System

Abstract

An object of the system according to the embodiment is to efficiently organize images captured at a travel destination and automatically generate an album of memories.SOLUTION: A system according to an embodiment includes an image collection unit, a scene selection unit, an album generation unit, and an order processing unit. The image collection unit collects images captured at a travel destination using a camera application equipped with a generation AI. The scene selection unit picks up a good scene from the images collected by the image collection unit. The album generation unit generates an album by combining the images selected by the scene selection unit. An order processing part performs printout and bookbinding when ordering the album generated by the album generation part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the task of organizing images taken at travel destinations and creating albums is time-consuming and difficult to do efficiently.

[0005] The system according to the embodiment aims to efficiently organize images taken at travel destinations and automatically create an album of memories. [Means for solving the problem]

[0006] The system according to the embodiment includes an image collection unit, a scene selection unit, an album creation unit, and an order processing unit. The image collection unit uses a camera app equipped with a generation AI to collect images taken at travel destinations. The scene selection unit selects good scenes from the images collected by the image collection unit. The album creation unit creates an album by combining the images selected by the scene selection unit. The order processing unit prints and binds the album created by the album creation unit when an order is placed. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently organize images taken at a travel destination and automatically create an album of memories. [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) The album creation system according to an embodiment of the present invention automatically collects images taken at travel destinations, uses a generation AI to select good scenes, combines the images to create an album, and prints and binds the images upon order. This allows the album creation system to automatically organize images taken at travel destinations and create a memory album.

[0029] An album generation system according to an embodiment includes an image collection unit, a scene selection unit, an album generation unit, and an order processing unit. The image collection unit collects images taken at travel destinations. For example, a user uploads images to storage via a camera app. The image collection unit then analyzes the image metadata (such as the date of capture and location information) to identify the period and location of the trip. For example, if a user uploads "travel photos from July 1 to July 7, 2023," the image collection unit automatically collects images taken during that period. The scene selection unit selects good scenes from the collected images. For example, the generation AI selects photos with beautiful scenery, photos with many smiling faces, photos featuring specific landmarks, etc. The generation AI then analyzes the images and selects good scenes based on prompts containing instructions from the user regarding what the generation AI should do. The album generation unit then combines the selected images to generate an album. For example, the generation AI may arrange the highlights of the trip in chronological order or divide the album into sections by theme. The generation AI also optimizes the layout and design based on the user's instructions to create a beautiful album. The order processing unit prints and binds the generated album when it is ordered. For example, when a user instructs, "I want to order this album," the order processing unit automatically performs the printing and binding procedures and delivers the completed album to the address specified by the user. This allows the album generation system according to the embodiment to automatically organize images taken at a travel destination and generate a memory album. For example, a user can easily create a beautiful album by simply uploading photos after returning from a trip. Customization options are also available to create an album tailored to the user's preferences.

[0030] The image collection unit recognizes not only metadata but also objects and people in images, allowing it to automatically identify travel themes and stories. For example, the image collection unit uses a generative AI to recognize objects and people in images and automatically identify travel themes and stories. For example, it recognizes landscapes such as beaches and mountains and sets a travel theme based on that. The image collection unit also recognizes people in images and automatically identifies the type of trip, such as a family trip or a trip with friends. For example, it analyzes photos with multiple people and classifies them as a group trip. The image collection unit also analyzes objects and backgrounds in images and automatically generates travel stories. For example, it recognizes landmarks at tourist spots and sets a story related to that location. This allows it to recognize objects and people in images and automatically identify travel themes and stories.

[0031] The image collection unit learns the user's past travel history and preferences and can suggest the best photo spots for the next trip. For example, the generation AI in the image collection unit learns the user's past travel history and suggests the best photo spots for the next trip. For example, it suggests spots that suit the user's preferences based on places visited in the past and photos taken. The image collection unit also learns the user's preferences and suggests tourist spots and photo spots to visit on the next trip. For example, for a user who likes natural scenery, it suggests natural parks and mountainous areas. The image collection unit also analyzes the user's past travel photos and suggests scenes and angles to take photos at on the next trip. For example, it suggests the best timing to take photos based on specific composition and lighting conditions. In this way, the generation AI can learn the user's past travel history and preferences and suggest the best photo spots for the next trip.

[0032] The image collection unit can simultaneously collect voice memos or video clips when collecting images, and generate a multimedia album. For example, when the generation AI collects images, the image collection unit simultaneously collects voice memos and video clips, and generates a multimedia album. For example, voice memos recorded during a trip are saved together with photos. The image collection unit also collects video clips along with images, and generates a multimedia album. For example, short video clips taken at tourist spots are incorporated into the album. The image collection unit also collects voice memos and video clips, and combines them with photos to generate a multimedia album. For example, voice memos recounting travel memories are saved together with photos. In this way, voice memos and video clips can be simultaneously collected, and a multimedia album can be generated.

[0033] The image collection unit can compare travel photos taken by other users and suggest photos with similar travel destinations or themes. For example, the generation AI compares travel photos taken by other users with travel photos taken by other users who visited the same tourist spot. The image collection unit also analyzes travel photos taken by other users and suggests photos with similar themes or scenes. For example, it suggests photos taken in the same season or at the same event. The image collection unit also analyzes travel photos taken by other users with travel photos with similar destinations or themes. For example, it refers to photos taken by users who visited the same country or city. This allows the generation AI to compare travel photos taken by other users with travel photos with similar destinations or themes.

[0034] The scene selection unit performs an aesthetic evaluation of the image and can select photos with a good balance of color and composition. For example, the generation AI analyzes the color and composition of the image and performs an aesthetic evaluation. For example, it selects photos based on color balance and symmetry of the composition. The scene selection unit also evaluates color harmony and composition balance to select beautiful photos. For example, it evaluates based on the use of natural light and the unity of the background. The scene selection unit also performs an aesthetic evaluation of the image and selects photos with a good balance of color and composition. For example, it selects based on color contrast and visual enhancement. This makes it possible to select photos with a good balance of color and composition.

[0035] The scene selection unit analyzes the movements and actions in an image and can prioritize picking out dynamic scenes. For example, the generation AI in the scene selection unit analyzes the movements and actions in an image and prioritizes picking out dynamic scenes. For example, it selects photos of people running or animals jumping. The scene selection unit also analyzes scenes with movement and selects photos that capture particularly dynamic moments. For example, it prioritizes photos of sporting events or dance performances. The scene selection unit also analyzes the movements and actions in an image and prioritizes picking out dynamic scenes. For example, it selects photos of children playing on the beach or trees swaying in the wind. This allows dynamic scenes to be prioritized.

[0036] The scene selection unit can refer to the user's social media reactions when picking images. For example, the generation AI in the scene selection unit refers to the user's social media reactions when picking images. For example, it selects photos based on the number of likes and the content of comments. The scene selection unit also analyzes reactions on social media and selects particularly popular photos. For example, it prioritizes photos with many likes and positive comments. The scene selection unit also refers to the user's social media reactions and selects photos that are particularly highly rated. For example, it selects photos based on the number of shares and retweets. This allows the generation AI to pick images by referring to the user's social media reactions.

[0037] The scene selection unit can pick out popular scenes based on the ratings of other users. For example, the generation AI in the scene selection unit picks out popular scenes based on the ratings of other users. For example, it prioritizes the selection of photos that other users have given high ratings. The scene selection unit also analyzes the rating data of other users and selects particularly popular scenes. For example, it selects photos based on reviews and rating scores. The scene selection unit also picks out particularly highly rated scenes based on the ratings of other users by the generation AI. For example, it prioritizes the selection of photos recommended by other users. This makes it possible to pick out popular scenes based on the ratings of other users.

[0038] The album generation unit can perform image storytelling to generate an album that naturally recreates the flow of a trip. For example, the generation AI in the album generation unit performs image storytelling to generate an album that naturally recreates the flow of a trip. For example, the flow from departure to return home is arranged in chronological order. The album generation unit also arranges the highlights of the trip in a storytelling format to generate an album. For example, photos are arranged in the order in which tourist spots were visited. The album generation unit also uses image storytelling to generate an album that naturally recreates the flow of a trip. For example, events and episodes that occurred during the trip are arranged along with the photos. This makes it possible to generate an album that naturally recreates the flow of a trip.

[0039] The album generation unit can unify the color tones and filters of images to increase the visual coherence of the entire album. For example, the generation AI of the album generation unit unifies the color tones and filters of images to increase the visual coherence of the entire album. For example, the same filter is applied to all photos. The album generation unit can also unify the color tones to increase the visual coherence of the entire album. For example, photos are adjusted based on a specific color tone or tone. The album generation unit can also unify the color tones and filters of images to increase the visual coherence of the entire album. For example, the same color correction is applied to all photos. This can increase the visual coherence of the entire album.

[0040] The album generation unit can combine photos from different travel destinations to generate a virtual travel album. For example, the generation AI of the album generation unit combines photos from different travel destinations to generate a virtual travel album. For example, highlights from multiple travel destinations are compiled into one album. The album generation unit also combines photos from different travel destinations to generate a virtual travel album. For example, photos of multiple cities that a user has visited are integrated into one album. The album generation unit also combines photos from different travel destinations to generate a virtual travel album. For example, tourist spots in different countries are compiled into one album. This makes it possible to generate a virtual travel album by combining photos from different travel destinations.

[0041] The album generation unit can generate an album that shows evolution and change by comparing with the user's past albums. For example, the generation AI of the album generation unit generates an album that shows evolution and change by comparing with the user's past albums. For example, it creates a page that compares past trips with current trips. The album generation unit also compares past albums with the current album to generate an album that shows changes in the user's travel style. For example, it displays past and current photos side by side. The album generation unit also generates an album that shows evolution and change by comparing with the user's past albums. For example, it creates a page that compares past travel destinations with current travel destinations. This makes it possible to generate an album that shows evolution and change by comparing with the user's past albums.

[0042] The order processing unit can learn the user's past order history and suggest the optimal print options. For example, the order processing unit uses a generation AI to learn the user's past order history and suggest the optimal print options. For example, suggestions are made based on print sizes and paper types selected in the past. The order processing unit can also analyze the user's order history and suggest the optimal print options. For example, suggestions are made based on past order data. The order processing unit can also use a generation AI to learn the user's past order history and suggest the optimal print options. For example, suggestions are made based on layouts and designs selected in the past. In this way, the order processing unit can learn the user's past order history and suggest the optimal print options.

[0043] The order processing unit can automatically optimize the album layout and design to improve print quality. In the order processing unit, for example, a generation AI automatically optimizes the album layout and design to improve print quality. For example, it adjusts the placement and size of photos. The order processing unit also optimizes the layout and design to improve print quality. For example, it adjusts the balance and margins of pages. The order processing unit also optimizes the album layout and design to improve print quality. For example, it adjusts the resolution and color tone of photos. In this way, the album layout and design can be automatically optimized to improve print quality.

[0044] The order processing unit can suggest popular print options by referring to the order histories of other users. For example, the order processing unit uses the generation AI to suggest popular print options by referring to the order histories of other users. For example, it may suggest print sizes and paper qualities that many users have chosen. The order processing unit also analyzes the order data of other users to suggest popular print options. For example, it may suggest highly rated print settings. The order processing unit also uses the generation AI to suggest popular print options by referring to the order histories of other users. For example, it may suggest layouts and designs that other users like. This allows the order processing unit to suggest popular print options by referring to the order histories of other users.

[0045] The order processing unit can simultaneously generate a digital version of the album, allowing the user to share it online. For example, the order processing unit can have a generation AI simultaneously generate a digital version of the album, allowing the user to share it online. For example, the order processing unit can generate the album in PDF format or e-book format. The order processing unit can also generate a digital version of the album, allowing the user to share it via social media or email. For example, the order processing unit can generate a link to share it. The order processing unit can also have a generation AI simultaneously generate a digital version of the album, allowing the user to share it online. For example, the order processing unit can save it in cloud storage and share it. In this way, the order processing unit can simultaneously generate a digital version of the album, allowing the user to share it online.

[0046] The customization option unit can learn the user's past customization history and suggest the optimal customization options. For example, the generation AI in the customization option unit learns the user's past customization history and suggests the optimal customization options. For example, suggestions are made based on layouts and designs selected in the past. The customization option unit can also analyze the user's customization history and suggest the optimal customization options. For example, suggestions are made based on past customization data. The customization option unit can also learn the user's past customization history and suggest the optimal customization options. For example, suggestions are made based on fonts and colors selected in the past. In this way, the generation AI can learn the user's past customization history and suggest the optimal customization options.

[0047] The customization option unit can display a real-time preview of the image layout and design during customization. For example, the customization option unit displays a real-time preview of the image layout and design during customization by the generation AI. For example, it instantly reflects settings selected by the user. The customization option unit also displays a real-time preview during customization, allowing the user to check changes. For example, it instantly displays changes to the layout and design. The customization option unit also displays a real-time preview of the image layout and design during customization by the generation AI. For example, it instantly reflects fonts and colors selected by the user. This allows the image layout and design to be previewed in real-time during customization.

[0048] The customization option unit can suggest popular customization options by referring to the customization history of other users. For example, the generation AI of the customization option unit suggests popular customization options by referring to the customization history of other users. For example, it suggests layouts and designs chosen by many users. The customization option unit also analyzes the customization data of other users to suggest popular customization options. For example, it suggests highly rated customization settings. The customization option unit also suggests popular customization options by referring to the customization history of other users. For example, it suggests fonts and colors that other users like. This allows the generation AI of the customization option unit to suggest popular customization options by referring to the customization history of other users.

[0049] The customization option unit can refer to the user's social media reactions when customizing. For example, the generation AI in the customization option unit refers to the user's social media reactions when customizing. For example, it suggests customization options based on the number of likes and the content of comments. The customization option unit also analyzes reactions on social media to suggest particularly popular customization options. For example, it suggests settings that have many likes and positive comments. The customization option unit also refers to the user's social media reactions when customizing. For example, it suggests customization options based on the number of shares and retweets. This allows the generation AI to refer to the user's social media reactions when customizing.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The album creation system may further include a voice recognition unit. The voice recognition unit may analyze voice memos recorded by a user during a trip and extract information related to the album. For example, if a user records their impressions of a tourist spot, the content may be converted into text and used as captions for the album. The voice recognition unit may also detect the user's voice talking about a specific place or event and optimize the composition of the album based on that information. Furthermore, the voice recognition unit may analyze the voice memos recorded by a user during a trip, extract specific keywords or phrases, and set themes and sections of the album based on those keywords or phrases.

[0052] The album creation system can further include a weather information acquisition unit. The weather information acquisition unit can collect weather information for the travel destination and reflect it in the contents of the album. For example, if the weather during the trip is sunny, that information can be added to the caption of the album. The weather information acquisition unit can also change the design and layout of the album based on the weather during the trip. For example, a rain effect can be added to photos taken on a rainy day. Furthermore, the weather information acquisition unit can suggest the best time to take photos to the user based on the weather forecast for the travel destination.

[0053] The album generation system may further include a health data acquisition unit that acquires the user's health data. The health data acquisition unit collects health data such as the user's heart rate and number of steps, and can reflect the user's activities during the trip in the album. For example, if the user records a large number of steps at a particular tourist spot, that information can be added to the album caption. The health data acquisition unit can also analyze changes in the user's heart rate, identify particularly exciting moments, and prioritize selecting photos of those moments. Furthermore, the health data acquisition unit can reflect highlights of the user's activities during the trip in the album based on the user's health data.

[0054] The album creation system can further include a social media integration unit that integrates with the user's social media accounts. The social media integration unit can collect photos and comments posted by the user during their trip and reflect them in the album. For example, photos posted by the user on Instagram can be automatically added to the album. The social media integration unit can also analyze reactions from the user's followers and prioritize the most popular photos. Furthermore, the social media integration unit can automatically generate captions and comments for the album based on the content posted by the user.

[0055] The album creation system can further include a travel planning support unit that supports the user's travel plans. The travel planning support unit can suggest next travel destinations and tourist spots based on the user's past travel data and preferences. For example, it can analyze places the user has visited in the past and photos they have taken to suggest next travel destinations. The travel planning support unit can also automatically generate and suggest travel plans that suit the user's preferences. Furthermore, the travel planning support unit can also suggest optimal photo spots and photo timing based on the user's travel plans.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The image collection unit collects images taken at a travel destination. For example, a user uploads images to storage via a camera app. The image collection unit also analyzes the image metadata (date of capture, location information, etc.) to identify the period and location of the trip. For example, if a user uploads "travel photos from July 1 to July 7, 2023," the image collection unit automatically collects images taken during that period. Step 2: The scene selection unit selects good scenes from the collected images. For example, the generation AI selects photos with beautiful scenery, many smiling faces, or photos that include specific landmarks. The generation AI also analyzes the images based on prompts containing instructions on what the user wants the generation AI to do, and selects good scenes. Step 3: The album generator combines the selected images to create an album. For example, the generator AI arranges the highlights of the trip in chronological order or divides sections by theme. The generator AI also optimizes the layout and design based on the user's instructions to create a beautiful album. Step 4: When the created album is ordered, the order processing unit prints and binds it. For example, when a user instructs "I want to order this album," the order processing unit automatically prints and binds it, and sends the completed album to the address specified by the user.

[0058] (Example 2) The album creation system according to an embodiment of the present invention automatically collects images taken at travel destinations, uses a generation AI to select good scenes, combines the images to create an album, and prints and binds the images upon order. This allows the album creation system to automatically organize images taken at travel destinations and create a memory album.

[0059] An album generation system according to an embodiment includes an image collection unit, a scene selection unit, an album generation unit, and an order processing unit. The image collection unit collects images taken at travel destinations. For example, a user uploads images to storage via a camera app. The image collection unit then analyzes the image metadata (such as the date of capture and location information) to identify the period and location of the trip. For example, if a user uploads "travel photos from July 1 to July 7, 2023," the image collection unit automatically collects images taken during that period. The scene selection unit selects good scenes from the collected images. For example, the generation AI selects photos with beautiful scenery, photos with many smiling faces, photos featuring specific landmarks, etc. The generation AI then analyzes the images and selects good scenes based on prompts containing instructions from the user regarding what the generation AI should do. The album generation unit then combines the selected images to generate an album. For example, the generation AI may arrange the highlights of the trip in chronological order or divide the album into sections by theme. The generation AI also optimizes the layout and design based on the user's instructions to create a beautiful album. The order processing unit prints and binds the generated album when it is ordered. For example, when a user instructs, "I want to order this album," the order processing unit automatically performs the printing and binding procedures and delivers the completed album to the address specified by the user. This allows the album generation system according to the embodiment to automatically organize images taken at a travel destination and generate a memory album. For example, a user can easily create a beautiful album by simply uploading photos after returning from a trip. Customization options are also available to create an album tailored to the user's preferences.

[0060] The image collection unit recognizes not only metadata but also objects and people in images, allowing it to automatically identify travel themes and stories. For example, the image collection unit uses a generative AI to recognize objects and people in images and automatically identify travel themes and stories. For example, it recognizes landscapes such as beaches and mountains and sets a travel theme based on that. The image collection unit also recognizes people in images and automatically identifies the type of trip, such as a family trip or a trip with friends. For example, it analyzes photos with multiple people and classifies them as a group trip. The image collection unit also analyzes objects and backgrounds in images and automatically generates travel stories. For example, it recognizes landmarks at tourist spots and sets a story related to that location. This allows it to recognize objects and people in images and automatically identify travel themes and stories.

[0061] The image collection unit learns the user's past travel history and preferences and can suggest the best photo spots for the next trip. For example, the generation AI in the image collection unit learns the user's past travel history and suggests the best photo spots for the next trip. For example, it suggests spots that suit the user's preferences based on places visited in the past and photos taken. The image collection unit also learns the user's preferences and suggests tourist spots and photo spots to visit on the next trip. For example, for a user who likes natural scenery, it suggests natural parks and mountainous areas. The image collection unit also analyzes the user's past travel photos and suggests scenes and angles to take photos at on the next trip. For example, it suggests the best timing to take photos based on specific composition and lighting conditions. In this way, the generation AI can learn the user's past travel history and preferences and suggest the best photo spots for the next trip.

[0062] The image collection unit can use the emotion estimation function to analyze the emotion the user felt when taking a photo and prioritize collecting photos that are particularly emotionally important. The image collection unit, for example, uses the emotion estimation function to analyze the emotion the user felt when taking a photo and prioritize collecting photos that are particularly emotionally important. For example, it detects expressions of smiles or surprise and prioritizes selecting those photos. The image collection unit also identifies emotionally important photos based on emotion data at the time of taking a photo. For example, it prioritizes collecting photos that capture moments when the user was particularly moved. The image collection unit also uses the emotion estimation function to select important photos based on the positive emotion the user felt when taking a photo. For example, it prioritizes collecting photos that show a strong sense of joy or happiness. This allows the image collection unit to analyze the emotion the user felt when taking a photo and prioritize collecting photos that are particularly emotionally important.

[0063] The image collection unit can simultaneously collect voice memos or video clips when collecting images, and generate a multimedia album. For example, when the generation AI collects images, the image collection unit simultaneously collects voice memos and video clips, and generates a multimedia album. For example, voice memos recorded during a trip are saved together with photos. The image collection unit also collects video clips along with images, and generates a multimedia album. For example, short video clips taken at tourist spots are incorporated into the album. The image collection unit also collects voice memos and video clips, and combines them with photos to generate a multimedia album. For example, voice memos recounting travel memories are saved together with photos. In this way, voice memos and video clips can be simultaneously collected, and a multimedia album can be generated.

[0064] The image collection unit can compare travel photos taken by other users and suggest photos with similar travel destinations or themes. For example, the generation AI compares travel photos taken by other users with travel photos taken by other users who visited the same tourist spot. The image collection unit also analyzes travel photos taken by other users and suggests photos with similar themes or scenes. For example, it suggests photos taken in the same season or at the same event. The image collection unit also analyzes travel photos taken by other users with travel photos with similar destinations or themes. For example, it refers to photos taken by users who visited the same country or city. This allows the generation AI to compare travel photos taken by other users with travel photos with similar destinations or themes.

[0065] The image collection unit can use the emotion estimation function to suggest the next travel destination based on the emotions the user felt during the trip. The image collection unit, for example, uses the emotion estimation function to suggest the next travel destination based on the emotions the user felt during the trip. For example, it prioritizes suggesting travel destinations associated with strong positive emotions. The image collection unit also analyzes emotion data collected during the trip to suggest the next travel destination. For example, it makes suggestions based on places and activities that the user particularly enjoyed. The image collection unit also uses the emotion estimation function to suggest the next travel destination based on the user's emotion data. For example, it suggests relaxing places and activities. This allows the image collection unit to suggest the next travel destination based on the emotions the user felt during the trip.

[0066] The scene selection unit performs an aesthetic evaluation of the image and can select photos with a good balance of color and composition. For example, the generation AI analyzes the color and composition of the image and performs an aesthetic evaluation. For example, it selects photos based on color balance and symmetry of the composition. The scene selection unit also evaluates color harmony and composition balance to select beautiful photos. For example, it evaluates based on the use of natural light and the unity of the background. The scene selection unit also performs an aesthetic evaluation of the image and selects photos with a good balance of color and composition. For example, it selects based on color contrast and visual enhancement. This makes it possible to select photos with a good balance of color and composition.

[0067] The scene selection unit analyzes the movements and actions in an image and can prioritize picking out dynamic scenes. For example, the generation AI in the scene selection unit analyzes the movements and actions in an image and prioritizes picking out dynamic scenes. For example, it selects photos of people running or animals jumping. The scene selection unit also analyzes scenes with movement and selects photos that capture particularly dynamic moments. For example, it prioritizes photos of sporting events or dance performances. The scene selection unit also analyzes the movements and actions in an image and prioritizes picking out dynamic scenes. For example, it selects photos of children playing on the beach or trees swaying in the wind. This allows dynamic scenes to be prioritized.

[0068] The scene selection unit can use the emotion estimation function to identify scenes that particularly moved the user and preferentially select those scenes. The scene selection unit, for example, uses the emotion estimation function to identify scenes that particularly moved the user and preferentially select those scenes. For example, it selects a photo of a person shedding tears of emotion. The scene selection unit also selects a photo that captures a moment that particularly moved the user based on emotion data. For example, it prioritizes special events and surprise moments. The scene selection unit also uses the emotion estimation function to identify scenes that particularly moved the user and preferentially select those scenes. For example, it selects a photo that captures a moving landscape or a special moment. This allows it to preferentially select scenes that particularly moved the user.

[0069] The scene selection unit can refer to the user's social media reactions when picking images. For example, the generation AI in the scene selection unit refers to the user's social media reactions when picking images. For example, it selects photos based on the number of likes and the content of comments. The scene selection unit also analyzes reactions on social media and selects particularly popular photos. For example, it prioritizes photos with many likes and positive comments. The scene selection unit also refers to the user's social media reactions and selects photos that are particularly highly rated. For example, it selects photos based on the number of shares and retweets. This allows the generation AI to pick images by referring to the user's social media reactions.

[0070] The scene selection unit can pick out popular scenes based on the ratings of other users. For example, the generation AI in the scene selection unit picks out popular scenes based on the ratings of other users. For example, it prioritizes the selection of photos that other users have given high ratings. The scene selection unit also analyzes the rating data of other users and selects particularly popular scenes. For example, it selects photos based on reviews and rating scores. The scene selection unit also picks out particularly highly rated scenes based on the ratings of other users by the generation AI. For example, it prioritizes the selection of photos recommended by other users. This makes it possible to pick out popular scenes based on the ratings of other users.

[0071] The scene selection unit can use the emotion estimation function to analyze the emotional reactions of the user's friends and family and select scenes that are likely to be empathetic. For example, the scene selection unit uses the emotion estimation function to analyze the emotional reactions of the user's friends and family and select scenes that are likely to be empathetic. For example, it selects photos of all family members smiling. The scene selection unit also selects scenes that are likely to be empathetic based on the emotional data of the friends and family. For example, it prioritizes photos of special events and anniversaries. The scene selection unit also uses the emotion estimation function to analyze the emotional reactions of the user's friends and family and select scenes that are likely to be empathetic. For example, it selects photos that capture moving moments. In this way, it is possible to analyze the emotional reactions of the user's friends and family and select scenes that are likely to be empathetic.

[0072] The album generation unit can perform image storytelling to generate an album that naturally recreates the flow of a trip. For example, the generation AI in the album generation unit performs image storytelling to generate an album that naturally recreates the flow of a trip. For example, the flow from departure to return home is arranged in chronological order. The album generation unit also arranges the highlights of the trip in a storytelling format to generate an album. For example, photos are arranged in the order in which tourist spots were visited. The album generation unit also uses image storytelling to generate an album that naturally recreates the flow of a trip. For example, events and episodes that occurred during the trip are arranged along with the photos. This makes it possible to generate an album that naturally recreates the flow of a trip.

[0073] The album generation unit can unify the color tones and filters of images to increase the visual coherence of the entire album. For example, the generation AI of the album generation unit unifies the color tones and filters of images to increase the visual coherence of the entire album. For example, the same filter is applied to all photos. The album generation unit can also unify the color tones to increase the visual coherence of the entire album. For example, photos are adjusted based on a specific color tone or tone. The album generation unit can also unify the color tones and filters of images to increase the visual coherence of the entire album. For example, the same color correction is applied to all photos. This can increase the visual coherence of the entire album.

[0074] The album generation unit can use the emotion estimation function to create an album that reflects the emotional ups and downs of the user. The album generation unit, for example, uses the emotion estimation function to create an album that reflects the emotional ups and downs of the user. For example, it creates a page that emphasizes moving moments. The album generation unit also creates an album that reflects the emotional ups and downs based on the user's emotion data. For example, it expresses changes in emotions during a trip in chronological order. The album generation unit also uses the emotion estimation function to create an album that reflects the emotional ups and downs of the user. For example, it places scenes with strong positive emotions in the center. This allows the album to be created that reflects the emotional ups and downs of the user.

[0075] The album generation unit can combine photos from different travel destinations to generate a virtual travel album. For example, the generation AI of the album generation unit combines photos from different travel destinations to generate a virtual travel album. For example, highlights from multiple travel destinations are compiled into one album. The album generation unit also combines photos from different travel destinations to generate a virtual travel album. For example, photos of multiple cities that a user has visited are integrated into one album. The album generation unit also combines photos from different travel destinations to generate a virtual travel album. For example, tourist spots in different countries are compiled into one album. This makes it possible to generate a virtual travel album by combining photos from different travel destinations.

[0076] The album generation unit can generate an album that shows evolution and change by comparing with the user's past albums. For example, the generation AI of the album generation unit generates an album that shows evolution and change by comparing with the user's past albums. For example, it creates a page that compares past trips with current trips. The album generation unit also compares past albums with the current album to generate an album that shows changes in the user's travel style. For example, it displays past and current photos side by side. The album generation unit also generates an album that shows evolution and change by comparing with the user's past albums. For example, it creates a page that compares past travel destinations with current travel destinations. This makes it possible to generate an album that shows evolution and change by comparing with the user's past albums.

[0077] The album generation unit can use the emotion estimation function to generate themed albums (e.g., emotional moments, happy moments) based on the user's emotions. The album generation unit, for example, uses the emotion estimation function to generate themed albums based on the user's emotions. For example, an album is created that collects emotional moments. The album generation unit also generates themed albums based on the user's emotion data. For example, an album is created that collects happy moments. The album generation unit also uses the emotion estimation function to generate themed albums based on the user's emotions. For example, an album is created that collects photos related to a specific emotion. In this way, it is possible to generate themed albums based on the user's emotions.

[0078] The order processing unit can learn the user's past order history and suggest the optimal print options. For example, the order processing unit uses a generation AI to learn the user's past order history and suggest the optimal print options. For example, suggestions are made based on print sizes and paper types selected in the past. The order processing unit can also analyze the user's order history and suggest the optimal print options. For example, suggestions are made based on past order data. The order processing unit can also use a generation AI to learn the user's past order history and suggest the optimal print options. For example, suggestions are made based on layouts and designs selected in the past. In this way, the order processing unit can learn the user's past order history and suggest the optimal print options.

[0079] The order processing unit can automatically optimize the album layout and design to improve print quality. In the order processing unit, for example, a generation AI automatically optimizes the album layout and design to improve print quality. For example, it adjusts the placement and size of photos. The order processing unit also optimizes the layout and design to improve print quality. For example, it adjusts the balance and margins of pages. The order processing unit also optimizes the album layout and design to improve print quality. For example, it adjusts the resolution and color tone of photos. In this way, the album layout and design can be automatically optimized to improve print quality.

[0080] The order processing unit can use the emotion estimation function to suggest a method of presenting an album that will most move the user. The order processing unit, for example, uses the emotion estimation function to suggest a method of presenting an album that will most move the user. For example, it suggests a layout or design that will elicit a specific emotion. The order processing unit also suggests a method of presentation that will most move the user based on the user's emotion data. For example, it creates a page that emphasizes a moving moment. The order processing unit also uses the emotion estimation function to suggest a method of presenting an album that will most move the user. For example, it places a photo related to a specific emotion in the center. This makes it possible to suggest a method of presenting an album that will most move the user.

[0081] The order processing unit can suggest popular print options by referring to the order histories of other users. For example, the order processing unit uses the generation AI to suggest popular print options by referring to the order histories of other users. For example, it may suggest print sizes and paper qualities that many users have chosen. The order processing unit also analyzes the order data of other users to suggest popular print options. For example, it may suggest highly rated print settings. The order processing unit also uses the generation AI to suggest popular print options by referring to the order histories of other users. For example, it may suggest layouts and designs that other users like. This allows the order processing unit to suggest popular print options by referring to the order histories of other users.

[0082] The order processing unit can simultaneously generate a digital version of the album, allowing the user to share it online. For example, the order processing unit can have a generation AI simultaneously generate a digital version of the album, allowing the user to share it online. For example, the order processing unit can generate the album in PDF format or e-book format. The order processing unit can also generate a digital version of the album, allowing the user to share it via social media or email. For example, the order processing unit can generate a link to share it. The order processing unit can also have a generation AI simultaneously generate a digital version of the album, allowing the user to share it online. For example, the order processing unit can save it in cloud storage and share it. In this way, the order processing unit can simultaneously generate a digital version of the album, allowing the user to share it online.

[0083] The order processing unit can use the emotion estimation function to suggest gift options (e.g., an emotional message card) based on the user's emotions. The order processing unit, for example, uses the emotion estimation function to suggest gift options based on the user's emotions. For example, it suggests an emotional message card. The order processing unit can also suggest emotion-based gift options based on the user's emotion data. For example, it can suggest a gift that elicits a specific emotion. The order processing unit can also use the emotion estimation function to suggest gift options based on the user's emotions. For example, it can suggest a gift that commemorates an emotional moment. This makes it possible to suggest gift options based on the user's emotions.

[0084] The customization option unit can learn the user's past customization history and suggest the optimal customization options. For example, the generation AI in the customization option unit learns the user's past customization history and suggests the optimal customization options. For example, suggestions are made based on layouts and designs selected in the past. The customization option unit can also analyze the user's customization history and suggest the optimal customization options. For example, suggestions are made based on past customization data. The customization option unit can also learn the user's past customization history and suggest the optimal customization options. For example, suggestions are made based on fonts and colors selected in the past. In this way, the generation AI can learn the user's past customization history and suggest the optimal customization options.

[0085] The customization option unit can display a real-time preview of the image layout and design during customization. For example, the customization option unit displays a real-time preview of the image layout and design during customization by the generation AI. For example, it instantly reflects settings selected by the user. The customization option unit also displays a real-time preview during customization, allowing the user to check changes. For example, it instantly displays changes to the layout and design. The customization option unit also displays a real-time preview of the image layout and design during customization by the generation AI. For example, it instantly reflects fonts and colors selected by the user. This allows the image layout and design to be previewed in real-time during customization.

[0086] The customization option unit can use the emotion estimation function to suggest the customization option that will most impress the user. For example, the customization option unit uses the emotion estimation function to suggest the customization option that will most impress the user. For example, it proposes a layout or design that will elicit a specific emotion. The customization option unit also suggests the customization option that will most impress the user based on the user's emotion data. For example, it proposes settings that emphasize emotional moments. The customization option unit also uses the emotion estimation function to suggest the customization option that will most impress the user. For example, it proposes fonts or colors associated with specific emotions. In this way, it is possible to suggest the customization option that will most impress the user.

[0087] The customization option unit can suggest popular customization options by referring to the customization history of other users. For example, the generation AI of the customization option unit suggests popular customization options by referring to the customization history of other users. For example, it suggests layouts and designs chosen by many users. The customization option unit also analyzes the customization data of other users to suggest popular customization options. For example, it suggests highly rated customization settings. The customization option unit also suggests popular customization options by referring to the customization history of other users. For example, it suggests fonts and colors that other users like. This allows the generation AI of the customization option unit to suggest popular customization options by referring to the customization history of other users.

[0088] The customization option unit can refer to the user's social media reactions when customizing. For example, the generation AI in the customization option unit refers to the user's social media reactions when customizing. For example, it suggests customization options based on the number of likes and the content of comments. The customization option unit also analyzes reactions on social media to suggest particularly popular customization options. For example, it suggests settings that have many likes and positive comments. The customization option unit also refers to the user's social media reactions when customizing. For example, it suggests customization options based on the number of shares and retweets. This allows the generation AI to refer to the user's social media reactions when customizing.

[0089] The customization option unit can use the emotion estimation function to analyze the emotional reactions of the user's friends and family and suggest customization options that are easy to empathize with. For example, the customization option unit can use the emotion estimation function to analyze the emotional reactions of the user's friends and family and suggest customization options that are easy to empathize with. For example, it can suggest layouts and designs that will make the whole family smile. The customization option unit can also suggest customization options that are easy to empathize with based on the emotional data of friends and family. For example, it can suggest settings that highlight photos of special events or anniversaries. The customization option unit can also use the emotion estimation function to analyze the emotional reactions of the user's friends and family and suggest customization options that are easy to empathize with. For example, it can suggest fonts and colors that commemorate moving moments. In this way, it is possible to analyze the emotional reactions of the user's friends and family and suggest customization options that are easy to empathize with.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The album creation system may further include a voice recognition unit. The voice recognition unit may analyze voice memos recorded by a user during a trip and extract information related to the album. For example, if a user records their impressions of a tourist spot, the content may be converted into text and used as captions for the album. The voice recognition unit may also detect the user's voice talking about a specific place or event and optimize the composition of the album based on that information. Furthermore, the voice recognition unit may analyze the voice memos recorded by a user during a trip, extract specific keywords or phrases, and set themes and sections of the album based on those keywords or phrases.

[0092] The album creation system can further include a weather information acquisition unit. The weather information acquisition unit can collect weather information for the travel destination and reflect it in the contents of the album. For example, if the weather during the trip is sunny, that information can be added to the caption of the album. The weather information acquisition unit can also change the design and layout of the album based on the weather during the trip. For example, a rain effect can be added to photos taken on a rainy day. Furthermore, the weather information acquisition unit can suggest the best time to take photos to the user based on the weather forecast for the travel destination.

[0093] The album generation system may further include a health data acquisition unit that acquires the user's health data. The health data acquisition unit collects health data such as the user's heart rate and number of steps, and can reflect the user's activities during the trip in the album. For example, if the user records a large number of steps at a particular tourist spot, that information can be added to the album caption. The health data acquisition unit can also analyze changes in the user's heart rate, identify particularly exciting moments, and prioritize selecting photos of those moments. Furthermore, the health data acquisition unit can reflect highlights of the user's activities during the trip in the album based on the user's health data.

[0094] The album creation system can further include a social media integration unit that integrates with the user's social media accounts. The social media integration unit can collect photos and comments posted by the user during their trip and reflect them in the album. For example, photos posted by the user on Instagram can be automatically added to the album. The social media integration unit can also analyze reactions from the user's followers and prioritize the most popular photos. Furthermore, the social media integration unit can automatically generate captions and comments for the album based on the content posted by the user.

[0095] The album creation system can further include a travel planning support unit that supports the user's travel plans. The travel planning support unit can suggest next travel destinations and tourist spots based on the user's past travel data and preferences. For example, it can analyze places the user has visited in the past and photos they have taken to suggest next travel destinations. The travel planning support unit can also automatically generate and suggest travel plans that suit the user's preferences. Furthermore, the travel planning support unit can also suggest optimal photo spots and photo timing based on the user's travel plans.

[0096] The album generation system can further use the emotion estimation function to suggest music that evokes specific emotions based on the emotions the user felt during their trip. For example, it can suggest inspiring music for a photo that captures a particularly moving moment for the user. The emotion estimation function can also be used to suggest enjoyable music based on the positive emotions the user felt during their trip. Furthermore, the emotion estimation function can also be used to automatically generate background music for an album based on the changes in emotions the user felt during their trip. This makes it possible to suggest music that evokes specific emotions based on the emotions the user felt during their trip.

[0097] The album generation system can further use the emotion estimation function to suggest effects that bring out specific emotions based on the emotions the user felt during the trip. For example, an emotional effect can be suggested for a photo that captured a moment that particularly moved the user. The emotion estimation function can also be used to suggest fun effects based on the positive emotions the user felt during the trip. Furthermore, the emotion estimation function can also be used to automatically generate album effects based on the changes in emotions the user felt during the trip. This makes it possible to suggest effects that bring out specific emotions based on the emotions the user felt during the trip.

[0098] The album generation system can further use the emotion estimation function to suggest captions that evoke specific emotions based on the emotions the user felt during their trip. For example, it can suggest an inspiring caption for a photo that captured a particularly moving moment for the user. The emotion estimation function can also be used to suggest fun captions based on the positive emotions the user felt during their trip. Furthermore, the emotion estimation function can also be used to automatically generate captions for the album based on the changes in emotions the user felt during their trip. This makes it possible to suggest captions that evoke specific emotions based on the emotions the user felt during their trip.

[0099] The album generation system can further use the emotion estimation function to propose a layout that evokes a specific emotion based on the emotion felt by the user during the trip. For example, an emotional layout can be proposed for a photo that captures a moment that particularly moved the user. The emotion estimation function can also be used to propose a fun layout based on the positive emotion felt by the user during the trip. Furthermore, the emotion estimation function can also be used to automatically generate an album layout based on the changes in the emotion felt by the user during the trip. This makes it possible to propose a layout that evokes a specific emotion based on the emotion felt by the user during the trip.

[0100] The album generation system can further use the emotion estimation function to suggest filters that bring out specific emotions based on the emotions the user felt during the trip. For example, an emotional filter can be suggested for a photo that captured a moment that particularly moved the user. The emotion estimation function can also be used to suggest fun filters based on the positive emotions the user felt during the trip. Furthermore, the emotion estimation function can also be used to automatically generate album filters based on the changes in emotions the user felt during the trip. This makes it possible to suggest filters that bring out specific emotions based on the emotions the user felt during the trip.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The image collection unit collects images taken at a travel destination. For example, a user uploads images to storage via a camera app. The image collection unit also analyzes the image metadata (date of capture, location information, etc.) to identify the period and location of the trip. For example, if a user uploads "travel photos from July 1 to July 7, 2023," the image collection unit automatically collects images taken during that period. Step 2: The scene selection unit selects good scenes from the collected images. For example, the generation AI selects photos with beautiful scenery, many smiling faces, or photos that include specific landmarks. The generation AI also analyzes the images based on prompts containing instructions on what the user wants the generation AI to do, and selects good scenes. Step 3: The album generator combines the selected images to create an album. For example, the generator AI arranges the highlights of the trip in chronological order or divides sections by theme. The generator AI also optimizes the layout and design based on the user's instructions to create a beautiful album. Step 4: When the created album is ordered, the order processing unit prints and binds it. For example, when a user instructs "I want to order this album," the order processing unit automatically prints and binds it, and sends the completed album to the address specified by the user.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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 image collection unit that collects images taken at travel destinations using a camera app equipped with generative AI; a scene selection unit that selects good scenes from the images collected by the image collection unit; an album creation unit that creates an album by combining the images selected by the scene selection unit; an order processing unit that prints out and binds the album generated by the album generating unit when the album is ordered; A system characterized by:

2. The image acquisition unit Recognizes objects and people in the images, as well as metadata, to automatically identify travel themes and stories 2. The system of claim 1.

3. The image acquisition unit Learns the user's past travel history and preferences and suggests the best photo spots for the next trip 2. The system of claim 1.

4. The image acquisition unit Analyzes the emotions felt by the user when taking a photo and prioritizes the collection of photos that are particularly emotionally significant.

2. The system of claim 1.

5. The image acquisition unit When collecting the images, audio memos or video clips are also collected at the same time to create a multimedia album.

2. The system of claim 1.

6. The image acquisition unit Compare your travel photos with those of other users and suggest photos with similar destinations or themes 2. The system of claim 1.

Images