System

Abstract

An object of the system according to the embodiment is to efficiently organize a large number of photographs and moving images and create a movie according to a usage scene.SOLUTION: A system according to an embodiment includes an upload unit, a recording unit, an organization unit, and a movie creation unit. The upload unit automatically uploads a photo or a moving image. The recording unit records information of the photograph or the moving image uploaded by the upload unit. Based on the information recorded by the recording unit, the organizing unit organizes photographs and moving images in accordance with the user's preference. The movie creation unit creates a movie according to the use scene on the basis of the photographs and the moving images organized by the organization unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has faced the challenge of making it difficult to efficiently organize large amounts of photos and videos and create movies tailored to specific usage scenarios.

[0005] The system according to the embodiment aims to efficiently organize a large number of photos and videos and create movies suited to the usage scenario. [Means for solving the problem]

[0006] The system according to the embodiment includes an upload unit, a recording unit, an organizing unit, and a movie creation unit. The upload unit automatically uploads photos and videos. The recording unit records information about the photos and videos uploaded by the upload unit. The organizing unit organizes the photos and videos according to the user's preferences based on the information recorded by the recording unit. The movie creation unit creates a movie tailored to the usage scenario based on the photos and videos organized by the organizing unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently organize a large number of photos and videos and create movies suited to the usage scenario. [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 platform according to an embodiment of the present invention is a system that automatically organizes large numbers of photos and videos taken with a smartphone or camera. This system is configured by a user in advance, and photos and videos are automatically uploaded to an AI-equipped platform. Information about the upload source is also recorded, preventing missed uploads. Furthermore, the uploaded photos and videos are automatically organized according to the user's preferences. For example, folders are organized for specific events or locations. Furthermore, movies tailored to specific usage scenarios are automatically created based on the stored data. This allows users to efficiently manage large numbers of photos and videos. This allows the platform to efficiently manage large numbers of photos and videos. For example, a user can set rules for organizing folders for specific events or locations by configuring the system in advance. Next, the photos and videos are automatically uploaded to the AI-equipped platform. Information about the upload source is also recorded, preventing missed uploads. Furthermore, the uploaded photos and videos are automatically organized according to the user's preferences. For example, folders are organized for specific events or locations. Furthermore, photos and videos that feature specific people or objects can be automatically classified. Finally, movies tailored to specific usage scenarios can be automatically created based on the stored data. For example, a movie can be created by combining photos and videos from a specific event. This allows users to efficiently manage large amounts of photos and videos.

[0029] The organization system according to the embodiment includes an upload unit, a recording unit, an organizing unit, and a movie creation unit. The upload unit automatically uploads photos and videos. For example, the upload unit can upload photos and videos directly from a smartphone or camera. The upload unit can also automatically upload photos and videos at specific times or under specific conditions. The recording unit records information about the photos and videos uploaded by the upload unit. For example, the recording unit can record information about the upload source. The recording unit can also automatically extract and record metadata (such as the shooting date and time, location, and device information) from the photos and videos. The organizing unit organizes the photos and videos according to the user's preferences based on the information recorded by the recording unit. For example, the organizing unit can organize photos and videos into folders for specific events or locations. The organizing unit can also automatically classify photos and videos that feature specific people or objects. The movie creation unit creates movies tailored to specific usage scenarios based on the photos and videos organized by the organizing unit. For example, the movie creation unit can create a movie by compiling photos and videos from a specific event. As a result, the organization system according to the embodiment can automatically upload photos and videos, organize them, and create movies.

[0030] The organizing unit can sort photos and videos into folders by event or location. The organizing unit automatically sorts photos and videos into folders based on, for example, the date and time the photos and videos were taken and the location. For example, the organizing unit can sort photos and videos into folders by year, month, or day based on the date and time the photos and videos were taken. The organizing unit can also sort photos and videos into folders by country, city, or location based on the location the photos were taken. Furthermore, the organizing unit can also sort photos and videos into folders by event based on both the date and time the photos were taken and the location. This allows photos and videos to be organized by specific events or locations.

[0031] The recording unit can record information about the upload source. The recording unit, for example, records device information about the upload source. For example, the recording unit can record information about a smartphone or camera. The recording unit can also record user information about the upload source. For example, the recording unit can record user account information. Furthermore, the recording unit can also record network information about the upload source. For example, the recording unit can record information about Wi-Fi or mobile data used at the time of upload. In this way, by recording the information about the upload source, it is possible to prevent missed uploads.

[0032] The organizing unit can automatically categorize photos and videos that include people or objects. The organizing unit can use, for example, facial recognition technology to identify people appearing in photos and videos and categorize the videos by person. For example, the organizing unit can automatically categorize photos and videos of family and friends. The organizing unit can also use object recognition technology to identify objects appearing in photos and videos and categorize the videos by object. For example, the organizing unit can automatically categorize photos and videos of pets or cars. Furthermore, the organizing unit can categorize photos and videos based on specific events or themes. For example, the organizing unit can automatically categorize photos and videos of weddings or birthdays. This makes it possible to automatically categorize photos and videos that include specific people or objects.

[0033] The movie creation unit can create a movie by compiling photos and videos of a specific event. For example, the movie creation unit can create a movie by arranging photos and videos of a specific event in chronological order. For example, the movie creation unit can create a movie by compiling photos and videos of a wedding or birthday. The movie creation unit can also select photos and videos based on a specific theme and create a movie. For example, the movie creation unit can create a movie by compiling photos and videos of travel or sports. Furthermore, the movie creation unit can analyze the content of the photos and videos and edit the movie based on a specific story. For example, the movie creation unit can create a movie based on the theme of family growth or memories with friends. In this way, a movie can be created by compiling photos and videos of a specific event.

[0034] The upload unit can upload photos and videos directly from a smartphone or camera. The upload unit uploads photos and videos directly from, for example, a smartphone or camera. For example, the upload unit can upload photos and videos using Wi-Fi or mobile data. The upload unit can also upload photos and videos using a USB cable. Furthermore, the upload unit can also upload photos and videos using Bluetooth (registered trademark). This allows photos and videos to be uploaded directly from a smartphone or camera.

[0035] The uploading unit can automatically determine the upload order based on the shooting date and time of the photos and videos. The uploading unit, for example, acquires the shooting date and time of the photos and videos and determines the upload order based on the date and time. For example, the uploading unit can upload photos and videos in order of oldest shooting date and time, so that the most recent photos and videos are uploaded last. The uploading unit can also upload photos and videos in order of newest shooting date and time, so that the most recent photos and videos are uploaded first. Furthermore, the uploading unit can upload photos and videos taken within a specific period of time together and organize them by event. This makes it possible to determine the upload order based on the shooting date and time of the photos and videos.

[0036] The upload unit can select an appropriate upload method based on the file size of the photo or video. For example, the upload unit measures the file size of the photo or video and selects the optimal upload method based on the size. For example, the upload unit can upload large files over a Wi-Fi connection to avoid using mobile data. The upload unit can also upload small files immediately to enable quick sharing. Furthermore, the upload unit can compress files before uploading them depending on the file size to save on communication traffic. This allows the optimal upload method to be selected based on the file size of the photo or video.

[0037] The upload unit can automatically apply appropriate upload settings by referring to the user's past upload history. The upload unit, for example, can refer to the user's past upload history and automatically apply optimal upload settings based on that history. For example, the upload unit can automatically apply upload settings (Wi-Fi only, specific time periods, etc.) that the user used in the past. The upload unit can also select optimal settings based on the types of files (photos, videos, etc.) that the user uploaded in the past. Furthermore, the upload unit can automatically set a periodic upload schedule based on the user's past upload frequency. This makes it possible to automatically apply optimal upload settings by referring to the user's past upload history.

[0038] The uploading unit can prioritize uploading highly relevant photos and videos based on the user's geographical location information. The uploading unit, for example, acquires the user's geographical location information and prioritizes uploading highly relevant photos and videos based on that information. For example, when the user is at a specific event venue, the uploading unit can prioritize uploading photos and videos related to the event. Furthermore, when the user is traveling, the uploading unit can prioritize uploading photos and videos of the user's travel destination. Furthermore, when the user is at home, the uploading unit can prioritize uploading photos and videos of family and friends. In this way, highly relevant photos and videos can be prioritized uploaded taking into account the user's geographical location information.

[0039] The upload unit can analyze the user's social media activity and automatically upload related photos and videos. The upload unit, for example, analyzes the user's social media activity and automatically uploads related photos and videos based on the activity. For example, the upload unit can automatically upload photos and videos related to places the user has checked in to on social media. The upload unit can also analyze the content of the user's social media posts and automatically upload related photos and videos. Furthermore, the upload unit can automatically upload related photos and videos based on the activity of the user's friends on social media. In this way, the user's social media activity can be analyzed and related photos and videos can be automatically uploaded.

[0040] The upload unit can customize the upload method by reflecting the user's past feedback. For example, the upload unit collects the user's past feedback and customizes the upload method based on the feedback. For example, the upload unit can customize the upload method based on feedback provided by the user in the past. The upload unit can also preferentially apply upload settings that the user previously preferred. Furthermore, the upload unit can analyze the user's past feedback and suggest an optimal upload method. In this way, the upload method can be customized by reflecting the user's past feedback.

[0041] The recording unit can automatically extract and record metadata from photos and videos. The recording unit automatically extracts and records metadata such as the date and time the photos and videos were taken, the location, and device information. For example, the recording unit can automatically extract and record the date and time the photos and videos were taken. The recording unit can also automatically extract and record the location where the photos and videos were taken. Furthermore, the recording unit can automatically extract and record information about the device used. This makes it possible to automatically extract and record metadata from photos and videos.

[0042] The recording unit can analyze the content of photos and videos and automatically assign specific keywords and tags. The recording unit can, for example, use image analysis technology to analyze the content of photos and videos and automatically assign specific keywords and tags based on the content. For example, the recording unit can analyze people appearing in photos and videos and assign their names as tags. The recording unit can also analyze the content of photos and videos and assign keywords related to specific events or places. Furthermore, the recording unit can analyze the content of photos and videos and assign keywords related to specific objects or scenery. In this way, the content of photos and videos can be analyzed and specific keywords and tags can be automatically assigned.

[0043] The recording unit can select an appropriate recording method by referring to the user's past recording history. For example, the recording unit can refer to the user's past recording history and select the optimal recording method based on that history. For example, the recording unit can automatically apply the recording method (metadata detail level, tag assignment method, etc.) that the user used in the past. The recording unit can also preferentially record information related to specific events or locations from the user's past recording history. Furthermore, the recording unit can analyze the user's past recording history and suggest the optimal recording method. In this way, the optimal recording method can be selected by referring to the user's past recording history.

[0044] The recording unit can prioritize recording highly relevant information based on the user's geographical location information. The recording unit, for example, acquires the user's geographical location information and prioritizes recording highly relevant information based on that information. For example, when the user is at a specific event venue, the recording unit can prioritize recording information related to the event. Furthermore, when the user is traveling, the recording unit can prioritize recording information about the travel destination. Furthermore, when the user is at home, the recording unit can prioritize recording information related to family and friends. In this way, highly relevant information can be prioritized and recorded taking into account the user's geographical location information.

[0045] The recording unit can analyze the user's social media activity and automatically record related information. The recording unit, for example, analyzes the user's social media activity and automatically records related information based on the activity. For example, the recording unit can automatically record information related to places where the user has checked in on social media. The recording unit can also analyze the content of the user's posts on social media and automatically record related information. Furthermore, the recording unit can automatically record related information based on the activities of the user's friends on social media. In this way, the user's social media activity can be analyzed and related information can be automatically recorded.

[0046] The recording unit can customize the recording method by reflecting the user's past feedback. For example, the recording unit collects the user's past feedback and customizes the recording method based on the feedback. For example, the recording unit can customize the recording method based on feedback provided by the user in the past. The recording unit can also preferentially apply recording settings that the user has previously preferred. Furthermore, the recording unit can analyze the user's past feedback and suggest an optimal recording method. In this way, the recording method can be customized by reflecting the user's past feedback.

[0047] The organizing unit can automatically sort photos and videos into folders based on the date and time and location at which the photos and videos were taken. For example, the organizing unit acquires the date and time and location at which the photos and videos were taken, and automatically sorts the photos and videos into folders based on that information. For example, the organizing unit can sort photos and videos into folders by year, month, or day based on the date and time at which the photos and videos were taken. The organizing unit can also sort photos and videos into folders by country, city, or location based on the location at which the photos and videos were taken. Furthermore, the organizing unit can also sort photos and videos into folders by event based on both the date and time at which the photos and videos were taken and the location at which the photos and videos were taken. This allows photos and videos to be automatically sorted into folders based on the date and time at which the photos and videos were taken and the location at which the photos and videos were taken.

[0048] The organizing unit can analyze the content of photos and videos and classify them by specific events or themes. The organizing unit can, for example, use image analysis technology to analyze the content of photos and videos and classify them by specific events or themes based on the content. For example, the organizing unit can analyze people appearing in photos and videos and classify them by those people. The organizing unit can also analyze the content of photos and videos and classify them by specific events (weddings, birthdays, etc.). Furthermore, the organizing unit can analyze the content of photos and videos and classify them by specific themes (travel, sports, etc.). In this way, the content of photos and videos can be analyzed and classified by specific events or themes.

[0049] The organizing unit can select an appropriate organizing method by referring to the user's past organizing history. For example, the organizing unit can refer to the user's past organizing history and select the optimal organizing method based on that history. For example, the organizing unit can automatically apply an organizing method (such as a folder sorting rule) that the user used in the past. The organizing unit can also select an organizing method based on a specific event or theme from the user's past organizing history. Furthermore, the organizing unit can analyze the user's past organizing history and suggest the optimal organizing method. In this way, the optimal organizing method can be selected by referring to the user's past organizing history.

[0050] The organizing unit can prioritize organizing highly relevant photos and videos based on the user's geographical location information. The organizing unit, for example, acquires the user's geographical location information and prioritizes organizing highly relevant photos and videos based on that information. For example, when the user is at a specific event venue, the organizing unit can prioritize organizing photos and videos related to the event. Furthermore, when the user is traveling, the organizing unit can prioritize organizing photos and videos from the travel destination. Furthermore, when the user is at home, the organizing unit can prioritize organizing photos and videos related to family and friends. In this way, highly relevant photos and videos can be prioritized and organized taking into account the user's geographical location information.

[0051] The organizing unit can analyze the user's social media activity and automatically organize related photos and videos. The organizing unit, for example, analyzes the user's social media activity and automatically organizes related photos and videos based on the activity. For example, the organizing unit can automatically organize photos and videos related to places the user has checked in to on social media. The organizing unit can also analyze the content of the user's social media posts and automatically organize related photos and videos. Furthermore, the organizing unit can automatically organize related photos and videos based on the activity of the user's friends on social media. In this way, the user's social media activity can be analyzed and related photos and videos can be automatically organized.

[0052] The organizing unit can customize the organizing method by reflecting the user's past feedback. The organizing unit, for example, collects the user's past feedback and customizes the organizing method based on the feedback. For example, the organizing unit can customize the organizing method based on feedback provided by the user in the past. The organizing unit can also preferentially apply organizing settings that the user preferred in the past. Furthermore, the organizing unit can analyze the user's past feedback and suggest an optimal organizing method. In this way, the organizing method can be customized by reflecting the user's past feedback.

[0053] The movie creation unit can automatically compose scenes based on the shooting date and time and location of photos and videos. The movie creation unit, for example, acquires the shooting date and time and location of photos and videos, and automatically composes scenes based on that information. For example, the movie creation unit can compose scenes in chronological order based on the shooting date and time. The movie creation unit can also compose scenes for each location based on the shooting location. Furthermore, the movie creation unit can also compose scenes for each event based on both the shooting date and time and location. This makes it possible to automatically compose scenes based on the shooting date and time and location of photos and videos.

[0054] The movie creation unit can analyze the content of photos and videos and edit them based on a specific theme or story. The movie creation unit can analyze the content of photos and videos using, for example, image analysis technology and edit the content based on a specific theme or story. For example, the movie creation unit can analyze people appearing in photos and videos and edit a story focusing on those people. The movie creation unit can also analyze the content of photos and videos and edit them based on a specific event (wedding, birthday, etc.). Furthermore, the movie creation unit can analyze the content of photos and videos and edit them based on a specific theme (travel, sports, etc.). This makes it possible to analyze the content of photos and videos and edit them based on a specific theme or story.

[0055] The movie creation unit can select the optimal editing method by referring to the user's past movie creation history. The movie creation unit, for example, refers to the user's past movie creation history and selects the optimal editing method based on that history. For example, the movie creation unit can automatically apply editing methods (effects, music, etc.) that the user has used in the past. The movie creation unit can also select an editing method based on a specific event or theme from the user's past movie creation history. Furthermore, the movie creation unit can analyze the user's past movie creation history and suggest the optimal editing method. In this way, the optimal editing method can be selected by referring to the user's past movie creation history.

[0056] The movie creation unit can prioritize editing highly relevant scenes based on the user's geographical location information. The movie creation unit, for example, acquires the user's geographical location information and prioritizes editing highly relevant scenes based on that information. For example, when the user is at a specific event venue, the movie creation unit can prioritize editing scenes related to the event. Furthermore, when the user is traveling, the movie creation unit can prioritize editing scenes from the travel destination. Furthermore, when the user is at home, the movie creation unit can prioritize editing scenes related to family and friends. In this way, highly relevant scenes can be prioritized and edited taking into account the user's geographical location information.

[0057] The movie creation unit can analyze the user's social media activity and automatically edit related scenes. The movie creation unit, for example, analyzes the user's social media activity and automatically edits related scenes based on the activity. For example, the movie creation unit can automatically edit scenes related to places where the user has checked in on social media. The movie creation unit can also analyze the content of the user's social media posts and automatically edit related scenes. Furthermore, the movie creation unit can automatically edit related scenes by referring to the activity of the user's friends on social media. In this way, the user's social media activity can be analyzed and related scenes can be automatically edited.

[0058] The movie creation unit can customize the editing method by reflecting the user's past feedback. For example, the movie creation unit collects the user's past feedback and customizes the editing method based on the feedback. For example, the movie creation unit can customize the editing method based on feedback provided by the user in the past. The movie creation unit can also preferentially apply editing settings that the user preferred in the past. Furthermore, the movie creation unit can analyze the user's past feedback and suggest an optimal editing method. In this way, the editing method can be customized by reflecting the user's past feedback.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The upload unit can select the appropriate upload method based on the file size of the photo or video. For example, large files can be uploaded when connected to Wi-Fi to avoid using mobile data. Small files can also be uploaded immediately to allow for quick sharing. Furthermore, depending on the file size, files can be compressed before uploading to save on data usage. This allows the optimal upload method to be selected based on the file size of the photo or video.

[0061] The recording unit can analyze the content of photos and videos and automatically assign specific keywords and tags. For example, it can use image analysis technology to analyze the content of photos and videos and automatically assign specific keywords and tags based on that content. For example, it can analyze the people appearing in photos and videos and assign their names as tags. It can also analyze the content of photos and videos and assign keywords related to specific events or places. It can also analyze the content of photos and videos and assign keywords related to specific objects or scenery. This makes it possible to analyze the content of photos and videos and automatically assign specific keywords and tags.

[0062] The organizing unit can automatically sort photos and videos into folders based on the date and time and location of their capture. For example, folders can be sorted by year, month, or day based on the date and time of capture. Folders can also be sorted by country, city, or location based on the location of the capture. Furthermore, folders can be sorted by event based on both the date and time of capture and the location of the capture. This allows photos and videos to be automatically sorted into folders based on the date and time of capture and the location of the capture.

[0063] The movie creation unit can analyze the content of photos and videos and edit them based on a specific theme or story. For example, it can use image analysis technology to analyze the content of photos and videos and edit them based on a specific theme or story. For example, it can analyze people in photos and videos and edit a story focusing on those people. It can also analyze the content of photos and videos and edit them based on a specific event (wedding, birthday, etc.). It can also analyze the content of photos and videos and edit them based on a specific theme (travel, sports, etc.). This makes it possible to analyze the content of photos and videos and edit them based on a specific theme or story.

[0064] The upload unit can automatically apply appropriate upload settings by referring to the user's past upload history. For example, the upload unit can automatically apply optimal upload settings based on the user's past upload history. For example, the upload unit can automatically apply upload settings that the user used in the past (Wi-Fi only, specific time period, etc.). The upload unit can also automatically select optimal settings based on the types of files (photos, videos, etc.) that the user uploaded in the past. Furthermore, the upload unit can automatically set a periodic upload schedule based on the user's past upload frequency. This makes it possible to automatically apply optimal upload settings by referring to the user's past upload history.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The upload section automatically uploads photos and videos. For example, you can upload photos and videos directly from your smartphone or camera, or you can automatically upload them at specific times or under specific conditions. Step 2: The recording unit records information about the photos and videos uploaded by the uploading unit. For example, it can automatically extract and record information about the upload source and metadata about the photos and videos (such as the shooting date and time, location, and device information). Step 3: The organizer organizes the photos and videos based on the information recorded by the recorder to suit the user's preferences. For example, photos and videos can be organized into folders for specific events or locations, or photos and videos containing specific people or objects can be automatically classified. Step 4: The movie creation unit creates a movie based on the photos and videos organized by the organization unit, tailored to the intended use scenario. For example, a movie can be created by combining photos and videos from a specific event.

[0067] (Example 2) A platform according to an embodiment of the present invention is a system that automatically organizes large numbers of photos and videos taken with a smartphone or camera. This system is configured by a user in advance, and photos and videos are automatically uploaded to an AI-equipped platform. Information about the upload source is also recorded, preventing missed uploads. Furthermore, the uploaded photos and videos are automatically organized according to the user's preferences. For example, they are organized into folders for specific events or locations. Furthermore, movies tailored to specific usage scenarios are automatically created based on the stored data. This allows users to efficiently manage large numbers of photos and videos. This allows the platform to efficiently manage large numbers of photos and videos. For example, a user can set rules for organizing folders for specific events or locations by configuring the system in advance. Next, the photos and videos are automatically uploaded to an AI-equipped platform. Information about the upload source is also recorded, preventing missed uploads. Furthermore, the uploaded photos and videos are automatically organized according to the user's preferences. For example, they are organized into folders for specific events or locations. Furthermore, photos and videos that feature specific people or objects can be automatically classified. Finally, movies tailored to specific usage scenarios can be automatically created based on the stored data. For example, a movie can be created by combining photos and videos from a specific event. This allows users to efficiently manage large amounts of photos and videos.

[0068] The organization system according to the embodiment includes an upload unit, a recording unit, an organizing unit, and a movie creation unit. The upload unit automatically uploads photos and videos. For example, the upload unit can upload photos and videos directly from a smartphone or camera. The upload unit can also automatically upload photos and videos at specific times or under specific conditions. The recording unit records information about the photos and videos uploaded by the upload unit. For example, the recording unit can record information about the upload source. The recording unit can also automatically extract and record metadata (such as the shooting date and time, location, and device information) from the photos and videos. The organizing unit organizes the photos and videos according to the user's preferences based on the information recorded by the recording unit. For example, the organizing unit can organize photos and videos into folders for specific events or locations. The organizing unit can also automatically classify photos and videos that feature specific people or objects. The movie creation unit creates movies tailored to specific usage scenarios based on the photos and videos organized by the organizing unit. For example, the movie creation unit can create a movie by compiling photos and videos from a specific event. As a result, the organization system according to the embodiment can automatically upload photos and videos, organize them, and create movies.

[0069] The organizing unit can sort photos and videos into folders by event or location. The organizing unit automatically sorts photos and videos into folders based on, for example, the date and time the photos and videos were taken and the location. For example, the organizing unit can sort photos and videos into folders by year, month, or day based on the date and time the photos and videos were taken. The organizing unit can also sort photos and videos into folders by country, city, or location based on the location the photos were taken. Furthermore, the organizing unit can also sort photos and videos into folders by event based on both the date and time the photos were taken and the location. This allows photos and videos to be organized by specific events or locations.

[0070] The recording unit can record information about the upload source. The recording unit, for example, records device information about the upload source. For example, the recording unit can record information about a smartphone or camera. The recording unit can also record user information about the upload source. For example, the recording unit can record user account information. Furthermore, the recording unit can also record network information about the upload source. For example, the recording unit can record information about Wi-Fi or mobile data used at the time of upload. In this way, by recording the information about the upload source, it is possible to prevent missed uploads.

[0071] The organizing unit can automatically categorize photos and videos that include people or objects. The organizing unit can use, for example, facial recognition technology to identify people appearing in photos and videos and categorize the videos by person. For example, the organizing unit can automatically categorize photos and videos of family and friends. The organizing unit can also use object recognition technology to identify objects appearing in photos and videos and categorize the videos by object. For example, the organizing unit can automatically categorize photos and videos of pets or cars. Furthermore, the organizing unit can categorize photos and videos based on specific events or themes. For example, the organizing unit can automatically categorize photos and videos of weddings or birthdays. This makes it possible to automatically categorize photos and videos that include specific people or objects.

[0072] The movie creation unit can create a movie by compiling photos and videos of a specific event. For example, the movie creation unit can create a movie by arranging photos and videos of a specific event in chronological order. For example, the movie creation unit can create a movie by compiling photos and videos of a wedding or birthday. The movie creation unit can also select photos and videos based on a specific theme and create a movie. For example, the movie creation unit can create a movie by compiling photos and videos of travel or sports. Furthermore, the movie creation unit can analyze the content of the photos and videos and edit the movie based on a specific story. For example, the movie creation unit can create a movie based on the theme of family growth or memories with friends. In this way, a movie can be created by compiling photos and videos of a specific event.

[0073] The upload unit can upload photos and videos directly from a smartphone or camera. The upload unit uploads photos and videos directly from, for example, a smartphone or camera. For example, the upload unit can upload photos and videos using Wi-Fi or mobile data. The upload unit can also upload photos and videos using a USB cable. Furthermore, the upload unit can also upload photos and videos using Bluetooth. This allows photos and videos to be uploaded directly from a smartphone or camera.

[0074] The upload unit can estimate the user's emotion and adjust the timing of uploading based on the estimated user's emotion. The upload unit can estimate the user's emotion using, for example, facial expression recognition technology. For example, the upload unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The upload unit can also estimate the user's emotion using voice analysis technology. For example, the upload unit can analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the upload unit can estimate the user's emotion using biometric data (heart rate and electrodermal activity). For example, the upload unit can estimate the emotion based on heart rate fluctuations. This makes it possible to adjust the timing of uploading based on the user's emotion. Emotion estimation is achieved using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] The uploading unit can automatically determine the upload order based on the shooting date and time of the photos and videos. The uploading unit, for example, acquires the shooting date and time of the photos and videos and determines the upload order based on the date and time. For example, the uploading unit can upload photos and videos in order of oldest shooting date and time, so that the most recent photos and videos are uploaded last. The uploading unit can also upload photos and videos in order of newest shooting date and time, so that the most recent photos and videos are uploaded first. Furthermore, the uploading unit can upload photos and videos taken within a specific period of time together and organize them by event. This makes it possible to determine the upload order based on the shooting date and time of the photos and videos.

[0076] The upload unit can select an appropriate upload method based on the file size of the photo or video. For example, the upload unit measures the file size of the photo or video and selects the optimal upload method based on the size. For example, the upload unit can upload large files over a Wi-Fi connection to avoid using mobile data. The upload unit can also upload small files immediately to enable quick sharing. Furthermore, the upload unit can compress files before uploading them depending on the file size to save on communication traffic. This allows the optimal upload method to be selected based on the file size of the photo or video.

[0077] The upload unit can automatically apply appropriate upload settings by referring to the user's past upload history. The upload unit, for example, can refer to the user's past upload history and automatically apply optimal upload settings based on that history. For example, the upload unit can automatically apply upload settings (Wi-Fi only, specific time periods, etc.) that the user used in the past. The upload unit can also select optimal settings based on the types of files (photos, videos, etc.) that the user uploaded in the past. Furthermore, the upload unit can automatically set a periodic upload schedule based on the user's past upload frequency. This makes it possible to automatically apply optimal upload settings by referring to the user's past upload history.

[0078] The upload unit can estimate a user's emotions and determine the priority of photos and videos to be uploaded based on the estimated user's emotions. The upload unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the upload unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The upload unit can also estimate the user's emotions using voice analysis technology. For example, the upload unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the upload unit can estimate the user's emotions using biometric data (heart rate and electrodermal activity). For example, the upload unit can estimate the emotions based on heart rate fluctuations. This makes it possible to determine the priority of photos and videos to be uploaded based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0079] The uploading unit can prioritize uploading highly relevant photos and videos based on the user's geographical location information. The uploading unit, for example, acquires the user's geographical location information and prioritizes uploading highly relevant photos and videos based on that information. For example, when the user is at a specific event venue, the uploading unit can prioritize uploading photos and videos related to the event. Furthermore, when the user is traveling, the uploading unit can prioritize uploading photos and videos of the user's travel destination. Furthermore, when the user is at home, the uploading unit can prioritize uploading photos and videos of family and friends. In this way, highly relevant photos and videos can be prioritized uploaded taking into account the user's geographical location information.

[0080] The upload unit can analyze the user's social media activity and automatically upload related photos and videos. The upload unit, for example, analyzes the user's social media activity and automatically uploads related photos and videos based on the activity. For example, the upload unit can automatically upload photos and videos related to places the user has checked in to on social media. The upload unit can also analyze the content of the user's social media posts and automatically upload related photos and videos. Furthermore, the upload unit can automatically upload related photos and videos based on the activity of the user's friends on social media. In this way, the user's social media activity can be analyzed and related photos and videos can be automatically uploaded.

[0081] The upload unit can customize the upload method by reflecting the user's past feedback. For example, the upload unit collects the user's past feedback and customizes the upload method based on the feedback. For example, the upload unit can customize the upload method based on feedback provided by the user in the past. The upload unit can also preferentially apply upload settings that the user previously preferred. Furthermore, the upload unit can analyze the user's past feedback and suggest an optimal upload method. In this way, the upload method can be customized by reflecting the user's past feedback.

[0082] The recording unit can estimate the user's emotion and adjust the level of detail of the information to be recorded based on the estimated user's emotion. The recording unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the recording unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The recording unit can also estimate the user's emotion using voice analysis technology. For example, the recording unit can analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the recording unit can estimate the user's emotion using biometric data (heart rate and electrodermal activity). For example, the recording unit can estimate the emotion based on heart rate fluctuations. This makes it possible to adjust the level of detail of the information to be recorded based on the user's emotion. Emotion estimation is achieved using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] The recording unit can automatically extract and record metadata from photos and videos. The recording unit automatically extracts and records metadata such as the date and time the photos and videos were taken, the location, and device information. For example, the recording unit can automatically extract and record the date and time the photos and videos were taken. The recording unit can also automatically extract and record the location where the photos and videos were taken. Furthermore, the recording unit can automatically extract and record information about the device used. This makes it possible to automatically extract and record metadata from photos and videos.

[0084] The recording unit can analyze the content of photos and videos and automatically assign specific keywords and tags. The recording unit can, for example, use image analysis technology to analyze the content of photos and videos and automatically assign specific keywords and tags based on the content. For example, the recording unit can analyze people appearing in photos and videos and assign their names as tags. The recording unit can also analyze the content of photos and videos and assign keywords related to specific events or places. Furthermore, the recording unit can analyze the content of photos and videos and assign keywords related to specific objects or scenery. In this way, the content of photos and videos can be analyzed and specific keywords and tags can be automatically assigned.

[0085] The recording unit can select an appropriate recording method by referring to the user's past recording history. For example, the recording unit can refer to the user's past recording history and select the optimal recording method based on that history. For example, the recording unit can automatically apply the recording method (metadata detail level, tag assignment method, etc.) that the user used in the past. The recording unit can also preferentially record information related to specific events or locations from the user's past recording history. Furthermore, the recording unit can analyze the user's past recording history and suggest the optimal recording method. In this way, the optimal recording method can be selected by referring to the user's past recording history.

[0086] The recording unit can estimate the user's emotions and determine the priority of information to be recorded based on the estimated user's emotions. The recording unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the recording unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The recording unit can also estimate the user's emotions using voice analysis technology. For example, the recording unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the recording unit can estimate the user's emotions using biometric data (heart rate and electrodermal activity). For example, the recording unit can estimate the emotions based on heart rate fluctuations. This makes it possible to determine the priority of information to be recorded based on the user's emotions. Emotion estimation is achieved using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] The recording unit can prioritize recording highly relevant information based on the user's geographical location information. The recording unit, for example, acquires the user's geographical location information and prioritizes recording highly relevant information based on that information. For example, when the user is at a specific event venue, the recording unit can prioritize recording information related to the event. Furthermore, when the user is traveling, the recording unit can prioritize recording information about the travel destination. Furthermore, when the user is at home, the recording unit can prioritize recording information related to family and friends. In this way, highly relevant information can be prioritized and recorded taking into account the user's geographical location information.

[0088] The recording unit can analyze the user's social media activity and automatically record related information. The recording unit, for example, analyzes the user's social media activity and automatically records related information based on the activity. For example, the recording unit can automatically record information related to places where the user has checked in on social media. The recording unit can also analyze the content of the user's posts on social media and automatically record related information. Furthermore, the recording unit can automatically record related information based on the activities of the user's friends on social media. In this way, the user's social media activity can be analyzed and related information can be automatically recorded.

[0089] The recording unit can customize the recording method by reflecting the user's past feedback. For example, the recording unit collects the user's past feedback and customizes the recording method based on the feedback. For example, the recording unit can customize the recording method based on feedback provided by the user in the past. The recording unit can also preferentially apply recording settings that the user has previously preferred. Furthermore, the recording unit can analyze the user's past feedback and suggest an optimal recording method. In this way, the recording method can be customized by reflecting the user's past feedback.

[0090] The organizing unit can estimate the user's emotions and adjust the organizing rules based on the estimated user's emotions. The organizing unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the organizing unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The organizing unit can also estimate the user's emotions using voice analysis technology. For example, the organizing unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the organizing unit can estimate the user's emotions using biometric data (heart rate and electrodermal activity). For example, the organizing unit can estimate the emotions based on heart rate fluctuations. This makes it possible to adjust the organizing rules based on the user's emotions. Emotion estimation is achieved using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0091] The organizing unit can automatically sort photos and videos into folders based on the date and time and location at which the photos and videos were taken. For example, the organizing unit acquires the date and time and location at which the photos and videos were taken, and automatically sorts the photos and videos into folders based on that information. For example, the organizing unit can sort photos and videos into folders by year, month, or day based on the date and time at which the photos and videos were taken. The organizing unit can also sort photos and videos into folders by country, city, or location based on the location at which the photos and videos were taken. Furthermore, the organizing unit can also sort photos and videos into folders by event based on both the date and time at which the photos and videos were taken and the location at which the photos and videos were taken. This allows photos and videos to be automatically sorted into folders based on the date and time at which the photos and videos were taken and the location at which the photos and videos were taken.

[0092] The organizing unit can analyze the content of photos and videos and classify them by specific events or themes. The organizing unit can, for example, use image analysis technology to analyze the content of photos and videos and classify them by specific events or themes based on the content. For example, the organizing unit can analyze people appearing in photos and videos and classify them by those people. The organizing unit can also analyze the content of photos and videos and classify them by specific events (weddings, birthdays, etc.). Furthermore, the organizing unit can analyze the content of photos and videos and classify them by specific themes (travel, sports, etc.). In this way, the content of photos and videos can be analyzed and classified by specific events or themes.

[0093] The organizing unit can select an appropriate organizing method by referring to the user's past organizing history. For example, the organizing unit can refer to the user's past organizing history and select the optimal organizing method based on that history. For example, the organizing unit can automatically apply an organizing method (such as a folder sorting rule) that the user used in the past. The organizing unit can also select an organizing method based on a specific event or theme from the user's past organizing history. Furthermore, the organizing unit can analyze the user's past organizing history and suggest the optimal organizing method. In this way, the optimal organizing method can be selected by referring to the user's past organizing history.

[0094] The organizing unit can estimate the user's emotions and determine the priority of photos and videos to be organized based on the estimated user's emotions. The organizing unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the organizing unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The organizing unit can also estimate the user's emotions using voice analysis technology. For example, the organizing unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the organizing unit can estimate the user's emotions using biometric data (heart rate and electrodermal activity). For example, the organizing unit can estimate emotions based on heart rate fluctuations. This makes it possible to determine the priority of photos and videos to be organized based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0095] The organizing unit can prioritize organizing highly relevant photos and videos based on the user's geographical location information. The organizing unit, for example, acquires the user's geographical location information and prioritizes organizing highly relevant photos and videos based on that information. For example, when the user is at a specific event venue, the organizing unit can prioritize organizing photos and videos related to the event. Furthermore, when the user is traveling, the organizing unit can prioritize organizing photos and videos from the travel destination. Furthermore, when the user is at home, the organizing unit can prioritize organizing photos and videos related to family and friends. In this way, highly relevant photos and videos can be prioritized and organized taking into account the user's geographical location information.

[0096] The organizing unit can analyze the user's social media activity and automatically organize related photos and videos. The organizing unit, for example, analyzes the user's social media activity and automatically organizes related photos and videos based on the activity. For example, the organizing unit can automatically organize photos and videos related to places the user has checked in to on social media. The organizing unit can also analyze the content of the user's social media posts and automatically organize related photos and videos. Furthermore, the organizing unit can automatically organize related photos and videos based on the activity of the user's friends on social media. In this way, the user's social media activity can be analyzed and related photos and videos can be automatically organized.

[0097] The organizing unit can customize the organizing method by reflecting the user's past feedback. The organizing unit, for example, collects the user's past feedback and customizes the organizing method based on the feedback. For example, the organizing unit can customize the organizing method based on feedback provided by the user in the past. The organizing unit can also preferentially apply organizing settings that the user preferred in the past. Furthermore, the organizing unit can analyze the user's past feedback and suggest an optimal organizing method. In this way, the organizing method can be customized by reflecting the user's past feedback.

[0098] The movie creation unit can estimate the user's emotions and adjust the editing method of the movie based on the estimated user's emotions. The movie creation unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the movie creation unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The movie creation unit can also estimate the user's emotions using voice analysis technology. For example, the movie creation unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the movie creation unit can estimate the user's emotions using biometric data (heart rate and electrodermal activity). For example, the movie creation unit can estimate the emotions based on heart rate fluctuations. This makes it possible to adjust the editing method of the movie based on the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0099] The movie creation unit can automatically compose scenes based on the shooting date and time and location of photos and videos. The movie creation unit, for example, acquires the shooting date and time and location of photos and videos, and automatically composes scenes based on that information. For example, the movie creation unit can compose scenes in chronological order based on the shooting date and time. The movie creation unit can also compose scenes for each location based on the shooting location. Furthermore, the movie creation unit can also compose scenes for each event based on both the shooting date and time and location. This makes it possible to automatically compose scenes based on the shooting date and time and location of photos and videos.

[0100] The movie creation unit can analyze the content of photos and videos and edit them based on a specific theme or story. The movie creation unit can analyze the content of photos and videos using, for example, image analysis technology and edit the content based on a specific theme or story. For example, the movie creation unit can analyze people appearing in photos and videos and edit a story focusing on those people. The movie creation unit can also analyze the content of photos and videos and edit them based on a specific event (wedding, birthday, etc.). Furthermore, the movie creation unit can analyze the content of photos and videos and edit them based on a specific theme (travel, sports, etc.). This makes it possible to analyze the content of photos and videos and edit them based on a specific theme or story.

[0101] The movie creation unit can select the optimal editing method by referring to the user's past movie creation history. The movie creation unit, for example, refers to the user's past movie creation history and selects the optimal editing method based on that history. For example, the movie creation unit can automatically apply editing methods (effects, music, etc.) that the user has used in the past. The movie creation unit can also select an editing method based on a specific event or theme from the user's past movie creation history. Furthermore, the movie creation unit can analyze the user's past movie creation history and suggest the optimal editing method. In this way, the optimal editing method can be selected by referring to the user's past movie creation history.

[0102] The movie creation unit can estimate the user's emotion and adjust the length of the movie based on the estimated user's emotion. The movie creation unit can estimate the user's emotion using, for example, facial expression recognition technology. For example, the movie creation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The movie creation unit can also estimate the user's emotion using voice analysis technology. For example, the movie creation unit can analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the movie creation unit can estimate the user's emotion using biometric data (heart rate and electrodermal activity). For example, the movie creation unit can estimate the emotion based on heart rate fluctuations. This makes it possible to adjust the length of the movie based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0103] The movie creation unit can prioritize editing highly relevant scenes based on the user's geographical location information. The movie creation unit, for example, acquires the user's geographical location information and prioritizes editing highly relevant scenes based on that information. For example, when the user is at a specific event venue, the movie creation unit can prioritize editing scenes related to the event. Furthermore, when the user is traveling, the movie creation unit can prioritize editing scenes from the travel destination. Furthermore, when the user is at home, the movie creation unit can prioritize editing scenes related to family and friends. In this way, highly relevant scenes can be prioritized and edited taking into account the user's geographical location information.

[0104] The movie creation unit can analyze the user's social media activity and automatically edit related scenes. The movie creation unit, for example, analyzes the user's social media activity and automatically edits related scenes based on the activity. For example, the movie creation unit can automatically edit scenes related to places where the user has checked in on social media. The movie creation unit can also analyze the content of the user's social media posts and automatically edit related scenes. Furthermore, the movie creation unit can automatically edit related scenes by referring to the activity of the user's friends on social media. In this way, the user's social media activity can be analyzed and related scenes can be automatically edited.

[0105] The movie creation unit can customize the editing method by reflecting the user's past feedback. For example, the movie creation unit collects the user's past feedback and customizes the editing method based on the feedback. For example, the movie creation unit can customize the editing method based on feedback provided by the user in the past. The movie creation unit can also preferentially apply editing settings that the user preferred in the past. Furthermore, the movie creation unit can analyze the user's past feedback and suggest an optimal editing method. In this way, the editing method can be customized by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the above-described upload unit, recording unit, organizing unit, and movie creation 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 control unit 46A of the smart device 14 and can upload photos and videos directly from a smartphone or camera. For example, the recording unit is realized by the specific processing unit 290 of the data processing device 12 and can record information about the upload source and metadata of the photos and videos. For example, the organizing unit is realized by the specific processing unit 290 of the data processing device 12 and can sort photos and videos into folders for specific events or locations. For example, the movie creation unit is realized by the specific processing unit 290 of the data processing device 12 and can create a movie based on the organized photos and videos. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described upload unit, recording unit, organizing unit, and movie creation 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 the control unit 46A of the smart glasses 214 and can upload photos and videos directly from a smartphone or camera. For example, the recording unit is realized by the specific processing unit 290 of the data processing device 12 and can record information about the upload source and metadata of the photos and videos. For example, the organizing unit is realized by the specific processing unit 290 of the data processing device 12 and can sort photos and videos into folders for specific events or locations. For example, the movie creation unit is realized by the specific processing unit 290 of the data processing device 12 and can create a movie based on the organized photos and videos. === Hard Collateral 1-3 === Each of the multiple elements including the above-described upload unit, recording unit, organizing unit, and movie creation 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 control unit 46A of the headset-type terminal 314 and can upload photos and videos directly from a smartphone or camera. For example, the recording unit is realized by the specific processing unit 290 of the data processing device 12 and can record information about the upload source and metadata of the photos and videos. For example, the organizing unit is realized by the specific processing unit 290 of the data processing device 12 and can sort photos and videos into folders for specific events or locations. For example, the movie creation unit is realized by the specific processing unit 290 of the data processing device 12 and can create a movie based on the organized photos and videos. === Hard Collateral 1-4 === Each of the multiple elements including the above-described upload unit, recording unit, organizing unit, and movie creation 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 control unit 46A of the robot 414 and can upload photos and videos directly from a smartphone or camera. For example, the recording unit is realized by the specific processing unit 290 of the data processing device 12 and can record information about the upload source and metadata of the photos and videos. For example, the organizing unit is realized by the specific processing unit 290 of the data processing device 12 and can sort photos and videos into folders for specific events or locations. For example, the movie creation unit is realized by the specific processing unit 290 of the data processing device 12 and can create a movie based on the organized photos and videos.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The upload unit can estimate the user's emotions and adjust the timing of uploading based on the estimated user emotions. For example, the upload unit can estimate the user's emotions using facial expression recognition technology and upload when the user is relaxed. It can also estimate the user's emotions using voice analysis technology and refrain from uploading when the user is excited. It can also estimate the user's emotions using biometric data (heart rate and electrodermal activity) and delay uploading when the user is feeling stressed. This makes it possible to adjust the timing of uploading based on the user's emotions.

[0108] The organizing unit can estimate the user's emotions and adjust the organizing rules based on the estimated user's emotions. For example, it can estimate the user's emotions using facial expression recognition technology and create folders with a cheerful theme when the user is happy. It can also estimate the user's emotions using voice analysis technology and apply a simple organizing method when the user is feeling down. Furthermore, it can estimate the user's emotions using biometric data (heart rate and electrodermal activity) and perform detailed organizing when the user is relaxed. This makes it possible to adjust the organizing rules based on the user's emotions.

[0109] The movie creation unit can estimate the user's emotions and adjust the editing method of the movie based on the estimated user's emotions. For example, facial expression recognition technology can be used to estimate the user's emotions and add moving music when the user is emotional. Audio analysis technology can also be used to estimate the user's emotions and add fun effects when the user is having fun. Furthermore, biometric data (heart rate and electrodermal activity) can be used to estimate the user's emotions and include more calm scenes when the user is relaxed. This allows the editing method of the movie to be adjusted based on the user's emotions.

[0110] The recording unit can estimate the user's emotions and adjust the level of detail of the information to be recorded based on the estimated user's emotions. For example, the recording unit can estimate the user's emotions using facial expression recognition technology and record detailed metadata when the user is excited. Alternatively, the recording unit can estimate the user's emotions using voice analysis technology and record simplified metadata when the user is relaxed. Furthermore, the recording unit can estimate the user's emotions using biometric data (heart rate and electrodermal activity) and refrain from recording when the user is feeling stressed. This makes it possible to adjust the level of detail of the information to be recorded based on the user's emotions.

[0111] The organizing unit can estimate the user's emotions and determine the priority of photos and videos to be organized based on the estimated user's emotions. For example, the user's emotions can be estimated using facial expression recognition technology, and family photos can be prioritized when the user is feeling happy. Alternatively, the user's emotions can be estimated using voice analysis technology, and travel photos can be prioritized when the user is excited. Furthermore, the user's emotions can be estimated using biometric data (heart rate and electrodermal activity), and landscape photos can be prioritized when the user is relaxed. This makes it possible to determine the priority of photos and videos to be organized based on the user's emotions.

[0112] The upload unit can select the appropriate upload method based on the file size of the photo or video. For example, large files can be uploaded when connected to Wi-Fi to avoid using mobile data. Small files can also be uploaded immediately to allow for quick sharing. Furthermore, depending on the file size, files can be compressed before uploading to save on data usage. This allows the optimal upload method to be selected based on the file size of the photo or video.

[0113] The recording unit can analyze the content of photos and videos and automatically assign specific keywords and tags. For example, it can use image analysis technology to analyze the content of photos and videos and automatically assign specific keywords and tags based on that content. For example, it can analyze the people appearing in photos and videos and assign their names as tags. It can also analyze the content of photos and videos and assign keywords related to specific events or places. It can also analyze the content of photos and videos and assign keywords related to specific objects or scenery. This makes it possible to analyze the content of photos and videos and automatically assign specific keywords and tags.

[0114] The organizing unit can automatically sort photos and videos into folders based on the date and time and location of their capture. For example, folders can be sorted by year, month, or day based on the date and time of capture. Folders can also be sorted by country, city, or location based on the location of the capture. Furthermore, folders can be sorted by event based on both the date and time of capture and the location of the capture. This allows photos and videos to be automatically sorted into folders based on the date and time of capture and the location of the capture.

[0115] The movie creation unit can analyze the content of photos and videos and edit them based on a specific theme or story. For example, it can use image analysis technology to analyze the content of photos and videos and edit them based on a specific theme or story. For example, it can analyze people in photos and videos and edit a story focusing on those people. It can also analyze the content of photos and videos and edit them based on a specific event (wedding, birthday, etc.). It can also analyze the content of photos and videos and edit them based on a specific theme (travel, sports, etc.). This makes it possible to analyze the content of photos and videos and edit them based on a specific theme or story.

[0116] The upload unit can automatically apply appropriate upload settings by referring to the user's past upload history. For example, the upload unit can automatically apply optimal upload settings based on the user's past upload history. For example, the upload unit can automatically apply upload settings that the user used in the past (Wi-Fi only, specific time period, etc.). The upload unit can also automatically select optimal settings based on the types of files (photos, videos, etc.) that the user uploaded in the past. Furthermore, the upload unit can automatically set a periodic upload schedule based on the user's past upload frequency. This makes it possible to automatically apply optimal upload settings by referring to the user's past upload history.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The upload section automatically uploads photos and videos. For example, you can upload photos and videos directly from your smartphone or camera, or you can automatically upload them at specific times or under specific conditions. Step 2: The recording unit records information about the photos and videos uploaded by the uploading unit. For example, it can automatically extract and record information about the upload source and metadata about the photos and videos (such as the shooting date and time, location, and device information). Step 3: The organizer organizes the photos and videos based on the information recorded by the recorder to suit the user's preferences. For example, photos and videos can be organized into folders for specific events or locations, or photos and videos containing specific people or objects can be automatically classified. Step 4: The movie creation unit creates a movie based on the photos and videos organized by the organization unit, tailored to the intended use scenario. For example, a movie can be created by combining photos and videos from a specific event.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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 upload section that automatically uploads photos and videos, a recording unit that records information about the photos and videos uploaded by the upload unit; an organizing unit that organizes photos and videos according to user preferences based on the information recorded by the recording unit; a movie creation unit that creates a movie according to the usage scene based on the photos and videos organized by the organization unit; A system characterized by:

2. The organizing unit Organize photos and videos into folders by event or location 2. The system of claim 1.

3. The recording unit Record the upload source information 2. The system of claim 1.

4. The organizing unit Automatically classify photos and videos containing people and objects 2. The system of claim 1.

5. The movie creation unit Create a movie by compiling photos and videos from a specific event 2. The system of claim 1.

6. The upload unit Upload photos and videos directly from your phone or camera 2. The system of claim 1.

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

8. The upload unit Automatically order uploads based on the date and time photos and videos were taken 2. The system of claim 1.

9. The upload unit Choose the right upload method based on the file size of your photos and videos 2. The system of claim 1.

10. The upload unit Automatically apply appropriate upload settings based on the user's upload history 2. The system of claim 1.

Images