System

Abstract

An object of the system according to the embodiment is to effectively edit a captured home video and repeatedly play the home video on a television in a living room.SOLUTION: A system includes an upload part, an analysis part, a generation part, an edition part, a download part, and a reproduction part. The uploading unit uploads the video to the cloud. The analysis unit analyzes the video uploaded by the upload unit. The generation unit generates a story line on the basis of a result analyzed by the analysis unit. The editing unit edits the video on the basis of the story line generated by the generation unit and converts the video into a file. The download unit downloads the video edited and filed by the editing unit to the dedicated mobile terminal. The reproduction unit connects the video downloaded by the download unit to the television and reproduces the video.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, there was a problem that home videos were buried and not effectively utilized.

[0005] The system according to the embodiment aims to effectively edit home videos that have been shot and play them repeatedly on a television in the living room. [Means for solving the problem]

[0006] The system according to the embodiment includes an upload unit, an analysis unit, a generation unit, an editing unit, a download unit, and a playback unit. The upload unit uploads videos to the cloud. The analysis unit analyzes the videos uploaded by the upload unit. The generation unit generates a storyline based on the analysis results by the analysis unit. The editing unit edits and files the video based on the storyline generated by the generation unit. The download unit downloads the video edited and filed by the editing unit to a dedicated mobile terminal. The playback unit connects the video downloaded by the download unit to a television and plays the video. [Effects of the Invention]

[0007] The system according to the embodiment allows users to effectively edit home videos and play them repeatedly on a television in the living room. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A home video editing system according to an embodiment of the present invention edits home videos to approximately 10 minutes and plays them repeatedly on a living room TV. In this system, users upload videos from their smartphones to cloud storage. A generation AI uses machine learning to analyze the footage, including children's facial expressions, language, and behavior, and automatically generates a storyline based on the video. The generation AI generates and adds background music and sound effects appropriate for the storyline. Users can also request video editing for special occasions (such as birthdays). The generation AI then edits and files the videos based on the storyline and downloads them to a dedicated mobile device. The dedicated mobile device is connected to the living room TV via HDMI (registered trademark), and the downloaded files are repeatedly displayed on the TV screen. The downloaded files are automatically updated approximately once a week, but manual updates are also possible. Users can rate the content, and the generation AI learns from the user's reactions and provides content tailored to their preferences. This allows the home video editing system to easily edit home videos and play them repeatedly on the living room TV. For example, a user can select a video using a smartphone app and upload it to the cloud simply by pressing the upload button. The generation AI analyzes uploaded videos and extracts children's smiles and special words to create a moving story. The generation AI adds background music and sound effects that fit the storyline, and edits the footage of special occasions in a way that best suits the theme and emotions. The generation AI downloads the edited footage to a dedicated mobile device, allowing users to easily enjoy home videos on their living room TV. Downloaded files are automatically updated approximately once a week, but manual updates are also possible. Users can rate the content, and the generation AI learns from their reactions and provides content that suits their preferences. This allows the home video editing system to easily edit home videos and play them repeatedly on the living room TV.

[0029] A home video editing system according to an embodiment includes an upload unit, an analysis unit, a generation unit, an editing unit, a download unit, and a playback unit. The upload unit allows a user to upload videos from their smartphone to cloud storage. For example, a user can select a video using a smartphone app and upload the video to the cloud simply by pressing an upload button. The analysis unit analyzes the video uploaded by the upload unit. The analysis unit uses a generation AI to analyze a child's facial expressions, language, behavior, etc. using machine learning. For example, the analysis unit extracts scenes in which the child is smiling or uttering special words. The generation unit generates a storyline based on the analysis results by the analysis unit. The generation unit uses the generation AI to automatically generate a storyline based on the analysis results. For example, the generation unit combines a child's smile and special words to create a moving story. The editing unit edits and files the video based on the storyline generated by the generation unit. The editing unit uses the generation AI to generate and add background music and sound effects appropriate for the storyline. For example, the editing unit adds celebratory music to a scene in which a child blows out the candles on a birthday cake. The download unit downloads the video edited and filed by the editing unit to a dedicated mobile terminal. The download unit can download to multiple dedicated mobile terminals. For example, if a user wants to play the same video on a TV at home and on a TV at their parents' home, they can download it to both terminals. The playback unit connects the video downloaded by the download unit to a TV and plays it. The playback unit is connected to the living room TV via HDMI and repeatedly displays the downloaded file on the TV screen. This allows the home video editing system according to the embodiment to easily edit home videos and repeatedly play them on the living room TV.

[0030] The generation unit can generate and add background music or sound effects appropriate for the storyline. The generation unit, for example, uses a generation AI to generate background music appropriate for the storyline. For example, the generation unit adds celebratory music to a scene where a child is blowing out the candles on a birthday cake. The generation unit can also use the generation AI to generate sound effects appropriate for the storyline. For example, the generation unit adds footstep sound effects to a scene where a child is running. The generation unit can also use the generation AI to simultaneously generate background music and sound effects appropriate for the storyline. For example, the generation unit adds cheerful music and laughter sound effects to a scene where children are playing. This allows the quality of the video to be improved by automatically adding background music and sound effects appropriate for the storyline.

[0031] The download unit can be downloaded to multiple dedicated mobile terminals. The download unit can be downloaded to, for example, multiple dedicated mobile terminals. For example, if a user wants to play the same video on a TV at home and on a TV at his or her parents' home, the video can be downloaded to both terminals. The download unit can also be downloaded to multiple dedicated mobile terminals simultaneously. For example, if a user wants to share the same video with multiple family members, the video can be downloaded to multiple terminals simultaneously. The download unit also makes it easy to register dedicated mobile terminals. For example, a user can register a dedicated mobile terminal in a home video editing system and specify it as a download destination. This allows the user to enjoy the same video in different locations by downloading it to multiple dedicated mobile terminals.

[0032] The playback unit can automatically update the downloaded file every week. For example, the playback unit can automatically update the downloaded file every week. For example, the playback unit automatically downloads new video every Monday and plays it on the television. The playback unit can also be updated manually by the user. For example, if the user wants to add a new video, the latest video will be played simply by manually pressing an update button. The playback unit can also be set by the user to update at a timing. For example, the user can set it to download new video every weekend. This allows the downloaded file to be automatically updated, allowing the user to always enjoy the latest video.

[0033] The upload unit can analyze the user's past upload history and select the optimal upload method. The upload unit can analyze the user's past upload history, for example, using a generative AI. For example, the upload unit can analyze the time periods during which the user frequently uploaded in the past. Next, the upload unit selects the optimal upload method. For example, the upload unit can prioritize upload methods (Wi-Fi, mobile data, etc.) that the user has used in the past. The upload unit can also prioritize uploading videos related to specific events or anniversaries from the user's past upload history. In this way, the optimal upload method can be provided by analyzing the user's past upload history.

[0034] The uploading unit can filter videos based on the user's current lifestyle and areas of interest when uploading them. The uploading unit, for example, uses generative AI to analyze the user's current lifestyle and areas of interest. For example, the uploading unit analyzes the user's social media posts and search history to identify areas of interest. Next, the uploading unit filters videos when uploading them. For example, if the user is traveling, videos related to the travel are prioritized for upload. Also, if the user is interested in a particular hobby, videos related to that hobby are prioritized for upload. In addition, the uploading unit can filter and upload appropriate videos depending on the user's lifestyle (work, home, etc.). This allows more relevant videos to be uploaded by filtering videos based on the user's lifestyle and areas of interest.

[0035] When uploading a video, the uploading unit can select the most appropriate uploading means depending on the user's input method. The uploading unit analyzes the user's input method using, for example, a generative AI. For example, if the user uses voice input, the uploading unit uploads the video using a voice command. Next, the uploading unit selects the optimal uploading means depending on the user's input method. For example, if the user uses text input, the uploading unit uploads the video using a text command. Also, if the user uses image input, the uploading unit can also upload the video using image recognition technology. This allows the convenience of uploading to be improved by selecting the optimal uploading means depending on the user's input method.

[0036] When uploading videos, the uploading unit can prioritize uploading videos that are highly relevant based on the user's geographical location information. The uploading unit, for example, uses a generative AI to analyze the user's geographical location information. For example, the uploading unit acquires the user's GPS data and identifies their current location. Next, the uploading unit prioritizes uploading videos that are highly relevant based on the user's geographical location information. For example, if the user is in a specific area, videos related to that area are prioritized for upload. Also, if the user is traveling, videos related to the travel destination are prioritized for upload. Also, if the user is participating in an event, videos related to the event can be prioritized for upload. In this way, highly relevant videos can be prioritized for upload by taking the user's geographical location information into consideration.

[0037] The uploading unit can analyze the user's social media activity when uploading a video and upload related videos. The uploading unit can, for example, use generative AI to analyze the user's social media activity. For example, the uploading unit can analyze the content of the user's social media posts and the number of likes. Next, the uploading unit uploads related videos based on the user's social media activity. For example, the uploading unit can prioritize uploading videos that the user has shared on social media. The uploading unit can also analyze the content of the user's social media posts and upload related videos. The uploading unit can also upload related videos based on the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to prioritize uploading related videos.

[0038] When uploading a video, the uploading unit can customize the uploading method by reflecting the user's past feedback. The uploading unit, for example, uses a generative AI to analyze the user's past feedback. For example, the uploading unit preferentially uses uploading methods that the user has previously rated highly. Next, the uploading unit customizes the uploading method by reflecting the user's past feedback. For example, the uploading unit analyzes the user's past feedback and suggests the optimal uploading method. The uploading unit can also customize the uploading method to avoid uploading methods that the user has previously been dissatisfied with. In this way, the optimal uploading method can be provided by reflecting the user's past feedback.

[0039] During analysis, the analysis unit can adjust the level of analysis detail based on the importance of the video. The analysis unit, for example, uses generative AI to evaluate the importance of the video. For example, the analysis unit evaluates the importance of the video based on the number of views and user ratings. Next, the analysis unit adjusts the level of analysis detail based on the importance of the video. For example, videos of important events (birthdays, weddings, etc.) are analyzed in detail. Meanwhile, videos of everyday life are analyzed more simply. Furthermore, for videos related to a specific theme, the level of analysis detail can be adjusted according to that theme. This allows for efficient analysis by adjusting the level of analysis detail based on the importance of the video.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the video category. The analysis unit classifies the video category using, for example, generative AI. For example, the analysis unit classifies videos into categories such as family, travel, and event. Next, the analysis unit applies different analysis algorithms depending on the video category. For example, the analysis unit applies an emotion analysis algorithm to family videos, a scenery analysis algorithm to travel videos, and an event analysis algorithm to event videos. This allows for more accurate analysis by applying different analysis algorithms depending on the video category.

[0041] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, uses a generation AI to analyze the user's past analysis results. For example, the analysis unit refers to analysis results that the user has given high ratings to in the past. Next, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit analyzes the user's past analysis results and suggests the optimal analysis method. It is also possible to improve the accuracy of the analysis by avoiding analysis results that the user has been dissatisfied with in the past. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0042] During analysis, the analysis unit can determine the analysis priority based on when the video was shot. The analysis unit, for example, uses generative AI to analyze when the video was shot. For example, the analysis unit identifies the date and period when the video was shot. Next, the analysis unit determines the analysis priority based on when the video was shot. For example, the analysis unit may prioritize analyzing recently shot videos. Alternatively, the analysis unit may prioritize analyzing videos of specific events (birthdays, weddings, etc.). It may also prioritize analyzing videos within a period specified by the user. In this way, by determining the analysis priority based on when the video was shot, it is possible to prioritize analyzing the most recent videos.

[0043] During analysis, the analysis unit can adjust the analysis order based on the relevance of the videos. The analysis unit, for example, uses generative AI to evaluate the relevance of the videos. For example, the analysis unit analyzes the topics and themes of the videos and evaluates the relevance. Next, the analysis unit adjusts the analysis order based on the relevance of the videos. For example, the analysis unit analyzes videos related to the same event consecutively. Also, the analysis unit analyzes videos related to the same theme consecutively. The analysis order can also be adjusted based on the relevance specified by the user. This allows for efficient analysis by adjusting the analysis order based on the relevance of the videos.

[0044] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit, for example, uses generative AI to evaluate the user's level of expertise. For example, the analysis unit evaluates the level of expertise based on the user's past feedback and evaluations. Next, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses a lot of technical terms. On the other hand, if the user does not have technical expertise, the analysis results are explained in simple terms. The analysis results can also be provided by selecting appropriate terms according to the user's level of expertise. In this way, by adjusting the technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy for the user to understand.

[0045] When generating a storyline, the generation unit can adjust the level of detail of the generation based on the importance of the video. The generation unit, for example, uses a generation AI to evaluate the importance of the video. For example, the generation unit evaluates the importance of the video based on the number of views and user ratings. Next, the generation unit adjusts the level of detail of the generation based on the importance of the video. For example, a detailed storyline is generated for videos of important events (birthdays, weddings, etc.). A simplified storyline is generated for everyday videos. Furthermore, the level of detail of the generation can be adjusted for videos related to a specific theme according to that theme. This allows for efficient storyline generation by adjusting the level of detail of the generation based on the importance of the video.

[0046] When generating a storyline, the generation unit can apply different generation algorithms depending on the video category. The generation unit, for example, uses a generation AI to classify the video category. For example, the generation unit classifies videos into categories such as family, travel, and event. Next, the generation unit applies different generation algorithms depending on the video category. For example, the generation unit applies an emotion analysis algorithm to family videos, a scenery analysis algorithm to travel videos, and an event analysis algorithm to event videos. This allows for more accurate storyline generation by applying different generation algorithms depending on the video category.

[0047] When generating a storyline, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit, for example, uses a generation AI to analyze the user's past generation results. For example, the generation unit refers to generation results that the user has given high ratings to. Next, the generation unit improves the accuracy of generation by referring to the user's past generation results. For example, the generation unit analyzes the user's past generation results and suggests an optimal generation method. It is also possible to improve the accuracy of generation so as to avoid generation results that the user has been dissatisfied with in the past. In this way, the accuracy of generation can be improved by referring to the user's past generation results.

[0048] When generating a storyline, the generation unit can determine the generation priority based on when the videos were shot. The generation unit, for example, uses a generation AI to analyze when the videos were shot. For example, the generation unit identifies the date and period when the videos were shot. Next, the generation unit determines the generation priority based on when the videos were shot. For example, the generation unit prioritizes incorporating recently shot videos into the storyline. Also, videos of specific events (birthdays, weddings, etc.) can be prioritized for incorporation into the storyline. Furthermore, videos within a period specified by the user can also be prioritized for incorporation into the storyline. In this way, by determining the generation priority based on when the videos were shot, the most recent videos can be prioritized for incorporation into the storyline.

[0049] When generating a storyline, the generation unit can adjust the generation order based on the relevance of the videos. The generation unit, for example, uses a generation AI to evaluate the relevance of the videos. For example, the generation unit analyzes the topics and themes of the videos and evaluates the relevance. Next, the generation unit adjusts the generation order based on the relevance of the videos. For example, the generation unit incorporates videos related to the same event consecutively into the storyline. Also, the generation unit incorporates videos related to the same theme consecutively into the storyline. The generation order can also be adjusted based on the relevance specified by the user. This enables efficient storyline generation by adjusting the generation order based on the relevance of the videos.

[0050] When generating a storyline, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. The generation unit, for example, uses a generation AI to evaluate the user's level of expertise. For example, the generation unit evaluates the expertise level based on the user's past feedback and evaluations. Next, the generation unit adjusts the use of technical terminology in the generation according to the user's level of expertise. For example, if the user has technical expertise, the generation unit uses a lot of technical terminology. On the other hand, if the user does not have technical expertise, the generation unit generates a storyline using simple language. The generation unit can also select appropriate terminology according to the user's level of expertise to generate a storyline. In this way, by adjusting the technical terminology in the generation according to the user's level of expertise, it is possible to provide a storyline that is easy for the user to understand.

[0051] When editing a video, the editing department can adjust the level of editing detail based on the importance of the video. The editing department may use, for example, generative AI to evaluate the importance of the video. For example, the editing department may evaluate the importance of the video based on the number of views or user ratings. Next, the editing department adjusts the level of editing detail based on the importance of the video. For example, videos of important events (birthdays, weddings, etc.) may be edited in detail. Meanwhile, videos of everyday life may be edited more simply. Furthermore, for videos related to a specific theme, the level of editing detail can be adjusted according to that theme. This allows for efficient video editing by adjusting the level of editing detail based on the importance of the video.

[0052] When editing a video, the editing department can apply different editing algorithms depending on the video category. The editing department, for example, uses generative AI to classify the video category. For example, the editing department classifies videos into categories such as family, travel, and event. Next, the editing department applies different editing algorithms depending on the video category. For example, the editing department applies an emotion analysis algorithm to family videos, a scenery analysis algorithm to travel videos, and an event analysis algorithm to event videos. This allows for more accurate video editing by applying different editing algorithms depending on the video category.

[0053] When editing video, the editing department can improve the accuracy of editing by referring to the user's past editing results. The editing department, for example, uses a generative AI to analyze the user's past editing results. For example, the editing department refers to editing results that the user has previously given high ratings to. Next, the editing department improves the accuracy of editing by referring to the user's past editing results. For example, the editing department analyzes the user's past editing results and suggests the optimal editing method. It is also possible to improve the accuracy of editing by avoiding editing results that the user has previously been dissatisfied with. In this way, the accuracy of editing can be improved by referring to the user's past editing results.

[0054] When editing video, the editing department can determine editing priorities based on when the video was shot. The editing department, for example, uses generative AI to analyze when the video was shot. For example, the editing department identifies the date and period when the video was shot. Next, the editing department determines editing priorities based on when the video was shot. For example, the editing department may prioritize editing of recently shot videos. Also, videos of specific events (birthdays, weddings, etc.) may be prioritized for editing. Furthermore, videos within a period specified by the user may be prioritized for editing. In this way, by determining editing priorities based on when the video was shot, the most recent videos can be prioritized for editing.

[0055] When editing videos, the editing department can adjust the editing order based on the relevance of the videos. The editing department, for example, uses generative AI to evaluate the relevance of the videos. For example, the editing department analyzes the topic or theme of the video and evaluates the relevance. Next, the editing department adjusts the editing order based on the relevance of the videos. For example, videos related to the same event may be edited consecutively. Also, videos related to the same theme may be edited consecutively. The editing order can also be adjusted based on the relevance specified by the user. This enables efficient video editing by adjusting the editing order based on the relevance of the videos.

[0056] When editing video, the editing department can adjust the use of editing terminology according to the user's level of expertise. The editing department, for example, uses generative AI to evaluate the user's level of expertise. For example, the editing department evaluates the expertise level based on the user's past feedback and evaluations. Next, the editing department adjusts the use of editing terminology according to the user's level of expertise. For example, if the user has expertise, the editing department will use a lot of terminology. On the other hand, if the user does not have expertise, the editing results will be explained in simple terms. It is also possible to select appropriate terminology according to the user's level of expertise and provide editing results that are easy for the user to understand. In this way, by adjusting the editing terminology according to the user's level of expertise, it is possible to provide editing results that are easy for the user to understand.

[0057] The download unit can adjust the download detail level based on the importance of the video when downloading. The download unit, for example, uses generative AI to evaluate the importance of the video. For example, the download unit evaluates the importance of the video based on the number of views and user ratings. Next, the download unit adjusts the download detail level based on the importance of the video. For example, videos of important events (birthdays, weddings, etc.) are downloaded in detail. Also, everyday videos are downloaded in a simplified manner. Furthermore, for videos related to a specific theme, the download detail level can be adjusted according to the theme. As a result, efficient downloading is possible by adjusting the download detail level based on the importance of the video.

[0058] The download unit can apply different download algorithms depending on the category of the video when downloading. The download unit classifies the category of the video using, for example, generative AI. For example, the download unit classifies videos into categories such as family, travel, and event. Next, the download unit applies different download algorithms depending on the category of the video. For example, it can apply an emotion analysis algorithm to family videos, a scenery analysis algorithm to travel videos, and an event analysis algorithm to event videos. This allows for more accurate downloads by applying different download algorithms depending on the video category.

[0059] When downloading, the download unit can improve the accuracy of the download by referring to the user's past download results. The download unit, for example, uses a generation AI to analyze the user's past download results. For example, the download unit refers to download results that the user has given high ratings to. Next, the download unit improves the accuracy of the download by referring to the user's past download results. For example, the download unit analyzes the user's past download results and suggests the optimal download method. The download accuracy can also be improved so as to avoid download results that the user has been dissatisfied with in the past. In this way, the download accuracy can be improved by referring to the user's past download results.

[0060] When downloading, the download unit can determine the download priority based on the time when the video was shot. The download unit, for example, uses a generation AI to analyze the time when the video was shot. For example, the download unit identifies the date and period when the video was shot. Next, the download unit determines the download priority based on the time when the video was shot. For example, the download unit prioritizes downloading recently shot videos. Also, videos of specific events (birthdays, weddings, etc.) can be downloaded preferentially. Furthermore, videos within a period specified by the user can be downloaded preferentially. In this way, by determining the download priority based on the time when the video was shot, the most recent videos can be downloaded preferentially.

[0061] The download unit can adjust the download order based on the relevance of the videos when downloading. The download unit, for example, uses generative AI to evaluate the relevance of the videos. For example, the download unit analyzes the topic or theme of the videos and evaluates the relevance. Next, the download unit adjusts the download order based on the relevance of the videos. For example, videos related to the same event are downloaded consecutively. Also, videos related to the same theme are downloaded consecutively. The download order can also be adjusted based on the relevance specified by the user. This allows for efficient downloading by adjusting the download order based on the relevance of the videos.

[0062] The download unit can adjust the use of technical terms for downloading according to the user's level of expertise during downloading. The download unit, for example, uses a generative AI to evaluate the user's level of expertise. For example, the download unit evaluates the level of expertise based on the user's past feedback and ratings. Next, the download unit adjusts the use of technical terms for downloading according to the user's level of expertise. For example, if the user has technical expertise, the download unit uses a lot of technical terms. On the other hand, if the user does not have technical expertise, the download results are explained in simple terms. The download results can also be provided by selecting appropriate terms according to the user's level of expertise. In this way, by adjusting the technical terms for downloading according to the user's level of expertise, download results that are easy for the user to understand can be provided.

[0063] The playback unit can adjust the level of detail of playback based on the importance of the video during playback. The playback unit, for example, uses generative AI to evaluate the importance of the video. For example, the playback unit evaluates the importance of the video based on the number of views and user ratings. Next, the playback unit adjusts the level of detail of playback based on the importance of the video. For example, videos of important events (birthdays, weddings, etc.) are played in detail. Meanwhile, videos of everyday life are played in a simplified manner. Furthermore, for videos related to a specific theme, the level of detail of playback can be adjusted according to that theme. This allows for efficient playback by adjusting the level of detail of playback based on the importance of the video.

[0064] The playback unit can apply different playback algorithms depending on the category of the video during playback. The playback unit classifies the category of the video using, for example, generative AI. For example, the playback unit classifies videos into categories such as family, travel, and event. Next, the playback unit applies different playback algorithms depending on the category of the video. For example, the playback unit applies an emotion analysis algorithm to family videos, a scenery analysis algorithm to travel videos, and an event analysis algorithm to event videos. This allows for more accurate playback by applying different playback algorithms depending on the video category.

[0065] During playback, the playback unit can improve playback accuracy by referring to the user's past playback results. The playback unit, for example, uses a generation AI to analyze the user's past playback results. For example, the playback unit refers to playback results that the user has given high ratings to in the past. Next, the playback unit improves playback accuracy by referring to the user's past playback results. For example, the playback unit analyzes the user's past playback results and suggests an optimal playback method. It is also possible to improve playback accuracy so as to avoid playback results that the user has been dissatisfied with in the past. In this way, playback accuracy can be improved by referring to the user's past playback results.

[0066] During playback, the playback unit can determine the playback priority based on the shooting date of the video. The playback unit, for example, uses a generative AI to analyze the shooting date of the video. For example, the playback unit identifies the shooting date and shooting period of the video. Next, the playback unit determines the playback priority based on the shooting date of the video. For example, the playback unit may prioritize playing back recently shot videos. Alternatively, the playback unit may prioritize playing back videos of specific events (birthdays, weddings, etc.). It may also prioritize playing back videos within a period specified by the user. In this way, by determining the playback priority based on the shooting date of the video, it is possible to prioritize playing back the most recent videos.

[0067] The playback unit can adjust the playback order based on the relevance of the videos during playback. The playback unit, for example, uses generative AI to evaluate the relevance of the videos. For example, the playback unit analyzes the topic or theme of the video and evaluates the relevance. Next, the playback unit adjusts the playback order based on the relevance of the videos. For example, videos related to the same event are played consecutively. Also, videos related to the same theme are played consecutively. The playback order can also be adjusted based on the relevance specified by the user. This enables efficient playback by adjusting the playback order based on the relevance of the videos.

[0068] During playback, the playback unit can adjust the use of technical terms in playback according to the user's level of expertise. The playback unit, for example, uses a generative AI to evaluate the user's level of expertise. For example, the playback unit evaluates the level of expertise based on the user's past feedback and evaluations. Next, the playback unit adjusts the use of technical terms in playback according to the user's level of expertise. For example, if the user has technical expertise, the playback unit uses a lot of technical terms. On the other hand, if the user does not have technical expertise, the playback results can be explained in simple terms. The playback results can also be provided by selecting appropriate terms according to the user's level of expertise. In this way, by adjusting the technical terms in playback according to the user's level of expertise, playback results that are easy for the user to understand can be provided.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The upload unit can analyze the user's past upload history and select the optimal upload method. For example, the upload unit analyzes the time periods during which the user frequently uploaded in the past. Then, the upload unit selects the optimal upload method. For example, the upload unit can prioritize upload methods (Wi-Fi, mobile data, etc.) that the user has used in the past. The upload unit can also prioritize uploading videos related to specific events or anniversaries based on the user's past upload history. In this way, the optimal upload method can be provided by analyzing the user's past upload history.

[0071] During analysis, the analysis unit can adjust the level of analysis detail based on the importance of the video. For example, the analysis unit evaluates the importance of the video based on the number of views and user ratings. Next, the analysis unit adjusts the level of analysis detail based on the importance of the video. For example, videos of important events (birthdays, weddings, etc.) are analyzed in detail. Meanwhile, videos of everyday life are analyzed more simply. Furthermore, for videos related to a specific theme, the level of analysis detail can be adjusted according to that theme. This allows for efficient analysis by adjusting the level of analysis detail based on the importance of the video.

[0072] When generating a storyline, the generation unit can apply different generation algorithms depending on the category of the video. For example, the generation unit classifies videos into categories such as family, travel, and event. Then, the generation unit applies different generation algorithms depending on the category of the video. For example, the generation unit can apply an emotion analysis algorithm to family videos, a scenery analysis algorithm to travel videos, and an event analysis algorithm to event videos. In this way, applying different generation algorithms depending on the video category enables more accurate storyline generation.

[0073] When editing video, the editing department can determine the editing priority based on the time when the video was shot. For example, the editing department identifies the date and period when the video was shot. Next, the editing department determines the editing priority based on the time when the video was shot. For example, the editing department may give priority to editing recently shot videos. Also, the editing department may give priority to editing videos of specific events (birthdays, weddings, etc.). It is also possible to give priority to editing videos within a period specified by the user. In this way, by determining the editing priority based on the time when the video was shot, it is possible to give priority to editing the most recent videos.

[0074] The playback unit can adjust the playback order based on the relevance of the videos during playback. For example, the playback unit analyzes the topic or theme of the videos and evaluates the relevance. Then, the playback unit adjusts the playback order based on the relevance of the videos. For example, videos related to the same event are played consecutively. Also, videos related to the same theme are played consecutively. The playback order can also be adjusted based on the relevance specified by the user. This allows for efficient playback by adjusting the playback order based on the relevance of the videos.

[0075] The processing flow of the first embodiment will be briefly explained below.

[0076] Step 1: In the upload section, users upload videos from their smartphones to cloud storage. Users simply select a video using the smartphone app and press the upload button to upload the video to the cloud. Step 2: The analysis unit analyzes the video uploaded by the upload unit. Using generative AI, the analysis unit uses machine learning to analyze the child's facial expressions, language, and behavior. For example, it extracts scenes in which the child is laughing or uttering special words. Step 3: The generator generates a storyline based on the results of the analysis. The generator uses AI to automatically generate a storyline based on the analysis results. For example, it creates a moving story by combining a child's smile and special words. Step 4: The editing team edits and files the footage based on the storyline generated by the generation team. The editing team uses the generative AI to generate and add background music and sound effects that fit the storyline. For example, they add celebratory music to a scene where a child blows out the candles on a birthday cake. Step 5: The download unit downloads the video edited and filed by the editing unit to a dedicated mobile device. The download unit can download to multiple dedicated mobile devices. For example, if a user wants to play the same video on a TV at home and at their parents' home, they can download it to both devices. Step 6: The playback unit connects to the TV and plays the video downloaded by the download unit. The playback unit is connected to the TV in the living room via HDMI, and repeatedly displays the downloaded file on the TV screen.

[0077] (Example 2) A home video editing system according to an embodiment of the present invention edits home videos to approximately 10 minutes and plays them repeatedly on a living room TV. In this system, users upload videos from their smartphones to cloud storage. A generation AI uses machine learning to analyze the footage, including children's facial expressions, language, and behavior, and automatically generates a storyline based on the video. The generation AI generates and adds background music and sound effects appropriate for the storyline. Users can also request video editing for special occasions (such as birthdays). The generation AI then edits and files the videos based on the storyline and downloads them to a dedicated mobile device. The dedicated mobile device is connected to the living room TV via HDMI, and the downloaded files are repeatedly displayed on the TV screen. The downloaded files are automatically updated approximately once a week, but manual updates are also possible. Users can rate the content, and the generation AI learns from the user's reactions and provides content tailored to their preferences. This allows the home video editing system to easily edit home videos and play them repeatedly on the living room TV. For example, a user can select a video using a smartphone app and upload it to the cloud simply by pressing the upload button. The generation AI analyzes uploaded videos and extracts children's smiles and special words to create a moving story. The generation AI adds background music and sound effects that fit the storyline, and edits the footage of special occasions in a way that best suits the theme and emotions. The generation AI downloads the edited footage to a dedicated mobile device, allowing users to easily enjoy home videos on their living room TV. Downloaded files are automatically updated approximately once a week, but manual updates are also possible. Users can rate the content, and the generation AI learns from their reactions and provides content that suits their preferences. This allows the home video editing system to easily edit home videos and play them repeatedly on the living room TV.

[0078] A home video editing system according to an embodiment includes an upload unit, an analysis unit, a generation unit, an editing unit, a download unit, and a playback unit. The upload unit allows a user to upload videos from their smartphone to cloud storage. For example, a user can select a video using a smartphone app and upload the video to the cloud simply by pressing an upload button. The analysis unit analyzes the video uploaded by the upload unit. The analysis unit uses a generation AI to analyze a child's facial expressions, language, behavior, etc. using machine learning. For example, the analysis unit extracts scenes in which the child is smiling or uttering special words. The generation unit generates a storyline based on the analysis results by the analysis unit. The generation unit uses the generation AI to automatically generate a storyline based on the analysis results. For example, the generation unit combines a child's smile and special words to create a moving story. The editing unit edits and files the video based on the storyline generated by the generation unit. The editing unit uses the generation AI to generate and add background music and sound effects appropriate for the storyline. For example, the editing unit adds celebratory music to a scene in which a child blows out the candles on a birthday cake. The download unit downloads the video edited and filed by the editing unit to a dedicated mobile terminal. The download unit can download to multiple dedicated mobile terminals. For example, if a user wants to play the same video on a TV at home and on a TV at their parents' home, they can download it to both terminals. The playback unit connects the video downloaded by the download unit to a TV and plays it. The playback unit is connected to the living room TV via HDMI and repeatedly displays the downloaded file on the TV screen. This allows the home video editing system according to the embodiment to easily edit home videos and repeatedly play them on the living room TV.

[0079] The generation unit can generate and add background music or sound effects appropriate for the storyline. The generation unit, for example, uses a generation AI to generate background music appropriate for the storyline. For example, the generation unit adds celebratory music to a scene where a child is blowing out the candles on a birthday cake. The generation unit can also use the generation AI to generate sound effects appropriate for the storyline. For example, the generation unit adds footstep sound effects to a scene where a child is running. The generation unit can also use the generation AI to simultaneously generate background music and sound effects appropriate for the storyline. For example, the generation unit adds cheerful music and laughter sound effects to a scene where children are playing. This allows the quality of the video to be improved by automatically adding background music and sound effects appropriate for the storyline.

[0080] The generation unit can request editing of a video for an anniversary, and can edit the video of a special day in a manner that best suits the theme and emotions. The generation unit can, for example, use a generation AI to request editing of the video for an anniversary. For example, a user can request editing of a birthday video from the generation unit. The generation unit uses the generation AI to edit the video of the special day in a manner that best suits the theme and emotions. For example, the generation unit edits the birthday video to emphasize children's smiles and celebratory scenes. The generation unit can also use the generation AI to edit wedding anniversary video. For example, the generation unit edits wedding anniversary video to emphasize the love between the couple and moving scenes. The generation unit can also use the generation AI to edit graduation ceremony video. For example, the generation unit edits graduation ceremony video to emphasize moving and joyful scenes of the graduates. In this way, by editing the video of the special day in a manner that best suits the theme and emotions, it is possible to provide the user with a moving video.

[0081] The download unit can be downloaded to multiple dedicated mobile terminals. The download unit can be downloaded to, for example, multiple dedicated mobile terminals. For example, if a user wants to play the same video on a TV at home and on a TV at his or her parents' home, the video can be downloaded to both terminals. The download unit can also be downloaded to multiple dedicated mobile terminals simultaneously. For example, if a user wants to share the same video with multiple family members, the video can be downloaded to multiple terminals simultaneously. The download unit also makes it easy to register dedicated mobile terminals. For example, a user can register a dedicated mobile terminal in a home video editing system and specify it as a download destination. This allows the user to enjoy the same video in different locations by downloading it to multiple dedicated mobile terminals.

[0082] The playback unit can automatically update the downloaded file every week. For example, the playback unit can automatically update the downloaded file every week. For example, the playback unit automatically downloads new video every Monday and plays it on the television. The playback unit can also be updated manually by the user. For example, if the user wants to add a new video, the latest video will be played simply by manually pressing an update button. The playback unit can also be set by the user to update at a timing. For example, the user can set it to download new video every weekend. This allows the downloaded file to be automatically updated, allowing the user to always enjoy the latest video.

[0083] The upload unit can estimate a user's emotions and adjust the timing of video uploads based on the estimated user emotions. The upload unit estimates the user's emotions using, for example, generative AI. For example, the upload unit analyzes the user's facial expressions and voice to estimate emotions. Next, the upload unit adjusts the timing of video uploads based on the estimated user emotions. For example, if the user is feeling stressed, the upload unit can automatically upload videos to save the user effort. Alternatively, if the user is relaxed, the upload unit can provide an option to upload manually, allowing the user to operate at their own pace. Alternatively, if the user is in a hurry, the upload can be automatically started in the background to speed up the upload. This improves user convenience by adjusting the upload timing according to the user's emotions.

[0084] The upload unit can analyze the user's past upload history and select the optimal upload method. The upload unit can analyze the user's past upload history, for example, using a generative AI. For example, the upload unit can analyze the time periods during which the user frequently uploaded in the past. Next, the upload unit selects the optimal upload method. For example, the upload unit can prioritize upload methods (Wi-Fi, mobile data, etc.) that the user has used in the past. The upload unit can also prioritize uploading videos related to specific events or anniversaries from the user's past upload history. In this way, the optimal upload method can be provided by analyzing the user's past upload history.

[0085] The uploading unit can filter videos based on the user's current lifestyle and areas of interest when uploading them. The uploading unit, for example, uses generative AI to analyze the user's current lifestyle and areas of interest. For example, the uploading unit analyzes the user's social media posts and search history to identify areas of interest. Next, the uploading unit filters videos when uploading them. For example, if the user is traveling, videos related to the travel are prioritized for upload. Also, if the user is interested in a particular hobby, videos related to that hobby are prioritized for upload. In addition, the uploading unit can filter and upload appropriate videos depending on the user's lifestyle (work, home, etc.). This allows more relevant videos to be uploaded by filtering videos based on the user's lifestyle and areas of interest.

[0086] When uploading a video, the uploading unit can select the most appropriate uploading means depending on the user's input method. The uploading unit analyzes the user's input method using, for example, a generative AI. For example, if the user uses voice input, the uploading unit uploads the video using a voice command. Next, the uploading unit selects the optimal uploading means depending on the user's input method. For example, if the user uses text input, the uploading unit uploads the video using a text command. Also, if the user uses image input, the uploading unit can also upload the video using image recognition technology. This allows the convenience of uploading to be improved by selecting the optimal uploading means depending on the user's input method.

[0087] The uploading unit can estimate a user's emotions and determine the priority of videos to be uploaded based on the estimated user's emotions. The uploading unit estimates the user's emotions using, for example, generative AI. For example, the uploading unit analyzes the user's facial expressions and voice to estimate emotions. Next, the uploading unit determines the priority of videos to be uploaded based on the estimated user's emotions. For example, if the user is emotional, the uploading unit prioritizes uploading emotional videos to share that emotion. Also, if the user is having fun, the uploading unit prioritizes uploading fun videos to share that fun. Also, if the user is sad, the uploading unit prioritizes uploading soothing videos to soothe the user's emotions. In this way, by prioritizing videos based on the user's emotions, more appropriate videos can be uploaded.

[0088] When uploading videos, the uploading unit can prioritize uploading videos that are highly relevant based on the user's geographical location information. The uploading unit, for example, uses a generative AI to analyze the user's geographical location information. For example, the uploading unit acquires the user's GPS data and identifies their current location. Next, the uploading unit prioritizes uploading videos that are highly relevant based on the user's geographical location information. For example, if the user is in a specific area, videos related to that area are prioritized for upload. Also, if the user is traveling, videos related to the travel destination are prioritized for upload. Also, if the user is participating in an event, videos related to the event can be prioritized for upload. In this way, highly relevant videos can be prioritized for upload by taking the user's geographical location information into consideration.

[0089] The uploading unit can analyze the user's social media activity when uploading a video and upload related videos. The uploading unit can, for example, use generative AI to analyze the user's social media activity. For example, the uploading unit can analyze the content of the user's social media posts and the number of likes. Next, the uploading unit uploads related videos based on the user's social media activity. For example, the uploading unit can prioritize uploading videos that the user has shared on social media. The uploading unit can also analyze the content of the user's social media posts and upload related videos. The uploading unit can also upload related videos based on the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to prioritize uploading related videos.

[0090] When uploading a video, the uploading unit can customize the uploading method by reflecting the user's past feedback. The uploading unit, for example, uses a generative AI to analyze the user's past feedback. For example, the uploading unit preferentially uses uploading methods that the user has previously rated highly. Next, the uploading unit customizes the uploading method by reflecting the user's past feedback. For example, the uploading unit analyzes the user's past feedback and suggests the optimal uploading method. The uploading unit can also customize the uploading method to avoid uploading methods that the user has previously been dissatisfied with. In this way, the optimal uploading method can be provided by reflecting the user's past feedback.

[0091] The analysis unit can estimate the user's emotions and adjust the video analysis method based on the estimated user's emotions. The analysis unit estimates the user's emotions, for example, using generative AI. For example, the analysis unit analyzes the user's facial expressions and voice to estimate emotions. Next, the analysis unit adjusts the video analysis method based on the estimated user's emotions. For example, if the user is moved, emotional scenes are prioritized for analysis. Also, if the user is having fun, happy scenes are prioritized for analysis. Also, if the user is sad, soothing scenes are prioritized for analysis. In this way, by adjusting the analysis method based on the user's emotions, more appropriate analysis results can be provided.

[0092] During analysis, the analysis unit can adjust the level of analysis detail based on the importance of the video. The analysis unit, for example, uses generative AI to evaluate the importance of the video. For example, the analysis unit evaluates the importance of the video based on the number of views and user ratings. Next, the analysis unit adjusts the level of analysis detail based on the importance of the video. For example, videos of important events (birthdays, weddings, etc.) are analyzed in detail. Meanwhile, videos of everyday life are analyzed more simply. Furthermore, for videos related to a specific theme, the level of analysis detail can be adjusted according to that theme. This allows for efficient analysis by adjusting the level of analysis detail based on the importance of the video.

[0093] During analysis, the analysis unit can apply different analysis algorithms depending on the video category. The analysis unit classifies the video category using, for example, generative AI. For example, the analysis unit classifies videos into categories such as family, travel, and event. Next, the analysis unit applies different analysis algorithms depending on the video category. For example, the analysis unit applies an emotion analysis algorithm to family videos, a scenery analysis algorithm to travel videos, and an event analysis algorithm to event videos. This allows for more accurate analysis by applying different analysis algorithms depending on the video category.

[0094] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, uses a generation AI to analyze the user's past analysis results. For example, the analysis unit refers to analysis results that the user has given high ratings to in the past. Next, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit analyzes the user's past analysis results and suggests the optimal analysis method. It is also possible to improve the accuracy of the analysis by avoiding analysis results that the user has been dissatisfied with in the past. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0095] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit estimates the user's emotions using, for example, generative AI. For example, the analysis unit analyzes the user's facial expressions and voice to estimate emotions. Next, the analysis unit adjusts the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis can be completed in a short time. Alternatively, if the user is relaxed, a detailed analysis can be performed. Alternatively, if the user is excited, a visually stimulating analysis can be performed. This allows the length of the analysis to be adjusted based on the user's emotions, making it possible to provide an analysis that meets the user's needs.

[0096] During analysis, the analysis unit can determine the analysis priority based on when the video was shot. The analysis unit, for example, uses generative AI to analyze when the video was shot. For example, the analysis unit identifies the date and period when the video was shot. Next, the analysis unit determines the analysis priority based on when the video was shot. For example, the analysis unit may prioritize analyzing recently shot videos. Alternatively, the analysis unit may prioritize analyzing videos of specific events (birthdays, weddings, etc.). It may also prioritize analyzing videos within a period specified by the user. In this way, by determining the analysis priority based on when the video was shot, it is possible to prioritize analyzing the most recent videos.

[0097] During analysis, the analysis unit can adjust the analysis order based on the relevance of the videos. The analysis unit, for example, uses generative AI to evaluate the relevance of the videos. For example, the analysis unit analyzes the topics and themes of the videos and evaluates the relevance. Next, the analysis unit adjusts the analysis order based on the relevance of the videos. For example, the analysis unit analyzes videos related to the same event consecutively. Also, the analysis unit analyzes videos related to the same theme consecutively. The analysis order can also be adjusted based on the relevance specified by the user. This allows for efficient analysis by adjusting the analysis order based on the relevance of the videos.

[0098] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit, for example, uses generative AI to evaluate the user's level of expertise. For example, the analysis unit evaluates the level of expertise based on the user's past feedback and evaluations. Next, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses a lot of technical terms. On the other hand, if the user does not have technical expertise, the analysis results are explained in simple terms. The analysis results can also be provided by selecting appropriate terms according to the user's level of expertise. In this way, by adjusting the technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy for the user to understand.

[0099] The generation unit can estimate the user's emotions and adjust the storyline generation method based on the estimated user's emotions. The generation unit estimates the user's emotions, for example, using a generation AI. For example, the generation unit analyzes the user's facial expressions and voice to estimate the emotions. Next, the generation unit adjusts the storyline generation method based on the estimated user's emotions. For example, if the user is moved, an emotional storyline is generated. Also, if the user is having fun, an enjoyable storyline is generated. Also, if the user is sad, a soothing storyline is generated. In this way, by adjusting the storyline generation method based on the user's emotions, a more emotional storyline can be provided.

[0100] When generating a storyline, the generation unit can adjust the level of detail of the generation based on the importance of the video. The generation unit, for example, uses a generation AI to evaluate the importance of the video. For example, the generation unit evaluates the importance of the video based on the number of views and user ratings. Next, the generation unit adjusts the level of detail of the generation based on the importance of the video. For example, a detailed storyline is generated for videos of important events (birthdays, weddings, etc.). A simplified storyline is generated for everyday videos. Furthermore, the level of detail of the generation can be adjusted for videos related to a specific theme according to that theme. This allows for efficient storyline generation by adjusting the level of detail of the generation based on the importance of the video.

[0101] When generating a storyline, the generation unit can apply different generation algorithms depending on the video category. The generation unit, for example, uses a generation AI to classify the video category. For example, the generation unit classifies videos into categories such as family, travel, and event. Next, the generation unit applies different generation algorithms depending on the video category. For example, the generation unit applies an emotion analysis algorithm to family videos, a scenery analysis algorithm to travel videos, and an event analysis algorithm to event videos. This allows for more accurate storyline generation by applying different generation algorithms depending on the video category.

[0102] When generating a storyline, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit, for example, uses a generation AI to analyze the user's past generation results. For example, the generation unit refers to generation results that the user has given high ratings to. Next, the generation unit improves the accuracy of generation by referring to the user's past generation results. For example, the generation unit analyzes the user's past generation results and suggests an optimal generation method. It is also possible to improve the accuracy of generation so as to avoid generation results that the user has been dissatisfied with in the past. In this way, the accuracy of generation can be improved by referring to the user's past generation results.

[0103] The generation unit can estimate the user's emotions and adjust the length of the storyline based on the estimated user emotions. The generation unit estimates the user's emotions using, for example, a generation AI. For example, the generation unit analyzes the user's facial expressions and voice to estimate emotions. Next, the generation unit adjusts the length of the storyline based on the estimated user emotions. For example, if the user is in a hurry, the generation unit generates a short, to-the-point storyline. If the user is relaxed, the generation unit generates a longer storyline with detailed explanations. If the user is excited, the generation unit generates a storyline with visually stimulating effects. In this way, by adjusting the length of the storyline based on the user's emotions, it is possible to provide a storyline that meets the user's needs.

[0104] When generating a storyline, the generation unit can determine the generation priority based on when the videos were shot. The generation unit, for example, uses a generation AI to analyze when the videos were shot. For example, the generation unit identifies the date and period when the videos were shot. Next, the generation unit determines the generation priority based on when the videos were shot. For example, the generation unit prioritizes incorporating recently shot videos into the storyline. Also, videos of specific events (birthdays, weddings, etc.) can be prioritized for incorporation into the storyline. Furthermore, videos within a period specified by the user can also be prioritized for incorporation into the storyline. In this way, by determining the generation priority based on when the videos were shot, the most recent videos can be prioritized for incorporation into the storyline.

[0105] When generating a storyline, the generation unit can adjust the generation order based on the relevance of the videos. The generation unit, for example, uses a generation AI to evaluate the relevance of the videos. For example, the generation unit analyzes the topics and themes of the videos and evaluates the relevance. Next, the generation unit adjusts the generation order based on the relevance of the videos. For example, the generation unit incorporates videos related to the same event consecutively into the storyline. Also, the generation unit incorporates videos related to the same theme consecutively into the storyline. The generation order can also be adjusted based on the relevance specified by the user. This enables efficient storyline generation by adjusting the generation order based on the relevance of the videos.

[0106] When generating a storyline, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. The generation unit, for example, uses a generation AI to evaluate the user's level of expertise. For example, the generation unit evaluates the expertise level based on the user's past feedback and evaluations. Next, the generation unit adjusts the use of technical terminology in the generation according to the user's level of expertise. For example, if the user has technical expertise, the generation unit uses a lot of technical terminology. On the other hand, if the user does not have technical expertise, the generation unit generates a storyline using simple language. The generation unit can also select appropriate terminology according to the user's level of expertise to generate a storyline. In this way, by adjusting the technical terminology in the generation according to the user's level of expertise, it is possible to provide a storyline that is easy for the user to understand.

[0107] The editing department can estimate the user's emotions and adjust the editing method of the video based on the estimated user's emotions. The editing department estimates the user's emotions, for example, using generative AI. For example, the editing department analyzes the user's facial expressions and voice to estimate emotions. Next, the editing department adjusts the editing method of the video based on the estimated user's emotions. For example, if the user is moved, editing is performed to emphasize moving scenes. Also, if the user is having fun, editing is performed to emphasize happy scenes. Also, if the user is sad, editing is performed to emphasize soothing scenes. In this way, by adjusting the editing method of the video based on the user's emotions, it is possible to provide more moving videos.

[0108] When editing a video, the editing department can adjust the level of editing detail based on the importance of the video. The editing department may use, for example, generative AI to evaluate the importance of the video. For example, the editing department may evaluate the importance of the video based on the number of views or user ratings. Next, the editing department adjusts the level of editing detail based on the importance of the video. For example, videos of important events (birthdays, weddings, etc.) may be edited in detail. Meanwhile, videos of everyday life may be edited more simply. Furthermore, for videos related to a specific theme, the level of editing detail can be adjusted according to that theme. This allows for efficient video editing by adjusting the level of editing detail based on the importance of the video.

[0109] When editing a video, the editing department can apply different editing algorithms depending on the video category. The editing department, for example, uses generative AI to classify the video category. For example, the editing department classifies videos into categories such as family, travel, and event. Next, the editing department applies different editing algorithms depending on the video category. For example, the editing department applies an emotion analysis algorithm to family videos, a scenery analysis algorithm to travel videos, and an event analysis algorithm to event videos. This allows for more accurate video editing by applying different editing algorithms depending on the video category.

[0110] When editing video, the editing department can improve the accuracy of editing by referring to the user's past editing results. The editing department, for example, uses a generative AI to analyze the user's past editing results. For example, the editing department refers to editing results that the user has previously given high ratings to. Next, the editing department improves the accuracy of editing by referring to the user's past editing results. For example, the editing department analyzes the user's past editing results and suggests the optimal editing method. It is also possible to improve the accuracy of editing by avoiding editing results that the user has previously been dissatisfied with. In this way, the accuracy of editing can be improved by referring to the user's past editing results.

[0111] The editorial department can estimate the user's emotions and adjust the length of the video based on the estimated user emotions. The editorial department, for example, uses generative AI to estimate the user's emotions. For example, the editorial department analyzes the user's facial expressions and voice to estimate emotions. Next, the editorial department adjusts the length of the video based on the estimated user emotions. For example, if the user is in a hurry, the editorial department edits a short video that gets to the point. If the user is relaxed, the editorial department edits a longer video that includes detailed explanations. If the user is excited, the editorial department edits a video that adds visually stimulating effects. In this way, by adjusting the length of the video based on the user's emotions, it is possible to provide videos that meet the user's needs.

[0112] When editing video, the editing department can determine editing priorities based on when the video was shot. The editing department, for example, uses generative AI to analyze when the video was shot. For example, the editing department identifies the date and period when the video was shot. Next, the editing department determines editing priorities based on when the video was shot. For example, the editing department may prioritize editing of recently shot videos. Also, videos of specific events (birthdays, weddings, etc.) may be prioritized for editing. Furthermore, videos within a period specified by the user may be prioritized for editing. In this way, by determining editing priorities based on when the video was shot, the most recent videos can be prioritized for editing.

[0113] When editing videos, the editing department can adjust the editing order based on the relevance of the videos. The editing department, for example, uses generative AI to evaluate the relevance of the videos. For example, the editing department analyzes the topic or theme of the video and evaluates the relevance. Next, the editing department adjusts the editing order based on the relevance of the videos. For example, videos related to the same event may be edited consecutively. Also, videos related to the same theme may be edited consecutively. The editing order can also be adjusted based on the relevance specified by the user. This enables efficient video editing by adjusting the editing order based on the relevance of the videos.

[0114] When editing video, the editing department can adjust the use of editing terminology according to the user's level of expertise. The editing department, for example, uses generative AI to evaluate the user's level of expertise. For example, the editing department evaluates the expertise level based on the user's past feedback and evaluations. Next, the editing department adjusts the use of editing terminology according to the user's level of expertise. For example, if the user has expertise, the editing department will use a lot of terminology. On the other hand, if the user does not have expertise, the editing results will be explained in simple terms. It is also possible to select appropriate terminology according to the user's level of expertise and provide editing results that are easy for the user to understand. In this way, by adjusting the editing terminology according to the user's level of expertise, it is possible to provide editing results that are easy for the user to understand.

[0115] The download unit can estimate the user's emotions and adjust the timing of downloads based on the estimated user emotions. The download unit estimates the user's emotions using, for example, a generation AI. For example, the download unit analyzes the user's facial expressions and voice to estimate emotions. Next, the download unit adjusts the timing of downloads based on the estimated user emotions. For example, if the user is in a hurry, the download is performed quickly. On the other hand, if the user is relaxed, the download is performed slowly. On the other hand, if the user is moved, moving scenes are downloaded preferentially. In this way, by adjusting the timing of downloads based on the user's emotions, user convenience can be improved.

[0116] The download unit can adjust the download detail level based on the importance of the video when downloading. The download unit, for example, uses generative AI to evaluate the importance of the video. For example, the download unit evaluates the importance of the video based on the number of views and user ratings. Next, the download unit adjusts the download detail level based on the importance of the video. For example, videos of important events (birthdays, weddings, etc.) are downloaded in detail. Also, everyday videos are downloaded in a simplified manner. Furthermore, for videos related to a specific theme, the download detail level can be adjusted according to the theme. As a result, efficient downloading is possible by adjusting the download detail level based on the importance of the video.

[0117] The download unit can apply different download algorithms depending on the category of the video when downloading. The download unit classifies the category of the video using, for example, generative AI. For example, the download unit classifies videos into categories such as family, travel, and event. Next, the download unit applies different download algorithms depending on the category of the video. For example, it can apply an emotion analysis algorithm to family videos, a scenery analysis algorithm to travel videos, and an event analysis algorithm to event videos. This allows for more accurate downloads by applying different download algorithms depending on the video category.

[0118] When downloading, the download unit can improve the accuracy of the download by referring to the user's past download results. The download unit, for example, uses a generation AI to analyze the user's past download results. For example, the download unit refers to download results that the user has given high ratings to. Next, the download unit improves the accuracy of the download by referring to the user's past download results. For example, the download unit analyzes the user's past download results and suggests the optimal download method. The download accuracy can also be improved so as to avoid download results that the user has been dissatisfied with in the past. In this way, the download accuracy can be improved by referring to the user's past download results.

[0119] The download unit can estimate the user's emotions and adjust the length of the download based on the estimated user emotions. The download unit, for example, uses a generative AI to estimate the user's emotions. For example, the download unit analyzes the user's facial expressions and voice to estimate emotions. Next, the download unit adjusts the length of the download based on the estimated user emotions. For example, if the user is in a hurry, the download can be completed in a short time. Alternatively, if the user is relaxed, a detailed download can be performed. Alternatively, if the user is excited, a visually stimulating download can be performed. In this way, by adjusting the length of the download based on the user's emotions, downloads can be tailored to the user's needs.

[0120] When downloading, the download unit can determine the download priority based on the time when the video was shot. The download unit, for example, uses a generation AI to analyze the time when the video was shot. For example, the download unit identifies the date and period when the video was shot. Next, the download unit determines the download priority based on the time when the video was shot. For example, the download unit prioritizes downloading recently shot videos. Also, videos of specific events (birthdays, weddings, etc.) can be downloaded preferentially. Furthermore, videos within a period specified by the user can be downloaded preferentially. In this way, by determining the download priority based on the time when the video was shot, the most recent videos can be downloaded preferentially.

[0121] The download unit can adjust the download order based on the relevance of the videos when downloading. The download unit, for example, uses generative AI to evaluate the relevance of the videos. For example, the download unit analyzes the topic or theme of the videos and evaluates the relevance. Next, the download unit adjusts the download order based on the relevance of the videos. For example, videos related to the same event are downloaded consecutively. Also, videos related to the same theme are downloaded consecutively. The download order can also be adjusted based on the relevance specified by the user. This allows for efficient downloading by adjusting the download order based on the relevance of the videos.

[0122] The download unit can adjust the use of technical terms for downloading according to the user's level of expertise during downloading. The download unit, for example, uses a generative AI to evaluate the user's level of expertise. For example, the download unit evaluates the level of expertise based on the user's past feedback and ratings. Next, the download unit adjusts the use of technical terms for downloading according to the user's level of expertise. For example, if the user has technical expertise, the download unit uses a lot of technical terms. On the other hand, if the user does not have technical expertise, the download results are explained in simple terms. The download results can also be provided by selecting appropriate terms according to the user's level of expertise. In this way, by adjusting the technical terms for downloading according to the user's level of expertise, download results that are easy for the user to understand can be provided.

[0123] The playback unit can estimate the user's emotions and adjust the timing of playback based on the estimated user emotions. The playback unit estimates the user's emotions using, for example, a generative AI. For example, the playback unit analyzes the user's facial expressions and voice to estimate emotions. Next, the playback unit adjusts the timing of playback based on the estimated user emotions. For example, if the user is in a hurry, playback is performed quickly. On the other hand, if the user is relaxed, playback is performed slowly. On the other hand, if the user is moved, moving scenes are preferentially played. In this way, by adjusting the timing of playback based on the user's emotions, user convenience can be improved.

[0124] The playback unit can adjust the level of detail of playback based on the importance of the video during playback. The playback unit, for example, uses generative AI to evaluate the importance of the video. For example, the playback unit evaluates the importance of the video based on the number of views and user ratings. Next, the playback unit adjusts the level of detail of playback based on the importance of the video. For example, videos of important events (birthdays, weddings, etc.) are played in detail. Meanwhile, videos of everyday life are played in a simplified manner. Furthermore, for videos related to a specific theme, the level of detail of playback can be adjusted according to that theme. This allows for efficient playback by adjusting the level of detail of playback based on the importance of the video.

[0125] The playback unit can apply different playback algorithms depending on the category of the video during playback. The playback unit classifies the category of the video using, for example, generative AI. For example, the playback unit classifies videos into categories such as family, travel, and event. Next, the playback unit applies different playback algorithms depending on the category of the video. For example, the playback unit applies an emotion analysis algorithm to family videos, a scenery analysis algorithm to travel videos, and an event analysis algorithm to event videos. This allows for more accurate playback by applying different playback algorithms depending on the video category.

[0126] During playback, the playback unit can improve playback accuracy by referring to the user's past playback results. The playback unit, for example, uses a generation AI to analyze the user's past playback results. For example, the playback unit refers to playback results that the user has given high ratings to in the past. Next, the playback unit improves playback accuracy by referring to the user's past playback results. For example, the playback unit analyzes the user's past playback results and suggests an optimal playback method. It is also possible to improve playback accuracy so as to avoid playback results that the user has been dissatisfied with in the past. In this way, playback accuracy can be improved by referring to the user's past playback results.

[0127] The playback unit can estimate the user's emotions and adjust the playback length based on the estimated user emotions. The playback unit, for example, uses a generative AI to estimate the user's emotions. For example, the playback unit analyzes the user's facial expressions and voice to estimate emotions. The playback unit then adjusts the playback length based on the estimated user emotions. For example, if the user is in a hurry, the playback can be completed in a short time. If the user is relaxed, detailed playback can be performed. If the user is excited, visually stimulating playback can be performed. This allows playback to be tailored to the user's needs by adjusting the playback length based on the user's emotions.

[0128] During playback, the playback unit can determine the playback priority based on the shooting date of the video. The playback unit, for example, uses a generative AI to analyze the shooting date of the video. For example, the playback unit identifies the shooting date and shooting period of the video. Next, the playback unit determines the playback priority based on the shooting date of the video. For example, the playback unit may prioritize playing back recently shot videos. Alternatively, the playback unit may prioritize playing back videos of specific events (birthdays, weddings, etc.). It may also prioritize playing back videos within a period specified by the user. In this way, by determining the playback priority based on the shooting date of the video, it is possible to prioritize playing back the most recent videos.

[0129] The playback unit can adjust the playback order based on the relevance of the videos during playback. The playback unit, for example, uses generative AI to evaluate the relevance of the videos. For example, the playback unit analyzes the topic or theme of the video and evaluates the relevance. Next, the playback unit adjusts the playback order based on the relevance of the videos. For example, videos related to the same event are played consecutively. Also, videos related to the same theme are played consecutively. The playback order can also be adjusted based on the relevance specified by the user. This enables efficient playback by adjusting the playback order based on the relevance of the videos.

[0130] During playback, the playback unit can adjust the use of technical terms in playback according to the user's level of expertise. The playback unit, for example, uses a generative AI to evaluate the user's level of expertise. For example, the playback unit evaluates the level of expertise based on the user's past feedback and evaluations. Next, the playback unit adjusts the use of technical terms in playback according to the user's level of expertise. For example, if the user has technical expertise, the playback unit uses a lot of technical terms. On the other hand, if the user does not have technical expertise, the playback results can be explained in simple terms. The playback results can also be provided by selecting appropriate terms according to the user's level of expertise. In this way, by adjusting the technical terms in playback according to the user's level of expertise, playback results that are easy for the user to understand can be provided. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned upload unit, analysis unit, generation unit, editing unit, download unit, and playback unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the upload unit is realized by the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart device 14. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the editing unit is realized by the control unit 46A of the smart device 14. For example, the download unit is realized by the specific processing unit 290 of the data processing device 12. For example, the playback unit is realized by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned upload unit, analysis unit, generation unit, editing unit, download unit, and playback unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the upload unit is realized by a specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by a control unit 46A of the smart glasses 214. For example, the generation unit is realized by a specific processing unit 290 of the data processing device 12. For example, the editing unit is realized by the control unit 46A of the smart glasses 214. For example, the download unit is realized by a specific processing unit 290 of the data processing device 12. For example, the playback unit is realized by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned upload unit, analysis unit, generation unit, editing unit, download unit, and playback unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the upload unit is realized by the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the headset type terminal 314. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the editing unit is realized by the control unit 46A of the headset type terminal 314. For example, the download unit is realized by the specific processing unit 290 of the data processing device 12. For example, the playback unit is realized by the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned upload unit, analysis unit, generation unit, editing unit, download unit, and playback unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the upload unit is realized by the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the robot 414. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the editing unit is realized by the control unit 46A of the robot 414. For example, the download unit is realized by the specific processing unit 290 of the data processing device 12. For example, the playback unit is realized by the control unit 46A of the robot 414.

[0131] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0132] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user's emotions. For example, if the user is emotional, it prioritizes analyzing emotional scenes. If the user is having fun, it prioritizes analyzing happy scenes. If the user is sad, it prioritizes analyzing soothing scenes. In this way, by determining the analysis priority based on the user's emotions, it is possible to provide more appropriate analysis results.

[0133] The generation unit can estimate the user's emotions and adjust the storyline generation method based on the estimated user emotions. For example, if the user is moved, an emotional storyline is generated. If the user is having fun, an enjoyable storyline is generated. If the user is sad, a comforting storyline is generated. In this way, by adjusting the storyline generation method based on the user's emotions, a more emotional storyline can be provided.

[0134] The editing department can estimate the user's emotions and adjust the video editing method based on the estimated user emotions. For example, if the user is moved, editing is performed to emphasize moving scenes. If the user is having fun, editing is performed to emphasize happy scenes. If the user is sad, editing is performed to emphasize soothing scenes. In this way, by adjusting the video editing method based on the user's emotions, it is possible to provide more moving videos.

[0135] The download unit can estimate the user's emotions and adjust the timing of downloads based on the estimated user emotions. For example, if the user is in a hurry, the download is performed quickly. If the user is relaxed, the download is performed slowly. If the user is moved, moving scenes are downloaded preferentially. In this way, by adjusting the download timing based on the user's emotions, it is possible to improve user convenience.

[0136] The playback unit can estimate the user's emotions and adjust the timing of playback based on the estimated user emotions. For example, if the user is in a hurry, playback can be performed quickly. If the user is relaxed, playback can be performed slowly. If the user is moved, moving scenes can be preferentially played back. In this way, user convenience can be improved by adjusting the timing of playback based on the user's emotions.

[0137] The upload unit can analyze the user's past upload history and select the optimal upload method. For example, the upload unit analyzes the time periods during which the user frequently uploaded in the past. Then, the upload unit selects the optimal upload method. For example, the upload unit can prioritize upload methods (Wi-Fi, mobile data, etc.) that the user has used in the past. The upload unit can also prioritize uploading videos related to specific events or anniversaries based on the user's past upload history. In this way, the optimal upload method can be provided by analyzing the user's past upload history.

[0138] During analysis, the analysis unit can adjust the level of analysis detail based on the importance of the video. For example, the analysis unit evaluates the importance of the video based on the number of views and user ratings. Next, the analysis unit adjusts the level of analysis detail based on the importance of the video. For example, videos of important events (birthdays, weddings, etc.) are analyzed in detail. Meanwhile, videos of everyday life are analyzed more simply. Furthermore, for videos related to a specific theme, the level of analysis detail can be adjusted according to that theme. This allows for efficient analysis by adjusting the level of analysis detail based on the importance of the video.

[0139] When generating a storyline, the generation unit can apply different generation algorithms depending on the category of the video. For example, the generation unit classifies videos into categories such as family, travel, and event. Then, the generation unit applies different generation algorithms depending on the category of the video. For example, the generation unit can apply an emotion analysis algorithm to family videos, a scenery analysis algorithm to travel videos, and an event analysis algorithm to event videos. In this way, applying different generation algorithms depending on the video category enables more accurate storyline generation.

[0140] When editing video, the editing department can determine the editing priority based on the time when the video was shot. For example, the editing department identifies the date and period when the video was shot. Next, the editing department determines the editing priority based on the time when the video was shot. For example, the editing department may give priority to editing recently shot videos. Also, the editing department may give priority to editing videos of specific events (birthdays, weddings, etc.). It is also possible to give priority to editing videos within a period specified by the user. In this way, by determining the editing priority based on the time when the video was shot, it is possible to give priority to editing the most recent videos.

[0141] The playback unit can adjust the playback order based on the relevance of the videos during playback. For example, the playback unit analyzes the topic or theme of the videos and evaluates the relevance. Then, the playback unit adjusts the playback order based on the relevance of the videos. For example, videos related to the same event are played consecutively. Also, videos related to the same theme are played consecutively. The playback order can also be adjusted based on the relevance specified by the user. This allows for efficient playback by adjusting the playback order based on the relevance of the videos.

[0142] The processing flow of the second embodiment will be briefly explained below.

[0143] Step 1: In the upload section, users upload videos from their smartphones to cloud storage. Users simply select a video using the smartphone app and press the upload button to upload the video to the cloud. Step 2: The analysis unit analyzes the video uploaded by the upload unit. Using generative AI, the analysis unit uses machine learning to analyze the child's facial expressions, language, and behavior. For example, it extracts scenes in which the child is laughing or uttering special words. Step 3: The generator generates a storyline based on the results of the analysis. The generator uses AI to automatically generate a storyline based on the analysis results. For example, it creates a moving story by combining a child's smile and special words. Step 4: The editing team edits and files the footage based on the storyline generated by the generation team. The editing team uses the generative AI to generate and add background music and sound effects that fit the storyline. For example, they add celebratory music to a scene where a child blows out the candles on a birthday cake. Step 5: The download unit downloads the video edited and filed by the editing unit to a dedicated mobile device. The download unit can download to multiple dedicated mobile devices. For example, if a user wants to play the same video on a TV at home and at their parents' home, they can download it to both devices. Step 6: The playback unit connects to the TV and plays the video downloaded by the download unit. The playback unit is connected to the TV in the living room via HDMI, and repeatedly displays the downloaded file on the TV screen.

[0144] 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.

[0145] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0146] 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.

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

[0148] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0149] 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.

[0150] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0151] The 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.

[0152] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0154] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0155] 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.

[0156] 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.

[0157] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0158] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] 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.

[0160] 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.

[0161] 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 AI 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.

[0162] 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.

[0163] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0164] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0174] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0175] 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.

[0176] 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.

[0177] 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 AI 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.

[0178] 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.

[0179] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0180] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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).

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0191] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0192] 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.

[0193] 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.

[0194] 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 AI 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.

[0195] 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.

[0196] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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).

[0201] 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.

[0202] 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."

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] [Explanation of symbols]

[0216] 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. an uploading unit for uploading videos to the cloud; an analysis unit that analyzes the video uploaded by the upload unit; a generation unit that generates a storyline based on the results of the analysis by the analysis unit; an editing unit that edits and files the video based on the storyline generated by the generation unit; a download unit that downloads the video edited and filed by the editing unit to a dedicated mobile terminal; a playback unit that connects the video downloaded by the download unit to a television and plays the video; Equipped with A system characterized by:

2. The generation unit Generate and add background music or sound effects that fit the storyline 2. The system of claim 1.

3. The generation unit You can request video editing for your special day, and we will edit the video to best suit your purpose and emotions.

2. The system of claim 1.

4. The download unit Download to multiple dedicated mobile devices 2. The system of claim 1.

5. The playback unit Automatically update download files weekly 2. The system of claim 1.

6. The upload unit Estimate user emotions and adjust video upload timing based on the estimated user emotions 2. The system of claim 1.

7. The upload unit Analyze the user's upload history and select the most suitable upload method 2. The system of claim 1.

8. The upload unit Filtering videos as they are uploaded based on the user's current life situation and interests 2. The system of claim 1.

9. The upload unit When uploading a video, choose the most appropriate upload method based on the user's input 2. The system of claim 1.

10. The upload unit Estimate user sentiment and prioritize videos to upload based on the estimated sentiment 2. The system of claim 1.

Images