system

Abstract

The system according to the embodiment aims to enable a user to easily create a commemorative photograph matching a specific scene at home. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a modification unit. The reception unit uploads a photograph. The generation unit analyzes the photograph uploaded by the reception unit and adds a background and decorations that match the specified scene. The modification unit allows the user to modify the commemorative photograph generated by the generation 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 [Overview of the Initiative] [Problem to be solved by the invention]

[0004] Traditional techniques required going to a studio to create commemorative photos tailored to specific scenes, which presented challenges in terms of time and expense.

[0005] The system according to the embodiment aims to enable a user to easily create a commemorative photograph matching a specific scene at home. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a correction unit. The reception unit uploads a photo. The generation unit analyzes the photo uploaded by the reception unit and adds a background and decorations that match a specified scene. The correction unit allows the user to correct the commemorative photo generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to easily create a commemorative photograph matching a specific scene at home. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A commemorative photo creation system according to an embodiment of the present invention uses AI to read photos of children taken with a smartphone and process them into commemorative photos tailored to a scene specified by the user. This commemorative photo creation system operates by allowing users to upload photos using a smartphone or PC and specify the desired scene (e.g., shrine visit, Shichi-Go-San, graduation ceremony, etc.). The AI ​​then analyzes the photos and automatically adds backgrounds and decorations appropriate for the specified scene. This allows users to create commemorative photos at home, anytime, without having to go to a studio. Users can also make any number of edits if they are not satisfied. Furthermore, there are no photography fees and the system is available at a reasonable price. For example, a user uploads a photo using a smartphone or PC. The user then selects a photo of their child and specifies the desired scene. For example, to specify a shrine visit scene, the user enters "shrine visit." This information is input into the AI. The AI ​​then analyzes the input information and automatically adds backgrounds and decorations appropriate to the specified scene. For example, if a shrine visit scene is specified, the AI ​​adds a shrine background and traditional clothing to the photo. This allows users to easily create commemorative photos. Furthermore, if the user is not satisfied with the result, they can make any number of corrections. For example, they can change the background color or the position of decorations. This allows the user to create commemorative photos that suit their preferences. This system allows users to create commemorative photos at home anytime without going to a studio. Users can easily create commemorative photos using their smartphones or PCs. In addition, there are no photography fees and the system is available at a reasonable price. For example, even if studio photography fees are expensive, this system allows commemorative photos to be created at low cost. This allows the commemorative photo generation system to easily create commemorative photos at home that match the scene specified by the user.

[0029] The commemorative photo generation system according to this embodiment comprises a reception unit, a generation unit, and a correction unit. The reception unit allows users to upload photos using their smartphones or PCs. Photos uploaded by users may include, but are not limited to, formats such as JPEG, PNG, and RAW. The reception unit can, for example, directly upload photos taken with a smartphone camera. The reception unit also supports file uploads from PCs, allowing users to select and upload photos saved on their PCs. Furthermore, the reception unit can also integrate with cloud storage services to directly upload photos saved by users in the cloud. For example, the reception unit can access the user's cloud account using the API of a cloud storage service and retrieve the photos. The generation unit uses AI to analyze the photos uploaded by the reception unit and add backgrounds and decorations that match the specified scene. The generation unit uses, for example, image recognition technology to analyze the content of the photos and select backgrounds and decorations appropriate for the scene. For example, if the user specifies "Omiyamairi" (a traditional Japanese shrine visit), the generation unit adds a shrine background and traditional clothing to the photo. The generation unit can also use an AI algorithm to adjust the color tone and brightness of the photos and generate an optimal image that matches the scene. For example, the generation unit uses a deep learning algorithm to automatically adjust the color tone of a photograph and create an atmosphere that matches the scene. The editing unit allows the user to edit the commemorative photo generated by the generation unit. The editing unit provides functions such as changing the background color and the position of decorations. For example, the editing unit provides a color palette for the user to change the background color and changes the background based on the color selected by the user. The editing unit also provides a function to change the position of decorations by drag and drop, allowing the user to freely adjust the position of decorations. Furthermore, the editing unit also provides functions such as applying filters and cropping, allowing the user to customize the photo to their liking. For example, the editing unit provides an option for the user to apply filters to the photo and applies the filter selected by the user to the photo.As a result, the commemorative photo creation system according to the embodiment allows the user to easily create a commemorative photo at home that matches a scene specified by the user.

[0030] The generation unit can add backgrounds and decorations that match the specified scene. For example, the generation unit automatically selects backgrounds and decorations based on a scene specified by the user and adds them to the photo. For example, if the user specifies "Omiya-mairi," the generation unit adds a shrine background and traditional clothing to the photo. Furthermore, if the user specifies "Shichi-Go-San," the generation unit can add bright backgrounds and decorations for children to the photo. Furthermore, if the user specifies "Graduation Ceremony," the generation unit can add formal backgrounds and decorations to the photo. For example, the generation unit places a photo holding a diploma in the center and adds backgrounds and decorations around it. This allows the generation unit to automatically add backgrounds and decorations that match the specified scene. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can automatically select backgrounds and decorations based on a scene specified by the user and add them to the photo using an AI model.

[0031] The modification unit may change the background color or the position of decorations. For example, the modification unit may provide a color palette for the user to change the background color and change the background based on the color selected by the user. For example, the modification unit may provide a color palette for the user to change the background color and change the background based on the color selected by the user. The modification unit may also provide a function for changing the position of decorations by dragging and dropping, allowing the user to freely adjust the position of decorations. For example, the modification unit may provide a function for the user to change the position of decorations by dragging and dropping, allowing the user to freely adjust the position of decorations. The modification unit may also provide functions such as applying filters and cropping, allowing the user to customize the photo to their preferences. For example, the modification unit may provide an option for the user to apply a filter to the photo and apply the filter selected by the user to the photo. This allows the modification unit to freely change the background color or the position of decorations. Some or all of the above-described processing in the modification unit may be performed using, for example, AI, or may be performed without using AI. For example, the modification unit may provide a color palette for the user to change the background color and change the background color using an AI model that changes the background based on the color selected by the user.

[0032] The reception unit can upload photos using a smartphone or a PC. For example, the reception unit can directly upload photos taken with a smartphone camera. For example, the reception unit can directly upload photos taken with a smartphone camera. The reception unit also supports file uploads from a PC, allowing a user to select and upload photos saved on a PC. For example, the reception unit also supports file uploads from a PC, allowing a user to select and upload photos saved on a PC. Furthermore, the reception unit can cooperate with a cloud storage service to directly upload photos saved on a cloud by a user. For example, the reception unit can access a user's cloud account and retrieve photos using an API of the cloud storage service. This allows the reception unit to easily upload photos using a smartphone or a PC. 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 upload photos using an AI model that directly uploads photos taken with a smartphone camera.

[0033] The generation unit can produce commemorative photos that are available at a low cost. For example, the generation unit automatically selects and adds backgrounds and decorations to photos based on a scene specified by the user. For example, if the user specifies "Omiyamairi" (a traditional Japanese ceremony for newborns), the generation unit will add a shrine background and traditional clothing to the photo. Also, if the user specifies "Shichi-Go-San" (a traditional Japanese rite of passage for children), the generation unit can add bright backgrounds and decorations suitable for children to the photo. Furthermore, if the user specifies "Graduation Ceremony," the generation unit can add formal backgrounds and decorations to the photo. For example, the generation unit can center a photo of someone holding a graduation certificate and add backgrounds and decorations around it. In this way, the generation unit can automatically add backgrounds and decorations that match the specified scene. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can add backgrounds and decorations using an AI model that automatically selects and adds backgrounds and decorations to photos based on a scene specified by the user. In this way, the generation unit can produce commemorative photos that are available at a low cost.

[0034] The reception unit can analyze the user's past photo upload history and select the optimal upload method. For example, the reception unit can prioritize and suggest an upload method (e.g., drag and drop) that the user has frequently used in the past. For example, the reception unit can prioritize and suggest an upload method (e.g., drag and drop) that the user has frequently used in the past. Furthermore, if the user tends to upload photos during a specific time period, the reception unit can send a notification during that time period. For example, if the user tends to upload photos during a specific time period, the reception unit can send a notification during that time period. Furthermore, the reception unit can analyze the size and format of photos previously uploaded by the user and suggest optimal settings. For example, the reception unit can analyze the size and format of photos previously uploaded by the user and suggest optimal settings. This allows the reception unit to suggest the optimal upload method based on the user's past history. 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 select the upload method using an AI model that analyzes the user's past photo upload history and selects the optimal upload method.

[0035] The reception desk can filter photos when they are uploaded based on the user's current projects and areas of interest. For example, the reception desk can suggest that the user upload only photos related to projects they are currently working on. The reception desk can also prioritize uploading photos that are highly relevant based on the user's areas of interest. Furthermore, if the user is attending a specific event, the reception desk can filter and upload photos related to that event. This allows the reception desk to upload photos that are highly relevant based on the user's areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can filter photos when they are uploaded using an AI model that filters based on the user's current projects and areas of interest.

[0036] When uploading photos, the reception unit can prioritize uploading highly relevant photos based on the user's geographical location information. For example, when the user is traveling, the reception unit prioritizes uploading photos taken at the travel destination. For example, when the user is traveling, the reception unit prioritizes uploading photos taken at the travel destination. Furthermore, when the user is participating in a specific event, the reception unit can prioritize uploading photos related to the event. For example, when the user is participating in a specific event, the reception unit can prioritize uploading photos related to the event. Furthermore, when the user is at home, the reception unit can prioritize uploading photos with family and friends. For example, when the user is at home, the reception unit can prioritize uploading photos with family and friends. This allows the reception unit to upload optimal photos based on 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, when uploading photos, the reception unit can upload photos using an AI model that prioritizes uploading highly relevant photos based on the user's geographical location information.

[0037] The reception desk can analyze a user's social media activity when they upload a photo and upload relevant photos. For example, the reception desk can prioritize uploading photos that the user has recently shared on social media. The reception desk can also prioritize uploading photos related to specific hashtags if the user is using them. Furthermore, if the reception desk is attending a specific event, it can prioritize uploading photos related to that event. This allows the reception desk to upload the most suitable photos based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can upload photos using an AI model that analyzes the user's social media activity and uploads relevant photos when they are uploaded.

[0038] The generation unit can adjust the level of detail of the background and decorations based on the importance of the photo during generation. For example, the generation unit adds detailed backgrounds and decorations to photos of important events (e.g., graduation ceremonies). For example, the generation unit adds detailed backgrounds and decorations to photos of important events (e.g., graduation ceremonies). The generation unit can also select simple backgrounds and decorations for everyday photos. For example, the generation unit can select simple backgrounds and decorations for everyday photos. The generation unit can also add luxurious backgrounds and decorations to photos of special anniversaries. For example, the generation unit can add luxurious backgrounds and decorations to photos of special anniversaries. In this way, the generation unit can add optimal backgrounds and decorations depending on the importance of the photo. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can adjust the level of detail of the background and decorations using an AI model that adjusts the level of detail of the background and decorations based on the importance of the photo.

[0039] The generation unit can apply different generation algorithms depending on the category of the scene during generation. For example, the generation unit applies an algorithm that adds traditional backgrounds and decorations to a shrine visit scene. For example, the generation unit applies an algorithm that adds traditional backgrounds and decorations to a shrine visit scene. The generation unit can also apply an algorithm that adds bright backgrounds and decorations for children to a Shichi-Go-San ceremony scene. For example, the generation unit can apply an algorithm that adds bright backgrounds and decorations for children to a Shichi-Go-San ceremony scene. The generation unit can also apply an algorithm that adds formal backgrounds and decorations to a graduation ceremony scene. For example, the generation unit can apply an algorithm that adds formal backgrounds and decorations to a graduation ceremony scene. This allows the generation unit to apply an optimal generation algorithm depending on the category of the scene. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can apply a generation algorithm using an AI model that applies different generation algorithms depending on the category of the scene.

[0040] The generation unit can determine the priority of backgrounds and decorations based on the time the scene was specified during generation. The generation unit, for example, prioritizes selecting backgrounds and decorations according to the season. For example, the generation unit prioritizes selecting backgrounds and decorations according to the season. The generation unit can also prioritize selecting backgrounds and decorations that are appropriate for the time of a specific event. For example, the generation unit can prioritize selecting backgrounds and decorations that are appropriate for the time of a specific event. Furthermore, the generation unit can prioritize selecting highly relevant backgrounds and decorations based on the time specified by the user. For example, the generation unit can prioritize selecting highly relevant backgrounds and decorations based on the time specified by the user. This allows the generation unit to select optimal backgrounds and decorations based on the time the scene was specified. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can determine the priority of backgrounds and decorations using an AI model that determines the priority of backgrounds and decorations based on the time the scene was specified.

[0041] The generation unit can adjust the order of backgrounds and decorations based on the relevance of the scenes during generation. For example, in a shrine visit scene, the generation unit first places the background of the shrine, and then adds decorations. For example, in a shrine visit scene, the generation unit first places the background of the shrine, and then adds decorations. The generation unit can also place a child's photo in the center of a Shichi-Go-San ceremony scene and add decorations around it. For example, in a Shichi-Go-San ceremony scene, the generation unit can place a child's photo in the center of a Shichi-Go-San ceremony scene and add decorations around it. The generation unit can also place a child's photo holding a diploma in the center of a graduation ceremony scene and add backgrounds and decorations around it. For example, the generation unit can place a child's photo holding a diploma in the center of a graduation ceremony scene and add backgrounds and decorations around it. This allows the generation unit to select an optimal order of backgrounds and decorations based on the relevance of the scenes. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can adjust the order of backgrounds and decorations using an AI model that adjusts the order of backgrounds and decorations based on the relevance of the scenes.

[0042] During correction, the correction unit can analyze the user's past correction history and select the optimal correction method. For example, the correction unit prioritizes suggesting correction methods that the user has frequently used in the past. For example, the correction unit prioritizes suggesting correction methods that the user has frequently used in the past. The correction unit can also analyze a specific pattern from the user's past correction history and suggest the optimal correction method. For example, the correction unit can analyze a specific pattern from the user's past correction history and suggest the optimal correction method. Furthermore, the correction unit can provide the optimal correction option based on the tendency of corrections made by the user in the past. For example, the correction unit can provide the optimal correction option based on the tendency of corrections made by the user in the past. In this way, the correction unit can suggest the optimal correction method based on the user's past correction history. Some or all of the above-described processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can select the correction method using an AI model that analyzes the user's past correction history and selects the optimal correction method.

[0043] The correction unit can customize the correction means based on the user's current living situation during correction. For example, if the user is busy, the correction unit provides a simple and quick correction option. For example, if the user is busy, the correction unit provides a simple and quick correction option. The correction unit can also provide detailed correction options if the user is relaxing. For example, if the user is relaxing, the correction unit can provide detailed correction options. Furthermore, if the user is attending a specific event, the correction unit can provide correction options related to the event. For example, if the user is attending a specific event, the correction unit can provide correction options related to the event. This allows the correction unit to provide the optimal correction means according to the user's living situation. Some or all of the above-described processing in the correction unit may be performed using AI, for example, or may be performed without using AI. For example, the correction unit can customize the correction means using an AI model that customizes the correction means based on the user's current living situation.

[0044] The correction unit can select the optimal correction method by taking into account the user's geographical location information when making corrections. For example, if the user is traveling, the correction unit suggests a background or decoration of the travel destination. For example, if the user is traveling, the correction unit suggests a background or decoration of the travel destination. Furthermore, if the user is at home, the correction unit can suggest a correction method suitable for a photo with family or friends. For example, if the user is at home, the correction unit can suggest a correction method suitable for a photo with family or friends. Furthermore, if the user is participating in a specific event, the correction unit can suggest a correction method related to the event. For example, if the user is participating in a specific event, the correction unit can suggest a correction method related to the event. This allows the correction unit to suggest the optimal correction method based on the user's geographical location information. Some or all of the above-mentioned processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can select the correction method using an AI model that selects the optimal correction method by taking into account the user's geographical location information.

[0045] During editing, the editing unit can analyze the user's social media activity to suggest editing measures. For example, the editing unit can suggest editing measures based on photos recently shared by the user on social media. Furthermore, if the user uses a specific hashtag, the editing unit can suggest editing measures related to the hashtag. For example, if the user uses a specific hashtag, the editing unit can suggest editing measures related to the hashtag. Furthermore, if the user is participating in a specific event, the editing unit can suggest editing measures related to the event. For example, if the user is participating in a specific event, the editing unit can suggest editing measures related to the event. This allows the editing unit to suggest optimal editing measures based on the user's social media activity. Some or all of the above-described processing in the editing unit may be performed using, for example, AI, or may be performed without using AI. For example, the editing unit can suggest editing measures using an AI model that analyzes the user's social media activity and suggests editing measures.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The reception unit can analyze the user's past upload history and suggest the optimal upload method. For example, it can prioritize and suggest upload methods (such as drag and drop) that the user has frequently used in the past. In addition, if the user tends to upload photos during a specific time period, the reception unit can send a notification during that time period. Furthermore, the reception unit can analyze the size and format of photos that the user has uploaded in the past and suggest optimal settings. This allows the reception unit to suggest the optimal upload method based on the user's past history.

[0048] The correction unit can analyze the user's past correction history and suggest the optimal correction method. For example, it can preferentially suggest correction methods that the user has frequently used in the past. It can also analyze specific patterns from the user's past correction history and suggest the optimal correction method. Furthermore, it can provide the optimal correction option based on the tendency of corrections made by the user in the past. This allows the correction unit to suggest the optimal correction method based on the user's past correction history.

[0049] The generation unit can determine the priority of backgrounds and decorations based on the time specified for the scene. For example, the generation unit can prioritize the selection of backgrounds and decorations according to the season. The generation unit can also prioritize the selection of backgrounds and decorations that are appropriate for the time of a specific event. Furthermore, the generation unit can prioritize the selection of highly relevant backgrounds and decorations based on the time specified by the user. This allows the generation unit to select the optimal backgrounds and decorations according to the time specified for the scene.

[0050] When uploading photos, the reception unit can filter the photos based on the user's current projects and areas of interest. For example, the reception unit can suggest that the user upload only photos related to the project currently in progress. The reception unit can also prioritize uploading highly relevant photos based on the user's areas of interest. Furthermore, if the user is participating in a specific event, the reception unit can filter and upload photos related to the event. This allows the reception unit to upload highly relevant photos based on the user's areas of interest.

[0051] The generation unit can apply different generation algorithms depending on the scene category during generation. For example, an algorithm that adds traditional backgrounds and decorations can be applied to a shrine visit scene. Also, an algorithm that adds bright backgrounds and decorations for children can be applied to a Shichi-Go-San ceremony scene. Furthermore, an algorithm that adds formal backgrounds and decorations can be applied to a graduation ceremony scene. This allows the generation unit to apply the optimal generation algorithm depending on the scene category.

[0052] The generation unit can adjust the level of detail of the background and decorations based on the importance of the photo during generation. For example, detailed backgrounds and decorations can be added to photos of important events (such as graduation ceremonies). Simple backgrounds and decorations can also be selected for everyday photos. Furthermore, luxurious backgrounds and decorations can be added to photos of special anniversaries. This allows the generation unit to add optimal backgrounds and decorations depending on the importance of the photo.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The reception unit allows the user to upload photos using a smartphone or PC. Photos uploaded by the user may be in formats such as, but not limited to, JPEG, PNG, and RAW. The reception unit can directly upload photos taken with a smartphone camera, for example. The reception unit also supports file uploads from a PC, allowing the user to select and upload photos stored on their PC. Furthermore, the reception unit can work with a cloud storage service to directly upload photos stored in the cloud by the user. For example, the reception unit can access the user's cloud account and retrieve photos using the API of the cloud storage service. Step 2: The generation unit uses AI to analyze the photo uploaded by the reception unit and add backgrounds and decorations that match the specified scene. The generation unit, for example, uses image recognition technology to analyze the content of the photo and select backgrounds and decorations that are appropriate for the scene. For example, if the user specifies "shrine visit," the generation unit adds a shrine background and traditional clothing to the photo. The generation unit can also use an AI algorithm to adjust the color tone and brightness of the photo and generate an optimal image that matches the scene. For example, the generation unit uses a deep learning algorithm to automatically adjust the color tone of the photo and create an atmosphere that matches the scene. Step 3: The modification unit allows the user to modify the commemorative photo generated by the generation unit. The modification unit provides, for example, a function to change the background color or the position of decorations. For example, the modification unit provides a color palette for the user to change the background color and changes the background based on the color selected by the user. The modification unit also provides a function to change the position of decorations by drag and drop, allowing the user to freely adjust the position of decorations. Furthermore, the modification unit also provides functions such as applying filters and cropping, allowing the user to customize the photo to their liking. For example, the modification unit provides an option for the user to apply a filter to the photo and applies the filter selected by the user to the photo.

[0055] (Example 2) A commemorative photo creation system according to an embodiment of the present invention uses AI to read photos of children taken with a smartphone and process them into commemorative photos tailored to a scene specified by the user. This commemorative photo creation system operates by allowing users to upload photos using a smartphone or PC and specify the desired scene (e.g., shrine visit, Shichi-Go-San, graduation ceremony, etc.). The AI ​​then analyzes the photos and automatically adds backgrounds and decorations appropriate for the specified scene. This allows users to create commemorative photos at home, anytime, without having to go to a studio. Users can also make any number of edits if they are not satisfied. Furthermore, there are no photography fees and the system is available at a reasonable price. For example, a user uploads a photo using a smartphone or PC. The user then selects a photo of their child and specifies the desired scene. For example, to specify a shrine visit scene, the user enters "shrine visit." This information is input into the AI. The AI ​​then analyzes the input information and automatically adds backgrounds and decorations appropriate to the specified scene. For example, if a shrine visit scene is specified, the AI ​​adds a shrine background and traditional clothing to the photo. This allows users to easily create commemorative photos. Furthermore, if the user is not satisfied with the result, they can make any number of corrections. For example, they can change the background color or the position of decorations. This allows the user to create commemorative photos that suit their preferences. This system allows users to create commemorative photos at home anytime without going to a studio. Users can easily create commemorative photos using their smartphones or PCs. In addition, there are no photography fees and the system is available at a reasonable price. For example, even if studio photography fees are expensive, this system allows commemorative photos to be created at low cost. This allows the commemorative photo generation system to easily create commemorative photos at home that match the scene specified by the user.

[0056] A commemorative photo creation system according to an embodiment includes a reception unit, a generation unit, and a correction unit. The reception unit allows users to upload photos using a smartphone or PC. Photos uploaded by users may be in formats such as, but not limited to, JPEG, PNG, and RAW. The reception unit can directly upload photos taken with a smartphone camera, for example. The reception unit also supports file uploads from a PC, allowing users to select and upload photos stored on their PCs. Furthermore, the reception unit can also directly upload photos stored in the cloud by linking with a cloud storage service. For example, the reception unit may access a user's cloud account using the API of a cloud storage service to retrieve photos. The generation unit uses AI to analyze the photos uploaded by the reception unit and add backgrounds and decorations appropriate for the specified scene. For example, the generation unit may use image recognition technology to analyze the content of the photo and select backgrounds and decorations appropriate for the scene. For example, if a user selects "shrine visit," the generation unit adds a shrine background and traditional clothing to the photo. The generation unit can also use an AI algorithm to adjust the color tone and brightness of the photo to create an optimal image for the scene. For example, the generation unit uses a deep learning algorithm to automatically adjust the color tone of a photo to create an atmosphere that matches the scene. The modification unit allows a user to modify the commemorative photo generated by the generation unit. The modification unit provides, for example, a function to change the background color or the position of decorations. For example, the modification unit provides a color palette for the user to change the background color and changes the background based on the color selected by the user. The modification unit also provides a function to change the position of decorations by drag and drop, allowing the user to freely adjust the position of decorations. Furthermore, the modification unit also provides functions such as applying filters and cropping, allowing the user to customize the photo to their liking. For example, the modification unit provides an option for the user to apply a filter to the photo and applies the filter selected by the user to the photo.As a result, the commemorative photo creation system according to the embodiment allows the user to easily create a commemorative photo at home that matches a scene specified by the user.

[0057] The generation unit can add backgrounds and decorations that match the specified scene. For example, the generation unit automatically selects backgrounds and decorations based on a scene specified by the user and adds them to the photo. For example, if the user specifies "Omiya-mairi," the generation unit adds a shrine background and traditional clothing to the photo. Furthermore, if the user specifies "Shichi-Go-San," the generation unit can add bright backgrounds and decorations for children to the photo. Furthermore, if the user specifies "Graduation Ceremony," the generation unit can add formal backgrounds and decorations to the photo. For example, the generation unit places a photo holding a diploma in the center and adds backgrounds and decorations around it. This allows the generation unit to automatically add backgrounds and decorations that match the specified scene. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can automatically select backgrounds and decorations based on a scene specified by the user and add them to the photo using an AI model.

[0058] The modification unit may change the background color or the position of decorations. For example, the modification unit may provide a color palette for the user to change the background color and change the background based on the color selected by the user. For example, the modification unit may provide a color palette for the user to change the background color and change the background based on the color selected by the user. The modification unit may also provide a function for changing the position of decorations by dragging and dropping, allowing the user to freely adjust the position of decorations. For example, the modification unit may provide a function for the user to change the position of decorations by dragging and dropping, allowing the user to freely adjust the position of decorations. The modification unit may also provide functions such as applying filters and cropping, allowing the user to customize the photo to their preferences. For example, the modification unit may provide an option for the user to apply a filter to the photo and apply the filter selected by the user to the photo. This allows the modification unit to freely change the background color or the position of decorations. Some or all of the above-described processing in the modification unit may be performed using, or without, AI. For example, the modification unit may provide a color palette for the user to change the background color and change the background color using an AI model that changes the background based on the color selected by the user.

[0059] The reception unit can upload photos using a smartphone or a PC. For example, the reception unit can directly upload photos taken with a smartphone camera. For example, the reception unit can directly upload photos taken with a smartphone camera. The reception unit also supports file uploads from a PC, allowing a user to select and upload photos saved on a PC. For example, the reception unit also supports file uploads from a PC, allowing a user to select and upload photos saved on a PC. Furthermore, the reception unit can cooperate with a cloud storage service to directly upload photos saved on a cloud by a user. For example, the reception unit can access a user's cloud account and retrieve photos using an API of the cloud storage service. This allows the reception unit to easily upload photos using a smartphone or a PC. 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 upload photos using an AI model that directly uploads photos taken with a smartphone camera.

[0060] The generation unit can generate commemorative photos that can be used at a low price. For example, the generation unit automatically selects a background and decorations based on a scene specified by a user and adds them to the photo. For example, if a user specifies "Omiya-mairi" (shrine visit), the generation unit adds a shrine background and traditional clothing to the photo. Furthermore, if a user specifies "Shichi-Go-San" (celebration of a child's first birthday), the generation unit can add a bright background and decorations for children to the photo. Furthermore, if a user specifies "Graduation Ceremony," the generation unit can add a formal background and decorations to the photo. For example, the generation unit may center a photo of a child holding a diploma and add background and decorations around it. This allows the generation unit to automatically add backgrounds and decorations that match the specified scene. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can automatically select a background and decorations based on a scene specified by a user and add the background and decorations using an AI model to add them to the photo. This allows the generation unit to generate commemorative photos that can be used at a low price.

[0061] The reception unit can estimate the user's emotions and adjust the timing of photo uploads based on the estimated user emotions. For example, if the user is relaxed, the reception unit sends a notification prompting the user to upload photos. For example, if the user is relaxed, the reception unit sends a notification prompting the user to upload photos. The reception unit can also set a reminder to upload photos later if the user is busy. For example, if the user is busy, the reception unit can also set a reminder to upload photos later. Furthermore, if the user is emotionally depressed, the reception unit can suggest that the user refrain from uploading. For example, if the user is emotionally depressed, the reception unit can suggest that the user refrain from uploading. This allows the reception unit to upload photos at the optimal timing depending on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can adjust the timing of photo uploads using an AI model that estimates the user's emotions and adjusts the timing of photo uploads based on the estimated user emotions.

[0062] The reception unit can analyze the user's past photo upload history and select the optimal upload method. For example, the reception unit can prioritize and suggest an upload method (e.g., drag and drop) that the user has frequently used in the past. For example, the reception unit can prioritize and suggest an upload method (e.g., drag and drop) that the user has frequently used in the past. Furthermore, if the user tends to upload photos during a specific time period, the reception unit can send a notification during that time period. For example, if the user tends to upload photos during a specific time period, the reception unit can send a notification during that time period. Furthermore, the reception unit can analyze the size and format of photos previously uploaded by the user and suggest optimal settings. For example, the reception unit can analyze the size and format of photos previously uploaded by the user and suggest optimal settings. This allows the reception unit to suggest the optimal upload method based on the user's past history. 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 select the upload method using an AI model that analyzes the user's past photo upload history and selects the optimal upload method.

[0063] The reception unit may filter photos based on the user's current project or area of ​​interest when uploading photos. For example, the reception unit may suggest that the user upload only photos related to the user's current project. For example, the reception unit may suggest that the user upload only photos related to the user's current project. The reception unit may also prioritize uploading highly relevant photos based on the user's area of ​​interest. For example, the reception unit may prioritize uploading highly relevant photos based on the user's area of ​​interest. Furthermore, if the user is participating in a specific event, the reception unit may filter and upload photos related to the event. For example, if the user is participating in a specific event, the reception unit may filter and upload photos related to the event. This allows the reception unit to upload highly relevant photos based on the user's area of ​​interest. 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 may perform filtering using an AI model that filters based on the user's current project or area of ​​interest when uploading photos.

[0064] The reception unit can estimate the user's emotions and determine the priority of photos to be uploaded based on the estimated user's emotions. For example, when the user is excited, the reception unit prioritizes uploading recently taken photos. For example, when the user is excited, the reception unit prioritizes uploading recently taken photos. Furthermore, when the user is relaxed, the reception unit can prioritize uploading photos of past memories. For example, when the user is relaxed, the reception unit can prioritize uploading photos of past memories. Furthermore, when the user is emotionally depressed, the reception unit can prioritize uploading positive photos. For example, when the user is emotionally depressed, the reception unit can prioritize uploading positive photos. This allows the reception unit to prioritize uploading optimal photos according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can determine the priority of photos using an AI model that estimates a user's emotions and determines the priority of photos to be uploaded based on the estimated user's emotions.

[0065] When uploading photos, the reception unit can prioritize uploading highly relevant photos based on the user's geographical location information. For example, when the user is traveling, the reception unit prioritizes uploading photos taken at the travel destination. For example, when the user is traveling, the reception unit prioritizes uploading photos taken at the travel destination. Furthermore, when the user is participating in a specific event, the reception unit can prioritize uploading photos related to the event. For example, when the user is participating in a specific event, the reception unit can prioritize uploading photos related to the event. Furthermore, when the user is at home, the reception unit can prioritize uploading photos with family and friends. For example, when the user is at home, the reception unit can prioritize uploading photos with family and friends. This allows the reception unit to upload optimal photos based on the user's geographical location information. 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, when uploading photos, the reception unit can upload photos using an AI model that prioritizes uploading highly relevant photos based on the user's geographical location information.

[0066] The reception unit may analyze the user's social media activity and upload relevant photos when uploading photos. For example, the reception unit may prioritize uploading photos recently shared by the user on social media. Furthermore, if the user uses a specific hashtag, the reception unit may prioritize uploading photos related to the hashtag. For example, if the user uses a specific hashtag, the reception unit may prioritize uploading photos related to the hashtag. Furthermore, if the user is participating in a specific event, the reception unit may prioritize uploading photos related to the event. For example, if the user is participating in a specific event, the reception unit may prioritize uploading photos related to the event. This allows the reception unit to upload optimal photos based on the user's social media activity. 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 may analyze the user's social media activity when uploading photos and upload the photos using an AI model that uploads relevant photos.

[0067] The generation unit can estimate the user's emotions and adjust the selection of background and decoration based on the estimated user's emotions. For example, if the user is relaxed, the generation unit selects a background and decoration with calm colors. For example, if the user is relaxed, the generation unit selects a background and decoration with calm colors. Furthermore, if the user is excited, the generation unit can select a background and decoration with vivid colors. For example, if the user is excited, the generation unit can select a background and decoration with vivid colors. Furthermore, if the user is emotionally depressed, the generation unit can select a background and decoration with a positive atmosphere. For example, if the user is emotionally depressed, the generation unit can select a background and decoration with a positive atmosphere. In this way, the generation unit can select the optimal background and decoration according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generator may adjust the selection of background and decoration using an AI model that estimates a user's emotion and adjusts the selection of background and decoration based on the estimated user's emotion.

[0068] The generation unit can adjust the level of detail of the background and decorations based on the importance of the photo during generation. For example, the generation unit adds detailed backgrounds and decorations to photos of important events (e.g., graduation ceremonies). For example, the generation unit adds detailed backgrounds and decorations to photos of important events (e.g., graduation ceremonies). The generation unit can also select simple backgrounds and decorations for everyday photos. For example, the generation unit can select simple backgrounds and decorations for everyday photos. The generation unit can also add luxurious backgrounds and decorations to photos of special anniversaries. For example, the generation unit can add luxurious backgrounds and decorations to photos of special anniversaries. In this way, the generation unit can add optimal backgrounds and decorations depending on the importance of the photo. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can adjust the level of detail of the background and decorations using an AI model that adjusts the level of detail of the background and decorations based on the importance of the photo.

[0069] The generation unit can apply different generation algorithms depending on the category of the scene during generation. For example, the generation unit applies an algorithm that adds traditional backgrounds and decorations to a shrine visit scene. For example, the generation unit applies an algorithm that adds traditional backgrounds and decorations to a shrine visit scene. The generation unit can also apply an algorithm that adds bright backgrounds and decorations for children to a Shichi-Go-San ceremony scene. For example, the generation unit can apply an algorithm that adds bright backgrounds and decorations for children to a Shichi-Go-San ceremony scene. The generation unit can also apply an algorithm that adds formal backgrounds and decorations to a graduation ceremony scene. For example, the generation unit can apply an algorithm that adds formal backgrounds and decorations to a graduation ceremony scene. This allows the generation unit to apply an optimal generation algorithm depending on the category of the scene. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can apply a generation algorithm using an AI model that applies different generation algorithms depending on the category of the scene.

[0070] The generation unit can estimate the user's emotions and adjust the length of the background and decorations based on the estimated user's emotions. For example, if the user is relaxed, the generation unit selects longer backgrounds and decorations. For example, if the user is relaxed, the generation unit selects longer backgrounds and decorations. The generation unit can also select shorter backgrounds and decorations if the user is in a hurry. For example, if the user is in a hurry, the generation unit can also select shorter backgrounds and decorations. Furthermore, if the user is emotionally depressed, the generation unit can select backgrounds and decorations with a positive atmosphere. For example, if the user is emotionally depressed, the generation unit can select backgrounds and decorations with a positive atmosphere. This allows the generation unit to select the optimal length of the background and decorations according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generator can adjust the length of the background and decoration using an AI model that estimates the user's emotions and adjusts the length of the background and decoration based on the estimated user's emotions.

[0071] The generation unit can determine the priority of backgrounds and decorations based on the time the scene was specified during generation. The generation unit, for example, prioritizes selecting backgrounds and decorations according to the season. For example, the generation unit prioritizes selecting backgrounds and decorations according to the season. The generation unit can also prioritize selecting backgrounds and decorations that match the time of a specific event. For example, the generation unit can prioritize selecting backgrounds and decorations that match the time of a specific event. Furthermore, the generation unit can prioritize selecting backgrounds and decorations that are highly relevant based on the time specified by the user. For example, the generation unit can prioritize selecting backgrounds and decorations that are highly relevant based on the time specified by the user. This allows the generation unit to select optimal backgrounds and decorations based on the time the scene was specified. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can determine the priority of backgrounds and decorations using an AI model that determines the priority of backgrounds and decorations based on the time the scene was specified.

[0072] The generation unit can adjust the order of backgrounds and decorations based on the relevance of the scenes during generation. For example, in a shrine visit scene, the generation unit first places the background of the shrine, and then adds decorations. For example, in a shrine visit scene, the generation unit first places the background of the shrine, and then adds decorations. The generation unit can also place a child's photo in the center of a Shichi-Go-San ceremony scene and add decorations around it. For example, in a Shichi-Go-San ceremony scene, the generation unit can place a child's photo in the center of a Shichi-Go-San ceremony scene and add decorations around it. The generation unit can also place a child's photo holding a diploma in the center of a graduation ceremony scene and add backgrounds and decorations around it. For example, the generation unit can place a child's photo holding a diploma in the center of a graduation ceremony scene and add backgrounds and decorations around it. This allows the generation unit to select an optimal order of backgrounds and decorations based on the relevance of the scenes. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can adjust the order of backgrounds and decorations using an AI model that adjusts the order of backgrounds and decorations based on the relevance of the scenes.

[0073] The correction unit can estimate the user's emotions and adjust the correction method based on the estimated user's emotions. For example, if the user is relaxed, the correction unit provides detailed correction options. For example, if the user is relaxed, the correction unit provides detailed correction options. The correction unit can also provide simple correction options if the user is in a hurry. For example, if the user is in a hurry, the correction unit can also provide simple correction options. Furthermore, if the user is emotionally depressed, the correction unit can also provide positive correction options. For example, if the user is emotionally depressed, the correction unit can also provide positive correction options. This allows the correction unit to provide an optimal correction method depending on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the correction unit may be performed using, for example, AI, or without AI. For example, the correction unit can adjust the correction method using an AI model that estimates the user's emotions and adjusts the correction method based on the estimated user's emotions.

[0074] During correction, the correction unit can analyze the user's past correction history and select the optimal correction method. For example, the correction unit prioritizes suggesting correction methods that the user has frequently used in the past. For example, the correction unit prioritizes suggesting correction methods that the user has frequently used in the past. The correction unit can also analyze a specific pattern from the user's past correction history and suggest the optimal correction method. For example, the correction unit can analyze a specific pattern from the user's past correction history and suggest the optimal correction method. Furthermore, the correction unit can provide the optimal correction option based on the tendency of corrections made by the user in the past. For example, the correction unit can provide the optimal correction option based on the tendency of corrections made by the user in the past. In this way, the correction unit can suggest the optimal correction method based on the user's past correction history. Some or all of the above-described processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can select the correction method using an AI model that analyzes the user's past correction history and selects the optimal correction method.

[0075] The correction unit can customize the correction means based on the user's current living situation during correction. For example, if the user is busy, the correction unit provides a simple and quick correction option. For example, if the user is busy, the correction unit provides a simple and quick correction option. The correction unit can also provide detailed correction options if the user is relaxing. For example, if the user is relaxing, the correction unit can provide detailed correction options. Furthermore, if the user is attending a specific event, the correction unit can provide correction options related to the event. For example, if the user is attending a specific event, the correction unit can provide correction options related to the event. This allows the correction unit to provide the optimal correction means according to the user's living situation. Some or all of the above-described processing in the correction unit may be performed using AI, for example, or may be performed without using AI. For example, the correction unit can customize the correction means using an AI model that customizes the correction means based on the user's current living situation.

[0076] The correction unit can estimate the user's emotions and determine the priority of corrections based on the estimated user's emotions. For example, if the user is excited, the correction unit prioritizes recent corrections. For example, if the user is excited, the correction unit prioritizes recent corrections. Furthermore, if the user is relaxed, the correction unit can prioritize past corrections. For example, if the user is relaxed, the correction unit can prioritize past corrections. Furthermore, if the user is emotionally depressed, the correction unit can prioritize positive corrections. For example, if the user is emotionally depressed, the correction unit can prioritize positive corrections. This allows the correction unit to prioritize optimal corrections according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the correction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the correction unit can determine the priority of corrections using an AI model that estimates the user's emotions and determines the priority of corrections based on the estimated user's emotions.

[0077] The correction unit can select the optimal correction method by taking into account the user's geographical location information when making corrections. For example, if the user is traveling, the correction unit suggests a background or decoration of the travel destination. For example, if the user is traveling, the correction unit suggests a background or decoration of the travel destination. Furthermore, if the user is at home, the correction unit can suggest a correction method suitable for a photo with family or friends. For example, if the user is at home, the correction unit can suggest a correction method suitable for a photo with family or friends. Furthermore, if the user is participating in a specific event, the correction unit can suggest a correction method related to the event. For example, if the user is participating in a specific event, the correction unit can suggest a correction method related to the event. This allows the correction unit to suggest the optimal correction method based on the user's geographical location information. Some or all of the above-mentioned processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can select the correction method using an AI model that selects the optimal correction method by taking into account the user's geographical location information.

[0078] During editing, the editing unit can analyze the user's social media activity to suggest editing measures. For example, the editing unit can suggest editing measures based on photos recently shared by the user on social media. Furthermore, if the user uses a specific hashtag, the editing unit can suggest editing measures related to the hashtag. For example, if the user uses a specific hashtag, the editing unit can suggest editing measures related to the hashtag. Furthermore, if the user is participating in a specific event, the editing unit can suggest editing measures related to the event. For example, if the user is participating in a specific event, the editing unit can suggest editing measures related to the event. This allows the editing unit to suggest optimal editing measures based on the user's social media activity. Some or all of the above-described processing in the editing unit may be performed using, for example, AI, or may be performed without using AI. For example, the editing unit can suggest editing measures using an AI model that analyzes the user's social media activity and suggests editing measures. === Hard Collateral 1-1 === Each of the multiple elements, including the above-described reception unit, generation unit, and correction unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, allowing a user to upload photos using a smartphone or PC. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzing the uploaded photo using AI and adding backgrounds and decorations that match the specified scene. The correction unit is realized, for example, by the control unit 46A of the smart device 14, allowing a user to correct the generated commemorative photo. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, generation unit, and correction 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 control unit 46A of the smart glasses 214, allowing a user to upload photos using a smartphone or PC. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzing the uploaded photo using AI and adding backgrounds and decorations that match the specified scene. The correction unit is realized, for example, by the control unit 46A of the smart glasses 214, allowing a user to correct the generated commemorative photo. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and correction 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 control unit 46A of the headset-type terminal 314, and allows a user to upload photos using a smartphone or PC. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the uploaded photos using AI and adds backgrounds and decorations that match the specified scene. The correction unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and allows a user to correct the generated commemorative photo. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and correction 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 control unit 46A of the robot 414, and allows a user to upload photos using a smartphone or PC. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the uploaded photo using AI and adds backgrounds and decorations that match the specified scene. The correction unit is realized, for example, by the control unit 46A of the robot 414, and allows a user to correct the generated commemorative photo.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The reception unit can analyze the user's past upload history and suggest the optimal upload method. For example, it can prioritize and suggest upload methods (such as drag and drop) that the user has frequently used in the past. In addition, if the user tends to upload photos during a specific time period, the reception unit can send a notification during that time period. Furthermore, the reception unit can analyze the size and format of photos that the user has uploaded in the past and suggest optimal settings. This allows the reception unit to suggest the optimal upload method based on the user's past history.

[0081] The generation unit can estimate the user's emotions and adjust the selection of background and decoration based on the estimated emotions. For example, if the user is relaxed, a background and decoration with calm colors can be selected. If the user is excited, a background and decoration with vivid colors can be selected. Furthermore, if the user is emotionally depressed, a background and decoration with a positive atmosphere can be selected. In this way, the generation unit can select the optimal background and decoration according to the user's emotions.

[0082] The correction unit can analyze the user's past correction history and suggest the optimal correction method. For example, it can preferentially suggest correction methods that the user has frequently used in the past. It can also analyze specific patterns from the user's past correction history and suggest the optimal correction method. Furthermore, it can provide the optimal correction option based on the tendency of corrections made by the user in the past. This allows the correction unit to suggest the optimal correction method based on the user's past correction history.

[0083] The reception unit can estimate the user's emotions and adjust the timing of photo uploads based on the estimated emotions. For example, if the user is relaxed, the reception unit can send a notification prompting the user to upload photos. If the user is busy, the reception unit can also set a reminder to upload photos later. Furthermore, if the user is feeling depressed, the reception unit can suggest that the user refrain from uploading photos. This allows the reception unit to upload photos at the optimal timing according to the user's emotions.

[0084] The generation unit can determine the priority of backgrounds and decorations based on the time specified for the scene. For example, the generation unit can prioritize the selection of backgrounds and decorations according to the season. The generation unit can also prioritize the selection of backgrounds and decorations that are appropriate for the time of a specific event. Furthermore, the generation unit can prioritize the selection of highly relevant backgrounds and decorations based on the time specified by the user. This allows the generation unit to select the optimal backgrounds and decorations according to the time specified for the scene.

[0085] The correction unit can estimate the user's emotion and adjust the correction method based on the estimated emotion. For example, if the user is relaxed, detailed correction options can be provided. If the user is in a hurry, simple correction options can be provided. Furthermore, if the user is emotionally depressed, positive correction options can be provided. In this way, the correction unit can provide the optimal correction method according to the user's emotion.

[0086] When uploading photos, the reception unit can filter the photos based on the user's current projects and areas of interest. For example, the reception unit can suggest that the user upload only photos related to the project currently in progress. The reception unit can also prioritize uploading highly relevant photos based on the user's areas of interest. Furthermore, if the user is participating in a specific event, the reception unit can filter and upload photos related to the event. This allows the reception unit to upload highly relevant photos based on the user's areas of interest.

[0087] The generation unit can apply different generation algorithms depending on the scene category during generation. For example, an algorithm that adds traditional backgrounds and decorations can be applied to a shrine visit scene. Also, an algorithm that adds bright backgrounds and decorations for children can be applied to a Shichi-Go-San ceremony scene. Furthermore, an algorithm that adds formal backgrounds and decorations can be applied to a graduation ceremony scene. This allows the generation unit to apply the optimal generation algorithm depending on the scene category.

[0088] The correction unit can estimate the user's emotions and determine the priority of corrections based on the estimated emotions. For example, if the user is excited, recent corrections can be prioritized. Also, if the user is relaxed, past corrections can be prioritized. Furthermore, if the user is emotionally depressed, positive corrections can be prioritized. This allows the correction unit to prioritize optimal corrections according to the user's emotions.

[0089] The generation unit can adjust the level of detail of the background and decorations based on the importance of the photo during generation. For example, detailed backgrounds and decorations can be added to photos of important events (such as graduation ceremonies). Simple backgrounds and decorations can also be selected for everyday photos. Furthermore, luxurious backgrounds and decorations can be added to photos of special anniversaries. This allows the generation unit to add optimal backgrounds and decorations depending on the importance of the photo.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The reception unit allows the user to upload photos using a smartphone or PC. Photos uploaded by the user may be in formats such as, but not limited to, JPEG, PNG, and RAW. The reception unit can directly upload photos taken with a smartphone camera, for example. The reception unit also supports file uploads from a PC, allowing the user to select and upload photos stored on their PC. Furthermore, the reception unit can work with a cloud storage service to directly upload photos stored in the cloud by the user. For example, the reception unit can access the user's cloud account and retrieve photos using the API of the cloud storage service. Step 2: The generation unit uses AI to analyze the photo uploaded by the reception unit and add backgrounds and decorations that match the specified scene. The generation unit, for example, uses image recognition technology to analyze the content of the photo and select backgrounds and decorations that are appropriate for the scene. For example, if the user specifies "shrine visit," the generation unit adds a shrine background and traditional clothing to the photo. The generation unit can also use an AI algorithm to adjust the color tone and brightness of the photo and generate an optimal image that matches the scene. For example, the generation unit uses a deep learning algorithm to automatically adjust the color tone of the photo and create an atmosphere that matches the scene. Step 3: The modification unit allows the user to modify the commemorative photo generated by the generation unit. The modification unit provides, for example, a function to change the background color or the position of decorations. For example, the modification unit provides a color palette for the user to change the background color and changes the background based on the color selected by the user. The modification unit also provides a function to change the position of decorations by drag and drop, allowing the user to freely adjust the position of decorations. Furthermore, the modification unit also provides functions such as applying filters and cropping, allowing the user to customize the photo to their liking. For example, the modification unit provides an option for the user to apply a filter to the photo and applies the filter selected by the user to the photo.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0150] 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."

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] [Explanation of symbols]

[0164] 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 desk where you can upload photos, a generation unit that analyzes the photo uploaded by the reception unit and adds a background and decoration that matches a specified scene; a correction unit that allows a user to correct the commemorative photo generated by the generation unit; A system characterized by:

2. The generation unit Add backgrounds and decorations to match the specified scene The system of claim 1 .

3. The correction unit Change the background color or decoration position The system of claim 1 .

4. The reception unit Upload photos using your smartphone or computer The system of claim 1 .

5. The generation unit Create affordable commemorative photos The system of claim 1 .

6. The reception unit Estimate user emotions and adjust the timing of photo uploads based on the estimated user emotions The system of claim 1 .

7. The reception unit Analyze the user's past photo upload history and select the optimal upload method The system of claim 1 .

8. The reception unit Filter photos based on your current projects and interests when uploading them The system of claim 1 .

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