System

Abstract

An object of the system according to the embodiment is to efficiently manage photos and videos captured by a smartphone and create and reproduce an optimal album.SOLUTION: A system includes an upload part, an analysis part, an album creation part, and a reproduction part. The upload unit uploads a photo or a video taken by the user with the smartphone to the cloud. The analysis unit analyzes the photo or video uploaded by the upload unit. The album creating section creates a recommended album based on the data analyzed by the analyzing section. The reproduction section reproduces the album created by the album creation section at a designated time.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to efficiently manage photos and videos taken with a smartphone and create optimal albums.

[0005] The system according to the embodiment aims to efficiently manage photos and videos taken with a smartphone and to create and play back optimal albums. [Means for solving the problem]

[0006] The system according to the embodiment includes an upload unit, an analysis unit, an album creation unit, and a playback unit. The upload unit uploads photos and videos taken by a user with a smartphone to the cloud. The analysis unit analyzes the photos and videos uploaded by the upload unit. The album creation unit creates a recommended album based on the data analyzed by the analysis unit. The playback unit plays the album created by the album creation unit for a specified time. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage photos and videos taken with a smartphone and create and play optimal albums. [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 cloud service system according to an embodiment of the present invention allows users to upload photos and videos taken with their smartphones to the cloud, where a generation AI analyzes the photos and videos to create recommended albums and play them at a specified time. This allows users to easily upload photos and videos taken with their smartphones and create and play optimal albums.

[0029] A cloud service system according to an embodiment includes an upload unit, an analysis unit, an album creation unit, and a playback unit. The upload unit uploads photos and videos taken by a user with a smartphone to the cloud. For example, the upload unit selects photos and videos from the smartphone's camera roll and saves them to cloud storage. The upload unit can also automatically upload photos and videos to a folder specified by the user. The upload unit also provides a function for uploading multiple photos and videos at once. For example, the upload unit uploads multiple photos and videos selected by the user to the cloud at once. The upload unit can also display the upload progress in real time. The analysis unit analyzes the photos and videos uploaded by the upload unit. For example, the analysis unit analyzes the content of photos using image recognition technology. The analysis unit can also analyze video scenes using machine learning algorithms. The analysis unit can also analyze metadata of photos and videos. For example, the analysis unit analyzes the date and time photos were taken and location information to help create an album. The album creation unit creates recommended albums based on the data analyzed by the analysis unit. For example, the album creation unit creates an album based on the user's preferences and past browsing history. The album creation unit can also create an album based on a specific theme. Furthermore, the album creation unit can create an album based on photos and videos specified by the user. For example, the album creation unit creates an album by collecting photos and videos selected by the user. The playback unit plays the album created by the album creation unit at a specified time. For example, the playback unit automatically plays the album at a time specified by the user. The playback unit can also manually set the playback time by the user. Furthermore, the playback unit can add interactive elements while the album is being played. For example, the playback unit displays a quiz or survey while the album is being played. This allows the cloud service system to easily upload photos and videos taken by the user with their smartphone and create and play optimal albums.

[0030] In the upload unit, when a user uploads photos or videos, the generation AI evaluates the quality of the images or videos in real time and can automatically adjust the optimal upload settings. For example, when a user uploads photos or videos, the generation AI evaluates the image resolution and video frame rate in real time and automatically adjusts the optimal upload settings. For example, it converts low-resolution images to high-resolution. In addition, when uploading, the generation AI analyzes the brightness and contrast of the video and automatically adjusts them to the optimal settings. For example, it applies a filter to brighten dark video. In addition, when a user uploads, the generation AI removes noise in real time to provide clear images and videos. For example, it applies a filter to reduce background noise in video. This allows the quality of the photos and videos uploaded by the user to be optimized.

[0031] In the upload unit, the generation AI automatically adds metadata to photos and videos when they are uploaded, which can be useful for later analysis. In the upload unit, for example, when a user uploads photos or videos, the generation AI automatically adds location information. For example, the location where the photo was taken is identified based on GPS data and saved as metadata. In addition, in the upload unit, the generation AI automatically adds the date and time the photo was taken when the photo was uploaded. For example, the accurate date and time of the photo was taken is saved as metadata based on the smartphone's internal clock. In addition, in the upload unit, the generation AI automatically adds weather information. For example, the weather information at the time is obtained based on the date and time of the photo and location information and saved as metadata. In this way, metadata is automatically added to photos and videos, which can be useful for later analysis.

[0032] The upload unit allows the user to select and upload photos and videos using voice commands when uploading. The upload unit provides a function that allows the user to select and upload specific photos and videos using voice commands. For example, the user may give a voice command such as "upload photos from my family trip." The upload unit also provides a function that allows the user to specify a folder of photos and videos to upload using voice commands. For example, the user may give a voice command such as "upload my 2023 album." The upload unit also provides a function that allows the user to check the progress of the upload using voice commands. For example, the user may give a voice command such as "tell me the progress of the upload." This allows the user to select and upload photos and videos using voice commands.

[0033] The upload unit uses a generation AI to analyze the content of photos and videos when uploading, and automatically generate and assign relevant tags. For example, when a user uploads photos or videos, the upload unit uses a generation AI to analyze the content and automatically generate relevant tags. For example, tags such as "nature" and "scenery" are assigned to landscape photos. The upload unit also uses a generation AI to analyze the scenes in the video when uploading, and assign tags to each scene. For example, tags such as "ocean" and "beach" are assigned to beach scenes. The upload unit also uses a generation AI to analyze the content of photos and videos, and automatically generate tags related to specific people or places. For example, tags such as "family" and "parent and child" are assigned to family photos. This allows the content of photos and videos to be analyzed, and relevant tags to be automatically generated and assigned.

[0034] When the generation AI analyzes the content of photos and videos, the analysis unit learns the user's past upload history and preferences, allowing it to create a more personalized album. For example, the analysis unit allows the generation AI to analyze the user's past upload history and learn their preferences. For example, if many family photos have been uploaded in the past, it will create a family-themed album. In addition, to learn the user's preferences, the analysis unit allows the generation AI to analyze the content of the uploaded photos and videos and extract specific themes and styles. For example, if there are many landscape photos, it will create a nature-themed album. In addition, the analysis unit allows the generation AI to create personalized albums based on the user's past upload history. For example, it will suggest an album that collects photos and videos related to a specific event or trip. This allows the generation AI to learn the user's past upload history and preferences and create a more personalized album.

[0035] The analysis unit can suggest new shooting methods and compositions to the user based on the results of the generative AI's analysis of photos and videos. For example, the analysis unit can suggest new shooting methods to the user based on the results of the generative AI's analysis of photos and videos. For example, it can suggest camera settings and angles that are suitable for a particular scene. The analysis unit can also suggest compositions to the user based on the results of the generative AI's analysis. For example, it can suggest compositions using the golden ratio or the rule of thirds. The analysis unit can also suggest new shooting techniques to the user based on the results of the generative AI's analysis of photos and videos. For example, it can suggest long exposure or panoramic shooting methods. This makes it possible to suggest new shooting methods and compositions to the user.

[0036] The analysis unit can provide a function for the generation AI to suggest related music and sound effects to the user based on the results of the photo and video analysis, and add them to an album. For example, the analysis unit can suggest related music to the user based on the results of the photo and video analysis. For example, relaxing music can be suggested for travel photos. The analysis unit can also suggest sound effects to the user based on the results of the video analysis. For example, the analysis unit can suggest the sound of waves for an ocean scene. The analysis unit can also provide a function for the generation AI to add music and sound effects to an album based on the results of the photo and video analysis. For example, music that matches a specific scene can be automatically added. This makes it possible to suggest related music and sound effects based on the results of the photo and video analysis, and add them to an album.

[0037] The analysis unit can provide the user with information about related travel destinations and events based on the results of the photo and video analysis by the generation AI. For example, the analysis unit can provide the user with information about related travel destinations based on the results of the photo and video analysis by the generation AI. For example, it can suggest new travel destinations based on places visited in the past. The analysis unit can also provide the user with information about related events based on the results of the video analysis by the generation AI. For example, it can suggest events related to a particular theme. The analysis unit can also provide the user with information about travel destinations and events based on the results of the photo and video analysis by the generation AI. For example, it can suggest recommended travel destinations for a particular season. This makes it possible to provide information about related travel destinations and events based on the results of the photo and video analysis.

[0038] In the album creation unit, the generation AI learns the user's past viewing history and ratings, allowing it to select more accurate recommended albums. In the album creation unit, for example, the generation AI analyzes the user's past viewing history and learns their preferences. For example, it selects recommended albums based on the themes and styles of albums previously viewed. In addition, in the album creation unit, the generation AI selects recommended albums based on the user's rating data. For example, it learns the characteristics of highly rated albums and suggests similar albums. In addition, in the album creation unit, the generation AI comprehensively analyzes the user's viewing history and ratings, allowing it to select more accurate recommended albums. For example, it suggests albums taking into account viewing time and number of plays. In this way, it is possible to learn the user's past viewing history and ratings, allowing it to select more accurate recommended albums.

[0039] The playback unit allows the generation AI to refer to the user's schedule and calendar information and suggest the optimal playback time. For example, the playback unit allows the generation AI to analyze the user's calendar information and suggest the optimal album playback time. For example, it may suggest playback during a time when the user is free. The playback unit also allows the generation AI to set a reminder to play an album based on the user's schedule. For example, it may play an album of memories before a specific event. The playback unit also allows the generation AI to refer to the user's schedule and suggest the optimal playback time. For example, it may play an album during a time when the user is able to relax. This allows the playback unit to refer to the user's schedule and calendar information and suggest the optimal playback time.

[0040] The playback unit allows the generation AI to add interactive elements (such as quizzes or surveys) while the album is being played based on the user's preferences. For example, the playback unit allows the generation AI to add a quiz while the album is being played based on the user's preferences. For example, the generation AI displays questions about photos and the user answers them. The playback unit also allows the generation AI to display a survey based on the user's preferences while the album is being played. For example, the generation AI asks the user what they think of a particular scene. The playback unit also allows the generation AI to add interactive elements while the album is being played based on the user's preferences. For example, the playback content changes depending on the user's selection of an option. This makes it possible to add interactive elements while the album is being played based on the user's preferences.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The cloud service system may further include a health data acquisition unit that acquires the user's health data and customizes the album content based on the user's health condition. For example, the system may monitor the user's heart rate and stress level to create an album with relaxing content. The health data acquisition unit may also suggest albums containing active scenes based on the user's exercise data. Furthermore, the health data acquisition unit may analyze the user's sleep data and play an album that helps the user relax before sleep. This allows the system to provide a more personalized album based on the user's health condition.

[0043] The cloud service system may further include a location information acquisition unit that acquires the user's location information and customizes the contents of the album based on the location information. For example, if the user is traveling, photos and videos related to the travel destination may be preferentially included in the album. Also, if the user is participating in a specific event, the location information acquisition unit may add content related to the event to the album. Furthermore, if the user is at home, the location information acquisition unit may suggest an album of past family photos. This allows a more appropriate album to be provided based on the user's current location information.

[0044] The cloud service system may further include a social media integration unit that integrates with a user's social media accounts and analyzes social media posts to customize the contents of the album. For example, photos and videos posted by the user on social media may be automatically added to the album. The social media integration unit may also analyze posts from the user's friends and followers and include related content in the album. Furthermore, the social media integration unit may suggest albums related to specific events or themes based on the user's social media activity history. This allows the system to provide a more personalized album based on the user's social media activity.

[0045] The cloud service system may further include a music linking unit that links with the user's music streaming service and customizes the contents of the album based on the user's music preferences. For example, background music that matches the user's favorite music genre may be added to the album. The music linking unit may also analyze the user's playlist and suggest music suitable for a specific scene. Furthermore, the music linking unit may include music related to a specific event or theme in the album based on the user's music playback history. This allows the cloud service system to provide a more appealing album based on the user's music preferences.

[0046] The cloud service system may further include a reading history acquisition unit that acquires a user's reading history and customizes the contents of the album based on the reading history. For example, photos and videos related to the contents of books the user has read may be added to the album. If the user has read many books in a particular genre, the reading history acquisition unit may also suggest an album with a theme related to that genre. Furthermore, the reading history acquisition unit may include content related to the authors and characters of the books the user has read in the album. This allows for a more personalized album to be provided based on the user's reading history.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The upload section uploads photos and videos taken by the user with their smartphone to the cloud. For example, it can select photos and videos from the smartphone's camera roll and save them to cloud storage. It can also automatically upload them to a user-specified folder, and it also provides a function to upload multiple photos and videos at once. It can also display the upload progress in real time. Step 2: The analysis unit analyzes the photos and videos uploaded by the upload unit. For example, it can use image recognition technology to analyze the content of photos and machine learning algorithms to analyze video scenes. Furthermore, it analyzes the metadata of photos and videos (such as shooting date and time, location information, etc.) to help create an album. Step 3: The album creation unit creates recommended albums based on the data analyzed by the analysis unit. For example, albums can be created based on the user's preferences and past browsing history, albums based on specific themes, or albums based on photos and videos specified by the user. Step 4: The playback unit plays the album created by the album creation unit at the specified time. For example, the album can be automatically played at a user-specified time, or the playback time can be manually set. Furthermore, interactive elements such as quizzes and surveys can be added during album playback.

[0049] (Example 2) A cloud service system according to an embodiment of the present invention allows users to upload photos and videos taken with their smartphones to the cloud, where a generation AI analyzes the photos and videos to create recommended albums and play them at a specified time. This allows users to easily upload photos and videos taken with their smartphones and create and play optimal albums.

[0050] A cloud service system according to an embodiment includes an upload unit, an analysis unit, an album creation unit, and a playback unit. The upload unit uploads photos and videos taken by a user with a smartphone to the cloud. For example, the upload unit selects photos and videos from the smartphone's camera roll and saves them to cloud storage. The upload unit can also automatically upload photos and videos to a folder specified by the user. The upload unit also provides a function for uploading multiple photos and videos at once. For example, the upload unit uploads multiple photos and videos selected by the user to the cloud at once. The upload unit can also display the upload progress in real time. The analysis unit analyzes the photos and videos uploaded by the upload unit. For example, the analysis unit analyzes the content of photos using image recognition technology. The analysis unit can also analyze video scenes using machine learning algorithms. The analysis unit can also analyze metadata of photos and videos. For example, the analysis unit analyzes the date and time photos were taken and location information to help create an album. The album creation unit creates recommended albums based on the data analyzed by the analysis unit. For example, the album creation unit creates an album based on the user's preferences and past browsing history. The album creation unit can also create an album based on a specific theme. Furthermore, the album creation unit can create an album based on photos and videos specified by the user. For example, the album creation unit creates an album by collecting photos and videos selected by the user. The playback unit plays the album created by the album creation unit at a specified time. For example, the playback unit automatically plays the album at a time specified by the user. The playback unit can also manually set the playback time by the user. Furthermore, the playback unit can add interactive elements while the album is being played. For example, the playback unit displays a quiz or survey while the album is being played. This allows the cloud service system to easily upload photos and videos taken by the user with their smartphone and create and play optimal albums.

[0051] In the upload unit, when a user uploads photos or videos, the generation AI evaluates the quality of the images or videos in real time and can automatically adjust the optimal upload settings. For example, when a user uploads photos or videos, the generation AI evaluates the image resolution and video frame rate in real time and automatically adjusts the optimal upload settings. For example, it converts low-resolution images to high-resolution. In addition, when uploading, the generation AI analyzes the brightness and contrast of the video and automatically adjusts them to the optimal settings. For example, it applies a filter to brighten dark video. In addition, when a user uploads, the generation AI removes noise in real time to provide clear images and videos. For example, it applies a filter to reduce background noise in video. This allows the quality of the photos and videos uploaded by the user to be optimized.

[0052] In the upload unit, the generation AI automatically adds metadata to photos and videos when they are uploaded, which can be useful for later analysis. In the upload unit, for example, when a user uploads photos or videos, the generation AI automatically adds location information. For example, the location where the photo was taken is identified based on GPS data and saved as metadata. In addition, in the upload unit, the generation AI automatically adds the date and time the photo was taken when the photo was uploaded. For example, the accurate date and time of the photo was taken is saved as metadata based on the smartphone's internal clock. In addition, in the upload unit, the generation AI automatically adds weather information. For example, the weather information at the time is obtained based on the date and time of the photo and location information and saved as metadata. In this way, metadata is automatically added to photos and videos, which can be useful for later analysis.

[0053] The upload unit uses the emotion estimation function to analyze the emotions of the user when uploading and can provide an interface for eliciting positive emotions. For example, when a user uploads a photo or video, the upload unit uses the generation AI to analyze facial expressions and estimate emotions. For example, when a photo of a smiling face is uploaded, a positive message is displayed. The upload unit also uses the generation AI to analyze voice tone and estimate emotions when uploading. For example, if the person is speaking in a happy voice, an encouraging message is displayed. The upload unit also uses the emotion estimation function to provide an interface for eliciting positive emotions when uploading. For example, an animation that makes the user smile is displayed. This makes it possible to analyze the user's emotions and provide an interface for eliciting positive emotions.

[0054] The upload unit allows the user to select and upload photos and videos using voice commands when uploading. The upload unit provides a function that allows the user to select and upload specific photos and videos using voice commands. For example, the user may give a voice command such as "upload photos from my family trip." The upload unit also provides a function that allows the user to specify a folder of photos and videos to upload using voice commands. For example, the user may give a voice command such as "upload my 2023 album." The upload unit also provides a function that allows the user to check the progress of the upload using voice commands. For example, the user may give a voice command such as "tell me the progress of the upload." This allows the user to select and upload photos and videos using voice commands.

[0055] The upload unit uses a generation AI to analyze the content of photos and videos when uploading, and automatically generate and assign relevant tags. For example, when a user uploads photos or videos, the upload unit uses a generation AI to analyze the content and automatically generate relevant tags. For example, tags such as "nature" and "scenery" are assigned to landscape photos. The upload unit also uses a generation AI to analyze the scenes in the video when uploading, and assign tags to each scene. For example, tags such as "ocean" and "beach" are assigned to beach scenes. The upload unit also uses a generation AI to analyze the content of photos and videos, and automatically generate tags related to specific people or places. For example, tags such as "family" and "parent and child" are assigned to family photos. This allows the content of photos and videos to be analyzed, and relevant tags to be automatically generated and assigned.

[0056] The upload unit can use the emotion estimation function to customize the message and interface displayed after uploading based on the emotion the user felt when uploading. For example, when a user uploads a photo or video, the upload unit uses the generation AI to estimate the emotion and display a message based on that emotion. For example, if the emotion is positive, it displays "What a great photo!". The upload unit also uses the generation AI to analyze the emotion at the time of uploading and provide an interface based on the emotion. For example, if the emotion is negative, it displays an encouraging message. The upload unit also uses the emotion estimation function to customize the interface after uploading based on the emotion the user felt when uploading. For example, it displays an animation that makes the user smile. This makes it possible to customize the message and interface displayed after uploading based on the user's emotion.

[0057] When the generation AI analyzes the content of photos and videos, the analysis unit learns the user's past upload history and preferences, allowing it to create a more personalized album. For example, the analysis unit allows the generation AI to analyze the user's past upload history and learn their preferences. For example, if many family photos have been uploaded in the past, it will create a family-themed album. In addition, to learn the user's preferences, the analysis unit allows the generation AI to analyze the content of the uploaded photos and videos and extract specific themes and styles. For example, if there are many landscape photos, it will create a nature-themed album. In addition, the analysis unit allows the generation AI to create personalized albums based on the user's past upload history. For example, it will suggest an album that collects photos and videos related to a specific event or trip. This allows the generation AI to learn the user's past upload history and preferences and create a more personalized album.

[0058] The analysis unit can suggest new shooting methods and compositions to the user based on the results of the generative AI's analysis of photos and videos. For example, the analysis unit can suggest new shooting methods to the user based on the results of the generative AI's analysis of photos and videos. For example, it can suggest camera settings and angles that are suitable for a particular scene. The analysis unit can also suggest compositions to the user based on the results of the generative AI's analysis. For example, it can suggest compositions using the golden ratio or the rule of thirds. The analysis unit can also suggest new shooting techniques to the user based on the results of the generative AI's analysis of photos and videos. For example, it can suggest long exposure or panoramic shooting methods. This makes it possible to suggest new shooting methods and compositions to the user.

[0059] The analysis unit can use the emotion estimation function to analyze the emotions of people in photos and videos and create themed albums based on the emotions. For example, the analysis unit uses a generation AI to analyze the facial expressions of people in photos and videos and estimate their emotions. For example, it can create an album with a positive theme that collects photos of smiling people. The analysis unit can also use the emotion estimation function to analyze the emotions of people in videos and create themed albums based on emotions. For example, it can create an album that collects moving scenes. The analysis unit can also use a generation AI to analyze the emotions of people in photos and videos and suggest themed albums based on specific emotions. For example, it can create an album that collects happy memories. This makes it possible to analyze the emotions of people in photos and videos and create themed albums based on emotions.

[0060] The analysis unit can provide a function for the generation AI to suggest related music and sound effects to the user based on the results of the photo and video analysis, and add them to an album. For example, the analysis unit can suggest related music to the user based on the results of the photo and video analysis. For example, relaxing music can be suggested for travel photos. The analysis unit can also suggest sound effects to the user based on the results of the video analysis. For example, the analysis unit can suggest the sound of waves for an ocean scene. The analysis unit can also provide a function for the generation AI to add music and sound effects to an album based on the results of the photo and video analysis. For example, music that matches a specific scene can be automatically added. This makes it possible to suggest related music and sound effects based on the results of the photo and video analysis, and add them to an album.

[0061] The analysis unit can provide the user with information about related travel destinations and events based on the results of the photo and video analysis by the generation AI. For example, the analysis unit can provide the user with information about related travel destinations based on the results of the photo and video analysis by the generation AI. For example, it can suggest new travel destinations based on places visited in the past. The analysis unit can also provide the user with information about related events based on the results of the video analysis by the generation AI. For example, it can suggest events related to a particular theme. The analysis unit can also provide the user with information about travel destinations and events based on the results of the photo and video analysis by the generation AI. For example, it can suggest recommended travel destinations for a particular season. This makes it possible to provide information about related travel destinations and events based on the results of the photo and video analysis.

[0062] The analysis unit uses the emotion estimation function to analyze the emotions a user feels when browsing an album and make suggestions to optimize the browsing experience. For example, the analysis unit uses the generation AI to analyze the user's facial expressions and estimate the emotions they feel when browsing an album. For example, if there are many smiles, a positive message is displayed. The analysis unit also uses the emotion estimation function to analyze the emotions a user feels when browsing an album and make suggestions to optimize the browsing experience. For example, moving scenes are emphasized. The analysis unit also provides an interface for the generation AI to analyze the user's emotions and optimize the browsing experience. For example, music that moves the user is played. This allows the analysis of the emotions a user feels when browsing an album and makes suggestions to optimize the browsing experience.

[0063] In the album creation unit, the generation AI learns the user's past viewing history and ratings, allowing it to select more accurate recommended albums. In the album creation unit, for example, the generation AI analyzes the user's past viewing history and learns their preferences. For example, it selects recommended albums based on the themes and styles of albums previously viewed. In addition, in the album creation unit, the generation AI selects recommended albums based on the user's rating data. For example, it learns the characteristics of highly rated albums and suggests similar albums. In addition, in the album creation unit, the generation AI comprehensively analyzes the user's viewing history and ratings, allowing it to select more accurate recommended albums. For example, it suggests albums taking into account viewing time and number of plays. In this way, it is possible to learn the user's past viewing history and ratings, allowing it to select more accurate recommended albums.

[0064] The album creation unit allows the generation AI to analyze the user's reactions in real time while the album is being played and reflect the results in creating the next album. For example, the album creation unit allows the generation AI to analyze the user's facial expressions while the album is being played and record the reactions in real time. For example, it detects smiling or surprised expressions and reflects the results in creating the next album. In addition, the album creation unit allows the generation AI to analyze the user's voice tone while the album is being played and estimate their emotions. For example, it detects a happy voice and reflects the results in creating the next album. In addition, the album creation unit allows the generation AI to analyze the user's reactions in real time while the album is being played and reflect the results in creating the next album. For example, it adjusts the content of the album based on the reaction to a specific scene. This allows the user's reactions to be analyzed in real time while the album is being played and reflect the results in creating the next album.

[0065] The album creation unit uses the emotion estimation function to analyze the emotion a user feels when playing an album, and customizes the playback order and content based on that emotion. In the album creation unit, for example, the generation AI analyzes the user's facial expressions and estimates the emotion when playing the album. For example, if there are a lot of smiles, positive scenes are prioritized for playback. The album creation unit also uses the emotion estimation function to analyze the emotion a user feels when playing an album, and customizes the playback order. For example, emotional scenes are played last. In the album creation unit, the generation AI analyzes the user's emotion, and customizes the content of the album based on that emotion. For example, scenes that move the user are emphasized. In this way, the emotion a user feels when playing an album can be analyzed, and the playback order and content can be customized based on that emotion.

[0066] The playback unit allows the generation AI to refer to the user's schedule and calendar information and suggest the optimal playback time. For example, the playback unit allows the generation AI to analyze the user's calendar information and suggest the optimal album playback time. For example, it may suggest playback during a time when the user is free. The playback unit also allows the generation AI to set a reminder to play an album based on the user's schedule. For example, it may play an album of memories before a specific event. The playback unit also allows the generation AI to refer to the user's schedule and suggest the optimal playback time. For example, it may play an album during a time when the user is able to relax. This allows the playback unit to refer to the user's schedule and calendar information and suggest the optimal playback time.

[0067] The playback unit allows the generation AI to add interactive elements (such as quizzes or surveys) while the album is being played based on the user's preferences. For example, the playback unit allows the generation AI to add a quiz while the album is being played based on the user's preferences. For example, the generation AI displays questions about photos and the user answers them. The playback unit also allows the generation AI to display a survey based on the user's preferences while the album is being played. For example, the generation AI asks the user what they think of a particular scene. The playback unit also allows the generation AI to add interactive elements while the album is being played based on the user's preferences. For example, the playback content changes depending on the user's selection of an option. This makes it possible to add interactive elements while the album is being played based on the user's preferences.

[0068] The playback unit can use the emotion estimation function to customize the interface and effects during playback based on the emotions the user feels when playing an album. For example, the generation AI in the playback unit analyzes the user's facial expressions and estimates the emotions they feel when playing an album. For example, if there are a lot of smiles, a bright effect is added. The playback unit also uses the emotion estimation function to analyze the emotions the user feels when playing an album and customize the interface during playback. For example, special effects are displayed during moving scenes. The playback unit also uses the generation AI to analyze the user's emotions and customize the effects during playback. For example, music that moves the user is played. This makes it possible to customize the interface and effects during playback based on the emotions the user feels when playing an album.

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

[0070] The cloud service system may further include a health data acquisition unit that acquires the user's health data and customizes the album content based on the user's health condition. For example, the system may monitor the user's heart rate and stress level to create an album with relaxing content. The health data acquisition unit may also suggest albums containing active scenes based on the user's exercise data. Furthermore, the health data acquisition unit may analyze the user's sleep data and play an album that helps the user relax before sleep. This allows the system to provide a more personalized album based on the user's health condition.

[0071] The cloud service system may further include a location information acquisition unit that acquires the user's location information and customizes the contents of the album based on the location information. For example, if the user is traveling, photos and videos related to the travel destination may be preferentially included in the album. Also, if the user is participating in a specific event, the location information acquisition unit may add content related to the event to the album. Furthermore, if the user is at home, the location information acquisition unit may suggest an album of past family photos. This allows a more appropriate album to be provided based on the user's current location information.

[0072] The cloud service system may further include a social media integration unit that integrates with a user's social media accounts and analyzes social media posts to customize the contents of the album. For example, photos and videos posted by the user on social media may be automatically added to the album. The social media integration unit may also analyze posts from the user's friends and followers and include related content in the album. Furthermore, the social media integration unit may suggest albums related to specific events or themes based on the user's social media activity history. This allows the system to provide a more personalized album based on the user's social media activity.

[0073] The cloud service system may further include a music linking unit that links with the user's music streaming service and customizes the contents of the album based on the user's music preferences. For example, background music that matches the user's favorite music genre may be added to the album. The music linking unit may also analyze the user's playlist and suggest music suitable for a specific scene. Furthermore, the music linking unit may include music related to a specific event or theme in the album based on the user's music playback history. This allows the cloud service system to provide a more appealing album based on the user's music preferences.

[0074] The cloud service system may further include a reading history acquisition unit that acquires a user's reading history and customizes the contents of the album based on the reading history. For example, photos and videos related to the contents of books the user has read may be added to the album. If the user has read many books in a particular genre, the reading history acquisition unit may also suggest an album with a theme related to that genre. Furthermore, the reading history acquisition unit may include content related to the authors and characters of the books the user has read in the album. This allows for a more personalized album to be provided based on the user's reading history.

[0075] The cloud service system may further include an emotion estimation unit that estimates the user's emotion and customizes the content of the album based on the estimated emotion. For example, if the user has positive emotions, an album containing happy memories may be created. If the user has negative emotions, the emotion estimation unit may also suggest an album with encouraging messages or relaxing content. Furthermore, the emotion estimation unit may customize the playback order and content of the album based on the user's emotion. This makes it possible to provide a more appropriate album based on the user's emotion.

[0076] The cloud service system may further include an emotion estimation unit that estimates a user's emotion and customizes messages and interfaces displayed during album playback based on the estimated emotion. For example, if the user has positive emotions, an encouraging message or positive feedback may be displayed. The emotion estimation unit may also provide a comforting message or a relaxing interface when the user has negative emotions. Furthermore, the emotion estimation unit may customize animations and effects displayed during album playback based on the user's emotion. This allows for more appropriate messages and interfaces to be provided based on the user's emotion.

[0077] The cloud service system may further include an emotion estimation unit that estimates the user's emotion and customizes the playback order and content of the album based on the estimated emotion. For example, if the user has a positive emotion, happy scenes may be preferentially played. The emotion estimation unit may also preferentially play relaxing scenes if the user has a negative emotion. Furthermore, the emotion estimation unit may also customize the playback order of the album based on the user's emotion. This makes it possible to provide a more appropriate playback order and content based on the user's emotion.

[0078] The cloud service system may further include an emotion estimation unit that estimates the user's emotion and customizes the content of the album based on the estimated emotion. For example, if the user has positive emotions, an album containing happy memories may be created. If the user has negative emotions, the emotion estimation unit may also suggest an album with encouraging messages or relaxing content. Furthermore, the emotion estimation unit may customize the playback order and content of the album based on the user's emotion. This makes it possible to provide a more appropriate album based on the user's emotion.

[0079] The cloud service system may further include an emotion estimation unit that estimates a user's emotion and customizes messages and interfaces displayed during album playback based on the estimated emotion. For example, if the user has positive emotions, an encouraging message or positive feedback may be displayed. The emotion estimation unit may also provide a comforting message or a relaxing interface when the user has negative emotions. Furthermore, the emotion estimation unit may customize animations and effects displayed during album playback based on the user's emotion. This allows for more appropriate messages and interfaces to be provided based on the user's emotion.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The upload section uploads photos and videos taken by the user with their smartphone to the cloud. For example, it can select photos and videos from the smartphone's camera roll and save them to cloud storage. It can also automatically upload them to a user-specified folder, and it also provides a function to upload multiple photos and videos at once. It can also display the upload progress in real time. Step 2: The analysis unit analyzes the photos and videos uploaded by the upload unit. For example, it can use image recognition technology to analyze the content of photos and machine learning algorithms to analyze video scenes. Furthermore, it analyzes the metadata of photos and videos (such as shooting date and time, location information, etc.) to help create an album. Step 3: The album creation unit creates recommended albums based on the data analyzed by the analysis unit. For example, albums can be created based on the user's preferences and past browsing history, albums based on specific themes, or albums based on photos and videos specified by the user. Step 4: The playback unit plays the album created by the album creation unit at the specified time. For example, the album can be automatically played at a user-specified time, or the playback time can be manually set. Furthermore, interactive elements such as quizzes and surveys can be added during album playback.

[0082] 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.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0111] 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.

[0112] 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.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

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

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

[0126] In the robot 414, 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 robot 414 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.

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

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

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an uploading unit that uploads photos and videos taken by users with their smartphones to the cloud; an analysis unit that analyzes the photos and videos uploaded by the upload unit; an album creation unit that creates a recommended album based on the data analyzed by the analysis unit; a playback unit that plays back the album created by the album creation unit at a specified time. A system characterized by:

2. The upload unit When users upload photos or videos, generative AI assesses the quality of the images and footage in real time and automatically adjusts optimal upload settings.

2. The system of claim 1.

3. The upload unit Upon upload, generative AI automatically adds metadata to the photo or video for later analysis.

2. The system of claim 1.

4. The upload unit Analyzes the emotions users express when uploading and provides an interface to elicit positive emotions 2. The system of claim 1.

5. The upload unit When uploading, the user selects and uploads the photos and videos using voice commands.

2. The system of claim 1.

6. The upload unit Upon upload, the AI ​​analyzes the content of the photo or video and automatically generates and assigns relevant tags.

2. The system of claim 1.

Images