system

Abstract

The system according to the embodiment aims to automatically generate the layout and design of an album based on photos and comments from a user. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, a provision unit, and a printing unit. The reception unit receives photos or comments from a user. The analysis unit analyzes the photos or comments received by the reception unit. The generation unit automatically generates the layout and design of an album based on the information analyzed by the analysis unit. The provision unit provides the user with a preview of the album generated by the generation unit. The printing unit prints the modified album based on the preview provided by the provision unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology requires the layout and design of albums to be done manually, which is time-consuming and labor-intensive.

[0005] The system according to the embodiment aims to automatically generate the layout and design of an album based on photos and comments from a user. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, a provision unit, and a printing unit. The reception unit receives photos or comments from a user. The analysis unit analyzes the photos or comments received by the reception unit. The generation unit automatically generates the layout and design of the album based on the information analyzed by the analysis unit. The provision unit provides the album generated by the generation unit as a preview to the user. The printing unit prints the album modified based on the preview provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate the layout and design of an album based on photos and comments from a user. [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) An automatic generation system according to an embodiment of the present invention utilizes a generation AI to automatically generate graduation albums and other images. In this automatic generation system, a user submits photos and comments via LINE (registered trademark) or other social networking services (SNS) or messaging apps. The generation AI analyzes the submitted photos and comments and automatically generates the layout and design of the album. The generated album is provided to the user as a preview, which the user can modify as needed. Finally, the modified album is printed and delivered to the user. For example, a user submits photos and comments via LINE or other social networking services (SNS) or messaging apps. The user can easily submit photos and comments, for example, photos from the graduation ceremony or comments about their children's memories. The generation AI then analyzes the submitted photos and comments. The generation AI understands the content of the photos and the context of the comments and determines appropriate layout and design elements, such as the placement of photos and font selection for comments. The generation AI then automatically generates the album layout and design. The generated album is provided to the user as a preview. The user can review the preview and make modifications as needed, for example, by changing the order of photos or adding comments. Finally, the revised album is printed and delivered to the user. The printing department prints the album in high quality, reflecting the user's revisions, and delivers it to the user. This service allows users to create beautiful graduation albums without much effort. In addition, integration with LINE makes it easy to send photos and comments, improving user convenience. This allows the automatic generation system to automatically analyze the user's photos and comments, generate the album layout and design, and deliver the revised album in high quality print.

[0029] An automatic generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, a provision unit, and a printing unit. The reception unit receives photos or comments from users. The photos or comments from users include, but are not limited to, photos in JPEG format, PNG format, and text comments. The reception unit can receive photos and comments via, for example, LINE, other social networking services, or messaging apps. The analysis unit analyzes the photos or comments received by the reception unit. The analysis unit understands the content of the submitted photos and the context of the comments using, for example, image recognition technology or natural language processing technology, and determines appropriate layouts and design elements. The generation unit automatically generates the layout and design of an album based on the information analyzed by the analysis unit. The generation unit performs, for example, photo placement and comment font selection. The provision unit provides the album generated by the generation unit as a preview to the user. The provision unit provides an interface for the user to view and modify the preview, for example, by displaying it on a web browser or within an application. The printing unit prints the modified album based on the preview provided by the providing unit and delivers it to the user. The printing unit prints the album with high quality, reflecting the user's modifications, taking into consideration, for example, high-quality print resolution, paper type, ink quality, etc. In this way, the automatic generation system according to the embodiment can automatically analyze the user's photos and comments, generate the album layout and design, and print and deliver the modified album with high quality.

[0030] The reception unit can accept photos or comments via LINE or other SNS or messaging apps. For example, the reception unit accepts photos and comments via LINE or other SNS or messaging apps. For example, a user can use LINE to send photos from a graduation ceremony or comments about their children's memories. The reception unit can also accept photos and comments via other SNS or messaging apps such as Facebook (registered trademark), Instagram (registered trademark), and WhatsApp (registered trademark). This allows users to easily send photos and comments via LINE or other SNS or messaging apps. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input photos and comments sent by users into an AI model to automate the reception of photos and comments.

[0031] The analysis unit can understand the content of the submitted photo or the context of the comment and determine the layout and design elements. The analysis unit can analyze the content of the submitted photo using, for example, image recognition technology. For example, the analysis unit can recognize people and scenery in the photo and determine an appropriate layout. The analysis unit can also understand the context of the submitted comment using natural language processing technology. For example, the analysis unit can analyze the content and sentiment of the comment and determine appropriate fonts and design elements. Furthermore, the analysis unit can analyze the relevance of the submitted photo and comment and optimize the album layout. For example, the analysis unit can evaluate the relevance between the photo and comment and place related photos and comments together. This makes it possible to understand the content of the submitted photo and comment and determine appropriate layout and design elements. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the submitted photo and comment into a generation AI and have the generation AI analyze the photo and comment.

[0032] The generation unit can arrange photos and select fonts for comments. The generation unit can, for example, automatically arrange photos. For example, the generation unit can arrange photos in an orderly manner using a grid layout. The generation unit can also freely arrange photos using a free layout. Furthermore, the generation unit can take into account the importance and relevance of photos when arranging photos. For example, the generation unit can arrange photos of important events in prominent positions and arrange related photos together. The generation unit can also automatically select fonts for comments. For example, the generation unit can select appropriate fonts based on the content and context of the comments. The generation unit can adjust the font type, size, color, etc. to make comments easier to read. This allows the generation unit to automatically arrange photos and select fonts for comments. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input photo and comment data into the generation AI and have the generation AI perform photo arrangement and comment font selection.

[0033] The providing unit can provide an interface for a user to check the preview and make corrections. The providing unit, for example, provides a preview display on a web browser. For example, the providing unit provides an interface for a user to check the album preview and make corrections through a web browser. The providing unit can also provide a preview display within an application. For example, the providing unit provides an interface for a user to check the album preview and make corrections through an application on a smartphone or tablet. Furthermore, the providing unit can be designed to enable a user to easily operate the preview display. For example, the providing unit can provide a drag-and-drop function to enable a user to easily change the order of photos. The providing unit can also provide a text editing function to enable a user to add or correct comments. In this way, the providing unit can provide an interface for a user to check the preview and make corrections as necessary. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input user operation data into an AI model and cause the AI ​​to optimize the preview display and correction interface.

[0034] The printing unit can print an album that reflects the user's edits and deliver it to the user. The printing unit, for example, provides high-quality print resolution. For example, the printing unit can print the album at a high resolution of 300 dpi or higher, clearly reproducing even the smallest details. The printing unit can also select the type of paper. For example, the printing unit can select paper, such as glossy or matte paper, according to the user's preference and print the album on that paper. The printing unit can also focus on ink quality. For example, the printing unit can use high-quality ink to achieve vibrant, long-lasting prints. This allows the printing unit to print a high-quality album that reflects the user's edits and deliver it to the user. Some or all of the above-described processing in the printing unit can be performed using, for example, AI, or without AI. For example, the printing unit can input the user's edited data into an AI model and have the AI ​​optimize the print settings. The printing unit also has a function for performing quality checks after printing. For example, the printing unit can automatically scan the printed album to check for quality issues. Furthermore, the printing department can reprint if there is a quality problem, which allows the printing department to always provide users with high-quality albums.

[0035] The reception unit can analyze the user's past posting history and select the reception method. The reception unit, for example, analyzes the user's past posting history. For example, the reception unit can analyze time periods in which the user frequently posted in the past and prioritize reception during those time periods. The reception unit can also analyze trends in the content the user has posted in the past and prioritize reception of similar content. Furthermore, the reception unit can analyze the posting method (text, image, video, etc.) used by the user in the past and suggest the optimal method. This allows the reception unit to analyze the user's past posting history and select the optimal reception method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past posting data into an AI model and have the AI ​​select the optimal reception method.

[0036] The reception unit may filter photos and comments based on the user's current events or areas of interest when receiving the photos or comments. The reception unit may, for example, identify the user's current events or areas of interest. For example, the reception unit may identify the user's current events or areas of interest based on calendar events or social media topics. This allows the reception unit to filter and receive photos and comments based on the user's current events and areas of interest. For example, if the user is attending a graduation ceremony, photos and comments related to the event may be preferentially received. Posts related to the user's specific areas of interest (e.g., sports or music) may also be preferentially received. Furthermore, related photos and comments may be filtered and received based on information about an event the user is currently participating in. This allows photos and comments to be filtered and received based on the user's current events and areas of interest. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit may input the user's event information and area of ​​interest data into an AI model and have the AI ​​perform the filtering.

[0037] When receiving photos or comments, the reception unit can prioritize receiving posts that are highly relevant based on the user's geographical location information. The reception unit, for example, acquires the user's geographical location information. For example, the reception unit can acquire the user's geographical location information using GPS data or an IP address. This allows the reception unit to prioritize receiving posts that are highly relevant in consideration of the user's geographical location information. For example, when the user is in a specific location, photos and comments related to that location can be prioritized. Also, when the user is traveling, photos and comments related to the travel destination can be prioritized. Furthermore, when the user is at home, photos and comments from family and friends can be prioritized. This allows posts that are highly relevant to be prioritized in consideration of the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location data into an AI model and cause the AI ​​to prioritize receiving posts that are highly relevant.

[0038] The reception unit can receive related posts based on the user's social media activity when receiving a photo or comment. The reception unit, for example, analyzes the user's social media activity. For example, the reception unit can analyze the content that the user frequently posts on social media and prioritize receiving related photos and comments. The reception unit can also analyze the content posted by accounts the user follows on social media and prioritize receiving related photos and comments. Furthermore, the reception unit can receive related photos and comments based on information about groups and events the user participates in on social media. In this way, the reception unit can analyze the user's social media activity and receive related posts. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into an AI model and cause the AI ​​to receive related posts.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the photo. The analysis unit, for example, evaluates the importance of the photo. For example, the analysis unit can evaluate the importance of the photo based on the content of the photo, the location where it was taken, the date and time it was taken, etc. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the photo. For example, the analysis unit can analyze photos of important events (such as a graduation ceremony) in detail and propose an optimal layout. It can also prioritize analysis of photos that the user particularly likes and propose a detailed design. It can also analyze photos that the user frequently posts in detail and place them at the center of the album. This allows the level of detail of the analysis to be adjusted based on the importance of the photo. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input photo data into the generation AI and cause the generation AI to adjust the level of detail of the analysis based on the importance.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the type of photo. The analysis unit, for example, classifies the type of photo. For example, the analysis unit can classify photos into types such as landscape photos, portrait photos, and event photos. This allows the analysis unit to apply different analysis algorithms depending on the type of photo. For example, a face recognition algorithm can be applied to portrait photos to suggest an optimal layout. A color analysis algorithm can be applied to landscape photos to suggest an attractive layout. Furthermore, an event recognition algorithm can be applied to event photos to arrange related photos together. This allows different analysis algorithms to be applied depending on the type of photo. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input photo data into the generation AI and have the generation AI apply an analysis algorithm depending on the type.

[0041] During analysis, the analysis unit can adjust the analysis order based on the photographic date and time of the photos. The analysis unit, for example, acquires the photographic date and time of the photos. For example, the analysis unit can acquire the photographic date and time from metadata. Alternatively, the analysis unit can use the photographic date and time manually entered by the user. This allows the analysis unit to adjust the analysis order based on the photographic date and time of the photos. For example, photos of a graduation ceremony can be analyzed first and placed at the beginning of the album. Also, photos taken by the user at a specific event can be analyzed preferentially and related photos can be placed together. Furthermore, photos taken by the user in the past can be analyzed in chronological order to enhance the narrative quality of the album. This allows the analysis order to be adjusted based on the photographic date and time of the photos. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input photographic date and time data of the photos to the generation AI and cause the generation AI to adjust the analysis order based on the photographic date and time.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the photos. The analysis unit, for example, evaluates the relevance of the photos. For example, the analysis unit can evaluate the relevance of the photos based on the similarity of the content of the photos or the proximity of the locations where the photos were taken. This allows the analysis unit to adjust the order of analysis based on the relevance of the photos. For example, photos taken at the same event can be analyzed preferentially and related photos can be arranged together. Also, photos related to a specific theme (e.g., sports or music) can be analyzed preferentially by the user. Furthermore, photos taken by the user with a specific person can be analyzed preferentially and arranged at the center of the album. This allows the order of analysis to be adjusted based on the relevance of the photos. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input photo data to the generation AI and cause the generation AI to adjust the order of analysis based on the relevance.

[0043] The generation unit can adjust the level of detail of the layout based on the importance of the photo during generation. The generation unit, for example, evaluates the importance of the photo. For example, the generation unit can evaluate the importance of the photo based on the content of the photo, the location where it was taken, the date and time it was taken, etc. This allows the generation unit to adjust the level of detail of the layout based on the importance of the photo. For example, the generation unit can arrange photos of important events (such as a graduation ceremony) in detail and propose an optimal layout. It can also preferentially arrange photos that the user particularly likes and propose a detailed design. Furthermore, it can arrange photos that the user frequently posts in detail and place them in the center of the album. This allows the level of detail of the layout to be adjusted based on the importance of the photo. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input photo data to the generation AI and cause the generation AI to adjust the level of detail of the layout based on the importance.

[0044] The generation unit can apply different layout algorithms depending on the type of photo during generation. The generation unit, for example, classifies the type of photo. For example, the generation unit can classify photos into types such as landscape photos, portrait photos, and event photos. This allows the generation unit to apply different layout algorithms depending on the type of photo. For example, a face recognition algorithm can be applied to portrait photos to suggest an optimal layout. A color analysis algorithm can be applied to landscape photos to suggest a beautiful layout. Furthermore, an event recognition algorithm can be applied to event photos to arrange related photos together. This allows different layout algorithms to be applied depending on the type of photo. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input photo data into the generation AI and have the generation AI apply a layout algorithm depending on the type.

[0045] The generation unit can adjust the layout order based on the date and time the photos were taken during generation. The generation unit, for example, acquires the date and time the photos were taken. For example, the generation unit can acquire the date and time the photos were taken from metadata. Alternatively, the generation unit can use the date and time manually entered by the user. This allows the generation unit to adjust the layout order based on the date and time the photos were taken. For example, a graduation ceremony photo can be placed first and placed at the beginning of the album. Furthermore, photos taken by the user at a specific event can be prioritized and related photos can be placed together. Furthermore, photos taken by the user in the past can be arranged in chronological order to enhance the story of the album. This allows the layout order to be adjusted based on the date and time the photos were taken. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input photo date and time data of the photos into the generation AI and cause the generation AI to adjust the layout order based on the date and time the photos were taken.

[0046] The generation unit can adjust the order of the layout based on the relevance of the photos during generation. The generation unit, for example, evaluates the relevance of the photos. For example, the generation unit can evaluate the relevance of the photos based on the similarity of the content of the photos or the proximity of the locations where the photos were taken. This allows the generation unit to adjust the order of the layout based on the relevance of the photos. For example, photos taken at the same event can be preferentially arranged, and related photos can be arranged together. Furthermore, the user can preferentially arrange photos related to a specific theme (e.g., sports or music). Furthermore, photos taken by the user with a specific person can be preferentially arranged and placed at the center of the album. This allows the order of the layout to be adjusted based on the relevance of the photos. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input photo data to the generation AI and cause the generation AI to adjust the order of the layout based on the relevance.

[0047] The providing unit can select a display method based on the user's past operation history when displaying a preview. The providing unit, for example, analyzes the user's past operation history. For example, the providing unit can prioritize and provide display methods that the user has used favorably in the past. The providing unit can also suggest an optimal display method by referring to operation procedures that the user has frequently used in the past. Furthermore, the providing unit can select an optimal display method when displaying a preview based on corrections made by the user in the past. This allows the providing unit to select an optimal display method by referring to the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's operation history data into an AI model and cause the AI ​​to select an optimal display method.

[0048] The providing unit can select a display method based on the user's current device information when displaying a preview. The providing unit, for example, acquires the user's current device information. For example, the providing unit can acquire information such as the device type, screen size, and resolution. This allows the providing unit to select the optimal display method in consideration of the user's current device information. For example, if the user is using a smartphone, a display method tailored to the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a personal computer, a high-resolution display method can be provided. This allows the optimal display method to be selected in consideration of the user's current device information. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's device information into an AI model and have the AI ​​select the optimal display method.

[0049] The providing unit can select the optimal display method by taking into consideration the user's device information when displaying a preview. The providing unit, for example, acquires the user's device information. For example, the providing unit can acquire information such as the device type, screen size, and resolution. This allows the providing unit to select the optimal display method by taking into consideration the user's device information. For example, if the user is using a smartphone, a display method tailored to the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a personal computer, a high-resolution display method can be provided. This allows the optimal display method to be selected by taking into consideration the user's device information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information into an AI model and cause the AI ​​to select the optimal display method.

[0050] The providing unit can provide a relevant display method based on the user's social media activity when displaying a preview. The providing unit, for example, analyzes the user's social media activity. For example, the providing unit can provide an optimal preview display method by referring to display methods frequently used by the user on social media. The providing unit can also provide a relevant preview display method by referring to display methods of accounts the user follows on social media. Furthermore, the providing unit can provide a relevant preview display method based on information about groups and events in which the user participates on social media. In this way, the providing unit can analyze the user's social media activity and provide a relevant display method. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media data into an AI model and cause the AI ​​to provide a relevant display method.

[0051] When printing, the printing unit can select a printing method based on the user's past printing history. The printing unit, for example, analyzes the user's past printing history. For example, the printing unit can prioritize the printing method that the user has used favorably in the past. The printing unit can also suggest the optimal printing method by referring to print settings that the user has used frequently in the past. Furthermore, the printing unit can select the optimal printing method based on the printing content that the user has performed in the past. This allows the printing unit to select the optimal printing method by referring to the user's past printing history. Some or all of the above-mentioned processing in the printing unit may be performed using, for example, AI, or may be performed without using AI. For example, the printing unit can input the user's printing history data into an AI model and have the AI ​​select the optimal printing method.

[0052] When printing, the printing unit can select a printing method based on the user's current device information. The printing unit, for example, acquires the user's current device information. For example, the printing unit can acquire information such as the device type, screen size, and resolution. This allows the printing unit to select the optimal printing method taking into account the user's current device information. For example, if the user is using a smartphone, a printing method optimized for the smartphone can be provided. Also, if the user is using a tablet, a printing method optimized for the tablet can be provided. Furthermore, if the user is using a PC, a high-resolution printing method can be provided. This allows the optimal printing method to be selected taking into account the user's current device information. Some or all of the above-described processing in the printing unit may be performed using, for example, AI, or may be performed without using AI. For example, the printing unit can input the user's device information into an AI model and have the AI ​​select the optimal printing method.

[0053] When printing, the printing unit can select a printing method based on the user's geographical location information. The printing unit, for example, acquires the user's geographical location information. For example, the printing unit can acquire the user's geographical location information using GPS data or an IP address. This allows the printing unit to select the optimal printing method taking the user's geographical location information into consideration. For example, if the user is in a specific location, the printing unit can provide the optimal printing method for that location. Also, if the user is traveling, the printing unit can provide the optimal printing method for the travel destination. Furthermore, if the user is at home, the printing unit can provide the optimal printing method for the home. This allows the optimal printing method to be selected taking the user's geographical location information into consideration. Some or all of the above-described processing in the printing unit may be performed using, for example, AI, or may be performed without using AI. For example, the printing unit can input the user's geographical location data into an AI model and have the AI ​​select the optimal printing method.

[0054] The printing unit can provide relevant printing methods based on the user's social media activity when printing. The printing unit, for example, analyzes the user's social media activity. For example, the printing unit can provide the optimal printing method by referring to printing methods frequently used by the user on social media. The printing unit can also provide relevant printing methods by referring to printing methods of accounts the user follows on social media. Furthermore, the printing unit can provide relevant printing methods based on information about groups and events the user participates in on social media. In this way, the printing unit can analyze the user's social media activity and provide relevant printing methods. Some or all of the above-described processing in the printing unit may be performed using, for example, AI, or may be performed without using AI. For example, the printing unit can input the user's social media data into an AI model and have the AI ​​provide relevant printing methods.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The reception unit can analyze the user's past posting history and customize the method for receiving photos and comments based on the content of the posts. For example, if the user has posted many times in the past about a particular theme (e.g., sports or music), photos and comments related to that theme can be preferentially received. Also, if the user has posted during a particular time period in the past, reception during that time period can be prioritized. Furthermore, the reception unit can analyze the posting method (text, image, video, etc.) used by the user in the past and suggest the optimal method. This allows the reception unit to analyze the user's past posting history and select the optimal reception method.

[0057] The reception unit can preferentially receive posts that are highly relevant based on the user's geographical location information. For example, when the user is in a specific location, photos and comments related to that location can be preferentially received. Also, when the user is traveling, photos and comments related to the travel destination can be preferentially received. Furthermore, when the user is at home, photos and comments from family and friends can be preferentially received. In this way, posts that are highly relevant can be preferentially received taking into account the user's geographical location information.

[0058] The analysis unit can adjust the level of detail in the analysis based on the importance of the photo. For example, it can analyze photos of important events (such as a graduation ceremony) in detail and suggest the optimal layout. It can also prioritize and analyze photos that the user particularly likes and suggest detailed designs. It can also analyze in detail photos that the user frequently posts and place them at the center of the album. This makes it possible to adjust the level of detail in the analysis based on the importance of the photo.

[0059] The generation unit can apply different layout algorithms depending on the type of photo. For example, a face recognition algorithm can be applied to portrait photos to suggest the optimal layout. A color analysis algorithm can be applied to landscape photos to suggest a beautiful layout. Furthermore, an event recognition algorithm can be applied to event photos to arrange related photos together. This makes it possible to apply different layout algorithms depending on the type of photo.

[0060] The providing unit can select a display method based on the user's past operation history. For example, it can provide a display method that the user has used favorably in the past with priority. It can also suggest an optimal display method by referring to operation procedures that the user has frequently used in the past. Furthermore, it can select an optimal display method for preview display based on corrections made by the user in the past. In this way, the providing unit can select an optimal display method by referring to the user's past operation history.

[0061] The printing unit can provide relevant printing methods based on the user's social media activity. For example, the printing unit can provide the optimal printing method by referring to the printing methods frequently used by the user on social media. The printing unit can also provide relevant printing methods by referring to the printing methods of accounts the user follows on social media. Furthermore, the printing unit can provide relevant printing methods based on information about groups and events the user participates in on social media. This allows the printing unit to analyze the user's social media activity and provide relevant printing methods.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The reception unit receives photos or comments from users. The photos or comments from users include, for example, photos in JPEG format, PNG format, text comments, etc. The reception unit can receive photos and comments via, for example, LINE, other SNS, or messaging apps. Step 2: The analysis unit analyzes the photos or comments received by the reception unit. The analysis unit uses, for example, image recognition technology or natural language processing technology, to understand the content of the submitted photos or the context of the comments, and determines appropriate layouts and design elements. Step 3: The generator automatically generates the layout and design of the album based on the information analyzed by the analyzer, for example, by arranging the photos and selecting the font for the comments. Step 4: The providing unit provides the album generated by the generating unit to the user as a preview. The providing unit provides an interface for the user to check and modify the preview, such as displaying it on a web browser or within an application. Step 5: The printing unit prints the modified album based on the preview provided by the providing unit and delivers it to the user. The printing unit prints the album with high quality, reflecting the user's modifications, taking into consideration, for example, high-quality print resolution, paper type, ink quality, etc.

[0064] (Example 2) An automatic generation system according to an embodiment of the present invention utilizes a generation AI to automatically generate graduation albums and other albums. In this automatic generation system, a user submits photos and comments via LINE, another social networking service (SNS), or a messaging app. The generation AI analyzes the submitted photos and comments and automatically generates the album layout and design. The generated album is provided to the user as a preview, allowing them to make modifications as needed. Finally, the modified album is printed and delivered to the user. For example, a user submits photos and comments via LINE, another social networking service (SNS), or a messaging app. The user can easily submit photos and comments, for example, photos from the graduation ceremony or comments about their children's memories. The generation AI then analyzes the submitted photos and comments. The generation AI understands the content of the photos and the context of the comments and determines appropriate layout and design elements, such as the placement of photos and font selection for comments. The generation AI then automatically generates the album layout and design. The generated album is provided to the user as a preview. The user can review the preview and make modifications as needed, for example, by changing the order of photos or adding comments. Finally, the revised album is printed and delivered to the user. The printing department prints the album in high quality, reflecting the user's revisions, and delivers it to the user. This service allows users to create beautiful graduation albums without much effort. In addition, integration with LINE makes it easy to send photos and comments, improving user convenience. This allows the automatic generation system to automatically analyze the user's photos and comments, generate the album layout and design, and deliver the revised album in high quality print.

[0065] An automatic generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, a provision unit, and a printing unit. The reception unit receives photos or comments from users. The photos or comments from users include, but are not limited to, photos in JPEG format, PNG format, and text comments. The reception unit can receive photos and comments via, for example, LINE, other social networking services, or messaging apps. The analysis unit analyzes the photos or comments received by the reception unit. The analysis unit understands the content of the submitted photos and the context of the comments using, for example, image recognition technology or natural language processing technology, and determines appropriate layouts and design elements. The generation unit automatically generates the layout and design of an album based on the information analyzed by the analysis unit. The generation unit performs, for example, photo placement and comment font selection. The provision unit provides the album generated by the generation unit as a preview to the user. The provision unit provides an interface for the user to view and modify the preview, for example, by displaying it on a web browser or within an application. The printing unit prints the modified album based on the preview provided by the providing unit and delivers it to the user. The printing unit prints the album with high quality, reflecting the user's modifications, taking into consideration, for example, high-quality print resolution, paper type, ink quality, etc. In this way, the automatic generation system according to the embodiment can automatically analyze the user's photos and comments, generate the album layout and design, and print and deliver the modified album with high quality.

[0066] The reception unit can accept photos or comments via LINE or other SNS or messaging apps. For example, the reception unit accepts photos and comments via LINE or other SNS or messaging apps. For example, a user can use LINE to send photos of a graduation ceremony or comments about their children's memories. The reception unit can also accept photos and comments via other SNS or messaging apps such as Facebook, Instagram, and WhatsApp. This allows users to easily send photos and comments via LINE or other SNS or messaging apps. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input photos and comments sent by users into an AI model to automate the reception of photos and comments.

[0067] The analysis unit can understand the content of the submitted photo or the context of the comment and determine the layout and design elements. The analysis unit can analyze the content of the submitted photo using, for example, image recognition technology. For example, the analysis unit can recognize people and scenery in the photo and determine an appropriate layout. The analysis unit can also understand the context of the submitted comment using natural language processing technology. For example, the analysis unit can analyze the content and sentiment of the comment and determine appropriate fonts and design elements. Furthermore, the analysis unit can analyze the relevance of the submitted photo and comment and optimize the album layout. For example, the analysis unit can evaluate the relevance between the photo and comment and place related photos and comments together. This makes it possible to understand the content of the submitted photo and comment and determine appropriate layout and design elements. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the submitted photo and comment into a generation AI and have the generation AI analyze the photo and comment.

[0068] The generation unit can arrange photos and select fonts for comments. The generation unit can, for example, automatically arrange photos. For example, the generation unit can arrange photos in an orderly manner using a grid layout. The generation unit can also freely arrange photos using a free layout. Furthermore, the generation unit can take into account the importance and relevance of photos when arranging photos. For example, the generation unit can arrange photos of important events in prominent positions and arrange related photos together. The generation unit can also automatically select fonts for comments. For example, the generation unit can select appropriate fonts based on the content and context of the comments. The generation unit can adjust the font type, size, color, etc. to make comments easier to read. This allows the generation unit to automatically arrange photos and select fonts for comments. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input photo and comment data into the generation AI and have the generation AI perform photo arrangement and comment font selection.

[0069] The providing unit can provide an interface for a user to check the preview and make corrections. The providing unit, for example, provides a preview display on a web browser. For example, the providing unit provides an interface for a user to check the album preview and make corrections through a web browser. The providing unit can also provide a preview display within an application. For example, the providing unit provides an interface for a user to check the album preview and make corrections through an application on a smartphone or tablet. Furthermore, the providing unit can be designed to enable a user to easily operate the preview display. For example, the providing unit can provide a drag-and-drop function to enable a user to easily change the order of photos. The providing unit can also provide a text editing function to enable a user to add or correct comments. In this way, the providing unit can provide an interface for a user to check the preview and make corrections as necessary. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input user operation data into an AI model and cause the AI ​​to optimize the preview display and correction interface.

[0070] The printing unit can print an album that reflects the user's edits and deliver it to the user. The printing unit, for example, provides high-quality print resolution. For example, the printing unit can print the album at a high resolution of 300 dpi or higher, clearly reproducing even the smallest details. The printing unit can also select the type of paper. For example, the printing unit can select paper, such as glossy or matte paper, according to the user's preference and print the album on that paper. The printing unit can also focus on ink quality. For example, the printing unit can use high-quality ink to achieve vibrant, long-lasting prints. This allows the printing unit to print a high-quality album that reflects the user's edits and deliver it to the user. Some or all of the above-described processing in the printing unit can be performed using, for example, AI, or without AI. For example, the printing unit can input the user's edited data into an AI model and have the AI ​​optimize the print settings. The printing unit also has a function for performing quality checks after printing. For example, the printing unit can automatically scan the printed album to check for quality issues. Furthermore, the printing department can reprint if there is a quality problem, which allows the printing department to always provide users with high-quality albums.

[0071] The reception unit can estimate the user's emotions and adjust the timing of receiving photos and comments based on the estimated user emotions. The reception unit, for example, uses an emotion engine to estimate the user's emotions. For example, the reception unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the reception unit can analyze the user's text comments and estimate the emotion using text analysis technology. In this way, the reception unit can estimate the user's emotions and adjust the timing of receiving photos and comments based on the estimated user emotions. For example, if the emotion engine estimates that the user is happy, the reception unit can immediately accept photos and comments and reflect them in real time. In addition, if the emotion engine estimates that the user is sad, the reception unit can slightly delay the reception of photos and comments until the user calms down. Furthermore, if the emotion engine estimates that the user is stressed, the reception unit can pause the reception and send the user a message to relax. In this way, the reception unit can adjust the timing of receiving photos and comments based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the reception timing based on the emotion.

[0072] The reception unit can analyze the user's past posting history and select the reception method. The reception unit, for example, analyzes the user's past posting history. For example, the reception unit can analyze time periods in which the user frequently posted in the past and prioritize reception during those time periods. The reception unit can also analyze trends in the content the user has posted in the past and prioritize reception of similar content. Furthermore, the reception unit can analyze the posting method (text, image, video, etc.) used by the user in the past and suggest the optimal method. This allows the reception unit to analyze the user's past posting history and select the optimal reception method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past posting data into an AI model and have the AI ​​select the optimal reception method.

[0073] The reception unit may filter photos and comments based on the user's current events or areas of interest when receiving the photos or comments. The reception unit may, for example, identify the user's current events or areas of interest. For example, the reception unit may identify the user's current events or areas of interest based on calendar events or social media topics. This allows the reception unit to filter and receive photos and comments based on the user's current events and areas of interest. For example, if the user is attending a graduation ceremony, photos and comments related to the event may be preferentially received. Posts related to the user's specific areas of interest (e.g., sports or music) may also be preferentially received. Furthermore, related photos and comments may be filtered and received based on information about an event the user is currently participating in. This allows photos and comments to be filtered and received based on the user's current events and areas of interest. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit may input the user's event information and area of ​​interest data into an AI model and have the AI ​​perform the filtering.

[0074] The reception unit can estimate the user's emotions and determine the priority of photos and comments to be received based on the estimated user's emotions. The reception unit, for example, uses an emotion engine to estimate the user's emotions. For example, the reception unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the reception unit can analyze the user's text comments and estimate the emotion using text analysis technology. In this way, the reception unit can estimate the user's emotions and determine the priority of photos and comments to be received based on the estimated user's emotions. For example, if the emotion engine estimates that the user is happy, photos and comments with positive content can be preferentially received. Furthermore, if the emotion engine estimates that the user is sad, encouraging comments and memorable photos can be preferentially received. Furthermore, if the emotion engine estimates that the user is stressed, photos and comments with relaxing content can be preferentially received. In this way, the priority of photos and comments to be received can be determined according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, or without, an AI. For example, the reception unit may input the user's emotion data into the generation AI and cause the generation AI to determine priorities based on emotions.

[0075] When receiving photos or comments, the reception unit can prioritize receiving posts that are highly relevant based on the user's geographical location information. The reception unit, for example, acquires the user's geographical location information. For example, the reception unit can acquire the user's geographical location information using GPS data or an IP address. This allows the reception unit to prioritize receiving posts that are highly relevant in consideration of the user's geographical location information. For example, when the user is in a specific location, photos and comments related to that location can be prioritized. Also, when the user is traveling, photos and comments related to the travel destination can be prioritized. Furthermore, when the user is at home, photos and comments from family and friends can be prioritized. This allows posts that are highly relevant to be prioritized in consideration of the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location data into an AI model and cause the AI ​​to prioritize receiving posts that are highly relevant.

[0076] The reception unit can receive related posts based on the user's social media activity when receiving a photo or comment. The reception unit, for example, analyzes the user's social media activity. For example, the reception unit can analyze the content that the user frequently posts on social media and prioritize receiving related photos and comments. The reception unit can also analyze the content posted by accounts the user follows on social media and prioritize receiving related photos and comments. Furthermore, the reception unit can receive related photos and comments based on information about groups and events the user participates in on social media. In this way, the reception unit can analyze the user's social media activity and receive related posts. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into an AI model and cause the AI ​​to receive related posts.

[0077] The analysis unit can estimate the user's emotions and adjust the analysis method of photos and comments based on the estimated user's emotions. The analysis unit, for example, uses an emotion engine to estimate the user's emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. The analysis unit can also analyze the user's text comments and estimate the emotions using text analysis technology. In this way, the analysis unit can estimate the user's emotions and adjust the analysis method of photos and comments based on the estimated user's emotions. For example, if the emotion engine estimates that the user is happy, photos and comments with positive content can be emphasized and analyzed. In addition, if the emotion engine estimates that the user is sad, encouraging comments and memorable photos can be prioritized in the analysis. In addition, if the emotion engine estimates that the user is stressed, photos and comments with relaxing content can be prioritized in the analysis. In this way, the analysis method of photos and comments can be adjusted according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may input user emotion data into the generation AI and cause the generation AI to adjust the analysis method based on the emotion.

[0078] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the photo. The analysis unit, for example, evaluates the importance of the photo. For example, the analysis unit can evaluate the importance of the photo based on the content of the photo, the location where it was taken, the date and time it was taken, etc. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the photo. For example, the analysis unit can analyze photos of important events (such as a graduation ceremony) in detail and propose an optimal layout. It can also prioritize analysis of photos that the user particularly likes and propose a detailed design. It can also analyze photos that the user frequently posts in detail and place them at the center of the album. This allows the level of detail of the analysis to be adjusted based on the importance of the photo. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input photo data into the generation AI and cause the generation AI to adjust the level of detail of the analysis based on the importance.

[0079] During analysis, the analysis unit can apply different analysis algorithms depending on the type of photo. The analysis unit, for example, classifies the type of photo. For example, the analysis unit can classify photos into types such as landscape photos, portrait photos, and event photos. This allows the analysis unit to apply different analysis algorithms depending on the type of photo. For example, a face recognition algorithm can be applied to portrait photos to suggest an optimal layout. A color analysis algorithm can be applied to landscape photos to suggest an attractive layout. Furthermore, an event recognition algorithm can be applied to event photos to arrange related photos together. This allows different analysis algorithms to be applied depending on the type of photo. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input photo data into the generation AI and have the generation AI apply an analysis algorithm depending on the type.

[0080] The analysis unit can determine the analysis priority based on the user's emotions. The analysis unit, for example, uses an emotion engine to estimate the user's emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the analysis unit can analyze the user's text comments and estimate the emotions using text analysis technology. In this way, the analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user emotions. For example, if the emotion engine estimates that the user is happy, photos and comments with positive content can be analyzed preferentially. Furthermore, if the emotion engine estimates that the user is sad, encouraging comments and memorable photos can be analyzed preferentially. Furthermore, if the emotion engine estimates that the user is stressed, photos and comments with relaxing content can be analyzed preferentially. In this way, the analysis priority can be determined according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input user emotion data into the generation AI and cause the generation AI to determine the priority of analysis based on emotion.

[0081] During analysis, the analysis unit can adjust the analysis order based on the photographic date and time of the photos. The analysis unit, for example, acquires the photographic date and time of the photos. For example, the analysis unit can acquire the photographic date and time from metadata. Alternatively, the analysis unit can use the photographic date and time manually entered by the user. This allows the analysis unit to adjust the analysis order based on the photographic date and time of the photos. For example, photos of a graduation ceremony can be analyzed first and placed at the beginning of the album. Also, photos taken by the user at a specific event can be analyzed preferentially and related photos can be placed together. Furthermore, photos taken by the user in the past can be analyzed in chronological order to enhance the narrative quality of the album. This allows the analysis order to be adjusted based on the photographic date and time of the photos. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input photographic date and time data of the photos to the generation AI and cause the generation AI to adjust the analysis order based on the photographic date and time.

[0082] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the photos. The analysis unit, for example, evaluates the relevance of the photos. For example, the analysis unit can evaluate the relevance of the photos based on the similarity of the content of the photos or the proximity of the locations where the photos were taken. This allows the analysis unit to adjust the order of analysis based on the relevance of the photos. For example, photos taken at the same event can be analyzed preferentially and related photos can be arranged together. Also, photos related to a specific theme (e.g., sports or music) can be analyzed preferentially by the user. Furthermore, photos taken by the user with a specific person can be analyzed preferentially and arranged at the center of the album. This allows the order of analysis to be adjusted based on the relevance of the photos. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input photo data to the generation AI and cause the generation AI to adjust the order of analysis based on the relevance.

[0083] The generation unit can estimate the user's emotions and adjust the layout and design of the album based on the estimated user's emotions. The generation unit, for example, uses an emotion engine to estimate the user's emotions. For example, the generation unit can capture the user's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The generation unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the generation unit can analyze the user's text comments and estimate the emotions using text analysis technology. This allows the generation unit to estimate the user's emotions and adjust the layout and design of the album based on the estimated user's emotions. For example, if the emotion engine estimates that the user is happy, the generation AI can generate a bright and positive design. If the emotion engine estimates that the user is sad, the generation AI can generate a calm design. Furthermore, if the emotion engine estimates that the user is stressed, the generation AI can generate a relaxing design. This allows the album layout and design to be adjusted according to the user's emotions. Emotion estimation is achieved, for example, using an emotion estimation function using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit may input user emotion data into the generation AI and cause the generation AI to adjust the layout and design based on the emotion.

[0084] The generation unit can adjust the level of detail of the layout based on the importance of the photo during generation. The generation unit, for example, evaluates the importance of the photo. For example, the generation unit can evaluate the importance of the photo based on the content of the photo, the location where it was taken, the date and time it was taken, etc. This allows the generation unit to adjust the level of detail of the layout based on the importance of the photo. For example, the generation unit can arrange photos of important events (such as a graduation ceremony) in detail and propose an optimal layout. It can also preferentially arrange photos that the user particularly likes and propose a detailed design. Furthermore, it can arrange photos that the user frequently posts in detail and place them in the center of the album. This allows the level of detail of the layout to be adjusted based on the importance of the photo. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input photo data to the generation AI and cause the generation AI to adjust the level of detail of the layout based on the importance.

[0085] The generation unit can apply different layout algorithms depending on the type of photo during generation. The generation unit, for example, classifies the type of photo. For example, the generation unit can classify photos into types such as landscape photos, portrait photos, and event photos. This allows the generation unit to apply different layout algorithms depending on the type of photo. For example, a face recognition algorithm can be applied to portrait photos to suggest an optimal layout. A color analysis algorithm can be applied to landscape photos to suggest a beautiful layout. Furthermore, an event recognition algorithm can be applied to event photos to arrange related photos together. This allows different layout algorithms to be applied depending on the type of photo. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input photo data into the generation AI and have the generation AI apply a layout algorithm depending on the type.

[0086] The generation unit can determine layout priorities based on the user's emotions. The generation unit, for example, uses an emotion engine to estimate the user's emotions. For example, the generation unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the generation unit can analyze the user's text comments and estimate the emotion using text analysis technology. In this way, the generation unit can estimate the user's emotions and determine layout priorities based on the estimated user emotions. For example, if the emotion engine estimates that the user is happy, photos and comments with positive content can be prioritized. Also, if the emotion engine estimates that the user is sad, encouraging comments and memorable photos can be prioritized. Furthermore, if the emotion engine estimates that the user is stressed, photos and comments with relaxing content can be prioritized. In this way, the layout priorities can be determined according to the user's emotions. Emotion estimation is realized, for example, using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit may input user emotion data into the generation AI and cause the generation AI to determine layout priorities based on the emotion.

[0087] The generation unit can adjust the layout order based on the date and time the photos were taken during generation. The generation unit, for example, acquires the date and time the photos were taken. For example, the generation unit can acquire the date and time the photos were taken from metadata. Alternatively, the generation unit can use the date and time manually entered by the user. This allows the generation unit to adjust the layout order based on the date and time the photos were taken. For example, a graduation ceremony photo can be placed first and placed at the beginning of the album. Furthermore, photos taken by the user at a specific event can be prioritized and related photos can be placed together. Furthermore, photos taken by the user in the past can be arranged in chronological order to enhance the story of the album. This allows the layout order to be adjusted based on the date and time the photos were taken. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input photo date and time data of the photos into the generation AI and cause the generation AI to adjust the layout order based on the date and time the photos were taken.

[0088] The generation unit can adjust the order of the layout based on the relevance of the photos during generation. The generation unit, for example, evaluates the relevance of the photos. For example, the generation unit can evaluate the relevance of the photos based on the similarity of the content of the photos or the proximity of the locations where the photos were taken. This allows the generation unit to adjust the order of the layout based on the relevance of the photos. For example, photos taken at the same event can be preferentially arranged, and related photos can be arranged together. Furthermore, the user can preferentially arrange photos related to a specific theme (e.g., sports or music). Furthermore, photos taken by the user with a specific person can be preferentially arranged and placed at the center of the album. This allows the order of the layout to be adjusted based on the relevance of the photos. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input photo data to the generation AI and cause the generation AI to adjust the order of the layout based on the relevance.

[0089] The providing unit can estimate the user's emotion and adjust the preview display method based on the estimated user's emotion. The providing unit, for example, uses an emotion engine to estimate the user's emotion. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the providing unit can analyze the user's text comments and estimate the emotion using text analysis technology. In this way, the providing unit can estimate the user's emotion and adjust the preview display method based on the estimated user's emotion. For example, if the emotion engine estimates that the user is happy, the preview can be displayed with a bright and positive design. On the other hand, if the emotion engine estimates that the user is sad, the preview can be displayed with a calm design. Furthermore, if the emotion engine estimates that the user is stressed, the preview can be displayed with a relaxing design. In this way, the preview display method can be adjusted according to the user's emotion. Emotion estimation is realized, for example, using an emotion estimation function using an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may input user emotion data into the generation AI and cause the generation AI to adjust the preview display method based on the emotion.

[0090] The providing unit can select a display method based on the user's past operation history when displaying a preview. The providing unit, for example, analyzes the user's past operation history. For example, the providing unit can prioritize and provide display methods that the user has used favorably in the past. The providing unit can also suggest an optimal display method by referring to operation procedures that the user has frequently used in the past. Furthermore, the providing unit can select an optimal display method when displaying a preview based on corrections made by the user in the past. This allows the providing unit to select an optimal display method by referring to the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's operation history data into an AI model and cause the AI ​​to select an optimal display method.

[0091] The providing unit can select a display method based on the user's current device information when displaying a preview. The providing unit, for example, acquires the user's current device information. For example, the providing unit can acquire information such as the device type, screen size, and resolution. This allows the providing unit to select the optimal display method in consideration of the user's current device information. For example, if the user is using a smartphone, a display method tailored to the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a personal computer, a high-resolution display method can be provided. This allows the optimal display method to be selected in consideration of the user's current device information. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's device information into an AI model and have the AI ​​select the optimal display method.

[0092] The providing unit can adjust the preview operation procedure based on the user's emotion. The providing unit, for example, uses an emotion engine to estimate the user's emotion. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the providing unit can analyze the user's text comments and estimate the emotion using text analysis technology. In this way, the providing unit can estimate the user's emotion and adjust the preview operation procedure based on the estimated user's emotion. For example, if the emotion engine estimates that the user is happy, intuitive and simple operation procedures can be provided. Furthermore, if the emotion engine estimates that the user is sad, the operation procedures can be simplified to reduce the user's burden. Furthermore, if the emotion engine estimates that the user is stressed, the operation procedures can be minimized to reduce the user's stress. In this way, the preview operation procedure can be adjusted according to the user's emotion. Emotion estimation is realized, for example, using an emotion estimation function using an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may input user emotion data into the generation AI and cause the generation AI to adjust the operation procedure based on the emotion.

[0093] The providing unit can select the optimal display method by taking into consideration the user's device information when displaying a preview. The providing unit, for example, acquires the user's device information. For example, the providing unit can acquire information such as the device type, screen size, and resolution. This allows the providing unit to select the optimal display method by taking into consideration the user's device information. For example, if the user is using a smartphone, a display method tailored to the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a personal computer, a high-resolution display method can be provided. This allows the optimal display method to be selected by taking into consideration the user's device information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information into an AI model and cause the AI ​​to select the optimal display method.

[0094] The providing unit can provide a relevant display method based on the user's social media activity when displaying a preview. The providing unit, for example, analyzes the user's social media activity. For example, the providing unit can provide an optimal preview display method by referring to display methods frequently used by the user on social media. The providing unit can also provide a relevant preview display method by referring to display methods of accounts the user follows on social media. Furthermore, the providing unit can provide a relevant preview display method based on information about groups and events in which the user participates on social media. In this way, the providing unit can analyze the user's social media activity and provide a relevant display method. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media data into an AI model and cause the AI ​​to provide a relevant display method.

[0095] The printing unit can estimate a user's emotions and adjust print quality based on the estimated user's emotions. The printing unit, for example, uses an emotion engine to estimate the user's emotions. For example, the printing unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The printing unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the printing unit can analyze the user's text comments and estimate the emotion using text analysis technology. This allows the printing unit to estimate the user's emotions and adjust print quality based on the estimated user's emotions. For example, if the emotion engine estimates that the user is happy, a bright and vivid print quality can be provided. If the emotion engine estimates that the user is sad, a calm print quality can be provided. Furthermore, if the emotion engine estimates that the user is stressed, a relaxing print quality can be provided. This allows print quality to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the printing unit may be performed using AI, or may be performed without using AI. For example, the printing unit may input user emotion data into the generation AI and have the generation AI adjust the print quality based on the emotion.

[0096] When printing, the printing unit can select a printing method based on the user's past printing history. The printing unit, for example, analyzes the user's past printing history. For example, the printing unit can prioritize the printing method that the user has used favorably in the past. The printing unit can also suggest the optimal printing method by referring to print settings that the user has used frequently in the past. Furthermore, the printing unit can select the optimal printing method based on the printing content that the user has performed in the past. This allows the printing unit to select the optimal printing method by referring to the user's past printing history. Some or all of the above-mentioned processing in the printing unit may be performed using, for example, AI, or may be performed without using AI. For example, the printing unit can input the user's printing history data into an AI model and have the AI ​​select the optimal printing method.

[0097] When printing, the printing unit can select a printing method based on the user's current device information. The printing unit, for example, acquires the user's current device information. For example, the printing unit can acquire information such as the device type, screen size, and resolution. This allows the printing unit to select the optimal printing method taking into account the user's current device information. For example, if the user is using a smartphone, a printing method optimized for the smartphone can be provided. Also, if the user is using a tablet, a printing method optimized for the tablet can be provided. Furthermore, if the user is using a PC, a high-resolution printing method can be provided. This allows the optimal printing method to be selected taking into account the user's current device information. Some or all of the above-described processing in the printing unit may be performed using, for example, AI, or may be performed without using AI. For example, the printing unit can input the user's device information into an AI model and have the AI ​​select the optimal printing method.

[0098] The printing unit can determine printing priorities based on the user's emotions. The printing unit, for example, uses an emotion engine to estimate the user's emotions. For example, the printing unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The printing unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the printing unit can analyze the user's text comments and estimate the emotion using text analysis technology. In this way, the printing unit can estimate the user's emotions and determine printing priorities based on the estimated user emotions. For example, if the emotion engine estimates that the user is happy, it can prioritize printing positive content. Also, if the emotion engine estimates that the user is sad, it can prioritize printing encouraging comments and memorable photos. Furthermore, if the emotion engine estimates that the user is stressed, it can prioritize printing relaxing content. In this way, it is possible to determine printing priorities according to the user's emotions. Emotion estimation is realized, for example, using an emotion estimation function using an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the printing unit may be performed using AI, or may be performed without using AI. For example, the printing unit may input user emotion data into the generation AI and have the generation AI determine printing priorities based on the emotion.

[0099] When printing, the printing unit can select a printing method based on the user's geographical location information. The printing unit, for example, acquires the user's geographical location information. For example, the printing unit can acquire the user's geographical location information using GPS data or an IP address. This allows the printing unit to select the optimal printing method taking the user's geographical location information into consideration. For example, if the user is in a specific location, the printing unit can provide the optimal printing method for that location. Also, if the user is traveling, the printing unit can provide the optimal printing method for the travel destination. Furthermore, if the user is at home, the printing unit can provide the optimal printing method for the home. This allows the optimal printing method to be selected taking the user's geographical location information into consideration. Some or all of the above-described processing in the printing unit may be performed using, for example, AI, or may be performed without using AI. For example, the printing unit can input the user's geographical location data into an AI model and have the AI ​​select the optimal printing method.

[0100] The printing unit can provide relevant printing methods based on the user's social media activity when printing. The printing unit, for example, analyzes the user's social media activity. For example, the printing unit can provide the optimal printing method by referring to printing methods frequently used by the user on social media. The printing unit can also provide relevant printing methods by referring to printing methods of accounts the user follows on social media. Furthermore, the printing unit can provide relevant printing methods based on information about groups and events the user participates in on social media. In this way, the printing unit can analyze the user's social media activity and provide relevant printing methods. Some or all of the above-described processing in the printing unit may be performed using, for example, AI, or may be performed without using AI. For example, the printing unit can input the user's social media data into an AI model and have the AI ​​provide relevant printing methods. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, analysis unit, generation unit, provision unit, and printing unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives photos and comments from users. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received photos and comments. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and automatically generates the layout and design of the album based on the analysis results. The provision unit is implemented, for example, by the output device 40 of the smart device 14 and provides the generated album as a preview to the user. The printing unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and prints the revised album at high quality and delivers it to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, provision unit, and printing unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives photos and comments from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received photos and comments. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically generates the layout and design of the album based on the analysis results. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the generated album as a preview to the user. The printing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and prints the modified album with high quality and delivers it to the user. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, provision unit, and printing unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives photos and comments from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received photos and comments. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically generates the layout and design of the album based on the analysis results. The provision unit is realized, for example, by the display 343 of the headset-type terminal 314 and provides the generated album as a preview to the user. The printing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and prints the revised album at high quality and delivers it to the user. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, provision unit, and printing unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives photos and comments from users. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received photos and comments. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically generates the layout and design of the album based on the analysis results. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides the generated album as a preview to the user. The printing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and prints the modified album at high quality and delivers it to the user.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The reception unit can analyze the user's past posting history and customize the method for receiving photos and comments based on the content of the posts. For example, if the user has posted many times in the past about a particular theme (e.g., sports or music), photos and comments related to that theme can be preferentially received. Also, if the user has posted during a particular time period in the past, reception during that time period can be prioritized. Furthermore, the reception unit can analyze the posting method (text, image, video, etc.) used by the user in the past and suggest the optimal method. This allows the reception unit to analyze the user's past posting history and select the optimal reception method.

[0103] The analysis unit can estimate the user's emotions and adjust the analysis method for photos and comments based on the estimated emotions. For example, if the emotion engine estimates that the user is happy, photos and comments with positive content can be emphasized in the analysis. Also, if the emotion engine estimates that the user is sad, encouraging comments and memorable photos can be prioritized in the analysis. Furthermore, if the emotion engine estimates that the user is stressed, photos and comments with relaxing content can be prioritized in the analysis. In this way, the analysis method for photos and comments can be adjusted according to the user's emotions.

[0104] The generation unit can estimate the user's emotions and adjust the layout and design of the album based on the estimated emotions. For example, if the emotion engine estimates that the user is happy, a bright and positive design can be generated. Alternatively, if the emotion engine estimates that the user is sad, a calm design can be generated. Furthermore, if the emotion engine estimates that the user is stressed, a relaxing design can be generated. In this way, the layout and design of the album can be adjusted according to the user's emotions.

[0105] The providing unit can estimate the user's emotion and adjust the preview display method based on the estimated emotion. For example, if the emotion engine estimates that the user is happy, the preview can be displayed with a bright and positive design. If the emotion engine estimates that the user is sad, the preview can be displayed with a calm design. Furthermore, if the emotion engine estimates that the user is stressed, the preview can be displayed with a relaxing design. In this way, the preview display method can be adjusted according to the user's emotion.

[0106] The printing unit can estimate the user's emotions and adjust the print quality based on the estimated emotions. For example, if the emotion engine estimates that the user is happy, it can provide bright and vivid print quality. If the emotion engine estimates that the user is sad, it can provide print quality with calming colors. Furthermore, if the emotion engine estimates that the user is stressed, it can provide print quality with relaxing colors. In this way, it is possible to adjust the print quality according to the user's emotions.

[0107] The reception unit can preferentially receive posts that are highly relevant based on the user's geographical location information. For example, when the user is in a specific location, photos and comments related to that location can be preferentially received. Also, when the user is traveling, photos and comments related to the travel destination can be preferentially received. Furthermore, when the user is at home, photos and comments from family and friends can be preferentially received. In this way, posts that are highly relevant can be preferentially received taking into account the user's geographical location information.

[0108] The analysis unit can adjust the level of detail in the analysis based on the importance of the photo. For example, it can analyze photos of important events (such as a graduation ceremony) in detail and suggest the optimal layout. It can also prioritize and analyze photos that the user particularly likes and suggest detailed designs. It can also analyze in detail photos that the user frequently posts and place them at the center of the album. This makes it possible to adjust the level of detail in the analysis based on the importance of the photo.

[0109] The generation unit can apply different layout algorithms depending on the type of photo. For example, a face recognition algorithm can be applied to portrait photos to suggest the optimal layout. A color analysis algorithm can be applied to landscape photos to suggest a beautiful layout. Furthermore, an event recognition algorithm can be applied to event photos to arrange related photos together. This makes it possible to apply different layout algorithms depending on the type of photo.

[0110] The providing unit can select a display method based on the user's past operation history. For example, it can provide a display method that the user has used favorably in the past with priority. It can also suggest an optimal display method by referring to operation procedures that the user has frequently used in the past. Furthermore, it can select an optimal display method for preview display based on corrections made by the user in the past. In this way, the providing unit can select an optimal display method by referring to the user's past operation history.

[0111] The printing unit can provide relevant printing methods based on the user's social media activity. For example, the printing unit can provide the optimal printing method by referring to the printing methods frequently used by the user on social media. The printing unit can also provide relevant printing methods by referring to the printing methods of accounts the user follows on social media. Furthermore, the printing unit can provide relevant printing methods based on information about groups and events the user participates in on social media. This allows the printing unit to analyze the user's social media activity and provide relevant printing methods.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The reception unit receives photos or comments from users. The photos or comments from users include, for example, photos in JPEG format, PNG format, text comments, etc. The reception unit can receive photos and comments via, for example, LINE, other SNS, or messaging apps. Step 2: The analysis unit analyzes the photos or comments received by the reception unit. The analysis unit uses, for example, image recognition technology or natural language processing technology, to understand the content of the submitted photos or the context of the comments, and determines appropriate layouts and design elements. Step 3: The generator automatically generates the layout and design of the album based on the information analyzed by the analyzer, for example, by arranging the photos and selecting the font for the comments. Step 4: The providing unit provides the album generated by the generating unit to the user as a preview. The providing unit provides an interface for the user to check and modify the preview, such as displaying it on a web browser or within an application. Step 5: The printing unit prints the modified album based on the preview provided by the providing unit and delivers it to the user. The printing unit prints the album with high quality, reflecting the user's modifications, taking into consideration, for example, high-quality print resolution, paper type, ink quality, etc.

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0116] 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0132] 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0145] 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.

[0146] 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.

[0147] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0162] 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.

[0163] 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.

[0164] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives photos or comments from users; an analysis unit that analyzes the photos or comments accepted by the acceptance unit; a generation unit that automatically generates a layout and design of an album based on the information analyzed by the analysis unit; a providing unit that provides a preview of the album generated by the generating unit to a user; a printing unit that prints a modified album based on the preview provided by the providing unit; Equipped with A system characterized by:

2. The reception unit Accept photos or comments through messaging apps 2. The system of claim 1.

3. The analysis unit Understand the context of the content of submitted photos or comments and determine layout and design elements 2. The system of claim 1.

4. The generation unit Place photos and choose fonts for comments 2. The system of claim 1.

5. The providing unit Provide an interface for users to see the preview and make corrections 2. The system of claim 1.

6. The printing unit Print the album that reflects the user's modifications and deliver it to the user.

2. The system of claim 1.

7. The reception unit The system estimates the user's emotions and adjusts the timing of accepting photos or comments based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past posting history and select the reception method 2. The system of claim 1.

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