system

Abstract

The system according to the embodiment aims to change the background of an image posted to an SNS in a natural way, thereby reducing the risk of location identification. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a feature detection unit, a modification unit, and a provision unit. The reception unit uploads an image. The analysis unit analyzes the background of the image uploaded by the reception unit. The feature detection unit detects features indicative of a specific location from the background analyzed by the analysis unit. The modification unit modifies the background based on the features detected by the feature detection unit. The provision unit provides an image with the background modified by the modification unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there was a risk that a person's location could be identified from the background of an image posted on social media, and it was difficult to avoid this in a natural way.

[0005] The system according to the embodiment aims to change the background of an image posted to an SNS in a natural way, thereby reducing the risk of location identification. [Means for solving the problem]

[0006] A system according to an embodiment includes a reception unit, an analysis unit, a feature detection unit, a modification unit, and a provision unit. The reception unit uploads an image. The analysis unit analyzes the background of the image uploaded by the reception unit. The feature detection unit detects features indicating a specific location from the background analyzed by the analysis unit. The modification unit modifies the background based on the features detected by the feature detection unit. The provision unit provides an image with the background modified by the modification unit. [Effects of the Invention]

[0007] The system according to the embodiment can change the background of an image posted to an SNS in a natural way, thereby reducing the risk of location identification. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An image background modification system according to an embodiment of the present invention provides natural-looking images while reducing the risk of location identification from the background of an image posted on a social networking site. In this system, a user uploads an image they wish to post, and AI analyzes the background of the image and detects features that indicate a specific location. Based on the detected features, the AI ​​changes the background in a natural manner. The background change is performed so as to blend seamlessly with the original image. Finally, an image with the modified background is generated and provided to the user. For example, a user uploads an image they wish to post. The user does not need to perform any special operations; the process can be performed in the same way as a regular image upload. Next, AI analyzes the background of the uploaded image. The AI ​​detects features that indicate a specific location within the image. For example, the AI ​​may change the shape of a building or replace the text on a sign with something else. Based on the detected features, the AI ​​changes the background in a natural manner. For example, the AI ​​may change the shape of a building or replace the text on a sign with something else. The changes are performed so as to blend seamlessly with the original image. Finally, an image with the modified background is generated and provided to the user. By posting this image on a social networking site, the user can reduce the risk of location identification. Furthermore, a feature has been added that allows the user to select the degree of modification desired. For example, users can choose whether to change to a completely different background or to make only minor changes to the original background. The system also includes algorithms that allow the AI ​​to maintain the atmosphere of the original image when changing the background. For example, it includes algorithms that adjust color tone and brightness. This allows the image background change system to naturally change the background of images posted by users on social media, providing natural-looking images while reducing the risk of being identified.

[0029] An image background changing system according to an embodiment includes a receiving unit, an analysis unit, a feature detection unit, a change unit, and a providing unit. The receiving unit allows a user to upload an image they wish to post. The user does not need to perform any special operations and can upload an image in the same way as a normal image upload. For example, the receiving unit allows the user to upload an image by dragging and dropping the image. The receiving unit also allows the user to select an image using a file selection dialog. The receiving unit also allows the user to upload an image taken in real time using a camera. The analysis unit analyzes the background of the uploaded image. The analysis unit extracts the background using, for example, an image processing algorithm. The analysis unit can also use image analysis technology to detect features that indicate a specific location within the image. For example, the analysis unit detects features such as the shape of a building or the characters on a sign. The feature detection unit detects features that indicate a specific location from the background analyzed by the analysis unit. The feature detection unit uses an image analysis algorithm to detect features such as landmarks and the shape of a building. The feature detection unit can also detect features such as the characters on a sign or a road sign. The change unit changes the background based on the features detected by the feature detection unit. The modification unit changes the background using, for example, image synthesis technology. The modification unit can also change the shape of a building or replace text on a sign with another one. For example, the modification unit reduces the risk of location identification by transforming the shape of a building. The modification unit can also reduce the risk of location identification by translating text on a sign into another language. The providing unit provides an image with a background modified by the modification unit. The providing unit, for example, allows a user to download the modified image. The providing unit can also provide a function for directly posting the modified image to an SNS. Furthermore, the providing unit can send the modified image by email. In this way, the image background modification system according to the embodiment can naturally change the background of an image posted by a user to an SNS, thereby providing a natural image while reducing the risk of location identification.

[0030] The reception unit includes a selection unit that allows the user to select a specific degree of background change. The selection unit provides an interface for the user to select the degree of background change. For example, the selection unit can adjust the degree of background change using a slider. The selection unit can also provide options such as a complete change or a partial change using a pull-down menu. The selection unit can also provide a preview function that allows the user to visually confirm the degree of background change. For example, when the user moves the slider, the selection unit displays a preview image that reflects the degree of background change in real time. This allows the user to intuitively select the degree of background change. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without AI. For example, the selection unit can input the user's selection history into AI and select the degree of background change using an AI model that suggests the optimal degree of background change. This allows the user to select the degree of background change.

[0031] The modification unit includes an adjustment unit that adjusts the hue and brightness based on a specific method for preserving the atmosphere of the original image. The adjustment unit adjusts the hue and brightness to preserve the atmosphere of the original image. For example, the adjustment unit uses a color correction algorithm to match the hue of the background to that of the original image. The adjustment unit can also use a brightness adjustment algorithm to match the brightness of the background to that of the original image. The adjustment unit can also adjust the contrast and saturation to preserve the atmosphere of the original image. For example, the adjustment unit analyzes the contrast of the original image and matches the contrast of the background. The adjustment unit can also analyze the saturation of the original image and match the saturation of the background. This makes it possible to change the background while preserving the atmosphere of the original image. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the hue and brightness of the original image into AI and adjust the hue and brightness using an AI model that suggests an optimal adjustment method. This makes it possible to change the background while preserving the atmosphere of the original image.

[0032] The analysis unit detects features that indicate a specific location within the image. The analysis unit uses image analysis technology to detect features that indicate a specific location within the image. For example, the analysis unit uses an image analysis algorithm to detect features such as landmarks and building shapes. The analysis unit can also detect features such as character strings on signs and road signs. For example, the analysis unit can analyze the shape of a building within the image to determine whether the shape indicates a specific location. The analysis unit can also analyze character strings on signs within the image to determine whether the character strings indicate a specific location. Furthermore, the analysis unit can analyze road signs within the image to determine whether the signs indicate a specific location. In this way, features that indicate a specific location within the image can be detected. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input features within the image into AI and detect features using an AI model that detects features that indicate a specific location. In this way, features that indicate a specific location within the image can be detected.

[0033] The modification unit specifically modifies the shape of the building. The modification unit uses image synthesis technology to modify the shape of the building. For example, the modification unit reduces the risk of location identification by deforming the shape of the building. The modification unit can also remove or add parts of the building. For example, the modification unit reduces the risk of location identification by changing the shape of the building's roof. The modification unit can also reduce the risk of location identification by changing the arrangement of the building's windows. The modification unit can also change the color of the building's exterior walls. For example, the modification unit reduces the risk of location identification by changing the color of the building's exterior walls. In this way, the risk of location identification can be reduced by changing the shape of the building. 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 can input the shape of the building into AI and modify the shape of the building using an AI model that suggests an optimal shape modification method. In this way, the risk of location identification can be reduced by modifying the shape of the building.

[0034] The modification unit specifically replaces the characters on the sign with different characters. The modification unit uses image synthesis technology to modify the characters on the sign. For example, the modification unit reduces the risk of location identification by translating the characters on the sign into another language. The modification unit can also change the font and size of the characters on the sign. For example, the modification unit reduces the risk of location identification by changing the font of the characters on the sign. The modification unit can also reduce the risk of location identification by changing the size of the characters on the sign. The modification unit can also change the content of the characters on the sign. For example, the modification unit reduces the risk of location identification by replacing the characters on the sign with different content. In this way, changing the characters on the sign can reduce the risk of location identification. Some or all of the above-described processing by the modification unit may be performed using, for example, AI, or may be performed without using AI. For example, the modification unit can input the characters on the sign into AI and change the characters on the sign using an AI model that suggests an optimal method for modifying the characters. In this way, changing the characters on the sign can reduce the risk of location identification.

[0035] The reception unit analyzes the user's past upload history and selects a specific upload method. The reception unit uses data analysis technology to analyze the user's past upload history. For example, the reception unit prioritizes and suggests upload methods (such as voice and text) that the user has frequently used in the past. The reception unit can also predict and suggest an upload method to be used during a specific time period based on the user's past upload history. Furthermore, the reception unit can suggest an optimal upload method based on the types of images the user has previously uploaded. For example, the reception unit analyzes the content of images the user has previously uploaded and suggests highly relevant upload methods. This makes it possible to suggest an optimal upload method based on the user's past upload 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 input the user's upload history data into AI and select an upload method using an AI model that suggests an optimal upload method. This makes it possible to suggest an optimal upload method based on the user's past upload history.

[0036] When uploading images, the reception unit filters the images based on the user's current project and areas of interest. The reception unit uses data analysis technology to identify the user's current project and areas of interest. For example, the reception unit filters the images so that only images related to the project the user is currently working on are uploaded. The reception unit can also prioritize uploading highly relevant images based on the user's areas of interest. Furthermore, the reception unit can refer to the user's past project history and filter and upload related images. For example, the reception unit analyzes the user's past project history and identifies images related to the current project. This makes it possible to upload highly relevant images based on the user's current project and areas 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 AI. For example, the reception unit can input data on the user's projects and areas of interest into AI and perform filtering using an AI model that suggests optimal images. This makes it possible to upload highly relevant images based on the user's current project and areas of interest.

[0037] When uploading images, the reception unit prioritizes uploading images that are specifically highly relevant, taking into account the user's geographical location information. The reception unit uses a location information service to acquire the user's geographical location information. For example, the reception unit may acquire the user's current location information using GPS data. The reception unit may also refer to the user's past location information and prioritize uploading highly relevant images. For example, the reception unit may analyze the user's past location information and identify images related to the current location information. Furthermore, the reception unit may combine the user's current location information and past location information to upload optimal images. For example, the reception unit prioritizes uploading images related to the user's current location. This makes it possible to upload highly relevant images 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, the reception unit may input the user's location information data into AI and perform uploading using an AI model that suggests optimal images. This makes it possible to upload highly relevant images based on the user's geographical location information.

[0038] The reception unit analyzes the user's social media activity when uploading an image and uploads relevant images. The reception unit uses data analysis technology to analyze the user's social media activity. For example, the reception unit uploads relevant images based on content recently shared by the user on social media. The reception unit can also upload optimal images based on the interests of the user's social media followers. The reception unit can also analyze the user's social media activity history and upload highly relevant images. For example, the reception unit analyzes the content of the user's social media posts and identifies relevant images. This makes it possible to upload highly relevant images 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 input the user's social media activity data into AI and perform uploading using an AI model that suggests optimal images. This makes it possible to upload highly relevant images based on the user's social media activity.

[0039] During analysis, the analysis unit adjusts the level of analysis detail based on the importance of specific images. The analysis unit uses data analysis technology to evaluate the importance of images. For example, the analysis unit evaluates the importance based on the content and intended use of the image. The analysis unit can also analyze the metadata and tag information of the image to evaluate the importance. For example, the analysis unit analyzes the content of the image and performs a detailed analysis on images with high importance. The analysis unit can also perform a simplified analysis on images with low importance. The analysis unit can also determine the priority of the analysis based on the importance of the image. For example, the analysis unit prioritizes the analysis of images with high importance and analyzes images with low importance later. This makes it possible to adjust the level of analysis detail based on the importance of the image. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input image importance data into AI and adjust the level of analysis detail using an AI model that suggests an optimal level of analysis detail. This makes it possible to adjust the level of analysis detail based on the importance of the image.

[0040] During analysis, the analysis unit applies different analysis algorithms depending on the specific image category. The analysis unit uses data analysis techniques to identify the image category. For example, the analysis unit identifies the category based on the content and metadata of the image. The analysis unit can also analyze the tag information of the image to identify the category. For example, the analysis unit applies a landscape analysis algorithm to landscape images and a face recognition algorithm to portrait images. The analysis unit can also apply a building analysis algorithm to building images. This allows an appropriate analysis algorithm to be applied depending on the image category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image category data into AI and apply an analysis algorithm using an AI model that proposes an optimal analysis algorithm. This allows an appropriate analysis algorithm to be applied depending on the image category.

[0041] During analysis, the analysis unit determines the analysis priority based on the time when a specific image was taken. The analysis unit uses metadata and timestamps to identify the time when the image was taken. For example, the analysis unit analyzes the metadata of the image to identify the time when the image was taken. The analysis unit can also analyze the timestamp of the image to identify the time when the image was taken. For example, the analysis unit prioritizes analyzing the most recent image and analyzes older images later. The analysis unit can also adjust the analysis priority according to the time when the image was taken. This makes it possible to determine the analysis priority based on the time when the image was taken. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image shooting time data into AI and determine the analysis priority using an AI model that proposes the optimal analysis priority. This makes it possible to determine the analysis priority based on the time when the image was taken.

[0042] During analysis, the analysis unit adjusts the analysis order based on the relevance of specific images. The analysis unit uses data analysis techniques to evaluate the relevance of images. For example, the analysis unit analyzes the similarity of image content and tag information to evaluate the relevance. The analysis unit can also analyze image metadata and shooting locations to evaluate the relevance. For example, the analysis unit prioritizes analyzing highly relevant images and analyzes less relevant images later. The analysis unit can also adjust the analysis order according to the relevance of images. In this way, the analysis order can be adjusted based on the relevance of images. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image relevance data into AI and adjust the analysis order using an AI model that suggests an optimal analysis order. In this way, the analysis order can be adjusted based on the relevance of images.

[0043] The feature detection unit improves the accuracy of feature detection by taking into account the interrelationships between images during feature detection. The feature detection unit uses data analysis techniques to evaluate the interrelationships between images. For example, the feature detection unit improves the accuracy of feature detection by taking into account the interrelationships between objects within an image. The feature detection unit can also improve the accuracy of feature detection by taking into account the relationship between the background and foreground of an image. For example, the feature detection unit analyzes the relationship between multiple objects within an image to improve the accuracy of feature detection. Furthermore, the feature detection unit can determine the priority of feature detection based on the interrelationships between images. For example, the feature detection unit prioritizes analysis of images that share a common subject or shooting location. This improves the accuracy of feature detection by taking into account the interrelationships between images. Some or all of the above-described processing in the feature detection unit may be performed using, for example, AI, or may be performed without AI. For example, the feature detection unit can input image interrelationship data into AI and use an AI model that proposes an optimal feature detection method to improve the accuracy of feature detection. This improves the accuracy of feature detection by taking into account the interrelationships between images.

[0044] The feature detection unit performs feature detection while taking into account the attribute information of the image photographer. The feature detection unit uses data analysis technology to acquire the attribute information of the image photographer. For example, the feature detection unit performs feature detection while taking into account the age and gender of the image photographer. The feature detection unit can also perform feature detection while taking into account the occupation and hobbies of the image photographer. For example, the feature detection unit performs feature detection by referring to the image photographer's past photography history. Furthermore, the feature detection unit can determine the priority of feature detection based on the attribute information of the image photographer. For example, the feature detection unit preferentially analyzes images of photographers with specific attributes. This allows feature detection to be performed while taking into account the attribute information of the image photographer. Some or all of the above-described processing in the feature detection unit may be performed using, for example, AI, or may be performed without AI. For example, the feature detection unit can input the photographer's attribute information data into AI and perform feature detection using an AI model that proposes an optimal feature detection method. This allows feature detection to be performed while taking into account the attribute information of the image photographer.

[0045] The feature detection unit performs feature detection while taking into account the geographic distribution of the image. The feature detection unit uses data analysis technology to evaluate the geographic distribution of the image. For example, the feature detection unit identifies the coordinates and region of the location where the image was taken. The feature detection unit can also improve the accuracy of feature detection based on the geographic distribution of the image. For example, the feature detection unit performs feature detection based on the location where the image was taken. The feature detection unit can also perform feature detection by referring to information related to the geographic distribution of the image. This allows feature detection to be performed while taking into account the geographic distribution of the image. Some or all of the above-described processing in the feature detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feature detection unit can input geographic distribution data of the image into AI and perform feature detection using an AI model that proposes an optimal feature detection method. This allows feature detection to be performed while taking into account the geographic distribution of the image.

[0046] The feature detection unit improves the accuracy of feature detection by referring to literature related to the image during feature detection. The feature detection unit uses data analysis technology to refer to literature related to the image. For example, the feature detection unit improves the accuracy of feature detection by referring to literature related to the image. The feature detection unit can also perform feature detection based on information obtained from literature related to the image. For example, the feature detection unit analyzes literature related to the image to improve the accuracy of feature detection. This makes it possible to improve the accuracy of feature detection by referring to literature related to the image. Some or all of the above-described processing in the feature detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feature detection unit can input literature data related to the image into AI and use an AI model that proposes an optimal feature detection method to improve the accuracy of feature detection. This makes it possible to improve the accuracy of feature detection by referring to literature related to the image.

[0047] The modification unit improves the accuracy of background modification by taking into account the interrelationships between images. The modification unit uses data analysis technology to evaluate the interrelationships between images. For example, the modification unit improves the accuracy of background modification by taking into account the interrelationships between objects within an image. The modification unit can also improve the accuracy of background modification by taking into account the relationship between the background and foreground of an image. For example, the modification unit analyzes the relationship between multiple objects within an image to improve the accuracy of background modification. Furthermore, the modification unit can determine the priority of background modification based on the interrelationships between images. For example, the modification unit prioritizes analysis of images that have a common subject or shooting location. This improves the accuracy of background modification by taking into account the interrelationships between images. 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 can input image interrelationship data into AI and use an AI model that suggests an optimal background modification method to improve the accuracy of background modification. This improves the accuracy of background modification by taking into account the interrelationships between images.

[0048] When changing the background, the change unit takes into account the attribute information of the image photographer. The change unit uses data analysis technology to acquire the attribute information of the image photographer. For example, the change unit takes into account the age and gender of the image photographer when changing the background. The change unit can also take into account the occupation and hobbies of the image photographer when changing the background. For example, the change unit references the image photographer's past photography history when changing the background. Furthermore, the change unit can determine the priority of background changes based on the attribute information of the image photographer. For example, the change unit preferentially analyzes images of photographers with specific attributes. This allows the background to be changed taking into account the attribute information of the image photographer. Some or all of the above-described processing in the change unit may be performed using, for example, AI, or may be performed without using AI. For example, the change unit can input the photographer's attribute information data into AI and use an AI model that suggests an optimal background change method to change the background. This allows the background to be changed taking into account the attribute information of the image photographer.

[0049] The modification unit performs the background change while taking into account the geographical distribution of the image. The modification unit uses data analysis technology to evaluate the geographical distribution of the image. For example, the modification unit identifies the coordinates and region of the location where the image was taken. The modification unit can also improve the accuracy of the background change based on the geographical distribution of the image. For example, the modification unit performs the background change based on the location where the image was taken. The modification unit can also perform the background change by referring to information related to the geographical distribution of the image. This allows the background change to take into account the geographical distribution of the image. 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 can input geographical distribution data of the image into AI and perform the background change using an AI model that suggests an optimal background change method. This allows the background change to take into account the geographical distribution of the image.

[0050] The modification unit improves the accuracy of the background modification by referring to related literature of the image. The modification unit uses data analysis technology to refer to related literature of the image. For example, the modification unit improves the accuracy of the background modification by referring to related literature of the image. The modification unit can also perform background modification based on information obtained from related literature of the image. For example, the modification unit analyzes related literature of the image and improves the accuracy of the background modification. This makes it possible to improve the accuracy of the background modification by referring to related literature of the image. 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 can input related literature data of the image into AI and use an AI model that suggests an optimal background modification method to improve the accuracy of the background modification. This makes it possible to improve the accuracy of the background modification by referring to related literature of the image.

[0051] The providing unit selects the optimal display method by referring to the user's past operation history when providing the data. The providing unit uses data analysis technology to analyze the user's past operation history. For example, the providing unit prioritizes suggesting display methods used by the user in the past. The providing unit can also predict and suggest a display method to be used during a specific time period based on the user's past operation history. Furthermore, the providing unit can analyze the user's past operation history and suggest an optimal display method. For example, the providing unit analyzes the user's operation date and time and operation content to identify the optimal display method. This makes it possible to suggest the optimal display method based on the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's operation history data into AI and select a display method using an AI model that suggests the optimal display method. This makes it possible to suggest the optimal display method based on the user's past operation history.

[0052] The providing unit selects the optimal display method based on the user's device information during the provisioning process. The providing unit uses data analysis technology to acquire the user's device information. For example, if the user is using a smartphone, the providing unit may provide a display method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit may also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit may also provide a display method that is simple and highly visible. This allows the optimal display method to be proposed based on the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit may input the user's device information data into AI and select the display method using an AI model that proposes the optimal display method. This allows the optimal display method to be proposed based on the user's device information.

[0053] When selecting the degree of background change, the selection unit refers to the user's past selection history and suggests the optimal degree. The selection unit uses data analysis technology to analyze the user's past selection history. For example, the selection unit suggests the optimal degree based on the degree of background change selected by the user in the past. The selection unit can also predict and suggest the degree of background change to be used at a specific time period from the user's past selection history. The selection unit can also analyze the user's past selection history and suggest the optimal degree of background change. For example, the selection unit analyzes the date and time of the user's selection and the content of the selection to identify the optimal degree of background change. This makes it possible to suggest the optimal degree of background change based on the user's past selection history. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without AI. For example, the selection unit can input the user's selection history data into AI and suggest the optimal degree of background change using an AI model that suggests the optimal degree of background change. This makes it possible to suggest the optimal degree of background change based on the user's past selection history.

[0054] When selecting the degree of background change, the selection unit considers the user's device information and suggests the optimal degree. The selection unit uses data analysis technology to acquire the user's device information. For example, if the user is using a smartphone, the selection unit may provide a degree of background change tailored to the screen size. Furthermore, if the user is using a tablet, the selection unit may provide a degree of background change optimized for a large screen. Furthermore, if the user is using a smartwatch, the selection unit may provide a concise and highly visible degree of background change. This makes it possible to suggest the optimal degree of background change based on the user's device information. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without AI. For example, the selection unit may input the user's device information data into AI and suggest the optimal degree of background change using an AI model that suggests the optimal degree of background change. This makes it possible to suggest the optimal degree of background change based on the user's device information.

[0055] When adjusting color tone and brightness, the adjustment unit selects the optimal adjustment method by referring to the image's past adjustment history. The adjustment unit uses data analysis technology to analyze the image's past adjustment history. For example, the adjustment unit suggests the optimal adjustment method based on the color tone and brightness selected by the user in the past. The adjustment unit can also predict and suggest the color tone and brightness to use at a specific time period based on the user's past adjustment history. The adjustment unit can also analyze the user's past adjustment history and suggest the optimal color tone and brightness. For example, the adjustment unit analyzes the user's adjustment date and time and adjustment content to identify the optimal color tone and brightness. This makes it possible to suggest the optimal color tone and brightness based on the user's past adjustment history. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without AI. For example, the adjustment unit can input the user's adjustment history data into AI and select the adjustment method using an AI model that suggests the optimal color tone and brightness adjustment method. This makes it possible to suggest the optimal color tone and brightness based on the user's past adjustment history.

[0056] When adjusting color tone and brightness, the adjustment unit selects the optimal adjustment method taking into account the user's device information. The adjustment unit uses data analysis technology to acquire the user's device information. For example, if the user is using a smartphone, the adjustment unit provides color tone and brightness that match the screen size. Furthermore, if the user is using a tablet, the adjustment unit can also provide color tone and brightness optimized for a large screen. Furthermore, if the user is using a smartwatch, the adjustment unit can also provide color tone and brightness that are simple and highly visible. This makes it possible to suggest optimal color tone and brightness based on the user's device information. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without AI. For example, the adjustment unit can input the user's device information data into AI and select the adjustment method using an AI model that suggests optimal color tone and brightness adjustment methods. This makes it possible to suggest optimal color tone and brightness based on the user's device information.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The reception unit can also analyze the user's past upload history and select a specific upload method. For example, the reception unit can prioritize and suggest upload methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest an upload method to be used during a specific time period based on the user's past upload history. Furthermore, the reception unit can also suggest the optimal upload method based on the types of images the user has uploaded in the past. This makes it possible to suggest the optimal upload method based on the user's past upload history.

[0059] The modification unit may improve the accuracy of the background modification by taking into account the interrelationships between images. For example, the modification unit may improve the accuracy of the background modification by taking into account the interrelationships between objects in the image. The modification unit may also improve the accuracy of the background modification by taking into account the relationship between the background and foreground of the image. Furthermore, the modification unit may determine the priority of the background modification based on the interrelationships between images. This improves the accuracy of the background modification by taking into account the interrelationships between images.

[0060] When providing the display method, the providing unit can also select the optimal display method by referring to the user's past operation history. For example, the providing unit can preferentially suggest display methods that the user has used in the past. The providing unit can also predict and suggest a display method to be used in a specific time period based on the user's past operation history. Furthermore, the providing unit can analyze the user's past operation history and suggest the optimal display method. This makes it possible to suggest the optimal display method based on the user's past operation history.

[0061] When selecting the degree of background change, the selection unit can also suggest an optimal degree by referring to the user's past selection history. For example, the selection unit can suggest an optimal degree based on the degree of background change selected by the user in the past. The selection unit can also predict and suggest the degree of background change to be used in a specific time period from the user's past selection history. Furthermore, the selection unit can analyze the user's past selection history and suggest an optimal degree of background change. In this way, it is possible to suggest an optimal degree of background change based on the user's past selection history.

[0062] When adjusting color tone or brightness, the adjustment unit can also select the optimal adjustment method by referring to the image's past adjustment history. For example, the adjustment unit can suggest the optimal adjustment method based on the color tone or brightness selected by the user in the past. The adjustment unit can also predict and suggest the color tone or brightness to be used at a specific time period based on the user's past adjustment history. Furthermore, the adjustment unit can analyze the user's past adjustment history and suggest the optimal color tone or brightness. This makes it possible to suggest the optimal color tone or brightness based on the user's past adjustment history.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The reception unit uploads the image that the user wants to post. The user does not need to perform any special operations, and can do so in the same way as normal image uploading. For example, the reception unit allows the user to upload an image by dragging and dropping it. The reception unit can also allow the user to select an image using a file selection dialog. Furthermore, the reception unit can also upload images that the user has taken in real time using the camera. Step 2: The analysis unit analyzes the background of the uploaded image. For example, the analysis unit uses an image processing algorithm to extract the background. The analysis unit can also use image analysis techniques to detect features that indicate specific locations within the image. For example, the analysis unit detects features such as the shape of a building or the letters on a sign. Step 3: The feature detection unit detects features that indicate a specific location from the background analyzed by the analysis unit. The feature detection unit uses image analysis algorithms to detect features such as landmarks and building shapes. The feature detection unit can also detect features such as letters on signs and road signs. Step 4: The modification unit modifies the background based on the features detected by the feature detection unit. For example, the modification unit modifies the background using image synthesis technology. The modification unit can also change the shape of a building or replace the text on a sign with something else. For example, the modification unit can reduce the risk of identifying the location by transforming the shape of a building. The modification unit can also reduce the risk of identifying the location by translating the text on a sign into another language. Step 5: The providing unit provides the image with the background changed by the changing unit. For example, the providing unit allows the user to download the changed image. The providing unit may also provide a function to directly post the changed image to an SNS. Furthermore, the providing unit may also send the changed image by email.

[0065] (Example 2) An image background modification system according to an embodiment of the present invention provides natural-looking images while reducing the risk of location identification from the background of an image posted on a social networking site. In this system, a user uploads an image they wish to post, and AI analyzes the background of the image and detects features that indicate a specific location. Based on the detected features, the AI ​​changes the background in a natural manner. The background change is performed so as to blend seamlessly with the original image. Finally, an image with the modified background is generated and provided to the user. For example, a user uploads an image they wish to post. The user does not need to perform any special operations; the process can be performed in the same way as a regular image upload. Next, AI analyzes the background of the uploaded image. The AI ​​detects features that indicate a specific location within the image. For example, the AI ​​may change the shape of a building or replace the text on a sign with something else. Based on the detected features, the AI ​​changes the background in a natural manner. For example, the AI ​​may change the shape of a building or replace the text on a sign with something else. The changes are performed so as to blend seamlessly with the original image. Finally, an image with the modified background is generated and provided to the user. By posting this image on a social networking site, the user can reduce the risk of location identification. Furthermore, a feature has been added that allows the user to select the degree of modification desired. For example, users can choose whether to change to a completely different background or to make only minor changes to the original background. The system also includes algorithms that allow the AI ​​to maintain the atmosphere of the original image when changing the background. For example, it includes algorithms that adjust color tone and brightness. This allows the image background change system to naturally change the background of images posted by users on social media, providing natural-looking images while reducing the risk of being identified.

[0066] An image background changing system according to an embodiment includes a receiving unit, an analysis unit, a feature detection unit, a change unit, and a providing unit. The receiving unit allows a user to upload an image they wish to post. The user does not need to perform any special operations and can upload an image in the same way as a normal image upload. For example, the receiving unit allows the user to upload an image by dragging and dropping the image. The receiving unit also allows the user to select an image using a file selection dialog. The receiving unit also allows the user to upload an image taken in real time using a camera. The analysis unit analyzes the background of the uploaded image. The analysis unit extracts the background using, for example, an image processing algorithm. The analysis unit can also use image analysis technology to detect features that indicate a specific location within the image. For example, the analysis unit detects features such as the shape of a building or the characters on a sign. The feature detection unit detects features that indicate a specific location from the background analyzed by the analysis unit. The feature detection unit uses an image analysis algorithm to detect features such as landmarks and the shape of a building. The feature detection unit can also detect features such as the characters on a sign or a road sign. The change unit changes the background based on the features detected by the feature detection unit. The modification unit changes the background using, for example, image synthesis technology. The modification unit can also change the shape of a building or replace text on a sign with another one. For example, the modification unit reduces the risk of location identification by transforming the shape of a building. The modification unit can also reduce the risk of location identification by translating text on a sign into another language. The providing unit provides an image with a background modified by the modification unit. The providing unit, for example, allows a user to download the modified image. The providing unit can also provide a function for directly posting the modified image to an SNS. Furthermore, the providing unit can send the modified image by email. In this way, the image background modification system according to the embodiment can naturally change the background of an image posted by a user to an SNS, thereby providing a natural image while reducing the risk of location identification.

[0067] The reception unit includes a selection unit that allows the user to select a specific degree of background change. The selection unit provides an interface for the user to select the degree of background change. For example, the selection unit can adjust the degree of background change using a slider. The selection unit can also provide options such as a complete change or a partial change using a pull-down menu. The selection unit can also provide a preview function that allows the user to visually confirm the degree of background change. For example, when the user moves the slider, the selection unit displays a preview image that reflects the degree of background change in real time. This allows the user to intuitively select the degree of background change. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without AI. For example, the selection unit can input the user's selection history into AI and select the degree of background change using an AI model that suggests the optimal degree of background change. This allows the user to select the degree of background change.

[0068] The modification unit includes an adjustment unit that adjusts the hue and brightness based on a specific method for preserving the atmosphere of the original image. The adjustment unit adjusts the hue and brightness to preserve the atmosphere of the original image. For example, the adjustment unit uses a color correction algorithm to match the hue of the background to that of the original image. The adjustment unit can also use a brightness adjustment algorithm to match the brightness of the background to that of the original image. The adjustment unit can also adjust the contrast and saturation to preserve the atmosphere of the original image. For example, the adjustment unit analyzes the contrast of the original image and matches the contrast of the background. The adjustment unit can also analyze the saturation of the original image and match the saturation of the background. This makes it possible to change the background while preserving the atmosphere of the original image. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the hue and brightness of the original image into AI and adjust the hue and brightness using an AI model that suggests an optimal adjustment method. This makes it possible to change the background while preserving the atmosphere of the original image.

[0069] The analysis unit detects features that indicate a specific location within the image. The analysis unit uses image analysis technology to detect features that indicate a specific location within the image. For example, the analysis unit uses an image analysis algorithm to detect features such as landmarks and building shapes. The analysis unit can also detect features such as character strings on signs and road signs. For example, the analysis unit can analyze the shape of a building within the image to determine whether the shape indicates a specific location. The analysis unit can also analyze character strings on signs within the image to determine whether the character strings indicate a specific location. Furthermore, the analysis unit can analyze road signs within the image to determine whether the signs indicate a specific location. In this way, features that indicate a specific location within the image can be detected. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input features within the image into AI and detect features using an AI model that detects features that indicate a specific location. In this way, features that indicate a specific location within the image can be detected.

[0070] The modification unit specifically modifies the shape of the building. The modification unit uses image synthesis technology to modify the shape of the building. For example, the modification unit reduces the risk of location identification by deforming the shape of the building. The modification unit can also remove or add parts of the building. For example, the modification unit reduces the risk of location identification by changing the shape of the building's roof. The modification unit can also reduce the risk of location identification by changing the arrangement of the building's windows. The modification unit can also change the color of the building's exterior walls. For example, the modification unit reduces the risk of location identification by changing the color of the building's exterior walls. In this way, the risk of location identification can be reduced by changing the shape of the building. 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 can input the shape of the building into AI and modify the shape of the building using an AI model that suggests an optimal shape modification method. In this way, the risk of location identification can be reduced by modifying the shape of the building.

[0071] The modification unit specifically replaces the characters on the sign with different characters. The modification unit uses image synthesis technology to modify the characters on the sign. For example, the modification unit reduces the risk of location identification by translating the characters on the sign into another language. The modification unit can also change the font and size of the characters on the sign. For example, the modification unit reduces the risk of location identification by changing the font of the characters on the sign. The modification unit can also reduce the risk of location identification by changing the size of the characters on the sign. The modification unit can also change the content of the characters on the sign. For example, the modification unit reduces the risk of location identification by replacing the characters on the sign with different content. In this way, changing the characters on the sign can reduce the risk of location identification. Some or all of the above-described processing by the modification unit may be performed using, for example, AI, or may be performed without using AI. For example, the modification unit can input the characters on the sign into AI and change the characters on the sign using an AI model that suggests an optimal method for modifying the characters. In this way, changing the characters on the sign can reduce the risk of location identification.

[0072] The reception unit estimates the user's emotion and adjusts the timing of image uploads based on the estimated user emotion. The reception unit uses an emotion estimation algorithm to estimate the user's emotion. For example, the reception unit may capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The reception unit may also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit may analyze the tone and speed of the user's voice to estimate the emotion. The reception unit may also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit may estimate the emotion based on heart rate fluctuations. This allows the user's emotion to be estimated. The reception unit adjusts the timing of image uploads based on the estimated user emotion. For example, if the user is feeling stressed, the reception unit may simplify the upload procedure to allow the user to upload images quickly. Alternatively, if the user is feeling relaxed, the reception unit may provide detailed upload options and suggest a customizable upload method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and upload images quickly. This makes it possible to adjust the timing of image uploads 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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's emotion data into AI and adjust the upload timing using an AI model that suggests the optimal upload timing. This makes it possible to adjust the timing of image uploads according to the user's emotions.

[0073] The reception unit analyzes the user's past upload history and selects a specific upload method. The reception unit uses data analysis technology to analyze the user's past upload history. For example, the reception unit prioritizes and suggests upload methods (such as voice and text) that the user has frequently used in the past. The reception unit can also predict and suggest an upload method to be used during a specific time period based on the user's past upload history. Furthermore, the reception unit can suggest an optimal upload method based on the types of images the user has previously uploaded. For example, the reception unit analyzes the content of images the user has previously uploaded and suggests highly relevant upload methods. This makes it possible to suggest an optimal upload method based on the user's past upload 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 input the user's upload history data into AI and select an upload method using an AI model that suggests an optimal upload method. This makes it possible to suggest an optimal upload method based on the user's past upload history.

[0074] When uploading images, the reception unit filters the images based on the user's current project and areas of interest. The reception unit uses data analysis technology to identify the user's current project and areas of interest. For example, the reception unit filters the images so that only images related to the project the user is currently working on are uploaded. The reception unit can also prioritize uploading highly relevant images based on the user's areas of interest. Furthermore, the reception unit can refer to the user's past project history and filter and upload related images. For example, the reception unit analyzes the user's past project history and identifies images related to the current project. This makes it possible to upload highly relevant images based on the user's current project and areas 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 AI. For example, the reception unit can input data on the user's projects and areas of interest into AI and perform filtering using an AI model that suggests optimal images. This makes it possible to upload highly relevant images based on the user's current project and areas of interest.

[0075] The reception unit estimates the user's emotion and determines the priority of images to be uploaded based on the estimated user's emotion. The reception unit uses an emotion estimation algorithm to estimate the user's emotion. For example, the reception unit may capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The reception unit may also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit may analyze the tone and speed of the user's voice to estimate the emotion. The reception unit may also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit may estimate the emotion based on heart rate fluctuations. This allows the user's emotion to be estimated. The reception unit determines the priority of images to be uploaded based on the estimated user's emotion. For example, if the user is excited, the reception unit may prioritize uploading the most recent image. Alternatively, if the user is relaxed, the reception unit may prioritize uploading older images. Furthermore, if the user is feeling stressed, the reception unit may prioritize uploading images of high importance. This allows the priority of images to be uploaded to be determined according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data into an AI and determine the upload priority using an AI model that suggests an optimal upload priority. This allows the priority of images to be uploaded to be determined according to the user's emotions.

[0076] When uploading images, the reception unit prioritizes uploading images that are specifically highly relevant, taking into account the user's geographical location information. The reception unit uses a location information service to acquire the user's geographical location information. For example, the reception unit may acquire the user's current location information using GPS data. The reception unit may also refer to the user's past location information and prioritize uploading highly relevant images. For example, the reception unit may analyze the user's past location information and identify images related to the current location information. Furthermore, the reception unit may combine the user's current location information and past location information to upload optimal images. For example, the reception unit prioritizes uploading images related to the user's current location. This makes it possible to upload highly relevant images 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, the reception unit may input the user's location information data into AI and perform uploading using an AI model that suggests optimal images. This makes it possible to upload highly relevant images based on the user's geographical location information.

[0077] The reception unit analyzes the user's social media activity when uploading an image and uploads relevant images. The reception unit uses data analysis technology to analyze the user's social media activity. For example, the reception unit uploads relevant images based on content recently shared by the user on social media. The reception unit can also upload optimal images based on the interests of the user's social media followers. The reception unit can also analyze the user's social media activity history and upload highly relevant images. For example, the reception unit analyzes the content of the user's social media posts and identifies relevant images. This makes it possible to upload highly relevant images 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 input the user's social media activity data into AI and perform uploading using an AI model that suggests optimal images. This makes it possible to upload highly relevant images based on the user's social media activity.

[0078] The analysis unit estimates the user's emotions and adjusts the presentation method of the analysis based on the estimated user's emotions. The analysis unit uses an emotion estimation algorithm to estimate the user's emotions. For example, the analysis unit may capture the user's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The analysis unit may also record the user's voice and estimate the emotions using voice analysis technology. For example, the analysis unit may analyze the tone and speed of the user's voice to estimate the emotions. The analysis unit may also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the analysis unit may estimate the emotions based on heart rate fluctuations. This allows the user's emotions to be estimated. The analysis unit adjusts the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit may provide a simple, highly visible analysis result. If the user is relaxed, the analysis unit may provide a detailed analysis result. If the user is in a hurry, the analysis unit may provide a concise analysis result. This allows the method of expressing the analysis to be adjusted according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's emotion data into AI and adjust the method of expressing the analysis using an AI model that suggests an optimal method of expressing the analysis. This allows the method of expressing the analysis to be adjusted according to the user's emotions.

[0079] During analysis, the analysis unit adjusts the level of analysis detail based on the importance of specific images. The analysis unit uses data analysis technology to evaluate the importance of images. For example, the analysis unit evaluates the importance based on the content and intended use of the image. The analysis unit can also analyze the metadata and tag information of the image to evaluate the importance. For example, the analysis unit analyzes the content of the image and performs a detailed analysis on images with high importance. The analysis unit can also perform a simplified analysis on images with low importance. The analysis unit can also determine the priority of the analysis based on the importance of the image. For example, the analysis unit prioritizes the analysis of images with high importance and analyzes images with low importance later. This makes it possible to adjust the level of analysis detail based on the importance of the image. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input image importance data into AI and adjust the level of analysis detail using an AI model that suggests an optimal level of analysis detail. This makes it possible to adjust the level of analysis detail based on the importance of the image.

[0080] During analysis, the analysis unit applies different analysis algorithms depending on the specific image category. The analysis unit uses data analysis techniques to identify the image category. For example, the analysis unit identifies the category based on the content and metadata of the image. The analysis unit can also analyze the tag information of the image to identify the category. For example, the analysis unit applies a landscape analysis algorithm to landscape images and a face recognition algorithm to portrait images. The analysis unit can also apply a building analysis algorithm to building images. This allows an appropriate analysis algorithm to be applied depending on the image category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image category data into AI and apply an analysis algorithm using an AI model that proposes an optimal analysis algorithm. This allows an appropriate analysis algorithm to be applied depending on the image category.

[0081] The analysis unit estimates the user's emotion and adjusts the length of the analysis based on the estimated user's emotion. The analysis unit uses an emotion estimation algorithm to estimate the user's emotion. For example, the analysis unit may capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The analysis unit may also record the user's voice and estimate the emotion using voice analysis technology. For example, the analysis unit may analyze the tone and speed of the user's voice to estimate the emotion. The analysis unit may also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit may estimate the emotion based on heart rate fluctuations. This allows the user's emotion to be estimated. The analysis unit adjusts the length of the analysis based on the estimated user's emotion. For example, if the user is in a hurry, the analysis unit may provide a short and concise analysis result. If the user is relaxed, the analysis unit may provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit may provide a visually stimulating analysis result. This allows the length of the analysis to be adjusted according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input user emotion data into AI and adjust the length of analysis using an AI model that suggests the optimal length of analysis. This allows the length of analysis to be adjusted according to the user's emotion.

[0082] During analysis, the analysis unit determines the analysis priority based on the time when a specific image was taken. The analysis unit uses metadata and timestamps to identify the time when the image was taken. For example, the analysis unit analyzes the metadata of the image to identify the time when the image was taken. The analysis unit can also analyze the timestamp of the image to identify the time when the image was taken. For example, the analysis unit prioritizes analyzing the most recent image and analyzes older images later. The analysis unit can also adjust the analysis priority according to the time when the image was taken. This makes it possible to determine the analysis priority based on the time when the image was taken. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image shooting time data into AI and determine the analysis priority using an AI model that proposes the optimal analysis priority. This makes it possible to determine the analysis priority based on the time when the image was taken.

[0083] During analysis, the analysis unit adjusts the analysis order based on the relevance of specific images. The analysis unit uses data analysis techniques to evaluate the relevance of images. For example, the analysis unit analyzes the similarity of image content and tag information to evaluate the relevance. The analysis unit can also analyze image metadata and shooting locations to evaluate the relevance. For example, the analysis unit prioritizes analyzing highly relevant images and analyzes less relevant images later. The analysis unit can also adjust the analysis order according to the relevance of images. In this way, the analysis order can be adjusted based on the relevance of images. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image relevance data into AI and adjust the analysis order using an AI model that suggests an optimal analysis order. In this way, the analysis order can be adjusted based on the relevance of images.

[0084] The feature detection unit estimates the user's emotion and adjusts the feature detection criteria based on the estimated user's emotion. The feature detection unit uses an emotion estimation algorithm to estimate the user's emotion. For example, the feature detection unit captures the user's facial expression with a camera and estimates the emotion using facial expression recognition technology. The feature detection unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the feature detection unit analyzes the tone and speed of the user's voice to estimate the emotion. The feature detection unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the feature detection unit estimates the emotion based on heart rate fluctuations. This allows the user's emotion to be estimated. The feature detection unit adjusts the feature detection criteria based on the estimated user's emotion. For example, if the user is nervous, the feature detection unit provides simple, highly visible feature detection criteria. Alternatively, if the user is relaxed, the feature detection unit can provide detailed feature detection criteria. Furthermore, if the user is in a hurry, the feature detection unit can provide feature detection criteria that focus on the key points. This allows the feature detection criteria to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the feature detection unit can be performed using, for example, AI, or without AI. For example, the feature detection unit can input user emotion data into AI and adjust the feature detection criteria using an AI model that proposes optimal feature detection criteria. This allows the feature detection criteria to be adjusted according to the user's emotions.

[0085] The feature detection unit improves the accuracy of feature detection by taking into account the interrelationships between images during feature detection. The feature detection unit uses data analysis techniques to evaluate the interrelationships between images. For example, the feature detection unit improves the accuracy of feature detection by taking into account the interrelationships between objects within an image. The feature detection unit can also improve the accuracy of feature detection by taking into account the relationship between the background and foreground of an image. For example, the feature detection unit analyzes the relationship between multiple objects within an image to improve the accuracy of feature detection. Furthermore, the feature detection unit can determine the priority of feature detection based on the interrelationships between images. For example, the feature detection unit prioritizes analysis of images that share a common subject or shooting location. This improves the accuracy of feature detection by taking into account the interrelationships between images. Some or all of the above-described processing in the feature detection unit may be performed using, for example, AI, or may be performed without AI. For example, the feature detection unit can input image interrelationship data into AI and use an AI model that proposes an optimal feature detection method to improve the accuracy of feature detection. This improves the accuracy of feature detection by taking into account the interrelationships between images.

[0086] The feature detection unit performs feature detection while taking into account the attribute information of the image photographer. The feature detection unit uses data analysis technology to acquire the attribute information of the image photographer. For example, the feature detection unit performs feature detection while taking into account the age and gender of the image photographer. The feature detection unit can also perform feature detection while taking into account the occupation and hobbies of the image photographer. For example, the feature detection unit performs feature detection by referring to the image photographer's past photography history. Furthermore, the feature detection unit can determine the priority of feature detection based on the attribute information of the image photographer. For example, the feature detection unit preferentially analyzes images of photographers with specific attributes. This allows feature detection to be performed while taking into account the attribute information of the image photographer. Some or all of the above-described processing in the feature detection unit may be performed using, for example, AI, or may be performed without AI. For example, the feature detection unit can input the photographer's attribute information data into AI and perform feature detection using an AI model that proposes an optimal feature detection method. This allows feature detection to be performed while taking into account the attribute information of the image photographer.

[0087] The feature detection unit estimates the user's emotion and adjusts the display order of the feature detection results based on the estimated user's emotion. The feature detection unit uses an emotion estimation algorithm to estimate the user's emotion. For example, the feature detection unit captures the user's facial expression with a camera and estimates the emotion using facial expression recognition technology. The feature detection unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the feature detection unit analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the feature detection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the feature detection unit estimates the emotion based on heart rate fluctuations. This allows the user's emotion to be estimated. The feature detection unit adjusts the display order of the feature detection results based on the estimated user's emotion. For example, if the user is nervous, the feature detection unit displays the feature detection results in a simple, highly visible order. Alternatively, if the user is relaxed, the feature detection unit can display the feature detection results in a detailed order. Furthermore, if the user is in a hurry, the feature detection unit can display the feature detection results in an order that emphasizes the key points. This allows the order in which the feature detection results are displayed to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feature detection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feature detection unit can input the user's emotion data into an AI and adjust the order in which the feature detection results are displayed using an AI model that suggests an optimal display order. This allows the order in which the feature detection results are displayed to be adjusted according to the user's emotions.

[0088] The feature detection unit performs feature detection while taking into account the geographic distribution of the image. The feature detection unit uses data analysis technology to evaluate the geographic distribution of the image. For example, the feature detection unit identifies the coordinates and region of the location where the image was taken. The feature detection unit can also improve the accuracy of feature detection based on the geographic distribution of the image. For example, the feature detection unit performs feature detection based on the location where the image was taken. The feature detection unit can also perform feature detection by referring to information related to the geographic distribution of the image. This allows feature detection to be performed while taking into account the geographic distribution of the image. Some or all of the above-described processing in the feature detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feature detection unit can input geographic distribution data of the image into AI and perform feature detection using an AI model that proposes an optimal feature detection method. This allows feature detection to be performed while taking into account the geographic distribution of the image.

[0089] The feature detection unit improves the accuracy of feature detection by referring to literature related to the image during feature detection. The feature detection unit uses data analysis technology to refer to literature related to the image. For example, the feature detection unit improves the accuracy of feature detection by referring to literature related to the image. The feature detection unit can also perform feature detection based on information obtained from literature related to the image. For example, the feature detection unit analyzes literature related to the image to improve the accuracy of feature detection. This makes it possible to improve the accuracy of feature detection by referring to literature related to the image. Some or all of the above-described processing in the feature detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feature detection unit can input literature data related to the image into AI and use an AI model that proposes an optimal feature detection method to improve the accuracy of feature detection. This makes it possible to improve the accuracy of feature detection by referring to literature related to the image.

[0090] The modification unit estimates the user's emotion and adjusts the background change method based on the estimated user's emotion. The modification unit uses an emotion estimation algorithm to estimate the user's emotion. For example, the modification unit may capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The modification unit may also record the user's voice and estimate the emotion using voice analysis technology. For example, the modification unit may analyze the tone and speed of the user's voice to estimate the emotion. The modification unit may also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the modification unit may estimate the emotion based on heart rate fluctuations. This allows the user's emotion to be estimated. The modification unit adjusts the background change method based on the estimated user's emotion. For example, if the user is relaxed, the modification unit may perform a natural background change. If the user is in a hurry, the modification unit may perform a quick background change. Furthermore, if the user is excited, the modification unit may perform a visually stimulating background change. This allows the background change method to be adjusted according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the modification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the modification unit may input user emotion data into the AI ​​and adjust the background modification method using an AI model that suggests an optimal background modification method. This allows the background modification method to be adjusted according to the user's emotion.

[0091] The modification unit improves the accuracy of background modification by taking into account the interrelationships between images. The modification unit uses data analysis technology to evaluate the interrelationships between images. For example, the modification unit improves the accuracy of background modification by taking into account the interrelationships between objects within an image. The modification unit can also improve the accuracy of background modification by taking into account the relationship between the background and foreground of an image. For example, the modification unit analyzes the relationship between multiple objects within an image to improve the accuracy of background modification. Furthermore, the modification unit can determine the priority of background modification based on the interrelationships between images. For example, the modification unit prioritizes analysis of images that have a common subject or shooting location. This improves the accuracy of background modification by taking into account the interrelationships between images. 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 can input image interrelationship data into AI and use an AI model that suggests an optimal background modification method to improve the accuracy of background modification. This improves the accuracy of background modification by taking into account the interrelationships between images.

[0092] When changing the background, the change unit takes into account the attribute information of the image photographer. The change unit uses data analysis technology to acquire the attribute information of the image photographer. For example, the change unit takes into account the age and gender of the image photographer when changing the background. The change unit can also take into account the occupation and hobbies of the image photographer when changing the background. For example, the change unit references the image photographer's past photography history when changing the background. Furthermore, the change unit can determine the priority of background changes based on the attribute information of the image photographer. For example, the change unit preferentially analyzes images of photographers with specific attributes. This allows the background to be changed taking into account the attribute information of the image photographer. Some or all of the above-described processing in the change unit may be performed using, for example, AI, or may be performed without using AI. For example, the change unit can input the photographer's attribute information data into AI and use an AI model that suggests an optimal background change method to change the background. This allows the background to be changed taking into account the attribute information of the image photographer.

[0093] The change unit estimates the user's emotion and determines the priority of background changes based on the estimated user's emotion. The change unit uses an emotion estimation algorithm to estimate the user's emotion. For example, the change unit captures the user's facial expression with a camera and estimates the emotion using facial expression recognition technology. The change unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the change unit analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the change unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the change unit estimates the emotion based on heart rate fluctuations. This allows the user's emotion to be estimated. The change unit determines the priority of background changes based on the estimated user's emotion. For example, if the user is excited, the change unit prioritizes the most recent background change. Also, if the user is relaxed, the change unit can prioritize past background changes. Furthermore, if the user is stressed, the change unit can prioritize background changes of high importance. This allows the priority of background changes to be determined according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the modification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the modification unit can input user emotion data into an AI and determine the priority of background changes using an AI model that proposes an optimal priority of background changes. This allows the priority of background changes to be determined according to the user's emotions.

[0094] The modification unit performs the background change while taking into account the geographical distribution of the image. The modification unit uses data analysis technology to evaluate the geographical distribution of the image. For example, the modification unit identifies the coordinates and region of the location where the image was taken. The modification unit can also improve the accuracy of the background change based on the geographical distribution of the image. For example, the modification unit performs the background change based on the location where the image was taken. The modification unit can also perform the background change by referring to information related to the geographical distribution of the image. This allows the background change to take into account the geographical distribution of the image. 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 can input geographical distribution data of the image into AI and perform the background change using an AI model that suggests an optimal background change method. This allows the background change to take into account the geographical distribution of the image.

[0095] The modification unit improves the accuracy of the background modification by referring to related literature of the image. The modification unit uses data analysis technology to refer to related literature of the image. For example, the modification unit improves the accuracy of the background modification by referring to related literature of the image. The modification unit can also perform background modification based on information obtained from related literature of the image. For example, the modification unit analyzes related literature of the image and improves the accuracy of the background modification. This makes it possible to improve the accuracy of the background modification by referring to related literature of the image. 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 can input related literature data of the image into AI and use an AI model that suggests an optimal background modification method to improve the accuracy of the background modification. This makes it possible to improve the accuracy of the background modification by referring to related literature of the image.

[0096] The providing unit estimates the user's emotion and adjusts the display method of the image to be provided based on the estimated user's emotion. The providing unit uses an emotion estimation algorithm to estimate the user's emotion. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using facial expression recognition technology. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the providing unit analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit estimates the emotion based on heart rate fluctuations. This allows the user's emotion to be estimated. The providing unit adjusts the display method of the image to be provided based on the estimated user's emotion. For example, if the user is nervous, the providing unit provides a simple, highly visible display method. Also, if the user is relaxed, the providing unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the providing unit can provide a display method that focuses on the main points. This makes it possible to adjust the display method of the image provided according to the user's emotions. Emotion estimation is realized, for example, by using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into an AI and adjust the display method using an AI model that suggests an optimal display method. This makes it possible to adjust the display method of the image provided according to the user's emotions.

[0097] The providing unit selects the optimal display method by referring to the user's past operation history when providing the data. The providing unit uses data analysis technology to analyze the user's past operation history. For example, the providing unit prioritizes suggesting display methods used by the user in the past. The providing unit can also predict and suggest a display method to be used during a specific time period based on the user's past operation history. Furthermore, the providing unit can analyze the user's past operation history and suggest an optimal display method. For example, the providing unit analyzes the user's operation date and time and operation content to identify the optimal display method. This makes it possible to suggest the optimal display method based on the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's operation history data into AI and select a display method using an AI model that suggests the optimal display method. This makes it possible to suggest the optimal display method based on the user's past operation history.

[0098] The providing unit estimates the user's emotion and adjusts the operation procedure of the image to be provided based on the estimated user's emotion. The providing unit uses an emotion estimation algorithm to estimate the user's emotion. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using facial expression recognition technology. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the providing unit analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit estimates the emotion based on heart rate fluctuations. This allows the user's emotion to be estimated. The providing unit adjusts the operation procedure of the image to be provided based on the estimated user's emotion. For example, if the user is nervous, the providing unit provides simple, highly visible operation procedures. Also, if the user is relaxed, the providing unit can provide detailed operation procedures. Furthermore, if the user is in a hurry, the providing unit can provide operation procedures that focus on the main points. This makes it possible to adjust the operation procedure of the image to be provided according to the user's emotions. Emotion estimation is realized, for example, by using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into an AI and adjust the operation procedure using an AI model that suggests the optimal operation procedure. This makes it possible to adjust the operation procedure of the image to be provided according to the user's emotions.

[0099] The providing unit selects the optimal display method based on the user's device information during the provisioning process. The providing unit uses data analysis technology to acquire the user's device information. For example, if the user is using a smartphone, the providing unit may provide a display method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit may also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit may also provide a display method that is simple and highly visible. This allows the optimal display method to be proposed based on the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit may input the user's device information data into AI and select the display method using an AI model that proposes the optimal display method. This allows the optimal display method to be proposed based on the user's device information.

[0100] The selection unit estimates the user's emotion and adjusts the degree of background change based on the estimated user's emotion. The selection unit uses an emotion estimation algorithm to estimate the user's emotion. For example, the selection unit may capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The selection unit may also record the user's voice and estimate the emotion using voice analysis technology. For example, the selection unit may analyze the tone and speed of the user's voice to estimate the emotion. The selection unit may also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the selection unit may estimate the emotion based on heart rate fluctuations. This allows the user's emotion to be estimated. The selection unit adjusts the degree of background change based on the estimated user's emotion. For example, if the user is relaxed, the selection unit may perform a natural background change. If the user is in a hurry, the selection unit may perform a quick background change. Furthermore, if the user is excited, the selection unit may perform a visually stimulating background change. This allows the degree of background change to be adjusted according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the selection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the selection unit can input user emotion data into an AI and adjust the degree of background change using an AI model that suggests an optimal degree of background change. This allows the degree of background change to be adjusted according to the user's emotion.

[0101] When selecting the degree of background change, the selection unit refers to the user's past selection history and suggests the optimal degree. The selection unit uses data analysis technology to analyze the user's past selection history. For example, the selection unit suggests the optimal degree based on the degree of background change selected by the user in the past. The selection unit can also predict and suggest the degree of background change to be used at a specific time period from the user's past selection history. The selection unit can also analyze the user's past selection history and suggest the optimal degree of background change. For example, the selection unit analyzes the date and time of the user's selection and the content of the selection to identify the optimal degree of background change. This makes it possible to suggest the optimal degree of background change based on the user's past selection history. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without AI. For example, the selection unit can input the user's selection history data into AI and suggest the optimal degree of background change using an AI model that suggests the optimal degree of background change. This makes it possible to suggest the optimal degree of background change based on the user's past selection history.

[0102] The selection unit estimates the user's emotion and determines a priority for the degree of background change based on the estimated user's emotion. The selection unit uses an emotion estimation algorithm to estimate the user's emotion. For example, the selection unit captures the user's facial expression with a camera and estimates the emotion using facial expression recognition technology. The selection unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the selection unit analyzes the tone and speed of the user's voice to estimate the emotion. The selection unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the selection unit estimates the emotion based on heart rate fluctuations. This allows the user's emotion to be estimated. The selection unit determines a priority for the degree of background change based on the estimated user's emotion. For example, if the user is excited, the selection unit prioritizes the most recent degree of background change. Also, if the user is relaxed, the selection unit can prioritize the degree of background change from a previous version. Furthermore, if the user is feeling stressed, the selection unit can prioritize the degree of background change that is of high importance. This allows the priority of the degree of background change to be determined 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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the selection unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the selection unit can input user emotion data into an AI and determine the priority of the degree of background change using an AI model that proposes an optimal priority of the degree of background change. This allows the priority of the degree of background change to be determined according to the user's emotions.

[0103] When selecting the degree of background change, the selection unit considers the user's device information and suggests the optimal degree. The selection unit uses data analysis technology to acquire the user's device information. For example, if the user is using a smartphone, the selection unit may provide a degree of background change tailored to the screen size. Furthermore, if the user is using a tablet, the selection unit may provide a degree of background change optimized for a large screen. Furthermore, if the user is using a smartwatch, the selection unit may provide a concise and highly visible degree of background change. This makes it possible to suggest the optimal degree of background change based on the user's device information. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without AI. For example, the selection unit may input the user's device information data into AI and suggest the optimal degree of background change using an AI model that suggests the optimal degree of background change. This makes it possible to suggest the optimal degree of background change based on the user's device information.

[0104] The adjustment unit estimates the user's emotion and changes the adjustment method for color tone and brightness based on the estimated user's emotion. The adjustment unit uses an emotion estimation algorithm to estimate the user's emotion. For example, the adjustment unit may capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The adjustment unit may also record the user's voice and estimate the emotion using voice analysis technology. For example, the adjustment unit may analyze the tone and speed of the user's voice to estimate the emotion. The adjustment unit may also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the adjustment unit may estimate the emotion based on heart rate fluctuations. This allows the user's emotion to be estimated. The adjustment unit changes the adjustment method for color tone and brightness based on the estimated user's emotion. For example, if the user is relaxed, the adjustment unit may prioritize warm colors. If the user is in a hurry, the adjustment unit may prioritize bright colors. Furthermore, if the user is excited, the adjustment unit may prioritize visually stimulating colors. This allows the method of adjusting color tone and brightness to be changed depending on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input user emotion data into AI and change the adjustment method using an AI model that suggests an optimal method of adjusting color tone and brightness. This allows the method of adjusting color tone and brightness to be changed depending on the user's emotions.

[0105] When adjusting color tone and brightness, the adjustment unit selects the optimal adjustment method by referring to the image's past adjustment history. The adjustment unit uses data analysis technology to analyze the image's past adjustment history. For example, the adjustment unit suggests the optimal adjustment method based on the color tone and brightness selected by the user in the past. The adjustment unit can also predict and suggest the color tone and brightness to use at a specific time period based on the user's past adjustment history. The adjustment unit can also analyze the user's past adjustment history and suggest the optimal color tone and brightness. For example, the adjustment unit analyzes the user's adjustment date and time and adjustment content to identify the optimal color tone and brightness. This makes it possible to suggest the optimal color tone and brightness based on the user's past adjustment history. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without AI. For example, the adjustment unit can input the user's adjustment history data into AI and select the adjustment method using an AI model that suggests the optimal color tone and brightness adjustment method. This makes it possible to suggest the optimal color tone and brightness based on the user's past adjustment history.

[0106] The adjustment unit estimates the user's emotion and determines the priority of color tone and brightness adjustment based on the estimated user's emotion. The adjustment unit uses an emotion estimation algorithm to estimate the user's emotion. For example, the adjustment unit may capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The adjustment unit may also record the user's voice and estimate the emotion using voice analysis technology. For example, the adjustment unit may analyze the tone and speed of the user's voice to estimate the emotion. The adjustment unit may also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the adjustment unit may estimate the emotion based on heart rate fluctuations. This allows the user's emotion to be estimated. The adjustment unit determines the priority of color tone and brightness adjustment based on the estimated user's emotion. For example, if the user is excited, the adjustment unit may prioritize the most recent color tone and brightness adjustment. Also, if the user is relaxed, the adjustment unit may prioritize the previous color tone and brightness adjustment. Furthermore, if the user is feeling stressed, the adjustment unit can prioritize adjustment of color tone and brightness with high importance. This allows the priority of color tone and brightness adjustment to be determined according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the adjustment unit can be performed using, for example, AI, or without AI. For example, the adjustment unit can input user emotion data into AI and determine the adjustment priority using an AI model that proposes optimal color tone and brightness adjustment priority. This allows the priority of color tone and brightness adjustment to be determined according to the user's emotions.

[0107] When adjusting color tone and brightness, the adjustment unit selects the optimal adjustment method taking into account the user's device information. The adjustment unit uses data analysis technology to acquire the user's device information. For example, if the user is using a smartphone, the adjustment unit provides color tone and brightness that match the screen size. Furthermore, if the user is using a tablet, the adjustment unit can also provide color tone and brightness optimized for a large screen. Furthermore, if the user is using a smartwatch, the adjustment unit can also provide color tone and brightness that are simple and highly visible. This makes it possible to suggest optimal color tone and brightness based on the user's device information. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without AI. For example, the adjustment unit can input the user's device information data into AI and select the adjustment method using an AI model that suggests optimal color tone and brightness adjustment methods. This makes it possible to suggest optimal color tone and brightness based on the user's device information. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, analysis unit, feature detection unit, change unit, and provision unit, is realized by, for example, 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 and receives an operation by a user when uploading an image. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the background of the uploaded image. The feature detection unit is realized by the identification processing unit 290 of the data processing device 12 and detects features indicating a specific location in the image. The change unit is realized by the identification processing unit 290 of the data processing device 12 and changes the background based on the detected features. The provision unit is realized by the control unit 46A of the smart device 14 and provides the user with an image with the changed background. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, analysis unit, feature detection unit, change unit, and provision 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 and receives an operation when a user uploads an image. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the background of the uploaded image. The feature detection unit is realized by the identification processing unit 290 of the data processing device 12 and detects features indicating a specific location in the image. The change unit is realized by the identification processing unit 290 of the data processing device 12 and changes the background based on the detected features. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides the user with an image with the changed background. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, feature detection unit, change unit, and provision 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 receives an operation when a user uploads an image. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the background of the uploaded image. The feature detection unit is realized by the identification processing unit 290 of the data processing device 12 and detects features that indicate a specific location in the image. The change unit is realized by the identification processing unit 290 of the data processing device 12 and changes the background based on the detected features. The provision unit is realized by the control unit 46A of the headset type terminal 314 and provides the user with an image with the changed background. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, feature detection unit, change unit, and provision 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 receives operations when a user uploads an image. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the background of the uploaded image. The feature detection unit is realized by the identification processing unit 290 of the data processing device 12 and detects features that indicate a specific location in the image. The change unit is realized by the identification processing unit 290 of the data processing device 12 and changes the background based on the detected features. The provision unit is realized by the control unit 46A of the robot 414 and provides the user with an image with the changed background.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] The reception unit can also estimate the user's emotions and adjust the timing of image uploads based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can simplify the upload procedure to enable quick image uploads. Alternatively, if the user is feeling relaxed, the reception unit can provide detailed upload options and suggest a customizable upload method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick image uploads. In this way, the timing of image uploads can be adjusted according to the user's emotions.

[0110] The reception unit can also analyze the user's past upload history and select a specific upload method. For example, the reception unit can prioritize and suggest upload methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest an upload method to be used during a specific time period based on the user's past upload history. Furthermore, the reception unit can also suggest the optimal upload method based on the types of images the user has uploaded in the past. This makes it possible to suggest the optimal upload method based on the user's past upload history.

[0111] The change unit can also estimate the user's emotion and adjust the background change method based on the estimated user's emotion. For example, if the user is relaxed, the change unit can change the background in a natural way. If the user is in a hurry, the change unit can change the background quickly. Furthermore, if the user is excited, the change unit can change the background in a visually stimulating way. In this way, the background change method can be adjusted according to the user's emotion.

[0112] The modification unit may improve the accuracy of the background modification by taking into account the interrelationships between images. For example, the modification unit may improve the accuracy of the background modification by taking into account the interrelationships between objects in the image. The modification unit may also improve the accuracy of the background modification by taking into account the relationship between the background and foreground of the image. Furthermore, the modification unit may determine the priority of the background modification based on the interrelationships between images. This improves the accuracy of the background modification by taking into account the interrelationships between images.

[0113] The providing unit can also estimate the user's emotions and adjust the display method of the images to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. If the user is relaxed, the providing unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. In this way, the display method of the images to be provided can be adjusted according to the user's emotions.

[0114] When providing the display method, the providing unit can also select the optimal display method by referring to the user's past operation history. For example, the providing unit can preferentially suggest display methods that the user has used in the past. The providing unit can also predict and suggest a display method to be used in a specific time period based on the user's past operation history. Furthermore, the providing unit can analyze the user's past operation history and suggest the optimal display method. This makes it possible to suggest the optimal display method based on the user's past operation history.

[0115] The selection unit can also estimate the user's emotion and adjust the degree of background change based on the estimated user's emotion. For example, if the user is relaxed, the selection unit can perform a natural background change. If the user is in a hurry, the selection unit can also perform a quick background change. Furthermore, if the user is excited, the selection unit can also perform a visually stimulating background change. In this way, the degree of background change can be adjusted according to the user's emotion.

[0116] When selecting the degree of background change, the selection unit can also suggest an optimal degree by referring to the user's past selection history. For example, the selection unit can suggest an optimal degree based on the degree of background change selected by the user in the past. The selection unit can also predict and suggest the degree of background change to be used in a specific time period from the user's past selection history. Furthermore, the selection unit can analyze the user's past selection history and suggest an optimal degree of background change. In this way, it is possible to suggest an optimal degree of background change based on the user's past selection history.

[0117] The adjustment unit can also estimate the user's emotions and change the adjustment method for color tone and brightness based on the estimated user's emotions. For example, if the user is relaxed, the adjustment unit can prioritize warm color tones. If the user is in a hurry, the adjustment unit can also prioritize bright color tones. Furthermore, if the user is excited, the adjustment unit can also prioritize visually stimulating color tones. In this way, the adjustment method for color tone and brightness can be changed according to the user's emotions.

[0118] When adjusting color tone or brightness, the adjustment unit can also select the optimal adjustment method by referring to the image's past adjustment history. For example, the adjustment unit can suggest the optimal adjustment method based on the color tone or brightness selected by the user in the past. The adjustment unit can also predict and suggest the color tone or brightness to be used at a specific time period based on the user's past adjustment history. Furthermore, the adjustment unit can analyze the user's past adjustment history and suggest the optimal color tone or brightness. This makes it possible to suggest the optimal color tone or brightness based on the user's past adjustment history.

[0119] The processing flow of the second embodiment will be briefly explained below.

[0120] Step 1: The reception unit uploads the image that the user wants to post. The user does not need to perform any special operations, and can do so in the same way as normal image uploading. For example, the reception unit allows the user to upload an image by dragging and dropping it. The reception unit can also allow the user to select an image using a file selection dialog. Furthermore, the reception unit can also upload images that the user has taken in real time using the camera. Step 2: The analysis unit analyzes the background of the uploaded image. For example, the analysis unit uses an image processing algorithm to extract the background. The analysis unit can also use image analysis techniques to detect features that indicate specific locations within the image. For example, the analysis unit detects features such as the shape of a building or the letters on a sign. Step 3: The feature detection unit detects features that indicate a specific location from the background analyzed by the analysis unit. The feature detection unit uses image analysis algorithms to detect features such as landmarks and building shapes. The feature detection unit can also detect features such as letters on signs and road signs. Step 4: The modification unit modifies the background based on the features detected by the feature detection unit. For example, the modification unit modifies the background using image synthesis technology. The modification unit can also change the shape of a building or replace the text on a sign with something else. For example, the modification unit can reduce the risk of identifying the location by transforming the shape of a building. The modification unit can also reduce the risk of identifying the location by translating the text on a sign into another language. Step 5: The providing unit provides the image with the background changed by the changing unit. For example, the providing unit allows the user to download the changed image. The providing unit may also provide a function to directly post the changed image to an SNS. Furthermore, the providing unit may also send the changed image by email.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] [Explanation of symbols]

[0193] 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 section for uploading images; an analysis unit that analyzes the background of the image uploaded by the reception unit; a feature detection unit that detects features that indicate a specific location from the background analyzed by the analysis unit; a modification unit that modifies a background based on the features detected by the feature detection unit; a providing unit that provides an image having the background changed by the changing unit; Equipped with A system characterized by:

2. A selection section is provided whereby the user can select a specific degree of background change.

2. The system of claim 1.

3. The change unit It has an adjustment section that adjusts the color tone and brightness based on a specific method to preserve the atmosphere of the original image.

2. The system of claim 1.

4. The analysis unit Detect features that indicate specific locations in an image 2. The system of claim 1.

5. The change unit Specifically changing the shape of the building 2. The system of claim 1.

6. The change unit Specifically replacing the text on the sign with something else 2. The system of claim 1.

7. The reception unit Estimate user emotions and adjust the timing of image uploads based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's upload history and select a specific upload method.

2. The system of claim 1.

Images