system

Abstract

An object of the system according to the embodiment is to automatically create a moving image with BGM based on a photograph and to easily share the moving image with friends and family.SOLUTION: A system according to an embodiment includes a photograph analysis unit, a moving image generation unit, a BGM generation unit, and a sharing unit. The photograph analysis unit analyzes a photograph. The moving image generation unit generates a moving image based on the photograph analyzed by the photograph analysis unit. The BGM generation unit generates BGM in accordance with the moving image generated by the moving image generation unit. The sharing unit shares the moving image and the BGM with friends or family.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] With conventional technology, the process of creating a video from photos, adding background music, and sharing it was time-consuming and difficult to do efficiently.

[0005] The system according to the embodiment aims to automatically create videos with background music based on photos and easily share them with friends and family. [Means for solving the problem]

[0006] The system according to the embodiment includes a photo analysis unit, a video generation unit, a background music generation unit, and a sharing unit. The photo analysis unit analyzes photos. The video generation unit generates videos based on the photos analyzed by the photo analysis unit. The background music generation unit generates background music to match the videos generated by the video generation unit. The sharing unit shares the videos and background music with friends or family. [Effects of the Invention]

[0007] The system according to the embodiment automatically creates videos with background music based on photos, allowing users to easily share them with friends and family. [Brief explanation of the drawings]

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

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

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

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

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

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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 video generation system according to an embodiment of the present invention is a system that automatically creates videos with background music using photos registered in a LINE album and shares them with friends and family. This system allows users to select a theme or create videos completely automatically. This allows the video generation system to easily create high-quality videos and share them with friends and family.

[0029] A video generation system according to an embodiment includes a photo analysis unit, a video generation unit, a background music (BGM) generation unit, and a sharing unit. The photo analysis unit analyzes photos posted to a LINE album by a user. For example, the photo analysis unit identifies people in the photos using face recognition technology. The photo analysis unit can also identify objects in the photos using object detection technology. The photo analysis unit can also analyze the color tones of the photos using color analysis technology. The video generation unit generates videos based on the photos analyzed by the photo analysis unit. For example, the video generation unit adjusts the frame rate to generate smooth videos. The video generation unit can also adjust the resolution to generate high-quality videos. The video generation unit can also apply effects to generate visually appealing videos. The background music (BGM) generation unit generates background music to match the video generated by the video generation unit. For example, the BGM generation unit selects a music genre to generate appropriate background music. The BGM generation unit can also adjust the tempo to generate background music that matches the video. The BGM generation unit can also adjust the volume to generate balanced background music. The sharing unit shares the video and background music with friends and family. For example, the sharing unit sends a link to the generated video via a LINE message. The sharing unit can also post the generated video to a social networking service platform. The sharing unit can also store the generated video in cloud storage and provide a sharing link. This allows the video generation system according to the embodiment to enable users to easily create high-quality videos and share them with friends and family. For example, a user can create a video summarizing travel memories and send it to family. A user can also share videos recording daily events with friends. Sharing on social networking services is also easy, allowing users to create short videos and have many people watch them.

[0030] The photo analysis unit can analyze the metadata of photos and generate a video that reflects the travel route or the flow of time. The photo analysis unit, for example, analyzes the location information of photos and displays the travel route on a map. For example, the route from the departure point to the destination of the trip is shown in the video, and photos taken at each location are displayed in sequence. The photo analysis unit can also analyze the shooting date and time and generate a video that reflects the flow of time. For example, photos are arranged in chronological order based on the shooting date and time to generate a video that shows the flow of time. The photo analysis unit can also combine the location information and the shooting date and time to generate a video that shows a more detailed travel route. This makes it possible to generate a more realistic video that reflects the travel route and the flow of time.

[0031] The photo analysis unit can analyze the color tone or brightness of photos and automatically apply a filter to unify the tone of the entire video. The photo analysis unit, for example, analyzes the color tone of photos and automatically applies a filter to unify the tone of the entire video. For example, if there are many warm-colored photos, a filter that unifies the overall warm tone is applied. The photo analysis unit can also analyze brightness and apply a filter that adjusts the brightness of the entire video. For example, if there are many dark photos, a filter that brightens the entire video is applied. The photo analysis unit can also apply a filter that combines color tone and brightness to achieve a more consistent tone. This unifies the tone of the entire video, thereby generating a more consistent video.

[0032] The video generation unit can learn the style of videos previously created by the user and generate new videos in a similar style. The video generation unit, for example, learns the style of videos previously created by the user and generates new videos in a similar style. For example, the video generation unit automatically applies filters and effects used in the past videos. The video generation unit can also generate new videos based on the frame rate and resolution of the past videos. For example, if the past videos were high resolution, the new videos will also be generated in high resolution. The video generation unit can also generate new videos based on the effects and transitions of the past videos. For example, the transition effects used in the past videos will be applied to the new video. In this way, by learning past styles, videos that suit the user's preferences can be generated.

[0033] The video generation unit can automatically suggest different themes based on the results of photo analysis, allowing the user to select from them. The video generation unit, for example, automatically suggests different themes based on the results of photo analysis. For example, it suggests themes such as travel, family, and friends, allowing the user to select from them. The video generation unit can also suggest themes based on the content of the photos. For example, if there are many natural landscapes, it suggests a theme such as "nature," and if there are many urban landscapes, it suggests a theme such as "city." The video generation unit can also suggest themes based on the user's past preferences. For example, it can suggest a new theme based on a theme selected in the past. This automatically suggests different themes, thereby expanding the user's options.

[0034] The BGM generation unit can automatically select a specific music genre based on the results of photo analysis and generate BGM based on that music genre. The BGM generation unit automatically selects a specific music genre based on the results of photo analysis. For example, classical music may be selected if there are many natural landscapes, and pop music may be selected if there are many urban landscapes. The BGM generation unit can also select a music genre based on the content of the photos. For example, pop music may be selected if there are many people in the photos, and classical music may be selected if there are many landscape photos. The BGM generation unit can also select a music genre based on the user's past preferences. For example, new BGM may be generated based on a music genre selected in the past. This allows the atmosphere of the video to be optimized by generating BGM based on a specific music genre.

[0035] The BGM generation unit can generate BGM incorporating natural sounds based on the results of photo analysis. The BGM generation unit generates BGM incorporating natural sounds based on the results of photo analysis, for example. For example, if there are many photos of the sea, the sound of waves can be incorporated, and if there are many photos of forests, the sound of birds chirping can be incorporated. The BGM generation unit can also select natural sounds based on the content of the photos. For example, if there are many photos of mountains, the sound of wind can be incorporated, and if there are many photos of rivers, the sound of flowing water can be incorporated. The BGM generation unit can also select natural sounds based on the user's past preferences. For example, new BGM can be generated based on previously selected natural sounds. This allows for the generation of more realistic videos using BGM incorporating natural sounds.

[0036] The BGM generation unit can learn the style of BGM previously selected by the user and generate new BGM in a similar style. For example, the BGM generation unit can learn the style of BGM previously selected by the user and generate new BGM in a similar style. For example, new BGM can be generated based on the tempo and melody of the BGM previously selected. The BGM generation unit can also generate new BGM based on the volume and instrument selection of the BGM previously selected. For example, new BGM can be generated based on the volume balance of the BGM previously selected. The BGM generation unit can also generate new BGM based on the genre of the BGM previously selected. In this way, by learning past styles, BGM that suits the user's preferences can be generated.

[0037] The BGM generation unit can automatically generate different BGM variations based on the results of photo analysis, allowing the user to select from them. The BGM generation unit automatically generates different BGM variations based on the results of photo analysis, for example. For example, it generates BGM with a fun atmosphere, BGM with an inspiring atmosphere, BGM with a relaxing atmosphere, etc. The BGM generation unit can also generate BGM variations based on the content of the photo. For example, it can generate pop music if the photo contains many people, and classical music if the photo contains many landscape photos. The BGM generation unit can also generate BGM variations based on the user's past preferences. For example, it can generate new variations based on the style of BGM selected in the past. This automatically generates different BGM variations, expanding the user's options.

[0038] The video generation unit can generate short videos in a format optimized for a specific social media platform based on the photo analysis results. For example, the video generation unit generates short videos in a format optimized for a specific social media platform based on the photo analysis results. For example, the video generation unit generates videos in a square format for Instagram and a landscape format for Twitter. The video generation unit can also generate videos based on the specifications of the social media platform. For example, the video generation unit generates videos in a portrait format for Instagram Stories. The video generation unit can also generate videos based on trends on the social media platform. For example, the video generation unit generates short videos that match trends on TikTok. By generating short videos in a format optimized for a specific social media platform, the videos can be shared more effectively.

[0039] The video generation unit can learn the style of short videos created by the user in the past and generate new short videos in a similar style. The video generation unit, for example, learns the style of short videos created by the user in the past and generates new short videos in a similar style. For example, the video generation unit automatically applies filters and effects used in the past videos. The video generation unit can also generate new short videos based on the frame rate and resolution of the past videos. For example, if the past videos were high resolution, the new videos will also be generated in high resolution. The video generation unit can also generate new short videos based on the effects and transitions of the past videos. For example, the transition effects used in the past videos will be applied to the new video. In this way, by learning past styles, short videos that suit the user's preferences can be generated.

[0040] The video generation unit can automatically generate different variations of short videos based on the results of photo analysis, allowing the user to select from them. The video generation unit automatically generates different variations of short videos based on, for example, the results of photo analysis. For example, it generates videos with a fun atmosphere, videos with an emotional atmosphere, videos with a relaxing atmosphere, etc. The video generation unit can also generate variations of short videos based on the content of the photos. For example, it can generate variations such as pop music if there are many people in the photos, or classical music if there are many landscape photos. The video generation unit can also generate variations of short videos based on the user's past preferences. For example, it can generate new variations based on a style selected in the past. This automatically generates different variations of short videos, thereby expanding the user's options.

[0041] The video generation unit can learn past user preferences based on the photo analysis results and generate a video in a completely automatic mode based on that. The video generation unit, for example, learns past user preferences based on the photo analysis results and generates a video in a completely automatic mode based on that. For example, a new video is generated based on a theme or background music selected in the past. The video generation unit can also adjust the style of the video based on past user preferences. For example, a new video is generated based on filters or effects selected in the past. The video generation unit can also adjust the content of the video based on past user preferences. For example, a new video is generated based on a scene composition selected in the past. In this way, by learning past preferences, a completely automatic mode video that suits the user's preferences can be generated.

[0042] The video generation unit can automatically adjust scene transitions and effects in a video based on the results of photo analysis. The video generation unit automatically adjusts scene transitions in a video based on the results of photo analysis. For example, it applies transition effects according to the content of the photo to smooth the flow of the video. The video generation unit can also automatically adjust effects based on the results of photo analysis. For example, it can apply effects to match the color tones of the photo to unify the tone of the entire video. The video generation unit can also adjust the length of scenes based on the results of photo analysis. For example, it can display important scenes for longer and supplementary scenes for shorter scenes. This makes it possible to generate more attractive videos by automatically adjusting scene transitions and effects.

[0043] The video generation unit can automatically generate a video generated in the completely automatic mode in different themes or styles, allowing the user to select. The video generation unit, for example, automatically generates a video generated in the completely automatic mode in different themes or styles. For example, it generates a video with a fun atmosphere, a video with an emotional atmosphere, a video with a relaxing atmosphere, etc. The video generation unit can also generate a video generated in the completely automatic mode in different themes or styles. For example, it generates a video based on a theme such as travel, family, or friends. The video generation unit can also generate a video generated in the completely automatic mode in different styles. For example, it generates a video based on a classic style, a modern style, a casual style, etc. This automatically generates videos with different themes and styles, thereby expanding the user's options.

[0044] The sharing unit can share the generated video at the optimal timing based on the recipient's past viewing history. For example, when sharing the generated video, the sharing unit analyzes the recipient's past viewing history and shares at the optimal timing. For example, the sharing unit shares the video at the same time based on the time period in which the recipient watched videos in the past. The sharing unit can also adjust the sharing timing based on the recipient's viewing history. For example, if the recipient often watches videos at night, the video is shared at night. The sharing unit can also select the sharing timing based on the recipient's viewing history and suggest it to the user. This allows videos to be shared more effectively by sharing at the optimal timing based on the recipient's viewing history.

[0045] The sharing unit can automatically convert the generated video into a format optimized for the recipient's device when sharing the generated video. For example, the sharing unit automatically converts the video into a format optimized for the recipient's device when sharing the generated video. For example, the sharing unit converts the video into a portrait format for smartphones and a landscape format for PCs. The sharing unit can also convert the video based on the specifications of the recipient's device. For example, the sharing unit converts the video to match the resolution of the smartphone. The sharing unit can also convert the video based on trends in the recipient's device. For example, the sharing unit converts the video into a format optimized for the latest smartphones. This allows the video to be shared more effectively by converting it into a format optimized for the recipient's device.

[0046] The sharing unit can add a function to automatically post the generated video to different social media platforms. The sharing unit adds a function to automatically post the generated video to different social media platforms. For example, the sharing unit can simultaneously post to Instagram, Twitter, Facebook, etc. The sharing unit can also post videos based on the specifications of the social media platforms. For example, the sharing unit can post videos in a vertical format for Instagram Stories. The sharing unit can also post videos based on trends on the social media platforms. For example, the sharing unit can post short videos that match TikTok trends. This allows the video to be shared with more people by automatically posting to different social media platforms.

[0047] The sharing unit can collect feedback from recipients when sharing the generated video and reflect it in the generation of the next video. For example, the sharing unit collects feedback from recipients when sharing the generated video and reflects the results in the generation of the next video. For example, the content of the video is adjusted based on the recipient's ratings and comments. The sharing unit can also analyze the feedback and reflect it in the generation of the next video. For example, the style of the video is adjusted based on the recipient's feedback. The sharing unit can also adjust the content of the video based on the feedback and suggest it to the user. In this way, by collecting feedback and reflecting it in the generation of the next video, a video that better suits the user's preferences can be generated.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The video generation system can further include a voice synthesis unit. The voice synthesis unit can add narration to the video based on the results of photo analysis. For example, if there are many travel photos, a narration telling stories from the travels can be added. The voice synthesis unit can also recognize the names of people in the photos and generate narration including those names. For example, if there are many family photos, a narration calling out the names of family members can be added. The voice synthesis unit can also generate narration based on text entered by the user. This allows for the generation of videos with narration, making the video feel more realistic.

[0050] The video generation system can also add infographics to videos based on the results of photo analysis. For example, it can display a map showing the travel route or information about the places visited. It can also add a timeline showing the flow of time based on the date and time the photos were taken. For example, it can display a timeline showing each day of the trip. It can also display statistical information about the places visited based on the location information of the photos. For example, it can add infographics showing the number of cities visited and the distance traveled. By adding infographics, it is possible to generate videos that are richer in information.

[0051] The video generation system can also add animations to videos based on the results of photo analysis. For example, it can add animations of people in the photo moving or objects moving. It can also add animations to the background based on the content of the photo. For example, if there are many natural landscapes, it can add animations of wind blowing or birds flying. It can also adjust the color of the animation based on the color tone of the photo. For example, it can add warm-colored animations to photos with warm colors. In this way, adding animations can generate videos with more movement.

[0052] The video generation system can also add text overlays to videos based on the results of photo analysis. For example, it can display the names of people in the photo or the name of the location where the photo was taken. It can also add captions based on the content of the photo. For example, it can display information about the travel destination for travel photos. It can also display the date and time based on the date and time the photo was taken. For example, it can display the date for photos taken on a specific date. By adding text overlays, it is possible to generate videos that are richer in information.

[0053] The video generation system can also add interactive elements to the video based on the results of photo analysis. For example, it can add an interactive map that displays detailed information when the user clicks on it. It can also add an interactive element that displays information about a person in a photo when the person is clicked on. It can also add an interactive menu that provides options that the user can select based on the content of the photo. For example, it can provide an option to display information about the travel destination for travel photos. By adding interactive elements, it is possible to generate videos that are more user-participatory.

[0054] The video generation system can also add 3D effects to videos based on the results of photo analysis. For example, people and objects in the photo can be recreated as 3D models and moved within the video. 3D effects can also be added to the background based on the content of the photo. For example, if there are many natural landscapes, 3D trees and mountains can be added. 3D models of places visited can also be displayed based on the location information of the photo. For example, buildings and landmarks from travel destinations can be recreated in 3D. By adding 3D effects, videos with a more three-dimensional feel can be generated.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The photo analysis unit analyzes photos that users have added to their LINE albums. For example, the photo analysis unit may use face recognition technology to identify people in the photos, object detection technology to identify objects in the photos, and color analysis technology to analyze the colors of the photos. Step 2: The video generator generates a video based on the photos analyzed by the photo analyzer. For example, the video generator adjusts the frame rate to generate a smooth video, adjusts the resolution to generate a high-quality video, and applies effects to generate a visually appealing video. Step 3: The BGM generator generates BGM to match the video generated by the video generator. For example, the BGM generator selects a music genre to generate appropriate BGM, adjusts the tempo to generate BGM that matches the video, and adjusts the volume to generate balanced BGM. Step 4: The sharing unit shares the video and background music with friends and family. For example, the sharing unit may send a link to the generated video via a LINE message, post the generated video to a social networking platform, save the generated video in cloud storage, and provide a sharing link.

[0057] (Example 2) The video generation system according to an embodiment of the present invention is a system that automatically creates videos with background music using photos registered in a LINE album and shares them with friends and family. This system allows users to select a theme or create videos completely automatically. This allows the video generation system to easily create high-quality videos and share them with friends and family.

[0058] A video generation system according to an embodiment includes a photo analysis unit, a video generation unit, a background music (BGM) generation unit, and a sharing unit. The photo analysis unit analyzes photos posted to a LINE album by a user. For example, the photo analysis unit identifies people in the photos using face recognition technology. The photo analysis unit can also identify objects in the photos using object detection technology. The photo analysis unit can also analyze the color tones of the photos using color analysis technology. The video generation unit generates videos based on the photos analyzed by the photo analysis unit. For example, the video generation unit adjusts the frame rate to generate smooth videos. The video generation unit can also adjust the resolution to generate high-quality videos. The video generation unit can also apply effects to generate visually appealing videos. The background music (BGM) generation unit generates background music to match the video generated by the video generation unit. For example, the BGM generation unit selects a music genre to generate appropriate background music. The BGM generation unit can also adjust the tempo to generate background music that matches the video. The BGM generation unit can also adjust the volume to generate balanced background music. The sharing unit shares the video and background music with friends and family. For example, the sharing unit sends a link to the generated video via a LINE message. The sharing unit can also post the generated video to a social networking service platform. The sharing unit can also store the generated video in cloud storage and provide a sharing link. This allows the video generation system according to the embodiment to enable users to easily create high-quality videos and share them with friends and family. For example, a user can create a video summarizing travel memories and send it to family. A user can also share videos recording daily events with friends. Sharing on social networking services is also easy, allowing users to create short videos and have many people watch them.

[0059] The photo analysis unit can estimate the emotions of people in photos and compose video scenes based on those emotions. For example, the photo analysis unit uses a generation AI to analyze the facial expressions of people in photos and estimate emotions. For example, if there are many smiling photos, the video will be composed mainly of happy scenes, and if there are many touching scenes, the video will be composed mainly of touching scenes. The photo analysis unit can also estimate emotions using voice analysis technology. For example, it can analyze the tone and speed of voice and calculate an emotion score. The photo analysis unit can also estimate emotions using text analysis technology. For example, it can analyze the content of text and calculate an emotion score. This makes it possible to generate more moving videos by composing scenes based on emotions.

[0060] The photo analysis unit can analyze the metadata of photos and generate a video that reflects the travel route or the flow of time. The photo analysis unit, for example, analyzes the location information of photos and displays the travel route on a map. For example, the route from the departure point to the destination of the trip is shown in the video, and photos taken at each location are displayed in sequence. The photo analysis unit can also analyze the shooting date and time and generate a video that reflects the flow of time. For example, photos are arranged in chronological order based on the shooting date and time to generate a video that shows the flow of time. The photo analysis unit can also combine the location information and the shooting date and time to generate a video that shows a more detailed travel route. This makes it possible to generate a more realistic video that reflects the travel route and the flow of time.

[0061] The photo analysis unit can analyze the color tone or brightness of photos and automatically apply a filter to unify the tone of the entire video. The photo analysis unit, for example, analyzes the color tone of photos and automatically applies a filter to unify the tone of the entire video. For example, if there are many warm-colored photos, a filter that unifies the overall warm tone is applied. The photo analysis unit can also analyze brightness and apply a filter that adjusts the brightness of the entire video. For example, if there are many dark photos, a filter that brightens the entire video is applied. The photo analysis unit can also apply a filter that combines color tone and brightness to achieve a more consistent tone. This unifies the tone of the entire video, thereby generating a more consistent video.

[0062] The video generation unit can learn the style of videos previously created by the user and generate new videos in a similar style. The video generation unit, for example, learns the style of videos previously created by the user and generates new videos in a similar style. For example, the video generation unit automatically applies filters and effects used in the past videos. The video generation unit can also generate new videos based on the frame rate and resolution of the past videos. For example, if the past videos were high resolution, the new videos will also be generated in high resolution. The video generation unit can also generate new videos based on the effects and transitions of the past videos. For example, the transition effects used in the past videos will be applied to the new video. In this way, by learning past styles, videos that suit the user's preferences can be generated.

[0063] The video generation unit can automatically suggest different themes based on the results of photo analysis, allowing the user to select from them. The video generation unit, for example, automatically suggests different themes based on the results of photo analysis. For example, it suggests themes such as travel, family, and friends, allowing the user to select from them. The video generation unit can also suggest themes based on the content of the photos. For example, if there are many natural landscapes, it suggests a theme such as "nature," and if there are many urban landscapes, it suggests a theme such as "city." The video generation unit can also suggest themes based on the user's past preferences. For example, it can suggest a new theme based on a theme selected in the past. This automatically suggests different themes, thereby expanding the user's options.

[0064] The video generation unit can use the emotion estimation function to automatically select a video theme based on the emotions of the people in the photos and suggest it to the user. The video generation unit, for example, uses the emotion estimation function to automatically select a video theme based on the emotions of the people in the photos. For example, if there are many smiling photos, the video generation unit suggests a theme such as "happy memories," and if there are many photos with touching expressions, the video generation unit suggests a theme such as "touching moments." The video generation unit can also use the emotion estimation function to analyze the emotions of the people in the photos and select a theme. For example, if the people in the photos have a happy expression, the video generation unit suggests a theme such as "happy memories," and if the people in the photos have a serious expression, the video generation unit suggests a theme such as "serious moments." The video generation unit can also use the emotion estimation function to select a theme based on the emotions of the people in the photos and suggest it to the user. This allows for the generation of more touching videos by selecting a theme based on emotions.

[0065] The BGM generation unit can automatically select a specific music genre based on the results of photo analysis and generate BGM based on that music genre. The BGM generation unit automatically selects a specific music genre based on the results of photo analysis. For example, classical music may be selected if there are many natural landscapes, and pop music may be selected if there are many urban landscapes. The BGM generation unit can also select a music genre based on the content of the photos. For example, pop music may be selected if there are many people in the photos, and classical music may be selected if there are many landscape photos. The BGM generation unit can also select a music genre based on the user's past preferences. For example, new BGM may be generated based on a music genre selected in the past. This allows the atmosphere of the video to be optimized by generating BGM based on a specific music genre.

[0066] The BGM generation unit can generate BGM incorporating natural sounds based on the results of photo analysis. The BGM generation unit generates BGM incorporating natural sounds based on the results of photo analysis, for example. For example, if there are many photos of the sea, the sound of waves can be incorporated, and if there are many photos of forests, the sound of birds chirping can be incorporated. The BGM generation unit can also select natural sounds based on the content of the photos. For example, if there are many photos of mountains, the sound of wind can be incorporated, and if there are many photos of rivers, the sound of flowing water can be incorporated. The BGM generation unit can also select natural sounds based on the user's past preferences. For example, new BGM can be generated based on previously selected natural sounds. This allows for the generation of more realistic videos using BGM incorporating natural sounds.

[0067] The BGM generation unit can learn the style of BGM previously selected by the user and generate new BGM in a similar style. For example, the BGM generation unit can learn the style of BGM previously selected by the user and generate new BGM in a similar style. For example, new BGM can be generated based on the tempo and melody of the BGM previously selected. The BGM generation unit can also generate new BGM based on the volume and instrument selection of the BGM previously selected. For example, new BGM can be generated based on the volume balance of the BGM previously selected. The BGM generation unit can also generate new BGM based on the genre of the BGM previously selected. In this way, by learning past styles, BGM that suits the user's preferences can be generated.

[0068] The BGM generation unit can automatically generate different BGM variations based on the results of photo analysis, allowing the user to select from them. The BGM generation unit automatically generates different BGM variations based on the results of photo analysis, for example. For example, it generates BGM with a fun atmosphere, BGM with an inspiring atmosphere, BGM with a relaxing atmosphere, etc. The BGM generation unit can also generate BGM variations based on the content of the photo. For example, it can generate pop music if the photo contains many people, and classical music if the photo contains many landscape photos. The BGM generation unit can also generate BGM variations based on the user's past preferences. For example, it can generate new variations based on the style of BGM selected in the past. This automatically generates different BGM variations, expanding the user's options.

[0069] The BGM generation unit can use the emotion estimation function to automatically adjust the atmosphere of the BGM based on the emotions of the people in the photo and suggest it to the user. The BGM generation unit, for example, uses the emotion estimation function to automatically adjust the atmosphere of the BGM based on the emotions of the people in the photo. For example, if there are many photos of people with happy expressions, it generates BGM with a bright and cheerful atmosphere. The BGM generation unit can also use the emotion estimation function to analyze the emotions of the people in the photo and adjust the atmosphere of the BGM. For example, if there are many photos of people with moving expressions, it generates BGM with a moving atmosphere. The BGM generation unit can also use the emotion estimation function to adjust the atmosphere of the BGM based on the emotions of the people in the photo and suggest it to the user. In this way, a more moving video can be generated by adjusting the atmosphere of the BGM based on emotions.

[0070] The video generation unit can estimate the emotions of people in photos and compose scenes for short videos based on those emotions. For example, the video generation unit uses a generation AI to analyze the facial expressions of people in photos and estimate emotions. For example, if there are many smiling photos, the video will be composed mainly of happy scenes, and if there are many moving scenes, the video will be composed mainly of moving scenes. The video generation unit can also estimate emotions using audio analysis technology. For example, it can analyze the tone and speed of audio and calculate an emotion score. The video generation unit can also estimate emotions using text analysis technology. For example, it can analyze the content of text and calculate an emotion score. This makes it possible to generate more moving short videos by composing scenes based on emotions.

[0071] The video generation unit can generate short videos in a format optimized for a specific social media platform based on the photo analysis results. For example, the video generation unit generates short videos in a format optimized for a specific social media platform based on the photo analysis results. For example, the video generation unit generates videos in a square format for Instagram and a landscape format for Twitter. The video generation unit can also generate videos based on the specifications of the social media platform. For example, the video generation unit generates videos in a portrait format for Instagram Stories. The video generation unit can also generate videos based on trends on the social media platform. For example, the video generation unit generates short videos that match trends on TikTok. By generating short videos in a format optimized for a specific social media platform, the videos can be shared more effectively.

[0072] The video generation unit can learn the style of short videos created by the user in the past and generate new short videos in a similar style. The video generation unit, for example, learns the style of short videos created by the user in the past and generates new short videos in a similar style. For example, the video generation unit automatically applies filters and effects used in the past videos. The video generation unit can also generate new short videos based on the frame rate and resolution of the past videos. For example, if the past videos were high resolution, the new videos will also be generated in high resolution. The video generation unit can also generate new short videos based on the effects and transitions of the past videos. For example, the transition effects used in the past videos will be applied to the new video. In this way, by learning past styles, short videos that suit the user's preferences can be generated.

[0073] The video generation unit can automatically generate different variations of short videos based on the results of photo analysis, allowing the user to select from them. The video generation unit automatically generates different variations of short videos based on, for example, the results of photo analysis. For example, it generates videos with a fun atmosphere, videos with an emotional atmosphere, videos with a relaxing atmosphere, etc. The video generation unit can also generate variations of short videos based on the content of the photos. For example, it can generate variations such as pop music if there are many people in the photos, or classical music if there are many landscape photos. The video generation unit can also generate variations of short videos based on the user's past preferences. For example, it can generate new variations based on a style selected in the past. This automatically generates different variations of short videos, thereby expanding the user's options.

[0074] The video generation unit can use the emotion estimation function to automatically select a theme for a short video based on the emotions of the people in the photo and suggest it to the user. For example, the video generation unit can use the emotion estimation function to automatically select a theme for a short video based on the emotions of the people in the photo. For example, if there are many smiling photos, the video generation unit can suggest a theme such as "happy memories," and if there are many photos with touching expressions, the video generation unit can suggest a theme such as "touching moments." The video generation unit can also use the emotion estimation function to analyze the emotions of the people in the photo and select a theme. For example, if the people in the photo have a happy expression, the video generation unit can suggest a theme such as "happy memories," and if the people in the photo have a serious expression, the video generation unit can suggest a theme such as "serious moments." The video generation unit can also use the emotion estimation function to select a theme based on the emotions of the people in the photo and suggest it to the user. In this way, more touching short videos can be generated by selecting a theme based on emotions.

[0075] The video generation unit can estimate the emotions of people in photos and generate videos in a completely automatic mode based on those emotions. For example, the video generation unit uses a generation AI to analyze the facial expressions of people in photos and estimate emotions. For example, if there are many smiling photos, the video will be composed mainly of happy scenes, and if there are many touching scenes, the video will be composed mainly of touching scenes. The video generation unit can also estimate emotions using voice analysis technology. For example, it can analyze the tone and speed of voices and calculate an emotion score. The video generation unit can also estimate emotions using text analysis technology. For example, it can analyze the content of text and calculate an emotion score. This allows for the generation of more moving videos in a completely automatic mode based on emotions.

[0076] The video generation unit can learn past user preferences based on the photo analysis results and generate a video in a completely automatic mode based on that. The video generation unit, for example, learns past user preferences based on the photo analysis results and generates a video in a completely automatic mode based on that. For example, a new video is generated based on a theme or background music selected in the past. The video generation unit can also adjust the style of the video based on past user preferences. For example, a new video is generated based on filters or effects selected in the past. The video generation unit can also adjust the content of the video based on past user preferences. For example, a new video is generated based on a scene composition selected in the past. In this way, by learning past preferences, a completely automatic mode video that suits the user's preferences can be generated.

[0077] The video generation unit can automatically adjust scene transitions and effects in a video based on the results of photo analysis. The video generation unit automatically adjusts scene transitions in a video based on the results of photo analysis. For example, it applies transition effects according to the content of the photo to smooth the flow of the video. The video generation unit can also automatically adjust effects based on the results of photo analysis. For example, it can apply effects to match the color tones of the photo to unify the tone of the entire video. The video generation unit can also adjust the length of scenes based on the results of photo analysis. For example, it can display important scenes for longer and supplementary scenes for shorter scenes. This makes it possible to generate more attractive videos by automatically adjusting scene transitions and effects.

[0078] The video generation unit can automatically generate a video generated in the completely automatic mode in different themes or styles, allowing the user to select. The video generation unit, for example, automatically generates a video generated in the completely automatic mode in different themes or styles. For example, it generates a video with a fun atmosphere, a video with an emotional atmosphere, a video with a relaxing atmosphere, etc. The video generation unit can also generate a video generated in the completely automatic mode in different themes or styles. For example, it generates a video based on a theme such as travel, family, or friends. The video generation unit can also generate a video generated in the completely automatic mode in different styles. For example, it generates a video based on a classic style, a modern style, a casual style, etc. This automatically generates videos with different themes and styles, thereby expanding the user's options.

[0079] The video generation unit can use the emotion estimation function to automatically select a theme for a video generated in the fully automatic mode based on the emotions of the people in the photos and suggest it to the user. The video generation unit, for example, uses the emotion estimation function to automatically select a theme for a video generated in the fully automatic mode based on the emotions of the people in the photos. For example, if there are many smiling photos, the video generation unit can suggest a theme such as "happy memories," and if there are many photos with touching expressions, the video generation unit can suggest a theme such as "touching moments." The video generation unit can also use the emotion estimation function to analyze the emotions of the people in the photos and select a theme. For example, if the people in the photos have a happy expression, the video generation unit can suggest a theme such as "happy memories," and if the people in the photos have a serious expression, the video generation unit can suggest a theme such as "serious moments." The video generation unit can also use the emotion estimation function to select a theme based on the emotions of the people in the photos and suggest it to the user. This allows for the generation of more touching videos by selecting a theme based on emotions.

[0080] The sharing unit can estimate the emotions of the recipient when sharing the generated video and suggest an optimal sharing method based on the emotions. For example, when sharing the generated video, the sharing unit estimates the emotions of the recipient and suggests an optimal sharing method based on the emotions. For example, if the recipient has positive emotions, it suggests sharing via direct message, and if the recipient has negative emotions, it suggests sharing later. The sharing unit can also analyze the emotions of the recipient and adjust the sharing method. For example, if the recipient is busy, it suggests sharing later. The sharing unit can also select a sharing method based on the emotions of the recipient and suggest it to the user. This allows videos to be shared more effectively by suggesting a sharing method based on the emotions of the recipient.

[0081] The sharing unit can share the generated video at the optimal timing based on the recipient's past viewing history. For example, when sharing the generated video, the sharing unit analyzes the recipient's past viewing history and shares at the optimal timing. For example, the sharing unit shares the video at the same time based on the time period in which the recipient watched videos in the past. The sharing unit can also adjust the sharing timing based on the recipient's viewing history. For example, if the recipient often watches videos at night, the video is shared at night. The sharing unit can also select the sharing timing based on the recipient's viewing history and suggest it to the user. This allows videos to be shared more effectively by sharing at the optimal timing based on the recipient's viewing history.

[0082] The sharing unit can automatically convert the generated video into a format optimized for the recipient's device when sharing the generated video. For example, the sharing unit automatically converts the video into a format optimized for the recipient's device when sharing the generated video. For example, the sharing unit converts the video into a portrait format for smartphones and a landscape format for PCs. The sharing unit can also convert the video based on the specifications of the recipient's device. For example, the sharing unit converts the video to match the resolution of the smartphone. The sharing unit can also convert the video based on trends in the recipient's device. For example, the sharing unit converts the video into a format optimized for the latest smartphones. This allows the video to be shared more effectively by converting it into a format optimized for the recipient's device.

[0083] The sharing unit can add a function to automatically post the generated video to different social media platforms. The sharing unit adds a function to automatically post the generated video to different social media platforms. For example, the sharing unit can simultaneously post to Instagram, Twitter, Facebook, etc. The sharing unit can also post videos based on the specifications of the social media platforms. For example, the sharing unit can post videos in a vertical format for Instagram Stories. The sharing unit can also post videos based on trends on the social media platforms. For example, the sharing unit can post short videos that match TikTok trends. This allows the video to be shared with more people by automatically posting to different social media platforms.

[0084] The sharing unit can collect feedback from recipients when sharing the generated video and reflect it in the generation of the next video. For example, the sharing unit collects feedback from recipients when sharing the generated video and reflects the results in the generation of the next video. For example, the content of the video is adjusted based on the recipient's ratings and comments. The sharing unit can also analyze the feedback and reflect it in the generation of the next video. For example, the style of the video is adjusted based on the recipient's feedback. The sharing unit can also adjust the content of the video based on the feedback and suggest it to the user. In this way, by collecting feedback and reflecting it in the generation of the next video, a video that better suits the user's preferences can be generated.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The video generation system can further include a voice synthesis unit. The voice synthesis unit can add narration to the video based on the results of photo analysis. For example, if there are many travel photos, a narration telling stories from the travels can be added. The voice synthesis unit can also recognize the names of people in the photos and generate narration including those names. For example, if there are many family photos, a narration calling out the names of family members can be added. The voice synthesis unit can also generate narration based on text entered by the user. This allows for the generation of videos with narration, making the video feel more realistic.

[0087] The video generation system can also use an emotion estimation function to automatically adjust video effects based on the emotions of people in the photos. For example, if there are many smiling photos, a bright and cheerful effect can be applied. On the other hand, if there are many photos with emotional expressions, an emotional effect can be applied. The emotion estimation function can also be used to analyze the emotions of people in the photos and adjust the strength of the effect. For example, a strong effect can be applied to photos that show strong emotions, and a gentle effect can be applied to photos that show gentle emotions. In this way, more emotional videos can be generated by adjusting effects based on emotions.

[0088] The video generation system can also add infographics to videos based on the results of photo analysis. For example, it can display a map showing the travel route or information about the places visited. It can also add a timeline showing the flow of time based on the date and time the photos were taken. For example, it can display a timeline showing each day of the trip. It can also display statistical information about the places visited based on the location information of the photos. For example, it can add infographics showing the number of cities visited and the distance traveled. By adding infographics, it is possible to generate videos that are richer in information.

[0089] The video generation system can also add animations to videos based on the results of photo analysis. For example, it can add animations of people in the photo moving or objects moving. It can also add animations to the background based on the content of the photo. For example, if there are many natural landscapes, it can add animations of wind blowing or birds flying. It can also adjust the color of the animation based on the color tone of the photo. For example, it can add warm-colored animations to photos with warm colors. In this way, adding animations can generate videos with more movement.

[0090] The video generation system can also use an emotion estimation function to automatically adjust the tempo of the background music based on the emotions of the people in the photos. For example, if there are many photos with happy expressions, it can generate background music with a faster tempo. On the other hand, if there are many photos with moving expressions, it can generate background music with a slower tempo. The emotion estimation function can also be used to analyze the emotions of the people in the photos and adjust the tempo of the background music. For example, fast background music can be applied to photos that show strong emotions, and slow background music can be applied to photos that show calm emotions. In this way, by adjusting the tempo of the background music based on emotions, it is possible to generate more moving videos.

[0091] The video generation system can also add text overlays to videos based on the results of photo analysis. For example, it can display the names of people in the photo or the name of the location where the photo was taken. It can also add captions based on the content of the photo. For example, it can display information about the travel destination for travel photos. It can also display the date and time based on the date and time the photo was taken. For example, it can display the date for photos taken on a specific date. By adding text overlays, it is possible to generate videos that are richer in information.

[0092] The video generation system can further use an emotion estimation function to automatically adjust video transition effects based on the emotions of people in photos. For example, if there are many photos with happy expressions, a bright and cheerful transition effect can be applied. On the other hand, if there are many photos with moving expressions, an emotional transition effect can be applied. The emotion estimation function can also analyze the emotions of people in photos and adjust the strength of the transition effect. For example, a strong transition effect can be applied to photos that show strong emotions, and a gentle transition effect can be applied to photos that show gentle emotions. In this way, more moving videos can be generated by adjusting the transition effects based on emotions.

[0093] The video generation system can also add interactive elements to the video based on the results of photo analysis. For example, it can add an interactive map that displays detailed information when the user clicks on it. It can also add an interactive element that displays information about a person in a photo when the person is clicked on. It can also add an interactive menu that provides options that the user can select based on the content of the photo. For example, it can provide an option to display information about the travel destination for travel photos. By adding interactive elements, it is possible to generate videos that are more user-participatory.

[0094] The video generation system can further use an emotion estimation function to automatically adjust the timing of scene changes in a video based on the emotions of people in the photos. For example, if there are many photos with happy expressions, the timing of scene changes can be made faster. On the other hand, if there are many photos with moving expressions, the timing of scene changes can be made slower. The emotion estimation function can also be used to analyze the emotions of people in photos and adjust the timing of scene changes. For example, fast scene changes can be applied to photos that show strong emotions, and slow scene changes can be applied to photos that show calm emotions. In this way, more moving videos can be generated by adjusting the timing of scene changes based on emotions.

[0095] The video generation system can also add 3D effects to videos based on the results of photo analysis. For example, people and objects in the photo can be recreated as 3D models and moved within the video. 3D effects can also be added to the background based on the content of the photo. For example, if there are many natural landscapes, 3D trees and mountains can be added. 3D models of places visited can also be displayed based on the location information of the photo. For example, buildings and landmarks from travel destinations can be recreated in 3D. By adding 3D effects, videos with a more three-dimensional feel can be generated.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The photo analysis unit analyzes photos that users have added to their LINE albums. For example, the photo analysis unit may use face recognition technology to identify people in the photos, object detection technology to identify objects in the photos, and color analysis technology to analyze the colors of the photos. Step 2: The video generator generates a video based on the photos analyzed by the photo analyzer. For example, the video generator adjusts the frame rate to generate a smooth video, adjusts the resolution to generate a high-quality video, and applies effects to generate a visually appealing video. Step 3: The BGM generator generates BGM to match the video generated by the video generator. For example, the BGM generator selects a music genre to generate appropriate BGM, adjusts the tempo to generate BGM that matches the video, and adjusts the volume to generate balanced BGM. Step 4: The sharing unit shares the video and background music with friends and family. For example, the sharing unit may send a link to the generated video via a LINE message, post the generated video to a social networking platform, save the generated video in cloud storage, and provide a sharing link.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 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.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The 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.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] Fig. 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.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] 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.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0130] 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.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0142] 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.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0144] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0146] 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.

[0147] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0148] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0149] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0150] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

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

[0152] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0153] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0154] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0155] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0156] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0157] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0158] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0159] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0160] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0161] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0162] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0163] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0164] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0165] 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 photos; a video generation unit that generates a video based on the photograph analyzed by the photograph analysis unit; a background music generating unit that generates background music in accordance with the video generated by the video generating unit; a sharing unit for sharing the video and the background music with friends or family. A system characterized by:

2. The photo analysis unit Estimating emotions of people in the photos and composing scenes of the video based on the emotions The system of claim 1 .

3. The video generation unit The style of the video created by the user in the past is learned, and a new video is generated in the same style. The system of claim 1 .

4. The BGM generation unit Based on the analysis results of the photo, the background music incorporating natural sounds is generated. The system of claim 1 .

5. The video generation unit Estimating the emotions of people in the photos and composing scenes of a short video based on the emotions The system of claim 1 .

6. The video generation unit Estimating the emotion of a person in the photo and generating the video in a completely automatic mode based on the emotion The system of claim 1 .

7. The common part is When sharing the generated video, the system estimates the recipient's emotions and suggests the optimal sharing method based on those emotions. The system of claim 1 .

8. The common part is When sharing the generated video, the video is shared at the optimal timing based on the viewing history of the recipient. The system of claim 1 .

Images