System

Abstract

An object of a system according to an embodiment is to automatically generate audio and video based on a photograph provided by a user.SOLUTION: A system according to an embodiment includes a photograph analysis unit, a sound generation unit, and a video generation unit. The photo analysis unit analyzes a photo provided by a user. The sound generation unit generates sound on the basis of a result of the analysis by the photograph analysis unit. The video generation unit generates a video by combining the photograph analyzed by the photograph analysis unit and the sound generated by the sound generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not adequately addressed the automatic generation of audio and video from photographs, and there is room for improvement.

[0005] The system according to the embodiment aims to automatically generate audio and video based on photos provided by a user. [Means for solving the problem]

[0006] The system according to the embodiment includes a photo analysis unit, an audio generation unit, and a video generation unit. The photo analysis unit analyzes photos provided by a user. The audio generation unit generates audio based on the results of the analysis by the photo analysis unit. The video generation unit generates video by combining the photos analyzed by the photo analysis unit and the audio generated by the audio generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate audio and video based on photos provided by the user. [Brief explanation of the drawings]

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

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

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

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

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

[0013] In the following embodiments, the coded storage is one or more 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) The audio / video album generation system according to an embodiment of the present invention automatically analyzes photos of a user's childhood or youth and generates an album that combines audio and video. This allows the user to easily save and share their memories.

[0029] An audio / video album generation system according to an embodiment includes a photo analysis unit, an audio generation unit, and a video generation unit. The photo analysis unit analyzes photos provided by a user. For example, the photo analysis unit selects photos of a specific person using facial recognition technology. The photo analysis unit can also select photos taking into account their quality and resolution. The photo analysis unit can also analyze background information of photos and automatically group photos related to specific locations or events. The audio generation unit generates audio based on the results of the analysis by the photo analysis unit. For example, the audio generation unit generates narration and music based on the content and background of the photos. The audio generation unit can also generate specific narration based on episode information provided by the user. The audio generation unit can also generate audio based on the emotions of people appearing in the photos using an emotion estimation function. The video generation unit generates video by combining the photos analyzed by the photo analysis unit and the audio generated by the audio generation unit. For example, the video generation unit displays photos in a slideshow format and adds audio and music to the background. The video generation unit can also automatically add transition effects and other effects between photos. The video generation unit can also generate a video based on the emotions of people in a photo using an emotion estimation function. This allows the audio / video album generation system according to the embodiment to easily save and share a user's memories. For example, the generated album is saved on the user's device. The user can also share the generated album with family and friends. For example, the album can be shared via social networking sites or email.

[0030] The photo analysis unit can select photos of a specific person using facial recognition technology. The photo analysis unit can select photos of a specific person using, for example, facial recognition technology. For example, facial recognition technology using deep learning can be used. The photo analysis unit can also perform facial recognition using a Haar feature classifier. The photo analysis unit can also identify a specific person using a pre-registered face database. This allows for the generation of a more personalized album by selecting photos of a specific person.

[0031] The audio generator can generate narration and music based on the content and background of a photo. For example, the audio generator can generate narration and music based on the content and background of a photo. For example, the narration can be generated using text-to-speech technology. The audio generator can also generate music using a music generation algorithm. The audio generator can also analyze background information of a photo and automatically select music related to a specific location or event. This allows for a more consistent album by generating audio based on the content and background of a photo.

[0032] The video generation unit can display photos in a slideshow format and add audio or music to the background. For example, the video generation unit can display photos in a slideshow format and add audio or music to the background. For example, the video generation unit can set the display time and transition effects of the slideshow format. The video generation unit can also automatically estimate the age of photos and sort them chronologically. For example, the video generation unit can analyze the metadata and content of photos to automatically estimate the age. This allows for the provision of a visually appealing album by displaying photos in a slideshow format and adding audio or music.

[0033] The photo analysis unit can analyze background information of photos and automatically group photos related to a specific place or event. The photo analysis unit can, for example, analyze background information of photos and automatically group photos related to a specific place or event. For example, it can analyze geographic information to identify a specific place. It can also analyze event information to identify a specific event. It can also analyze the content of photos to automatically group photos related to a specific place or event. This makes it possible to provide a more organized album by grouping photos related to a specific place or event.

[0034] The photo analysis unit can automatically estimate the age of photos and sort them in chronological order. The photo analysis unit can automatically estimate the age by, for example, analyzing the metadata and content of photos. For example, the age can be determined based on the date and time the photo was taken and the person's growth rate. The age can also be estimated by analyzing the content of the photo. Time stamps can also be used to sort photos in chronological order. This makes it possible to provide an album in chronological order by sorting photos in chronological order.

[0035] In addition to analyzing photos, the photo analysis unit can also analyze video clips provided by the user to generate an album that combines still images and videos. The photo analysis unit can, for example, analyze video clips provided by the user to generate an album that combines still images and videos. For example, specific scenes from a video clip can be cut out and used as still images. The photo analysis unit can also analyze the contents of a video clip to generate an album that combines still images and videos. It can also perform frame analysis of a video clip to extract specific scenes. This allows for the provision of a richer album by combining still images and videos.

[0036] The photo analysis unit can automatically identify photos of different family members and generate individual albums. The photo analysis unit can automatically identify photos of different family members and generate individual albums using, for example, facial recognition technology. For example, a pre-registered face database can be used to identify family members. Real-time facial recognition can also be performed to identify family members. Photos can also be selected for each family member and individual albums can be generated. This allows for more personalized memories by identifying photos of different family members and generating individual albums.

[0037] The audio generation unit can automatically select music related to a particular season or event based on the content of the photo. For example, the audio generation unit analyzes the content of the photo and automatically selects music related to a particular season or event. For example, music can be selected using a music library for each season. A music template according to the event can also be used. Music related to a particular season or event can also be selected by analyzing background information of the photo. This allows for a more consistent album to be provided by selecting music related to a particular season or event.

[0038] The audio generation unit can generate a narration about a specific episode or memory based on text information provided by the user. The audio generation unit generates a narration about a specific episode or memory based on, for example, text information provided by the user. For example, the narration can be generated using text analysis technology. The length of the narration can also be adjusted based on the importance of the episode. The narration about a specific episode or memory can also be generated based on the text information provided by the user. This allows for a more personalized album to be provided by generating a narration based on the text information provided by the user.

[0039] In addition to generating audio, the audio generation unit can analyze audio messages provided by the user and incorporate them into the album. The audio generation unit, for example, analyzes audio messages provided by the user and incorporates them into the album. For example, the audio generation unit can analyze audio messages using voice recognition technology. It can also analyze the format of an audio file and convert it into an appropriate format. It can also analyze the content of the audio message and use it as narration for the album. In this way, by incorporating audio messages provided by the user into the album, it is possible to provide a more personalized album.

[0040] The audio generation unit can automatically generate narration in different languages ​​to create an album from an international perspective. The audio generation unit can, for example, automatically generate narration in different languages ​​to create an album from an international perspective. For example, the audio generation unit can translate the narration into different languages ​​using translation technology. Narration in different languages ​​can also be generated using speech synthesis technology. Customization according to cultural backgrounds is also possible. In this way, by generating narration in different languages, an album from an international perspective can be provided.

[0041] The video generation unit can automatically generate video with a specific theme or storyline based on the content of the photo. For example, the video generation unit can analyze the content of the photo and automatically generate video with a specific theme or storyline. For example, the video generation unit can visualize the story of a family trip. It can also build a storyline based on a specific event or occurrence. It can also apply effects and transitions according to the theme. This allows for the generation of video with a specific theme or storyline, thereby providing more consistent video.

[0042] The video generation unit can dynamically change the transition effects between photos based on the content and background of the photos. For example, the video generation unit analyzes the content and background of the photos and dynamically changes the transition effects based on that. For example, a fade-in / fade-out effect can be used for photos of natural scenery, and a slide effect can be used for photos of urban landscapes. Transition effects can also be customized according to the content of the photos. This allows for more visually appealing images to be provided by dynamically changing the transition effects based on the content and background of the photos.

[0043] In addition to generating videos, the video generation unit can generate hybrid videos that combine video clips provided by the user. For example, the video generation unit analyzes video clips provided by the user and generates hybrid videos that combine them with still images. For example, it can extract specific scenes and display them together with the still images. It can also analyze the contents of the video clips to generate videos that combine still images and moving images. It can also perform frame analysis of the video clips to extract specific scenes. This allows for the generation of hybrid videos that combine video clips, thereby providing richer videos.

[0044] The video generation unit can generate an album video of the entire family by combining photos of different family members. The video generation unit can automatically identify photos of different family members using, for example, facial recognition technology, and generate an album video of the entire family. For example, it can display separate sections for each family member. It can also combine photos of the entire family into a single video. It can also set selection criteria for photos for each family member. This allows the generation of an album video of the entire family, providing memories with a more united feeling.

[0045] The album customization unit can suggest customization related to specific episodes or memories based on text information provided by the user. The album customization unit can suggest customization related to specific episodes or memories based on, for example, text information provided by the user. For example, the album customization unit can evaluate the importance of an episode using text analysis technology and suggest customization. Customization suggestions can also be made based on the content of the episode. Customization related to specific episodes or memories can also be made based on the text information provided by the user. In this way, by suggesting customization based on the text information provided by the user, a more personalized album can be provided.

[0046] The album customization unit can dynamically change the customization options based on the content and background of the photo. For example, the album customization unit analyzes the content and background of the photo and dynamically changes the customization options based on the content and background of the photo. For example, a fade-in / fade-out effect can be suggested for photos of natural scenery. A slide effect can also be suggested for photos of urban landscapes. The customization options can also be customized according to the content of the photo. This makes it possible to provide a more visually appealing album by dynamically changing the customization options based on the content and background of the photo.

[0047] In addition to customization, the album customization unit can provide customization options that combine video clips provided by the user. For example, the album customization unit analyzes video clips provided by the user and provides customization options that combine still images. For example, it can extract specific scenes and display them together with still images. It can also analyze the content of video clips and provide customization options that combine still images and moving images. It can also perform frame analysis of video clips and extract specific scenes. This allows for providing customization options that combine video clips, thereby providing a richer album.

[0048] The album customization unit can provide a customized album for the entire family by combining photos of different family members. The album customization unit can automatically identify photos of different family members using, for example, facial recognition technology, and provide a customized album for the entire family. For example, it can display separate sections for each family member. It can also combine photos of the entire family into a single album. It can also set photo selection criteria for each family member. This allows for customized albums for the entire family, creating memories that are more unified.

[0049] The album storage unit can make suggestions for saving and sharing specific episodes and memories based on text information provided by the user. The album storage unit can make suggestions for saving and sharing specific episodes and memories based on, for example, text information provided by the user. For example, the album storage unit can use text analysis technology to evaluate the importance of an episode and make suggestions for saving and sharing. It can also make suggestions for saving and sharing based on the content of the episode. It can also make suggestions for saving and sharing specific episodes and memories based on text information provided by the user. This makes it possible to provide a more personalized album by making suggestions for saving and sharing based on the text information provided by the user.

[0050] The album storage unit can dynamically change the save and share options based on the content and background of the photo. For example, the album storage unit analyzes the content and background of the photo and dynamically changes the save and share options based on the content and background of the photo. For example, a fade-in / fade-out effect can be suggested for photos of natural scenery. A slide effect can also be suggested for photos of cityscapes. The save and share options can also be customized according to the content of the photo. This allows for a more visually appealing album to be provided by dynamically changing the save and share options based on the content and background of the photo.

[0051] The album storage unit can provide storage and sharing options that combine video clips provided by the user in addition to storage and sharing. For example, the album storage unit can analyze video clips provided by the user and provide storage and sharing options that combine still images. For example, it can extract specific scenes and display them together with still images. It can also analyze the content of video clips to provide storage and sharing options that combine still images and videos. It can also perform frame analysis of video clips to extract specific scenes. This allows for a richer album to be provided by providing storage and sharing options that combine video clips.

[0052] The album storage unit can provide an option to store and share an album for the entire family that combines photos of different family members. The album storage unit can automatically identify photos of different family members using, for example, facial recognition technology, and provide an option to store and share an album for the entire family. For example, a separate section can be displayed for each family member. Photos of the entire family can also be combined into a single album. Photo selection criteria can also be set for each family member. This allows for an option to store and share an album for the entire family, thereby providing a more unified memory.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The audio / video album generation system may further include a geographic information analysis unit that can analyze geographic information provided by the user and incorporate it into the album. For example, the geographic information of places visited by the user may be analyzed and photos and videos related to the places may be automatically grouped. The geographic information analysis unit may also generate music and narration related to a specific location. Furthermore, the geographic information analysis unit may incorporate maps and location information into the album to provide a more coherent story. Thus, by analyzing geographic information provided by the user and incorporating it into the album, a more personalized album may be provided.

[0055] The audio / video album generation system may further include a music analysis unit that can analyze a music library provided by a user and incorporate the music into the album. For example, the system may analyze a user's music library and automatically select music that matches a particular photo or video. The music analysis unit may also adjust the transitions and effects of the album based on the tempo and mood of the music. Furthermore, the music analysis unit may provide a customized album based on the user's musical preferences. This allows the system to provide a more personalized album by analyzing a user's music library and incorporating the music into the album.

[0056] The audio / video album generation system may further include a calendar analysis unit that can analyze calendar information provided by a user and incorporate it into an album. For example, the system may analyze the user's calendar information and automatically group photos and videos related to specific events or anniversaries. The calendar analysis unit may also generate narration and music related to specific events. Furthermore, the calendar analysis unit may incorporate a timeline of events into the album to provide a more coherent story. This allows the system to provide a more personalized album by analyzing the calendar information provided by a user and incorporating it into the album.

[0057] The audio / video album generation system may further include a message analysis unit that can analyze emails and messages provided by the user and incorporate them into the album. For example, by analyzing emails and messages sent and received by the user in the past and incorporating them into the album, a more consistent story can be provided. The message analysis unit may also generate narration and music based on the content of the messages. Furthermore, the message analysis unit may provide a more emotional album by emphasizing specific keywords and phrases. In this way, by analyzing emails and messages provided by the user and incorporating them into the album, a more personalized album can be provided.

[0058] The audio / video album generation system may further include a cloud analysis unit that can analyze cloud storage data provided by the user and incorporate it into an album. For example, by analyzing photos and videos stored by the user in cloud storage and incorporating them into an album, a more coherent story can be provided. The cloud analysis unit may also analyze metadata from the cloud storage and automatically group content related to a specific event or location. The cloud analysis unit may also generate narration and music based on the cloud storage data. In this way, by analyzing cloud storage data provided by the user and incorporating it into an album, a more personalized album can be provided.

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

[0060] Step 1: The photo analyzer analyzes the photos provided by the user. For example, the photo analyzer may use facial recognition technology to select photos of specific people. The photo analyzer may also consider the quality and resolution of the photos when selecting photos. Furthermore, the photo analyzer may analyze the background information of the photos and automatically group photos related to specific places or events. Step 2: The audio generation unit generates audio based on the results of the analysis by the photo analysis unit. For example, the audio generation unit generates narration and music based on the content and background of the photo. The audio generation unit can also generate specific narration based on episode information provided by the user. Furthermore, the audio generation unit can use an emotion estimation function to generate audio based on the emotions of people appearing in the photo. Step 3: The video generation unit generates a video by combining the photos analyzed by the photo analysis unit and the audio generated by the audio generation unit. For example, the video generation unit displays the photos in a slideshow format and adds audio or music to the background. The video generation unit can also automatically add transitions and effects between photos. Furthermore, the video generation unit can use an emotion estimation function to generate a video based on the emotions of the people in the photos.

[0061] (Example 2) The audio / video album generation system according to an embodiment of the present invention automatically analyzes photos of a user's childhood or youth and generates an album that combines audio and video. This allows the user to easily save and share their memories.

[0062] An audio / video album generation system according to an embodiment includes a photo analysis unit, an audio generation unit, and a video generation unit. The photo analysis unit analyzes photos provided by a user. For example, the photo analysis unit selects photos of a specific person using facial recognition technology. The photo analysis unit can also select photos taking into account their quality and resolution. The photo analysis unit can also analyze background information of photos and automatically group photos related to specific locations or events. The audio generation unit generates audio based on the results of the analysis by the photo analysis unit. For example, the audio generation unit generates narration and music based on the content and background of the photos. The audio generation unit can also generate specific narration based on episode information provided by the user. The audio generation unit can also generate audio based on the emotions of people appearing in the photos using an emotion estimation function. The video generation unit generates video by combining the photos analyzed by the photo analysis unit and the audio generated by the audio generation unit. For example, the video generation unit displays photos in a slideshow format and adds audio and music to the background. The video generation unit can also automatically add transition effects and other effects between photos. The video generation unit can also generate a video based on the emotions of people in a photo using an emotion estimation function. This allows the audio / video album generation system according to the embodiment to easily save and share a user's memories. For example, the generated album is saved on the user's device. The user can also share the generated album with family and friends. For example, the album can be shared via social networking sites or email.

[0063] The photo analysis unit can select photos of a specific person using facial recognition technology. The photo analysis unit can select photos of a specific person using, for example, facial recognition technology. For example, facial recognition technology using deep learning can be used. The photo analysis unit can also perform facial recognition using a Haar feature classifier. The photo analysis unit can also identify a specific person using a pre-registered face database. This allows for the generation of a more personalized album by selecting photos of a specific person.

[0064] The audio generator can generate narration and music based on the content and background of a photo. For example, the audio generator can generate narration and music based on the content and background of a photo. For example, the narration can be generated using text-to-speech technology. The audio generator can also generate music using a music generation algorithm. The audio generator can also analyze background information of a photo and automatically select music related to a specific location or event. This allows for a more consistent album by generating audio based on the content and background of a photo.

[0065] The video generation unit can display photos in a slideshow format and add audio or music to the background. For example, the video generation unit can display photos in a slideshow format and add audio or music to the background. For example, the video generation unit can set the display time and transition effects of the slideshow format. The video generation unit can also automatically estimate the age of photos and sort them chronologically. For example, the video generation unit can analyze the metadata and content of photos to automatically estimate the age. This allows for the provision of a visually appealing album by displaying photos in a slideshow format and adding audio or music.

[0066] The photo analysis unit can use the emotion estimation function to analyze the emotions of people in photos and generate an album that tracks the evolution of their emotions. The photo analysis unit can, for example, use the emotion estimation function to analyze the emotions of people in photos and generate an album that tracks the evolution of their emotions. For example, emotions can be analyzed using facial expression recognition technology. Emotions can also be analyzed using voice analysis technology. Furthermore, changes in emotions can be displayed along a time axis to track the evolution of emotions. This makes it possible to provide a more emotional album by tracking the evolution of emotions.

[0067] The photo analysis unit can analyze background information of photos and automatically group photos related to a specific place or event. The photo analysis unit can, for example, analyze background information of photos and automatically group photos related to a specific place or event. For example, it can analyze geographic information to identify a specific place. It can also analyze event information to identify a specific event. It can also analyze the content of photos to automatically group photos related to a specific place or event. This makes it possible to provide a more organized album by grouping photos related to a specific place or event.

[0068] The photo analysis unit can automatically estimate the age of photos and sort them in chronological order. The photo analysis unit can automatically estimate the age by, for example, analyzing the metadata and content of photos. For example, the age can be determined based on the date and time the photo was taken and the person's growth rate. The age can also be estimated by analyzing the content of the photo. Time stamps can also be used to sort photos in chronological order. This makes it possible to provide an album in chronological order by sorting photos in chronological order.

[0069] The photo analysis unit can use the emotion estimation function to generate an album that emphasizes a specific emotion based on the emotion of a person appearing in a photo. The photo analysis unit, for example, uses the emotion estimation function to analyze the emotion of a person appearing in a photo and generate an album that emphasizes a specific emotion. For example, the photo analysis unit can analyze emotions using facial expression recognition technology and perform filtering based on the intensity of the emotion. It can also apply effects according to the emotion. It can also adjust the layout of the album based on the type of emotion. This makes it possible to provide a more emotional album by emphasizing a specific emotion.

[0070] In addition to analyzing photos, the photo analysis unit can also analyze video clips provided by the user to generate an album that combines still images and videos. The photo analysis unit can, for example, analyze video clips provided by the user to generate an album that combines still images and videos. For example, specific scenes from a video clip can be cut out and used as still images. The photo analysis unit can also analyze the contents of a video clip to generate an album that combines still images and videos. It can also perform frame analysis of a video clip to extract specific scenes. This allows for the provision of a richer album by combining still images and videos.

[0071] The photo analysis unit can automatically identify photos of different family members and generate individual albums. The photo analysis unit can automatically identify photos of different family members and generate individual albums using, for example, facial recognition technology. For example, a pre-registered face database can be used to identify family members. Real-time facial recognition can also be performed to identify family members. Photos can also be selected for each family member and individual albums can be generated. This allows for more personalized memories by identifying photos of different family members and generating individual albums.

[0072] The audio generation unit can use the emotion estimation function to generate music and narration that match the emotions of the people in the photos based on their emotions. For example, the audio generation unit can use the emotion estimation function to analyze the emotions of the people in the photos and generate music and narration that match those emotions. For example, the audio generation unit can select music based on the emotion analysis results. It can also adjust the tone of the narration. It can also apply audio effects according to the emotions. This allows the audio generation unit to generate music and narration that match the emotions, thereby providing a more emotional album.

[0073] The audio generation unit can automatically select music related to a particular season or event based on the content of the photo. For example, the audio generation unit analyzes the content of the photo and automatically selects music related to a particular season or event. For example, music can be selected using a music library for each season. A music template according to the event can also be used. Music related to a particular season or event can also be selected by analyzing background information of the photo. This allows for a more consistent album to be provided by selecting music related to a particular season or event.

[0074] The audio generation unit can generate a narration about a specific episode or memory based on text information provided by the user. The audio generation unit generates a narration about a specific episode or memory based on, for example, text information provided by the user. For example, the narration can be generated using text analysis technology. The length of the narration can also be adjusted based on the importance of the episode. The narration about a specific episode or memory can also be generated based on the text information provided by the user. This allows for a more personalized album to be provided by generating a narration based on the text information provided by the user.

[0075] In addition to generating audio, the audio generation unit can analyze audio messages provided by the user and incorporate them into the album. The audio generation unit, for example, analyzes audio messages provided by the user and incorporates them into the album. For example, the audio generation unit can analyze audio messages using voice recognition technology. It can also analyze the format of an audio file and convert it into an appropriate format. It can also analyze the content of the audio message and use it as narration for the album. In this way, by incorporating audio messages provided by the user into the album, it is possible to provide a more personalized album.

[0076] The audio generation unit can automatically generate narration in different languages ​​to create an album from an international perspective. The audio generation unit can, for example, automatically generate narration in different languages ​​to create an album from an international perspective. For example, the audio generation unit can translate the narration into different languages ​​using translation technology. Narration in different languages ​​can also be generated using speech synthesis technology. Customization according to cultural backgrounds is also possible. In this way, by generating narration in different languages, an album from an international perspective can be provided.

[0077] The audio generation unit can use the emotion estimation function to generate audio that emphasizes a specific emotion based on the user's emotion. For example, the audio generation unit can analyze the user's emotion using the emotion estimation function and generate audio that emphasizes a specific emotion. For example, audio effects can be applied according to the emotion. The tone of the audio can also be adjusted. Music can also be selected based on the emotion. In this way, audio that emphasizes a specific emotion can be generated, thereby providing a more emotional album.

[0078] The image generation unit can use the emotion estimation function to generate an image that visually expresses the transition of emotions based on the emotions of the person appearing in the photograph. For example, the image generation unit uses the emotion estimation function to analyze the emotions of the person appearing in the photograph and generate an image that visually expresses the transition of emotions. For example, the transition of emotions can be displayed using a graph or chart. Color changes according to emotions can also be applied. Animations showing the transition of emotions can also be added. In this way, a more emotional image can be provided by visually expressing the transition of emotions.

[0079] The video generation unit can automatically generate video with a specific theme or storyline based on the content of the photo. For example, the video generation unit can analyze the content of the photo and automatically generate video with a specific theme or storyline. For example, the video generation unit can visualize the story of a family trip. It can also build a storyline based on a specific event or occurrence. It can also apply effects and transitions according to the theme. This allows for the generation of video with a specific theme or storyline, thereby providing more consistent video.

[0080] The video generation unit can dynamically change the transition effects between photos based on the content and background of the photos. For example, the video generation unit analyzes the content and background of the photos and dynamically changes the transition effects based on that. For example, a fade-in / fade-out effect can be used for photos of natural scenery, and a slide effect can be used for photos of urban landscapes. Transition effects can also be customized according to the content of the photos. This allows for more visually appealing images to be provided by dynamically changing the transition effects based on the content and background of the photos.

[0081] In addition to generating videos, the video generation unit can generate hybrid videos that combine video clips provided by the user. For example, the video generation unit analyzes video clips provided by the user and generates hybrid videos that combine them with still images. For example, it can extract specific scenes and display them together with the still images. It can also analyze the contents of the video clips to generate videos that combine still images and moving images. It can also perform frame analysis of the video clips to extract specific scenes. This allows for the generation of hybrid videos that combine video clips, thereby providing richer videos.

[0082] The video generation unit can generate an album video of the entire family by combining photos of different family members. The video generation unit can automatically identify photos of different family members using, for example, facial recognition technology, and generate an album video of the entire family. For example, it can display separate sections for each family member. It can also combine photos of the entire family into a single video. It can also set selection criteria for photos for each family member. This allows the generation of an album video of the entire family, providing memories with a more united feeling.

[0083] The video generation unit can use the emotion estimation function to generate a video that emphasizes a specific emotion based on the user's emotion. For example, the video generation unit can use the emotion estimation function to analyze the user's emotion and generate a video that emphasizes a specific emotion. For example, the video generation unit can apply an effect according to the emotion. It can also adjust the color of the video. It can also select music based on the emotion. In this way, it is possible to generate a video that emphasizes a specific emotion and provide a more emotional video.

[0084] The album customization unit can make customization suggestions based on the user's emotions using the emotion estimation function. The album customization unit, for example, analyzes the user's emotions using the emotion estimation function and makes customization suggestions. For example, it can suggest moving music or effects for moving moments. It can also make customization suggestions based on the user's preferences. It can also make suggestions based on past customization history. In this way, by making customization suggestions based on the user's emotions, it is possible to provide a more personalized album.

[0085] The album customization unit can suggest customization related to specific episodes or memories based on text information provided by the user. The album customization unit can suggest customization related to specific episodes or memories based on, for example, text information provided by the user. For example, the album customization unit can evaluate the importance of an episode using text analysis technology and suggest customization. Customization suggestions can also be made based on the content of the episode. Customization related to specific episodes or memories can also be made based on the text information provided by the user. In this way, by suggesting customization based on the text information provided by the user, a more personalized album can be provided.

[0086] The album customization unit can dynamically change the customization options based on the content and background of the photo. For example, the album customization unit analyzes the content and background of the photo and dynamically changes the customization options based on the content and background of the photo. For example, a fade-in / fade-out effect can be suggested for photos of natural scenery. A slide effect can also be suggested for photos of urban landscapes. The customization options can also be customized according to the content of the photo. This makes it possible to provide a more visually appealing album by dynamically changing the customization options based on the content and background of the photo.

[0087] In addition to customization, the album customization unit can provide customization options that combine video clips provided by the user. For example, the album customization unit analyzes video clips provided by the user and provides customization options that combine still images. For example, it can extract specific scenes and display them together with still images. It can also analyze the content of video clips and provide customization options that combine still images and moving images. It can also perform frame analysis of video clips and extract specific scenes. This allows for providing customization options that combine video clips, thereby providing a richer album.

[0088] The album customization unit can provide a customized album for the entire family by combining photos of different family members. The album customization unit can automatically identify photos of different family members using, for example, facial recognition technology, and provide a customized album for the entire family. For example, it can display separate sections for each family member. It can also combine photos of the entire family into a single album. It can also set photo selection criteria for each family member. This allows for customized albums for the entire family, creating memories that are more unified.

[0089] The album customization unit can use the emotion estimation function to suggest customizations that emphasize specific emotions based on the user's emotions. The album customization unit, for example, uses the emotion estimation function to analyze the user's emotions and suggest customizations that emphasize specific emotions. For example, it can suggest moving music or effects for moving moments. It can also suggest color changes according to emotions. It can also suggest layout adjustments based on emotions. In this way, it is possible to provide a more emotional album by suggesting customizations that emphasize specific emotions.

[0090] The album storage unit can make suggestions for saving and sharing based on the user's emotions using the emotion estimation function. The album storage unit, for example, analyzes the user's emotions using the emotion estimation function and makes suggestions for saving and sharing. For example, it can suggest moving music or effects for moving moments. It can also make suggestions for saving and sharing based on the user's preferences. It can also make suggestions based on past saving history. In this way, it is possible to provide a more personalized album by making suggestions for saving and sharing based on the user's emotions.

[0091] The album storage unit can make suggestions for saving and sharing specific episodes and memories based on text information provided by the user. The album storage unit can make suggestions for saving and sharing specific episodes and memories based on, for example, text information provided by the user. For example, the album storage unit can use text analysis technology to evaluate the importance of an episode and make suggestions for saving and sharing. It can also make suggestions for saving and sharing based on the content of the episode. It can also make suggestions for saving and sharing specific episodes and memories based on text information provided by the user. This makes it possible to provide a more personalized album by making suggestions for saving and sharing based on the text information provided by the user.

[0092] The album storage unit can dynamically change the save and share options based on the content and background of the photo. For example, the album storage unit analyzes the content and background of the photo and dynamically changes the save and share options based on the content and background of the photo. For example, a fade-in / fade-out effect can be suggested for photos of natural scenery. A slide effect can also be suggested for photos of cityscapes. The save and share options can also be customized according to the content of the photo. This allows for a more visually appealing album to be provided by dynamically changing the save and share options based on the content and background of the photo.

[0093] The album storage unit can provide storage and sharing options that combine video clips provided by the user in addition to storage and sharing. For example, the album storage unit can analyze video clips provided by the user and provide storage and sharing options that combine still images. For example, it can extract specific scenes and display them together with still images. It can also analyze the content of video clips to provide storage and sharing options that combine still images and videos. It can also perform frame analysis of video clips to extract specific scenes. This allows for a richer album to be provided by providing storage and sharing options that combine video clips.

[0094] The album storage unit can provide an option to store and share an album for the entire family that combines photos of different family members. The album storage unit can automatically identify photos of different family members using, for example, facial recognition technology, and provide an option to store and share an album for the entire family. For example, a separate section can be displayed for each family member. Photos of the entire family can also be combined into a single album. Photo selection criteria can also be set for each family member. This allows for an option to store and share an album for the entire family, thereby providing a more unified memory.

[0095] The album storage unit can use the emotion estimation function to suggest save and share options that emphasize a particular emotion based on the user's emotion. For example, the album storage unit can analyze the user's emotion using the emotion estimation function and suggest save and share options that emphasize a particular emotion. For example, the album storage unit can suggest emotional music or effects for an emotional moment. It can also suggest color changes according to the emotion. It can also suggest layout adjustments based on the emotion. In this way, by suggesting save and share options that emphasize a particular emotion, it is possible to provide a more emotional album.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The audio-visual album generation system may also include a recording unit that can record the user's voice in real time and incorporate it into the album. For example, a more personalized narration can be added by the user sharing their thoughts and memories while browsing the album. The recording unit can also analyze the user's voice and emphasize specific keywords or phrases. The recording unit can also analyze the tone and emotion of the user's voice and adjust the music and effects accordingly. This allows the user's voice to be recorded in real time and incorporated into the album, providing a more personal and emotional album.

[0098] The audio / video album generation system may further include a text analysis unit that can analyze text messages provided by the user and incorporate them into the album. For example, a user can input stories or memories related to a photo in text, and generate a narration based on that content. The text analysis unit may also analyze the emotion of the text and adjust the music and effects based on that emotion. Furthermore, the text analysis unit may provide a more emotional album by emphasizing specific keywords and phrases. This allows a more personalized album to be provided by analyzing text messages provided by the user and incorporating them into the album.

[0099] The audio / video album generation system may further include a video analysis unit that can analyze video messages provided by users and incorporate them into an album. For example, a user may record a video message related to a specific photo or event and incorporate it into an album, thereby providing richer content. The video analysis unit may also analyze the content of a video and extract specific scenes to use as still images. The video analysis unit may also analyze the emotions in the video and adjust the music and effects based on the analysis. In this way, a more emotional album can be provided by analyzing video messages provided by users and incorporating them into an album.

[0100] The audio / video album generation system may further include a social media analysis unit that can analyze a user's social media posts and incorporate them into an album. For example, by analyzing photos and comments that a user has previously posted on social media and incorporating them into an album, a more coherent story can be provided. The social media analysis unit may also analyze the emotions of posts and adjust music and effects based on the analysis. Furthermore, the social media analysis unit may provide a more emotional album by emphasizing specific hashtags and keywords. In this way, a more personalized album can be provided by analyzing a user's social media posts and incorporating them into an album.

[0101] The audio / video album generation system may further include a biometric information analysis unit that can analyze the user's biometric information and reflect it in the album. For example, the user's heart rate and electrodermal activity may be analyzed and the data reflected in the emotional expression of the album. The biometric information analysis unit may also adjust music and effects based on the user's biometric information. Furthermore, the biometric information analysis unit may provide a more emotional album by emphasizing a specific emotional state. In this way, a more personalized album can be provided by analyzing the user's biometric information and reflecting it in the album.

[0102] The audio / video album generation system may further include a geographic information analysis unit that can analyze geographic information provided by the user and incorporate it into the album. For example, the geographic information of places visited by the user may be analyzed and photos and videos related to the places may be automatically grouped. The geographic information analysis unit may also generate music and narration related to a specific location. Furthermore, the geographic information analysis unit may incorporate maps and location information into the album to provide a more coherent story. Thus, by analyzing geographic information provided by the user and incorporating it into the album, a more personalized album may be provided.

[0103] The audio / video album generation system may further include a music analysis unit that can analyze a music library provided by a user and incorporate the music into the album. For example, the system may analyze a user's music library and automatically select music that matches a particular photo or video. The music analysis unit may also adjust the transitions and effects of the album based on the tempo and mood of the music. Furthermore, the music analysis unit may provide a customized album based on the user's musical preferences. This allows the system to provide a more personalized album by analyzing a user's music library and incorporating the music into the album.

[0104] The audio / video album generation system may further include a calendar analysis unit that can analyze calendar information provided by a user and incorporate it into an album. For example, the system may analyze the user's calendar information and automatically group photos and videos related to specific events or anniversaries. The calendar analysis unit may also generate narration and music related to specific events. Furthermore, the calendar analysis unit may incorporate a timeline of events into the album to provide a more coherent story. This allows the system to provide a more personalized album by analyzing the calendar information provided by a user and incorporating it into the album.

[0105] The audio / video album generation system may further include a message analysis unit that can analyze emails and messages provided by the user and incorporate them into the album. For example, by analyzing emails and messages sent and received by the user in the past and incorporating them into the album, a more consistent story can be provided. The message analysis unit may also generate narration and music based on the content of the messages. Furthermore, the message analysis unit may provide a more emotional album by emphasizing specific keywords and phrases. In this way, by analyzing emails and messages provided by the user and incorporating them into the album, a more personalized album can be provided.

[0106] The audio / video album generation system may further include a cloud analysis unit that can analyze cloud storage data provided by the user and incorporate it into an album. For example, by analyzing photos and videos stored by the user in cloud storage and incorporating them into an album, a more coherent story can be provided. The cloud analysis unit may also analyze metadata from the cloud storage and automatically group content related to a specific event or location. The cloud analysis unit may also generate narration and music based on the cloud storage data. In this way, by analyzing cloud storage data provided by the user and incorporating it into an album, a more personalized album can be provided.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The photo analyzer analyzes the photos provided by the user. For example, the photo analyzer may use facial recognition technology to select photos of specific people. The photo analyzer may also consider the quality and resolution of the photos when selecting photos. Furthermore, the photo analyzer may analyze the background information of the photos and automatically group photos related to specific places or events. Step 2: The audio generation unit generates audio based on the results of the analysis by the photo analysis unit. For example, the audio generation unit generates narration and music based on the content and background of the photo. The audio generation unit can also generate specific narration based on episode information provided by the user. Furthermore, the audio generation unit can use an emotion estimation function to generate audio based on the emotions of people appearing in the photo. Step 3: The video generation unit generates a video by combining the photos analyzed by the photo analysis unit and the audio generated by the audio generation unit. For example, the video generation unit displays the photos in a slideshow format and adds audio or music to the background. The video generation unit can also automatically add transitions and effects between photos. Furthermore, the video generation unit can use an emotion estimation function to generate a video based on the emotions of the people in the photos.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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]

[0176] 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 analysis unit that analyzes a photo provided by a user; a sound generating unit that generates sound based on the result of the analysis by the photo analyzing unit; a video generation unit that generates a video by combining the photo analyzed by the photo analysis unit and the audio generated by the audio generation unit. A system characterized by:

2. The photo analysis unit Use facial recognition technology to select photos of specific people 2. The system of claim 1.

3. The voice generation unit Automatically select music related to a particular season or event based on the content of the photo.

2. The system of claim 1.

4. The image generation unit In addition to generating a video, a hybrid video is generated by combining the video clips provided by the user.

2. The system of claim 1.

5. Album customization section: Using an emotion estimation function, customization suggestions are made based on the user's emotions.

2. The system of claim 1.

6. The album storage department Using emotion estimation functionality, suggestions for saving and sharing are made based on the user's emotions.

2. The system of claim 1.

7. The photo analysis unit Using an emotion estimation function, the emotions of the people in the photos are analyzed, and an album is generated that tracks the changes in the emotions.

2. The system of claim 1.

Images