music generation unit can also automatically generate animations based on the results of photo analysis. For example, it can generate animations in which characters move in time with the melody of the music. This allows for the automatic generation of visual effects and animations to provide visual enjoyment as well.

[0038] The sharing unit can automatically remix the generated music to suit the user's preferences and provide multiple versions. The sharing unit, for example, develops an algorithm that automatically remixes the generated music to suit the user's preferences. For example, the sharing unit can generate multiple versions by changing the tempo or the type of instrument. The sharing unit can also change the arrangement of the music based on the user's preferences. For example, the sharing unit can remix the music to suit the user's preferred genre or style. This allows for more diverse ways of enjoying the music by remixing the music to suit the user's preferences and providing multiple versions.

[0039] The sharing unit allows the generation AI to automatically generate hashtags or captions when a song is shared, promoting its spread on social media. The sharing unit develops an algorithm that allows the generation AI to automatically generate hashtags when a song is shared. For example, the sharing unit may suggest related hashtags based on the genre or theme of the song. The sharing unit also allows the generation AI to automatically generate captions. For example, the sharing unit may create captions based on the content or emotion of the song. This allows the automatic generation of hashtags and captions to promote its spread on social media.

[0040] The sharing unit can provide a function that allows the generated music to be set as a ringtone or alarm sound for the user's smartphone. The sharing unit can, for example, provide a function that allows the generated music to be set as a ringtone for the user's smartphone. For example, a portion of the music can be cut out and used as a ringtone. The sharing unit can also set the generated music as an alarm sound. For example, the intro part of the music can be set as an alarm sound. This allows the generated music to be used in a wider variety of ways by being set as a ringtone or alarm sound.

[0041] The sharing unit can have the generation AI automatically generate a music video when a song is shared, making it enjoyable as visual content. The sharing unit, for example, develops an algorithm that allows the generation AI to automatically generate a music video when a song is shared. For example, it generates video that matches the rhythm and melody of the song. The sharing unit can also have the generation AI create a storyboard based on the content of the song and generate a music video based on that. For example, it can generate video that matches the lyrics of the song. In this way, automatically generated music videos can be enjoyed as visual content.

[0042] When detecting features in a photo, the analysis unit can also analyze the subject's movements or pose and adjust the rhythm and tempo of the music based on that. For example, when the generation AI detects features in a photo, the analysis unit analyzes the subject's movements and adjusts the rhythm of the music based on that. For example, it can generate music with a faster rhythm for moving photos and a slower rhythm for still photos. The analysis unit can also analyze the subject's pose and adjust the tempo of the music based on that. For example, it can generate music with a faster tempo for dynamic poses and a slower tempo for relaxed poses. This makes it possible to generate more dynamic music by analyzing the subject's movements and poses.

[0043] The analysis unit can estimate the subject's age or gender when detecting photo features and customize the style of the music based on that. For example, the analysis unit estimates the subject's age when the generation AI detects photo features and customizes the style of the music based on that. For example, it can generate music in a bright and fun style for photos of children and a more subdued style for photos of adults. The analysis unit can also estimate the subject's gender and customize the style of the music based on that. For example, it can generate a soft melody for photos of women and a powerful melody for photos of men. This makes it possible to generate more personalized music by analyzing the subject's age and gender.

[0044] The analysis unit can also analyze the subject's clothing or accessories when detecting photo features and determine the genre and style of the music based on that. For example, when the generation AI detects photo features, the analysis unit analyzes the subject's clothing and determines the genre of the music based on that. For example, pop music can be generated for casual clothing, and classical music for formal clothing. The analysis unit can also analyze the subject's accessories and determine the style of the music based on that. For example, an energetic style can be generated for sportswear, and an elegant style can be generated for elegant accessories. This makes it possible to generate a wider variety of music by analyzing the subject's clothing and accessories.

[0045] The analysis unit can cause the generation AI to automatically generate related artwork or illustrations based on the results of detecting features in the photo, and provide them as cover art for the song. The analysis unit can cause the generation AI to automatically generate related artwork based on the results of detecting features in the photo. For example, the analysis unit can generate an abstract painting that matches the color and atmosphere of the photo, and provide it as cover art for the song. The analysis unit can also cause the generation AI to generate illustrations based on the results of detecting features in the photo. For example, the analysis unit can generate an illustration based on the subject of the photo, and provide it as cover art for the song. This allows for the automatic generation of related artwork and illustrations, providing visual enjoyment.

[0046] When customizing a song, the music generation unit can refer to the user's past music preference data and provide more personalized songs. For example, the music generation unit uses a generation AI to refer to the user's past music preference data and customize the song based on that. For example, it generates a song that incorporates the user's favorite genres and artist styles. The music generation unit can also adjust elements of the song based on the user's playback history and rating data. For example, it can generate melodies and rhythms that reflect the characteristics of songs that the user has given high ratings. This makes it possible to generate more personalized songs by referring to the user's past music preference data.

[0047] The music generation unit can reflect user feedback in real time when customizing a song and instantly adjust the song. For example, the music generation unit builds a system in which a generation AI reflects user feedback in real time when customizing a song and instantly adjusts the song. For example, if a user wants to change the tempo, this is immediately reflected. The music generation unit can also dynamically change elements of the song based on user feedback. For example, if a user wants to change part of the melody, this can be immediately reflected. This allows the song to be instantly adjusted by reflecting user feedback in real time.

[0048] The music generation unit can recommend related artists and songs based on the instrument or tone selected by the user when customizing a song. For example, the generation AI of the music generation unit recommends related artists based on the instrument or tone selected by the user. For example, if a guitar is selected, guitarist artists will be recommended. The music generation unit can also recommend related songs based on the tone selected by the user. For example, if a piano tone is selected, songs using piano can be recommended. This allows for a more diverse musical experience by recommending related artists and songs based on the instrument or tone selected by the user.

[0049] The music generation unit can provide a function in which the generation AI automatically generates lyrics when customizing a song and the user can edit the lyrics. For example, the music generation unit can provide a function in which the generation AI automatically generates lyrics when customizing a song and the user can edit the lyrics. For example, the music generation unit can allow the user to change part of the generated lyrics. The music generation unit can also allow the user to add or delete lyrics. For example, the user can create lyrics that reflect their own emotions or memories. This allows the user to edit the automatically generated lyrics, making it possible to create more personalized music.

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

[0051] The analysis unit can analyze videos taken by the user at the same time as uploading photos and generate music that matches the scenes in the video. For example, the analysis unit can analyze videos taken at the same time as uploading photos and generate music based on the scenes in the video. For example, the analysis unit can analyze the movement and sound of the video to generate dynamic music. The analysis unit can also analyze scenes in the video and generate music that matches the scene. For example, if the video shows an ocean scene, it can generate music that incorporates the sound of waves. By analyzing both photos and videos, a wider variety of music can be generated.

[0052] The analysis unit can estimate the background sound of a photo and determine the elements of a song based on that sound. For example, the analysis unit uses an algorithm that allows the generation AI to estimate background sounds when analyzing a photo, generating sounds that match the scene in the photo. For example, the analysis unit can estimate the sound of waves for a photo of the ocean, and birdsong for a photo of a forest. The analysis unit can also analyze the background sound of a photo and determine the elements of a song based on that sound. For example, if the background sound of a photo is quiet, it can generate a calm melody, and if the background sound is lively, it can generate a lively rhythm. This makes it possible to determine the elements of a song based on the background sound, creating a more immersive song.

[0053] The analysis unit can also analyze the subject's clothing or accessories when detecting photo features and determine the genre and style of the music based on that. For example, when the generation AI detects photo features, the analysis unit analyzes the subject's clothing and determines the genre of the music based on that. For example, pop music can be generated for casual clothing, and classical music for formal clothing. The analysis unit can also analyze the subject's accessories and determine the style of the music based on that. For example, an energetic style can be generated for sportswear, and an elegant style can be generated for elegant accessories. This makes it possible to generate a wider variety of music by analyzing the subject's clothing and accessories.

[0054] The music generation unit can automatically generate visual effects or animations for the music based on the results of photo analysis, providing visual enjoyment as well. For example, the music generation unit uses a generation AI to automatically generate visual effects that match the music based on the results of photo analysis. For example, it can display light effects in time with the rhythm of the music. The music generation unit can also automatically generate animations based on the results of photo analysis. For example, it can generate animations in which characters move in time with the melody of the music. This allows for the automatic generation of visual effects and animations to provide visual enjoyment as well.

[0055] The music generation unit can recommend related artists and songs based on the instrument or tone selected by the user when customizing a song. For example, the generation AI of the music generation unit recommends related artists based on the instrument or tone selected by the user. For example, if a guitar is selected, guitarist artists will be recommended. The music generation unit can also recommend related songs based on the tone selected by the user. For example, if a piano tone is selected, songs using piano can be recommended. This allows for a more diverse musical experience by recommending related artists and songs based on the instrument or tone selected by the user.

[0056] The music generation unit can provide a function in which the generation AI automatically generates lyrics when customizing a song and the user can edit the lyrics. For example, the music generation unit can provide a function in which the generation AI automatically generates lyrics when customizing a song and the user can edit the lyrics. For example, the music generation unit can allow the user to change part of the generated lyrics. The music generation unit can also allow the user to add or delete lyrics. For example, the user can create lyrics that reflect their own emotions or memories. This allows the user to edit the automatically generated lyrics, making it possible to create more personalized music.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The photo upload unit uploads photos stored on the user's smartphone. For example, the user can select and upload photos from the smartphone's gallery. They can also select and upload photos from cloud storage. They can also upload photos taken in real time using the camera function. Step 2: The analysis unit analyzes the uploaded photo's atmosphere, color, and the emotions and scenes perceived from the subject. For example, image recognition technology can be used to analyze the color tone and brightness of the photo to infer emotions. It can also analyze the shape and placement of the subject to determine the scene. Furthermore, facial expression analysis technology can be used to analyze the subject's emotions. Step 3: The music generation unit generates music based on the results of the analysis by the analysis unit. For example, the generation AI can be used to create a genre, melody, harmony, and rhythm that matches the analysis results. The generation AI can also be used to select instruments that match the atmosphere of the photo. Furthermore, the generation AI can also be used to set the tempo and key based on the emotion of the photo. Step 4: The sharing unit allows the user to share the generated song on social media or save it as a personal keepsake. For example, the sharing unit may provide a function to post the generated song to a social networking site. It may also provide a function to save the song to cloud storage. It may also provide a function to download the generated song and save it locally.

[0059] (Example 2) A music generation system according to an embodiment of the present invention is a system that, when a user uploads a photo stored on their smartphone, analyzes the atmosphere, color, and emotions and scenes perceived from the subject of the photo and automatically generates original music that matches the uploaded photo. This allows the music generation system to generate original music based on the user's photos, which can then be shared and saved.

[0060] A music generation system according to an embodiment includes a photo upload unit, an analysis unit, a music generation unit, and a sharing unit. The photo upload unit uploads photos stored on a user's smartphone. For example, the user can select and upload photos from the smartphone's gallery. The photo upload unit can also select and upload photos from cloud storage. The photo upload unit can also upload photos taken in real time using a camera function. The analysis unit analyzes the atmosphere, color, and emotions and scenes perceived from the subjects of the uploaded photos. For example, the analysis unit uses image recognition technology to analyze the color tone and brightness of the photos to infer emotions. The analysis unit can also analyze the shape and placement of the subjects to determine the scene. The analysis unit can also analyze the emotions of the subjects using facial expression analysis technology. The music generation unit generates music based on the results of the analysis by the analysis unit. For example, the music generation unit uses a generation AI to create a genre, melody, harmony, and rhythm that match the analysis results. The music generation unit can also use a generation AI to select instruments that match the atmosphere of the photo. Furthermore, the music generation unit can use the generation AI to set a tempo and key based on the emotion of the photo. The sharing unit allows the user to share the generated music on social media or save it as a personal keepsake. For example, the sharing unit provides a function for posting the generated music to a social networking site. The sharing unit can also provide a function for saving the generated music in cloud storage. The sharing unit can also provide a function for downloading the generated music and saving it locally. This enables the music generation system according to the embodiment to generate, share, and save original music based on a user's photos. For example, the user can post the generated music to a social networking site to share it with friends. The user can also save the generated music as a keepsake of a special event. The user can also download the generated music and enjoy it offline.

[0061] The analysis unit can automatically acquire location information or time information and reflect it in the analysis when analyzing the atmosphere, color, and emotions and scenes perceived from the subject of a photo. For example, the analysis unit can automatically acquire location information using the smartphone's GPS function when uploading a photo and reflect that information in the analysis. For example, music can be generated taking into account the geographical features and cultural background of the location where the photo was taken. The analysis unit can also automatically acquire the date and time the photo was taken and reflect that information in the analysis. For example, the atmosphere of the music can be adjusted based on the time of day or season when the photo was taken. In this way, by reflecting location information and time information in the analysis, more accurate music generation is possible.

[0062] The analysis unit can estimate the background sound of a photo and determine the elements of a song based on that sound. For example, the analysis unit uses an algorithm that allows the generation AI to estimate background sounds when analyzing a photo, generating sounds that match the scene in the photo. For example, the analysis unit can estimate the sound of waves for a photo of the ocean, and birdsong for a photo of a forest. The analysis unit can also analyze the background sound of a photo and determine the elements of a song based on that sound. For example, if the background sound of a photo is quiet, it can generate a calm melody, and if the background sound is lively, it can generate a lively rhythm. This makes it possible to determine the elements of a song based on the background sound, creating a more immersive song.

[0063] The analysis unit can analyze the user's emotions in real time when uploading photos and reflect those emotions in the music generation. For example, the analysis unit analyzes the user's facial expressions with a camera when uploading photos and estimates the emotions in real time. For example, it generates a cheerful song for a photo of a smiling face and a calm song for a photo of a sad face. The analysis unit can also analyze the user's voice and estimate the emotions in real time. For example, it can analyze the tone and speed of the voice and calculate an emotion score. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and analyze emotions in real time. For example, it can calculate an emotion score based on fluctuations in heart rate. In this way, the user's emotions can be analyzed in real time and reflected in the music generation, enabling more personalized music generation.

[0064] The analysis unit can analyze videos taken by the user at the same time as uploading photos and generate music that matches the scenes in the video. For example, the analysis unit can analyze videos taken at the same time as uploading photos and generate music based on the scenes in the video. For example, the analysis unit can analyze the movement and sound of the video to generate dynamic music. The analysis unit can also analyze scenes in the video and generate music that matches the scene. For example, if the video shows an ocean scene, it can generate music that incorporates the sound of waves. By analyzing both photos and videos, a wider variety of music can be generated.

[0065] The analysis unit can automatically generate poems and lyrics relevant to the user based on the results of photo analysis and incorporate them into a song. For example, the analysis unit uses a generation AI to automatically generate poems and lyrics relevant to the user based on the results of photo analysis. For example, for a photo of a sunset, it can generate a poem such as "A beautiful sunset is setting" and incorporate it into a song. The analysis unit can also generate poems and lyrics based on the emotion of the photo. For example, for a photo with a relaxing atmosphere, it can generate calm lyrics and incorporate them into a song. In this way, by automatically generating poems and lyrics based on the results of photo analysis and incorporating them into a song, it is possible to create a song with a more unified feel.

[0066] The analysis unit can analyze the user's emotions when uploading a photo and suggest filtering or effects for the photo based on the emotions. For example, the analysis unit can analyze the user's emotions when uploading a photo and suggest filtering or effects suitable for the photo based on the emotions. For example, it can suggest a bright filter for positive emotions and a monochrome filter for negative emotions. The analysis unit can also suggest photo effects based on the user's emotions. For example, it can suggest a vivid effect for emotions of joy and a calm effect for emotions of sadness. This allows for more personalized photo editing by suggesting filtering or effects for the photo based on the user's emotions.

[0067] The music generation unit can dynamically change the tempo or key of the music based on the results of photo analysis, thereby generating a wider variety of music. For example, the music generation unit uses a generation AI to dynamically change the tempo of the music based on the results of photo analysis. For example, it can generate music with a faster tempo for photos with movement and a slower tempo for quiet photos. The music generation unit can also dynamically change the key of the music based on the results of photo analysis. For example, it can generate music with a higher key for photos with a bright atmosphere and a lower key for photos with a calm atmosphere. This makes it possible to dynamically change the tempo and key of the music, thereby generating a wider variety of music.

[0068] The music generation unit can specially design a song's intro or outro based on the results of photo analysis. For example, the music generation unit uses a generative AI to specially design a song's intro based on the results of photo analysis. For example, it generates a quiet intro or a dramatic intro to match the atmosphere of the photo. The music generation unit can also specially design a song's outro based on the results of photo analysis. For example, it can generate an emotional outro or a calm outro based on the emotion of the photo. This allows for the creation of more unique songs by specially designing song intros and outros.

[0069] The music generation unit can use the emotion estimation function to estimate the user's emotion from the photo analysis results and adjust the dynamics or expression of the music based on the emotion. The music generation unit, for example, uses the emotion estimation function to estimate the user's emotion from the photo analysis results and adjusts the dynamics of the music based on the emotion. For example, it applies dynamic expressions to strong emotions and quiet expressions to calm emotions. The music generation unit can also use the emotion estimation function to estimate the user's emotion from the photo analysis results and adjust the expression of the music based on the emotion. For example, it can apply a bright melody to emotions of joy and a sentimental melody to emotions of sadness. This makes it possible to generate more emotional music by adjusting the dynamics and expression of the music based on the user's emotion.

[0070] The music generation unit generates not only music but also environmental sounds or sound effects based on the results of photo analysis, providing a more realistic audio experience. For example, the music generation unit uses a generation AI to generate environmental sounds in addition to music based on the results of photo analysis. For example, birdsong and wind sounds can be added to photos of natural landscapes. The music generation unit can also generate sound effects based on the results of photo analysis. For example, the sounds of cars and people talking can be added to photos of cities. This allows for the generation of environmental sounds and sound effects, providing a more realistic audio experience.

[0071] The music generation unit can automatically generate visual effects or animations for the music based on the results of photo analysis, providing visual enjoyment as well. For example, the music generation unit uses a generation AI to automatically generate visual effects that match the music based on the results of photo analysis. For example, it can display light effects in time with the rhythm of the music. The music generation unit can also automatically generate animations based on the results of photo analysis. For example, it can generate animations in which characters move in time with the melody of the music. This allows for the automatic generation of visual effects and animations to provide visual enjoyment as well.

[0072] The music generation unit can use the emotion estimation function to estimate the user's emotion from the photo analysis results and automatically generate lyrics or a message for the music based on the emotion. The music generation unit, for example, uses the emotion estimation function to estimate the user's emotion from the photo analysis results and automatically generate lyrics for the music based on the emotion. For example, positive lyrics are generated for emotions of joy, and sentimental lyrics are generated for emotions of sadness. The music generation unit can also use the emotion estimation function to estimate the user's emotion from the photo analysis results and automatically generate a message based on the emotion. For example, a message of gratitude can be generated for emotions of gratitude, and a message of support can be generated for emotions of encouragement. This makes it possible to automatically generate lyrics and messages based on the user's emotions, thereby creating music that is richer in emotion.

[0073] The sharing unit can automatically remix the generated music to suit the user's preferences and provide multiple versions. The sharing unit, for example, develops an algorithm that automatically remixes the generated music to suit the user's preferences. For example, the sharing unit can generate multiple versions by changing the tempo or the type of instrument. The sharing unit can also change the arrangement of the music based on the user's preferences. For example, the sharing unit can remix the music to suit the user's preferred genre or style. This allows for more diverse ways of enjoying the music by remixing the music to suit the user's preferences and providing multiple versions.

[0074] The sharing unit allows the generation AI to automatically generate hashtags or captions when a song is shared, promoting its spread on social media. The sharing unit develops an algorithm that allows the generation AI to automatically generate hashtags when a song is shared. For example, the sharing unit may suggest related hashtags based on the genre or theme of the song. The sharing unit also allows the generation AI to automatically generate captions. For example, the sharing unit may create captions based on the content or emotion of the song. This allows the automatic generation of hashtags and captions to promote its spread on social media.

[0075] The sharing unit can use the emotion estimation function to analyze the user's emotion when sharing a song and customize the shared message based on that emotion. For example, the sharing unit analyzes the user's emotion when sharing a song and customizes the shared message based on that emotion. For example, it generates a positive message for an emotion of joy and an encouraging message for an emotion of sadness. The sharing unit can also use the emotion estimation function to analyze the user's emotion in real time and adjust the shared message based on that emotion. For example, it can dynamically change the content of the message according to changes in emotion. This allows for more personalized sharing by customizing the shared message based on the user's emotion.

[0076] The sharing unit can provide a function that allows the generated music to be set as a ringtone or alarm sound for the user's smartphone. The sharing unit can, for example, provide a function that allows the generated music to be set as a ringtone for the user's smartphone. For example, a portion of the music can be cut out and used as a ringtone. The sharing unit can also set the generated music as an alarm sound. For example, the intro part of the music can be set as an alarm sound. This allows the generated music to be used in a wider variety of ways by being set as a ringtone or alarm sound.

[0077] The sharing unit can have the generation AI automatically generate a music video when a song is shared, making it enjoyable as visual content. The sharing unit, for example, develops an algorithm that allows the generation AI to automatically generate a music video when a song is shared. For example, it generates video that matches the rhythm and melody of the song. The sharing unit can also have the generation AI create a storyboard based on the content of the song and generate a music video based on that. For example, it can generate video that matches the lyrics of the song. In this way, automatically generated music videos can be enjoyed as visual content.

[0078] The sharing unit can use the emotion estimation function to analyze the user's emotion when sharing a song and suggest a sharing platform based on that emotion. For example, the sharing unit can analyze the user's emotion when sharing a song and suggest the optimal sharing platform based on that emotion. For example, it can suggest a social networking site for positive emotions and a private messaging app for negative emotions. The sharing unit can also use the emotion estimation function to analyze the user's emotion in real time and dynamically change the sharing platform based on that emotion. For example, it can adjust the sharing platform according to changes in emotion. This enables more appropriate sharing by suggesting a sharing platform based on the user's emotion.

[0079] When detecting features in a photo, the analysis unit can also analyze the subject's movements or pose and adjust the rhythm and tempo of the music based on that. For example, when the generation AI detects features in a photo, the analysis unit analyzes the subject's movements and adjusts the rhythm of the music based on that. For example, it can generate music with a faster rhythm for moving photos and a slower rhythm for still photos. The analysis unit can also analyze the subject's pose and adjust the tempo of the music based on that. For example, it can generate music with a faster tempo for dynamic poses and a slower tempo for relaxed poses. This makes it possible to generate more dynamic music by analyzing the subject's movements and poses.

[0080] The analysis unit can estimate the subject's age or gender when detecting photo features and customize the style of the music based on that. For example, the analysis unit estimates the subject's age when the generation AI detects photo features and customizes the style of the music based on that. For example, it can generate music in a bright and fun style for photos of children and a more subdued style for photos of adults. The analysis unit can also estimate the subject's gender and customize the style of the music based on that. For example, it can generate a soft melody for photos of women and a powerful melody for photos of men. This makes it possible to generate more personalized music by analyzing the subject's age and gender.

[0081] The analysis unit can use the emotion estimation function to analyze the user's emotion when detecting features in a photo and improve the accuracy of feature detection based on the emotion. For example, the analysis unit can analyze the user's emotion when detecting features in a photo and improve the accuracy of feature detection based on the emotion. For example, it can emphasize bright colors for positive emotions and dark colors for negative emotions. The analysis unit can also use the emotion estimation function to analyze the user's emotion in real time and adjust the feature detection algorithm based on the emotion. For example, it can dynamically change detection parameters according to changes in emotion. This improves the accuracy of feature detection based on the user's emotion, enabling more accurate music generation.

[0082] The analysis unit can also analyze the subject's clothing or accessories when detecting photo features and determine the genre and style of the music based on that. For example, when the generation AI detects photo features, the analysis unit analyzes the subject's clothing and determines the genre of the music based on that. For example, pop music can be generated for casual clothing, and classical music for formal clothing. The analysis unit can also analyze the subject's accessories and determine the style of the music based on that. For example, an energetic style can be generated for sportswear, and an elegant style can be generated for elegant accessories. This makes it possible to generate a wider variety of music by analyzing the subject's clothing and accessories.

[0083] The analysis unit can cause the generation AI to automatically generate related artwork or illustrations based on the results of detecting features in the photo, and provide them as cover art for the song. The analysis unit can cause the generation AI to automatically generate related artwork based on the results of detecting features in the photo. For example, the analysis unit can generate an abstract painting that matches the color and atmosphere of the photo, and provide it as cover art for the song. The analysis unit can also cause the generation AI to generate illustrations based on the results of detecting features in the photo. For example, the analysis unit can generate an illustration based on the subject of the photo, and provide it as cover art for the song. This allows for the automatic generation of related artwork and illustrations, providing visual enjoyment.

[0084] The analysis unit can use the emotion estimation function to analyze the user's emotion when detecting features in a photo and suggest editing or processing of the photo based on that emotion. For example, the analysis unit can analyze the user's emotion when detecting features in a photo and suggest editing or processing of the photo based on that emotion. For example, it can suggest bright colors for positive emotions and monochrome filters for negative emotions. The analysis unit can also use the emotion estimation function to analyze the user's emotion in real time and dynamically change editing or processing options based on that emotion. For example, it can adjust the type of filter or effect depending on changes in emotion. This enables more personalized photo editing by suggesting editing or processing of the photo based on the user's emotion.

[0085] When customizing a song, the music generation unit can refer to the user's past music preference data and provide more personalized songs. For example, the music generation unit uses a generation AI to refer to the user's past music preference data and customize the song based on that. For example, it generates a song that incorporates the user's favorite genres and artist styles. The music generation unit can also adjust elements of the song based on the user's playback history and rating data. For example, it can generate melodies and rhythms that reflect the characteristics of songs that the user has given high ratings. This makes it possible to generate more personalized songs by referring to the user's past music preference data.

[0086] The music generation unit can reflect user feedback in real time when customizing a song and instantly adjust the song. For example, the music generation unit builds a system in which a generation AI reflects user feedback in real time when customizing a song and instantly adjusts the song. For example, if a user wants to change the tempo, this is immediately reflected. The music generation unit can also dynamically change elements of the song based on user feedback. For example, if a user wants to change part of the melody, this can be immediately reflected. This allows the song to be instantly adjusted by reflecting user feedback in real time.

[0087] The music generation unit can use the emotion estimation function to analyze the user's emotions when customizing a song and make customization suggestions based on those emotions. For example, the music generation unit can analyze the user's emotions when customizing a song and make customization suggestions based on those emotions. For example, it can suggest a bright melody for positive emotions and a calm melody for negative emotions. The music generation unit can also use the emotion estimation function to analyze the user's emotions in real time and dynamically change customization options based on those emotions. For example, it can adjust the selection of instruments and the application of effects in response to changes in emotions. This allows for more personalized music generation by suggesting customizations based on the user's emotions.

[0088] The music generation unit can recommend related artists and songs based on the instrument or tone selected by the user when customizing a song. For example, the generation AI of the music generation unit recommends related artists based on the instrument or tone selected by the user. For example, if a guitar is selected, guitarist artists will be recommended. The music generation unit can also recommend related songs based on the tone selected by the user. For example, if a piano tone is selected, songs using piano can be recommended. This allows for a more diverse musical experience by recommending related artists and songs based on the instrument or tone selected by the user.

[0089] The music generation unit can provide a function in which the generation AI automatically generates lyrics when customizing a song and the user can edit the lyrics. For example, the music generation unit can provide a function in which the generation AI automatically generates lyrics when customizing a song and the user can edit the lyrics. For example, the music generation unit can allow the user to change part of the generated lyrics. The music generation unit can also allow the user to add or delete lyrics. For example, the user can create lyrics that reflect their own emotions or memories. This allows the user to edit the automatically generated lyrics, making it possible to create more personalized music.

[0090] The music generation unit can use the emotion estimation function to analyze the user's emotions when customizing a song and suggest customization options based on those emotions. For example, the music generation unit can analyze the user's emotions when customizing a song and suggest customization options based on those emotions. For example, it can suggest a bright melody for positive emotions and a calm melody for negative emotions. The music generation unit can also use the emotion estimation function to analyze the user's emotions in real time and dynamically change the customization options based on those emotions. For example, it can adjust the selection of instruments and the application of effects in response to changes in emotions. This allows for more personalized music generation by suggesting customization options based on the user's emotions.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The analysis unit can automatically generate poems and lyrics relevant to the user based on the results of photo analysis and incorporate them into a song. For example, the analysis unit uses a generation AI to automatically generate poems and lyrics relevant to the user based on the results of photo analysis. For example, for a photo of a sunset, it can generate a poem such as "A beautiful sunset is setting" and incorporate it into a song. The analysis unit can also generate poems and lyrics based on the emotion of the photo. For example, for a photo with a relaxing atmosphere, it can generate calm lyrics and incorporate them into a song. In this way, by automatically generating poems and lyrics based on the results of photo analysis and incorporating them into a song, it is possible to create a song with a more unified feel.

[0093] The analysis unit can analyze videos taken by the user at the same time as uploading photos and generate music that matches the scenes in the video. For example, the analysis unit can analyze videos taken at the same time as uploading photos and generate music based on the scenes in the video. For example, the analysis unit can analyze the movement and sound of the video to generate dynamic music. The analysis unit can also analyze scenes in the video and generate music that matches the scene. For example, if the video shows an ocean scene, it can generate music that incorporates the sound of waves. By analyzing both photos and videos, a wider variety of music can be generated.

[0094] The analysis unit can analyze the user's emotions in real time when uploading photos and reflect those emotions in the music generation. For example, the analysis unit analyzes the user's facial expressions with a camera when uploading photos and estimates the emotions in real time. For example, it generates a cheerful song for a photo of a smiling face and a calm song for a photo of a sad face. The analysis unit can also analyze the user's voice and estimate the emotions in real time. For example, it can analyze the tone and speed of the voice and calculate an emotion score. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and analyze emotions in real time. For example, it can calculate an emotion score based on fluctuations in heart rate. In this way, the user's emotions can be analyzed in real time and reflected in the music generation, enabling more personalized music generation.

[0095] The analysis unit can estimate the background sound of a photo and determine the elements of a song based on that sound. For example, the analysis unit uses an algorithm that allows the generation AI to estimate background sounds when analyzing a photo, generating sounds that match the scene in the photo. For example, the analysis unit can estimate the sound of waves for a photo of the ocean, and birdsong for a photo of a forest. The analysis unit can also analyze the background sound of a photo and determine the elements of a song based on that sound. For example, if the background sound of a photo is quiet, it can generate a calm melody, and if the background sound is lively, it can generate a lively rhythm. This makes it possible to determine the elements of a song based on the background sound, creating a more immersive song.

[0096] The analysis unit can also analyze the subject's clothing or accessories when detecting photo features and determine the genre and style of the music based on that. For example, when the generation AI detects photo features, the analysis unit analyzes the subject's clothing and determines the genre of the music based on that. For example, pop music can be generated for casual clothing, and classical music for formal clothing. The analysis unit can also analyze the subject's accessories and determine the style of the music based on that. For example, an energetic style can be generated for sportswear, and an elegant style can be generated for elegant accessories. This makes it possible to generate a wider variety of music by analyzing the subject's clothing and accessories.

[0097] The music generation unit can automatically generate visual effects or animations for the music based on the results of photo analysis, providing visual enjoyment as well. For example, the music generation unit uses a generation AI to automatically generate visual effects that match the music based on the results of photo analysis. For example, it can display light effects in time with the rhythm of the music. The music generation unit can also automatically generate animations based on the results of photo analysis. For example, it can generate animations in which characters move in time with the melody of the music. This allows for the automatic generation of visual effects and animations to provide visual enjoyment as well.

[0098] The music generation unit can use the emotion estimation function to estimate the user's emotion from the photo analysis results and adjust the dynamics or expression of the music based on the emotion. The music generation unit, for example, uses the emotion estimation function to estimate the user's emotion from the photo analysis results and adjusts the dynamics of the music based on the emotion. For example, it applies dynamic expressions to strong emotions and quiet expressions to calm emotions. The music generation unit can also use the emotion estimation function to estimate the user's emotion from the photo analysis results and adjust the expression of the music based on the emotion. For example, it can apply a bright melody to emotions of joy and a sentimental melody to emotions of sadness. This makes it possible to generate more emotional music by adjusting the dynamics and expression of the music based on the user's emotion.

[0099] The music generation unit can use the emotion estimation function to analyze the user's emotions when customizing a song and make customization suggestions based on those emotions. For example, the music generation unit can analyze the user's emotions when customizing a song and make customization suggestions based on those emotions. For example, it can suggest a bright melody for positive emotions and a calm melody for negative emotions. The music generation unit can also use the emotion estimation function to analyze the user's emotions in real time and dynamically change customization options based on those emotions. For example, it can adjust the selection of instruments and the application of effects in response to changes in emotions. This allows for more personalized music generation by suggesting customizations based on the user's emotions.

[0100] The music generation unit can recommend related artists and songs based on the instrument or tone selected by the user when customizing a song. For example, the generation AI of the music generation unit recommends related artists based on the instrument or tone selected by the user. For example, if a guitar is selected, guitarist artists will be recommended. The music generation unit can also recommend related songs based on the tone selected by the user. For example, if a piano tone is selected, songs using piano can be recommended. This allows for a more diverse musical experience by recommending related artists and songs based on the instrument or tone selected by the user.

[0101] The music generation unit can provide a function in which the generation AI automatically generates lyrics when customizing a song and the user can edit the lyrics. For example, the music generation unit can provide a function in which the generation AI automatically generates lyrics when customizing a song and the user can edit the lyrics. For example, the music generation unit can allow the user to change part of the generated lyrics. The music generation unit can also allow the user to add or delete lyrics. For example, the user can create lyrics that reflect their own emotions or memories. This allows the user to edit the automatically generated lyrics, making it possible to create more personalized music.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The photo upload unit uploads photos stored on the user's smartphone. For example, the user can select and upload photos from the smartphone's gallery. They can also select and upload photos from cloud storage. They can also upload photos taken in real time using the camera function. Step 2: The analysis unit analyzes the uploaded photo's atmosphere, color, and the emotions and scenes perceived from the subject. For example, image recognition technology can be used to analyze the color tone and brightness of the photo to infer emotions. It can also analyze the shape and placement of the subject to determine the scene. Furthermore, facial expression analysis technology can be used to analyze the subject's emotions. Step 3: The music generation unit generates music based on the results of the analysis by the analysis unit. For example, the generation AI can be used to create a genre, melody, harmony, and rhythm that matches the analysis results. The generation AI can also be used to select instruments that match the atmosphere of the photo. Furthermore, the generation AI can also be used to set the tempo and key based on the emotion of the photo. Step 4: The sharing unit allows the user to share the generated song on social media or save it as a personal keepsake. For example, the sharing unit may provide a function to post the generated song to a social networking site. It may also provide a function to save the song to cloud storage. It may also provide a function to download the generated song and save it locally.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] 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.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] 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.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0148] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0149] 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.

[0150] 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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.

[0153] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0161] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0162] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0163] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0164] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0165] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0166] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0171] 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 photo upload unit for uploading photos stored in the user's smartphone; an analysis unit that analyzes the atmosphere, color, and emotions and scenes perceived from the subject of the photo uploaded by the photo upload unit; a music composition unit that generates music based on the analysis results of the analysis unit; a sharing unit that allows users to share the music generated by the music generating unit on social media or save it as a personal keepsake. A system characterized by:

2. The analysis unit The user's emotions when uploading the photo are analyzed in real time, and the emotions are reflected in the music generated by the music generating unit.

2. The system of claim 1.

3. The analysis unit At the same time as uploading the photo, a video taken by the user is also analyzed, and a piece of music that matches the scene in the video is generated by the music generation unit.

2. The system of claim 1.

4. The music generation unit Based on the results of the photo analysis, the tempo or key of the music is dynamically changed to generate more diverse music.

2. The system of claim 1.

5. The common part is The generated music is automatically remixed to suit the user's tastes, and multiple versions are provided.

2. The system of claim 1.

6. The analysis unit When detecting the features of the photo, the subject's movement or pose is also analyzed and the rhythm and tempo of the music are adjusted accordingly.

2. The system of claim 1.

7. The music generation unit When customizing the music, the user's past music preference data is referenced to provide more personalized music.

2. The system of claim 1.

8. The music generation unit Using an emotion estimation function, the emotion of the user is analyzed when customizing the song, and customization suggestions are made based on the emotion.

2. The system of claim 1.

Images