system

Abstract

The system according to the embodiment aims to process the pupils of images posted to SNS so that they cannot be read by others. [Solution] A system according to an embodiment includes a selection unit, a detection unit, an analysis unit, and a processing unit. The selection unit selects an image to post to a social networking service. The detection unit detects the pupil portion of the image selected by the selection unit. The analysis unit analyzes the scenery reflected in the pupil detected by the detection unit. The processing unit processes the pupil portion based on the scenery information analyzed by the analysis 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 is a risk that the scenery reflected in the eyes of a person in an image posted to a social networking site may be read by others.

[0005] The system according to the embodiment aims to process the pupils of images posted to SNS so that they cannot be read by others. [Means for solving the problem]

[0006] The system according to the embodiment includes a selection unit, a detection unit, an analysis unit, and a processing unit. The selection unit selects an image to post to an SNS. The detection unit detects the pupil portion of the image selected by the selection unit. The analysis unit analyzes the scenery reflected in the pupil detected by the detection unit. The processing unit processes the pupil portion based on the scenery information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can process the pupils of an image posted to an SNS so that they cannot be read by others. [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 processing system according to an embodiment of the present invention is a system that reduces the risk of a scene reflected in the eyes of an image posted to a social networking site (SNS) being read by others. This system uses an image generation AI to process images of eyes. For example, a user selects an image to post to the SNS, and the image generation AI detects the eye portion of the selected image and analyzes the scene reflected in the eye. Based on the analyzed scene information, the image generation AI processes the eye portion to prevent problems even if a scene is read by others. For example, the scene reflected in the eye can be blurred or replaced with a different scene. This allows the image processing system to allow users to post images to a social networking site with peace of mind. For example, when posting photos taken at a travel destination to a social networking site, the risk of a scene reflected in the eyes being read by others can be reduced. Furthermore, when posting photos taken in a private location to a social networking site, the risk of a scene reflected in the eyes being read by others can be reduced.

[0029] An image processing system according to an embodiment includes a selection unit, a detection unit, an analysis unit, and a processing unit. The selection unit selects an image to be posted to a social networking service (SNS). For example, the selection unit can eliminate the need for a user to pay particular attention to the pupil area when selecting an image to post to the SNS. The detection unit detects the pupil area of ​​the image selected by the selection unit. The detection unit identifies the pupil area using, for example, face recognition technology. The detection unit can also detect the pupil area by combining brightness and hue analysis. The analysis unit analyzes a scene reflected in the pupil detected by the detection unit. The analysis unit analyzes a scene reflected in the pupil using, for example, object recognition technology and scene analysis technology. The processing unit processes the pupil area based on the scene information analyzed by the analysis unit. For example, the processing unit can blur the scene reflected in the pupil to reduce the risk of it being read by others. The processing unit can also replace the scene reflected in the pupil with a different scene. As a result, the image processing system according to an embodiment can reduce the risk of it being read by others of the scene reflected in the pupil of an image to be posted to a social networking service.

[0030] The detection unit can detect the pupil area by combining face recognition technology with brightness and hue analysis. Face recognition technology includes, for example, face recognition technology using deep learning and conventional face recognition algorithms. Brightness and hue analysis includes, for example, histogram analysis and color space conversion. The detection unit, for example, uses face recognition technology to detect the entire face and then identifies the pupil area from within it. The detection unit can also use brightness analysis to adjust the brightness of the pupil area to improve detection accuracy. Furthermore, the detection unit can also use hue analysis to identify the color of the pupil area to improve detection accuracy. As a result, the combination of face recognition technology and brightness and hue analysis improves the detection accuracy of the pupil area.

[0031] The analysis unit can analyze the scene reflected in the pupil using object recognition technology and scene analysis technology. Object recognition technology includes, for example, YOLO, SSD, and Faster R-CNN. Scene analysis technology includes, for example, a scene classification algorithm and scene segmentation. The analysis unit can, for example, identify objects in the scene reflected in the pupil using object recognition technology. The analysis unit can also identify the background of the scene reflected in the pupil using scene analysis technology. Furthermore, the analysis unit can combine object recognition technology and scene analysis technology to analyze the scene reflected in the pupil in detail. As a result, the combination of object recognition technology and scene analysis technology improves the analysis accuracy of the scene reflected in the pupil.

[0032] The processing unit can perform a process to blur the scenery reflected in the eyes. Examples of blurring processes include Gaussian blur and motion blur. For example, if the scenery reflected in the eyes is clearly visible, the processing unit increases the intensity of the blur to blur the scenery. Also, if the scenery reflected in the eyes is blurry, the processing unit can adjust the intensity of the blur to blur the scenery appropriately. Furthermore, if the range of the scenery reflected in the eyes is wide, the processing unit can also widen the range of the blur to blur the scenery. In this way, by blurring the scenery reflected in the eyes, the risk of it being read by others can be reduced.

[0033] The processing unit can perform a process to replace the scenery reflected in the eyes with another scenery. The replacement process includes, for example, image synthesis technology and criteria for selecting the scenery to be replaced. For example, if the scenery reflected in the eyes is a specific place, the processing unit replaces it with another scenery of the same category. Furthermore, if the scenery reflected in the eyes is a private place, the processing unit can replace it with a scenery of a public place. Furthermore, if the scenery reflected in the eyes is a specific object, the processing unit can replace it with another object of the same category. In this way, by replacing the scenery reflected in the eyes with a different scenery, it is possible to prevent problems from occurring even if it is read by others.

[0034] The processing unit can use a technique to smooth the edges of the processed image. Edge smoothing techniques include, for example, anti-aliasing and edge smoothing algorithms. For example, if the edges of the processed image are unnatural, the processing unit can apply an edge smoothing technique to achieve a natural finish. In addition, if the edges of the processed image are jagged, the processing unit can also apply an edge smoothing technique to smooth them. Furthermore, if the edges of the processed image are blurred, the processing unit can also apply an edge smoothing technique to make them clear. In this way, by smoothing the edges of the processed image, a natural finish can be achieved.

[0035] The processing unit can use a technique for adjusting the color tone of the processed image. Techniques for adjusting the color tone include, for example, a color correction algorithm and white balance adjustment. For example, if the color tone of the processed image is unnatural, the processing unit applies a color tone adjustment technique to make it natural. Furthermore, if the color tone of the processed image is dark, the processing unit can adjust the brightness to improve the overall balance. Furthermore, if the color tone of the processed image is too vivid, the processing unit can adjust the saturation to improve the overall balance. In this way, by adjusting the color tone of the processed image, the overall balance can be improved.

[0036] The selection unit can analyze the user's past posting history and suggest the optimal image selection method. For example, the selection unit analyzes the trends of images posted by the user in the past and preferentially suggests similar images. The selection unit can also analyze the time period in which the user posted images in the past and suggest the optimal posting timing. Furthermore, the selection unit can analyze the categories of images posted by the user in the past and suggest related images. In this way, the optimal image selection method can be suggested by analyzing the user's past posting history.

[0037] When selecting images, the selection unit can perform filtering based on the user's current areas of interest and activities. For example, the selection unit can preferentially display images related to topics in which the user is currently interested. The selection unit can also preferentially display images related to places the user has recently visited. Furthermore, the selection unit can also preferentially display images related to events in which the user is currently participating. In this way, by filtering images based on the user's current areas of interest and activities, highly relevant images can be selected.

[0038] The selection unit can provide an optimal selection means depending on the user's input method when selecting an image. For example, when the user uses voice input, the selection unit selects an image based on a voice command. When the user uses text input, the selection unit can also select an image based on a keyword. Furthermore, when the user uses image input, the selection unit can perform a similar image search and select related images. This makes it easier to select an image by providing an optimal selection means depending on the user's input method.

[0039] When selecting an image, the selection unit can prioritize selecting highly relevant images by taking into consideration the user's geographical location information. For example, the selection unit can prioritize displaying images related to the user's current location. The selection unit can also prioritize displaying images related to places the user has visited in the past. Furthermore, the selection unit can also prioritize displaying images related to places the user plans to visit in the future. In this way, highly relevant images can be selected by taking into consideration the user's geographical location information.

[0040] When selecting an image, the selection unit can analyze the user's social media activity and suggest related images. For example, the selection unit can suggest images related to images shared by the user on social media. The selection unit can also suggest images related to images shared by the user's friends. Furthermore, the selection unit can suggest images related to images that the user has "liked" on social media. In this way, related images can be suggested by analyzing the user's social media activity.

[0041] The selection unit can customize the selection method by reflecting the user's past feedback when selecting an image. For example, the selection unit analyzes the trend of images that the user has previously rated highly and preferentially displays similar images. The selection unit can also analyze the trend of images that the user has previously rated poorly and avoid displaying them. Furthermore, the selection unit can analyze the trend of images on which the user has previously left comments and preferentially display related images. In this way, the selection method can be customized by reflecting the user's past feedback.

[0042] The detection unit can improve accuracy when detecting the pupil by combining face recognition technology with brightness and hue analysis. For example, the detection unit uses face recognition technology to detect the entire face and then identify the pupil from within. The detection unit can also improve detection accuracy by adjusting the brightness of the pupil using brightness analysis. Furthermore, the detection unit can also improve detection accuracy by identifying the color of the pupil using hue analysis. In this way, the detection accuracy of the pupil is improved by combining face recognition technology with brightness and hue analysis.

[0043] When detecting the pupil portion, the detection unit can apply different detection algorithms depending on the resolution and quality of the image. For example, the detection unit applies a high-precision detection algorithm to a high-resolution image. The detection unit can also apply a simple detection algorithm to a low-resolution image. Furthermore, when the image quality is low, the detection unit can also improve the detection accuracy by applying a noise removal algorithm. In this way, by applying different detection algorithms depending on the resolution and quality of the image, the detection accuracy is improved.

[0044] When detecting the pupil portion, the detection unit can improve detection accuracy by referring to the user's past detection results. For example, the detection unit analyzes the user's past detection results to improve detection accuracy. The detection unit can also improve detection accuracy by learning specific patterns from the user's past detection results. Furthermore, the detection unit can optimize the detection algorithm based on the user's past detection results. In this way, detection accuracy is improved by referring to the user's past detection results.

[0045] When detecting the pupil portion, the detection unit can improve detection accuracy by taking into account the location and time of image capture. For example, if the location where the image was captured is bright, the detection unit applies a normal detection algorithm. Also, if the location where the image was captured is dark, the detection unit can perform brightness correction to improve detection accuracy. Furthermore, if the image was captured during the daytime, the detection unit can apply a normal detection algorithm. In this way, detection accuracy is improved by taking into account the location and time of image capture.

[0046] When detecting the pupil portion, the detection unit can improve detection accuracy by referring to background information of the image. For example, if the background of the image is simple, the detection unit applies a normal detection algorithm. Also, if the background of the image is complex, the detection unit can improve detection accuracy by applying a background removal algorithm. Furthermore, if the background of the image is bright, the detection unit can perform brightness correction to improve detection accuracy. In this way, by referring to the background information of the image, detection accuracy is improved.

[0047] The detection unit can improve the detection accuracy by combining different face recognition technologies when detecting the pupil portion. For example, the detection unit can improve the detection accuracy of the pupil portion by combining different face recognition technologies. The detection unit can also improve the detection accuracy of the pupil portion by combining face recognition technology and object recognition technology. Furthermore, the detection unit can improve the detection accuracy of the pupil portion by combining face recognition technology and scene analysis technology. In this way, the detection accuracy is improved by combining different face recognition technologies.

[0048] The analysis unit can improve accuracy by combining object recognition technology and scene analysis technology when analyzing the scenery reflected in the pupil. For example, the analysis unit uses object recognition technology to identify objects in the scenery reflected in the pupil. The analysis unit can also use scene analysis technology to identify the background of the scenery reflected in the pupil. Furthermore, the analysis unit can combine object recognition technology and scene analysis technology to analyze the scenery reflected in the pupil in detail. Thus, by combining object recognition technology and scene analysis technology, the accuracy of scenery analysis is improved.

[0049] When analyzing the scene reflected in the pupil, the analysis unit can apply different analysis algorithms depending on the resolution and quality of the image. For example, the analysis unit applies a high-precision analysis algorithm to a high-resolution image. The analysis unit can also apply a simple analysis algorithm to a low-resolution image. Furthermore, when the image quality is low, the analysis unit can also apply a noise removal algorithm to improve the analysis accuracy. In this way, by applying different analysis algorithms depending on the resolution and quality of the image, the analysis accuracy is improved.

[0050] When analyzing the scene reflected in the eyes, the analysis unit can improve the analysis accuracy by referring to the user's past analysis results. For example, the analysis unit analyzes the user's past analysis results to improve the analysis accuracy. The analysis unit can also learn specific patterns from the user's past analysis results to improve the analysis accuracy. Furthermore, the analysis unit can optimize the analysis algorithm based on the user's past analysis results. In this way, the analysis accuracy is improved by referring to the user's past analysis results.

[0051] When analyzing the scenery reflected in the pupil, the analysis unit can improve the analysis accuracy by taking into account the location and time of the image capture. For example, if the location where the image was captured is bright, the analysis unit applies a normal analysis algorithm. Also, if the location where the image was captured is dark, the analysis unit can perform brightness correction to improve the analysis accuracy. Furthermore, if the image was captured during the daytime, the analysis unit can apply a normal analysis algorithm. In this way, the analysis accuracy is improved by taking into account the location and time of the image capture.

[0052] When analyzing the scenery reflected in the pupil, the analysis unit can improve the analysis accuracy by referring to the background information of the image. For example, if the background of the image is simple, the analysis unit applies a normal analysis algorithm. Also, if the background of the image is complex, the analysis unit can apply a background removal algorithm to improve the analysis accuracy. Furthermore, if the background of the image is bright, the analysis unit can perform brightness correction to improve the analysis accuracy. In this way, by referring to the background information of the image, the analysis accuracy is improved.

[0053] The analysis unit can improve the analysis accuracy by combining different object recognition technologies when analyzing the scene reflected in the pupil. For example, the analysis unit can improve the analysis accuracy of the scene reflected in the pupil by combining different object recognition technologies. The analysis unit can also improve the analysis accuracy of the scene reflected in the pupil by combining object recognition technology and scene analysis technology. Furthermore, the analysis unit can improve the analysis accuracy of the scene reflected in the pupil by combining object recognition technology and background removal technology. As a result, the analysis accuracy is improved by combining different object recognition technologies.

[0054] The processing unit can adjust the intensity and range of the blur when blurring the scenery reflected in the pupil. For example, when the scenery reflected in the pupil is clearly visible, the processing unit increases the intensity of the blur to blur the scenery. Also, when the scenery reflected in the pupil is blurry, the processing unit can adjust the intensity of the blur to blur the scenery appropriately. Furthermore, when the range of the scenery reflected in the pupil is wide, the processing unit can widen the range of the blur to blur the scenery. In this way, by adjusting the intensity and range of the blur, the scenery reflected in the pupil can be blurred appropriately.

[0055] The processing unit can set selection criteria for the scenery to be replaced when replacing the scenery reflected in the eyes with another scenery. For example, if the scenery reflected in the eyes is a specific place, the processing unit replaces it with another scenery of the same category. Also, if the scenery reflected in the eyes is a private place, the processing unit can replace it with a scenery of a public place. Furthermore, if the scenery reflected in the eyes is a specific object, the processing unit can replace it with another object of the same category. In this way, by setting selection criteria for the scenery to be replaced, it is possible to replace it with an appropriate scenery.

[0056] The processing unit can achieve a natural finish by using a technique for smoothing the edges of the processed image. For example, if the edges of the processed image are unnatural, the processing unit can apply edge smoothing technology to achieve a natural finish. In addition, if the edges of the processed image are jagged, the processing unit can also apply edge smoothing technology to smooth them. Furthermore, if the edges of the processed image are blurred, the processing unit can also apply edge smoothing technology to make them clear. In this way, by smoothing the edges of the processed image, a natural finish can be achieved.

[0057] The processing unit can select a blur pattern or style when blurring the scenery reflected in the eyes. For example, the processing unit applies Gaussian blur when blurring the scenery reflected in the eyes. The processing unit can also apply motion blur when blurring the scenery reflected in the eyes. Furthermore, the processing unit can also apply lens blur when blurring the scenery reflected in the eyes. In this way, by selecting a blur pattern or style, the scenery reflected in the eyes can be blurred appropriately.

[0058] The processing unit can customize the scenery according to the user's preferences when replacing the scenery reflected in the eyes with another scenery. For example, the processing unit pre-registers the scenery that the user likes and replaces it with that scenery. The processing unit can also customize the scenery to match the user's preferred color or style. Furthermore, the processing unit can customize the scenery to match the user's preferred season or time of day. This allows for more satisfying image processing by customizing the scenery according to the user's preferences.

[0059] The processing unit can adjust the overall balance by using a technique for adjusting the color tone of the processed image. For example, if the color tone of the processed image is unnatural, the processing unit applies a color tone adjustment technique to make it natural. In addition, if the color tone of the processed image is dark, the processing unit can adjust the brightness to adjust the overall balance. Furthermore, if the color tone of the processed image is too vivid, the processing unit can adjust the saturation to adjust the overall balance. In this way, by adjusting the color tone of the processed image, the overall balance can be adjusted.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The providing unit can analyze the user's past behavior history and suggest the optimal image processing method. For example, it can prioritize suggesting processing methods that the user has used favorably in the past. It can also exclude from suggestions processing methods that the user has avoided in the past. It can also suggest processing methods that the user has given high ratings to in the past. In this way, it is possible to suggest the optimal image processing method by analyzing the user's past behavior history.

[0062] The providing unit can analyze the user's current activity status and suggest the optimal image processing method. For example, if the user is currently traveling, a travel-related processing method can be suggested. If the user is currently working, a work-related processing method can be suggested. Furthermore, if the user is currently relaxing, a relaxation-related processing method can be suggested. In this way, the optimal image processing method can be suggested by analyzing the user's current activity status.

[0063] The providing unit can analyze the user's social media activity and suggest the optimal image processing method. For example, it can suggest processing methods related to images shared by the user on social media. It can also suggest processing methods related to images shared by the user's friends. It can also suggest processing methods related to images that the user has "liked" on social media. In this way, it is possible to suggest the optimal image processing method by analyzing the user's social media activity.

[0064] The providing unit can propose an optimal image processing method by taking into consideration the user's geographical location information. For example, it can propose a processing method related to the user's current location. It can also propose a processing method related to places the user has visited in the past. It can also propose a processing method related to places the user plans to visit in the future. In this way, it is possible to propose an optimal image processing method by taking into consideration the user's geographical location information.

[0065] The providing unit can suggest the optimal image processing method depending on the user's input method. For example, if the user uses voice input, the providing unit can suggest a processing method based on a voice command. If the user uses text input, the providing unit can also suggest a processing method based on keywords. Furthermore, if the user uses image input, the providing unit can perform a similar image search and suggest related processing methods. This makes it easier to process images by suggesting the optimal image processing method depending on the user's input method.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The selection unit selects an image to be posted to an SNS. When a user selects an image to be posted to an SNS, the user does not need to pay particular attention to the pupil area. Step 2: The detection unit detects the pupil area of ​​the image selected by the selection unit. The detection unit identifies the pupil area by combining face recognition technology and brightness / hue analysis. Step 3: The analysis unit analyzes the scene reflected in the pupil detected by the detection unit. The analysis unit analyzes the scene reflected in the pupil using object recognition technology and scene analysis technology. Step 4: The processing unit processes the pupil area based on the scenery information analyzed by the analysis unit. The processing unit blurs the scenery reflected in the pupil to reduce the risk of it being read by others. It can also replace the scenery reflected in the pupil with a different scenery.

[0068] (Example 2) An image processing system according to an embodiment of the present invention is a system that reduces the risk of a scene reflected in the eyes of an image posted to a social networking site (SNS) being read by others. This system uses an image generation AI to process images of eyes. For example, a user selects an image to post to the SNS, and the image generation AI detects the eye portion of the selected image and analyzes the scene reflected in the eye. Based on the analyzed scene information, the image generation AI processes the eye portion to prevent problems even if a scene is read by others. For example, the scene reflected in the eye can be blurred or replaced with a different scene. This allows the image processing system to allow users to post images to a social networking site with peace of mind. For example, when posting photos taken at a travel destination to a social networking site, the risk of a scene reflected in the eyes being read by others can be reduced. Furthermore, when posting photos taken in a private location to a social networking site, the risk of a scene reflected in the eyes being read by others can be reduced.

[0069] An image processing system according to an embodiment includes a selection unit, a detection unit, an analysis unit, and a processing unit. The selection unit selects an image to be posted to a social networking service (SNS). For example, the selection unit can eliminate the need for a user to pay particular attention to the pupil area when selecting an image to post to the SNS. The detection unit detects the pupil area of ​​the image selected by the selection unit. The detection unit identifies the pupil area using, for example, face recognition technology. The detection unit can also detect the pupil area by combining brightness and hue analysis. The analysis unit analyzes a scene reflected in the pupil detected by the detection unit. The analysis unit analyzes a scene reflected in the pupil using, for example, object recognition technology and scene analysis technology. The processing unit processes the pupil area based on the scene information analyzed by the analysis unit. For example, the processing unit can blur the scene reflected in the pupil to reduce the risk of it being read by others. The processing unit can also replace the scene reflected in the pupil with a different scene. As a result, the image processing system according to an embodiment can reduce the risk of it being read by others of the scene reflected in the pupil of an image to be posted to a social networking service.

[0070] The detection unit can detect the pupil area by combining face recognition technology with brightness and hue analysis. Face recognition technology includes, for example, face recognition technology using deep learning and conventional face recognition algorithms. Brightness and hue analysis includes, for example, histogram analysis and color space conversion. The detection unit, for example, uses face recognition technology to detect the entire face and then identifies the pupil area from within it. The detection unit can also use brightness analysis to adjust the brightness of the pupil area to improve detection accuracy. Furthermore, the detection unit can also use hue analysis to identify the color of the pupil area to improve detection accuracy. As a result, the combination of face recognition technology and brightness and hue analysis improves the detection accuracy of the pupil area.

[0071] The analysis unit can analyze the scene reflected in the pupil using object recognition technology and scene analysis technology. Object recognition technology includes, for example, YOLO, SSD, and Faster R-CNN. Scene analysis technology includes, for example, a scene classification algorithm and scene segmentation. The analysis unit can, for example, identify objects in the scene reflected in the pupil using object recognition technology. The analysis unit can also identify the background of the scene reflected in the pupil using scene analysis technology. Furthermore, the analysis unit can combine object recognition technology and scene analysis technology to analyze the scene reflected in the pupil in detail. As a result, the combination of object recognition technology and scene analysis technology improves the analysis accuracy of the scene reflected in the pupil.

[0072] The processing unit can perform a process to blur the scenery reflected in the eyes. Examples of blurring processes include Gaussian blur and motion blur. For example, if the scenery reflected in the eyes is clearly visible, the processing unit increases the intensity of the blur to blur the scenery. Also, if the scenery reflected in the eyes is blurry, the processing unit can adjust the intensity of the blur to blur the scenery appropriately. Furthermore, if the range of the scenery reflected in the eyes is wide, the processing unit can also widen the range of the blur to blur the scenery. In this way, by blurring the scenery reflected in the eyes, the risk of it being read by others can be reduced.

[0073] The processing unit can perform a process to replace the scenery reflected in the eyes with another scenery. The replacement process includes, for example, image synthesis technology and criteria for selecting the scenery to be replaced. For example, if the scenery reflected in the eyes is a specific place, the processing unit replaces it with another scenery of the same category. Furthermore, if the scenery reflected in the eyes is a private place, the processing unit can replace it with a scenery of a public place. Furthermore, if the scenery reflected in the eyes is a specific object, the processing unit can replace it with another object of the same category. In this way, by replacing the scenery reflected in the eyes with a different scenery, it is possible to prevent problems from occurring even if it is read by others.

[0074] The processing unit can use a technique to smooth the edges of the processed image. Edge smoothing techniques include, for example, anti-aliasing and edge smoothing algorithms. For example, if the edges of the processed image are unnatural, the processing unit can apply an edge smoothing technique to achieve a natural finish. In addition, if the edges of the processed image are jagged, the processing unit can also apply an edge smoothing technique to smooth them. Furthermore, if the edges of the processed image are blurred, the processing unit can also apply an edge smoothing technique to make them clear. In this way, by smoothing the edges of the processed image, a natural finish can be achieved.

[0075] The processing unit can use a technique for adjusting the color tone of the processed image. Techniques for adjusting the color tone include, for example, a color correction algorithm and white balance adjustment. For example, if the color tone of the processed image is unnatural, the processing unit applies a color tone adjustment technique to make it natural. Furthermore, if the color tone of the processed image is dark, the processing unit can adjust the brightness to improve the overall balance. Furthermore, if the color tone of the processed image is too vivid, the processing unit can adjust the saturation to improve the overall balance. In this way, by adjusting the color tone of the processed image, the overall balance can be improved.

[0076] The selection unit can estimate the user's emotions and adjust the timing of image selection based on the estimated user emotions. For example, if the user is feeling stressed, the selection unit can delay the timing of image selection to allow the user to select in a relaxed state. Furthermore, if the user is excited, the selection unit can also accelerate the timing of image selection to allow the user to select quickly. Furthermore, if the user is tired, the selection unit can adjust the timing of image selection to allow the user to select easily. In this way, by adjusting the timing of image selection according to the user's emotions, the image can be selected at a more appropriate time. 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.

[0077] The selection unit can analyze the user's past posting history and suggest the optimal image selection method. For example, the selection unit analyzes the trends of images posted by the user in the past and preferentially suggests similar images. The selection unit can also analyze the time period in which the user posted images in the past and suggest the optimal posting timing. Furthermore, the selection unit can analyze the categories of images posted by the user in the past and suggest related images. In this way, the optimal image selection method can be suggested by analyzing the user's past posting history.

[0078] When selecting images, the selection unit can perform filtering based on the user's current areas of interest and activities. For example, the selection unit can preferentially display images related to topics in which the user is currently interested. The selection unit can also preferentially display images related to places the user has recently visited. Furthermore, the selection unit can also preferentially display images related to events in which the user is currently participating. In this way, by filtering images based on the user's current areas of interest and activities, highly relevant images can be selected.

[0079] The selection unit can provide an optimal selection means depending on the user's input method when selecting an image. For example, when the user uses voice input, the selection unit selects an image based on a voice command. When the user uses text input, the selection unit can also select an image based on a keyword. Furthermore, when the user uses image input, the selection unit can perform a similar image search and select related images. This makes it easier to select an image by providing an optimal selection means depending on the user's input method.

[0080] The selection unit can estimate the user's emotions and determine the priority of images to be selected based on the estimated user's emotions. For example, if the user is relaxed, the selection unit can preferentially display landscape images. Furthermore, if the user is excited, the selection unit can preferentially display action scene images. Furthermore, if the user is sad, the selection unit can preferentially display soothing images. In this way, by determining the priority of images according to the user's emotions, more appropriate images can be selected. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0081] When selecting an image, the selection unit can prioritize selecting highly relevant images by taking into consideration the user's geographical location information. For example, the selection unit can prioritize displaying images related to the user's current location. The selection unit can also prioritize displaying images related to places the user has visited in the past. Furthermore, the selection unit can also prioritize displaying images related to places the user plans to visit in the future. In this way, highly relevant images can be selected by taking into consideration the user's geographical location information.

[0082] When selecting an image, the selection unit can analyze the user's social media activity and suggest related images. For example, the selection unit can suggest images related to images shared by the user on social media. The selection unit can also suggest images related to images shared by the user's friends. Furthermore, the selection unit can suggest images related to images that the user has "liked" on social media. In this way, related images can be suggested by analyzing the user's social media activity.

[0083] The selection unit can customize the selection method by reflecting the user's past feedback when selecting an image. For example, the selection unit analyzes the trend of images that the user has previously rated highly and preferentially displays similar images. The selection unit can also analyze the trend of images that the user has previously rated poorly and avoid displaying them. Furthermore, the selection unit can analyze the trend of images on which the user has previously left comments and preferentially display related images. In this way, the selection method can be customized by reflecting the user's past feedback.

[0084] The detection unit can estimate the user's emotion and adjust the detection accuracy of the pupil area based on the estimated user emotion. For example, when the user is relaxed, the detection unit detects the pupil area with normal detection accuracy. Furthermore, when the user is nervous, the detection unit can increase the detection accuracy to accurately detect the pupil area. Furthermore, when the user is excited, the detection unit can adjust the detection accuracy to quickly detect the pupil area. This allows for more accurate detection by adjusting the detection accuracy of the pupil area according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] The detection unit can improve accuracy when detecting the pupil by combining face recognition technology with brightness and hue analysis. For example, the detection unit uses face recognition technology to detect the entire face and then identify the pupil from within. The detection unit can also improve detection accuracy by adjusting the brightness of the pupil using brightness analysis. Furthermore, the detection unit can also improve detection accuracy by identifying the color of the pupil using hue analysis. In this way, the detection accuracy of the pupil is improved by combining face recognition technology with brightness and hue analysis.

[0086] When detecting the pupil portion, the detection unit can apply different detection algorithms depending on the resolution and quality of the image. For example, the detection unit applies a high-precision detection algorithm to a high-resolution image. The detection unit can also apply a simple detection algorithm to a low-resolution image. Furthermore, when the image quality is low, the detection unit can also improve the detection accuracy by applying a noise removal algorithm. In this way, by applying different detection algorithms depending on the resolution and quality of the image, the detection accuracy is improved.

[0087] When detecting the pupil portion, the detection unit can improve detection accuracy by referring to the user's past detection results. For example, the detection unit analyzes the user's past detection results to improve detection accuracy. The detection unit can also improve detection accuracy by learning specific patterns from the user's past detection results. Furthermore, the detection unit can optimize the detection algorithm based on the user's past detection results. In this way, detection accuracy is improved by referring to the user's past detection results.

[0088] The detection unit can estimate the user's emotion and adjust the display method of the detection result based on the estimated user's emotion. For example, if the user is relaxed, the detection unit displays the detection result in a normal display method. Furthermore, if the user is nervous, the detection unit can provide a simple, highly visible display method. Furthermore, if the user is excited, the detection unit can provide a visually stimulating display method. This improves visibility by adjusting the display method of the detection result according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0089] When detecting the pupil portion, the detection unit can improve detection accuracy by taking into account the location and time of image capture. For example, if the location where the image was captured is bright, the detection unit applies a normal detection algorithm. Also, if the location where the image was captured is dark, the detection unit can perform brightness correction to improve detection accuracy. Furthermore, if the image was captured during the daytime, the detection unit can apply a normal detection algorithm. In this way, detection accuracy is improved by taking into account the location and time of image capture.

[0090] When detecting the pupil portion, the detection unit can improve detection accuracy by referring to background information of the image. For example, if the background of the image is simple, the detection unit applies a normal detection algorithm. Also, if the background of the image is complex, the detection unit can improve detection accuracy by applying a background removal algorithm. Furthermore, if the background of the image is bright, the detection unit can perform brightness correction to improve detection accuracy. In this way, by referring to the background information of the image, detection accuracy is improved.

[0091] The detection unit can improve the detection accuracy by combining different face recognition technologies when detecting the pupil portion. For example, the detection unit can improve the detection accuracy of the pupil portion by combining different face recognition technologies. The detection unit can also improve the detection accuracy of the pupil portion by combining face recognition technology and object recognition technology. Furthermore, the detection unit can improve the detection accuracy of the pupil portion by combining face recognition technology and scene analysis technology. In this way, the detection accuracy is improved by combining different face recognition technologies.

[0092] The analysis unit can estimate the user's emotions and adjust the accuracy of the scene analysis based on the estimated user's emotions. For example, when the user is relaxed, the analysis unit analyzes the scene with normal analysis accuracy. Furthermore, when the user is nervous, the analysis unit can increase the analysis accuracy to accurately analyze the scene. Furthermore, when the user is excited, the analysis unit can adjust the analysis accuracy to quickly analyze the scene. This allows for more accurate analysis by adjusting the scene analysis accuracy according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0093] The analysis unit can improve accuracy by combining object recognition technology and scene analysis technology when analyzing the scenery reflected in the pupil. For example, the analysis unit uses object recognition technology to identify objects in the scenery reflected in the pupil. The analysis unit can also use scene analysis technology to identify the background of the scenery reflected in the pupil. Furthermore, the analysis unit can combine object recognition technology and scene analysis technology to analyze the scenery reflected in the pupil in detail. Thus, by combining object recognition technology and scene analysis technology, the accuracy of scenery analysis is improved.

[0094] When analyzing the scene reflected in the pupil, the analysis unit can apply different analysis algorithms depending on the resolution and quality of the image. For example, the analysis unit applies a high-precision analysis algorithm to a high-resolution image. The analysis unit can also apply a simple analysis algorithm to a low-resolution image. Furthermore, when the image quality is low, the analysis unit can also apply a noise removal algorithm to improve the analysis accuracy. In this way, by applying different analysis algorithms depending on the resolution and quality of the image, the analysis accuracy is improved.

[0095] When analyzing the scene reflected in the eyes, the analysis unit can improve the analysis accuracy by referring to the user's past analysis results. For example, the analysis unit analyzes the user's past analysis results to improve the analysis accuracy. The analysis unit can also learn specific patterns from the user's past analysis results to improve the analysis accuracy. Furthermore, the analysis unit can optimize the analysis algorithm based on the user's past analysis results. In this way, the analysis accuracy is improved by referring to the user's past analysis results.

[0096] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is relaxed, the analysis unit displays the analysis results in a normal display method. Furthermore, if the user is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating display method. This improves visibility by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0097] When analyzing the scenery reflected in the pupil, the analysis unit can improve the analysis accuracy by taking into account the location and time of the image capture. For example, if the location where the image was captured is bright, the analysis unit applies a normal analysis algorithm. Also, if the location where the image was captured is dark, the analysis unit can perform brightness correction to improve the analysis accuracy. Furthermore, if the image was captured during the daytime, the analysis unit can apply a normal analysis algorithm. In this way, the analysis accuracy is improved by taking into account the location and time of the image capture.

[0098] When analyzing the scenery reflected in the pupil, the analysis unit can improve the analysis accuracy by referring to the background information of the image. For example, if the background of the image is simple, the analysis unit applies a normal analysis algorithm. Also, if the background of the image is complex, the analysis unit can apply a background removal algorithm to improve the analysis accuracy. Furthermore, if the background of the image is bright, the analysis unit can perform brightness correction to improve the analysis accuracy. In this way, by referring to the background information of the image, the analysis accuracy is improved.

[0099] The analysis unit can improve the analysis accuracy by combining different object recognition technologies when analyzing the scene reflected in the pupil. For example, the analysis unit can improve the analysis accuracy of the scene reflected in the pupil by combining different object recognition technologies. The analysis unit can also improve the analysis accuracy of the scene reflected in the pupil by combining object recognition technology and scene analysis technology. Furthermore, the analysis unit can improve the analysis accuracy of the scene reflected in the pupil by combining object recognition technology and background removal technology. As a result, the analysis accuracy is improved by combining different object recognition technologies.

[0100] The processing unit can estimate the user's emotion and adjust the processing method for the pupil area based on the estimated user emotion. For example, when the user is relaxed, the processing unit processes the pupil area using a normal processing method. Furthermore, when the user is nervous, the processing unit can increase the processing accuracy to accurately process the pupil area. Furthermore, when the user is excited, the processing unit can adjust the processing accuracy to quickly process the pupil area. This enables more appropriate processing by adjusting the processing method for the pupil area according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0101] The processing unit can adjust the intensity and range of the blur when blurring the scenery reflected in the pupil. For example, when the scenery reflected in the pupil is clearly visible, the processing unit increases the intensity of the blur to blur the scenery. Also, when the scenery reflected in the pupil is blurry, the processing unit can adjust the intensity of the blur to blur the scenery appropriately. Furthermore, when the range of the scenery reflected in the pupil is wide, the processing unit can widen the range of the blur to blur the scenery. In this way, by adjusting the intensity and range of the blur, the scenery reflected in the pupil can be blurred appropriately.

[0102] The processing unit can set selection criteria for the scenery to be replaced when replacing the scenery reflected in the eyes with another scenery. For example, if the scenery reflected in the eyes is a specific place, the processing unit replaces it with another scenery of the same category. Also, if the scenery reflected in the eyes is a private place, the processing unit can replace it with a scenery of a public place. Furthermore, if the scenery reflected in the eyes is a specific object, the processing unit can replace it with another object of the same category. In this way, by setting selection criteria for the scenery to be replaced, it is possible to replace it with an appropriate scenery.

[0103] The processing unit can achieve a natural finish by using a technique for smoothing the edges of the processed image. For example, if the edges of the processed image are unnatural, the processing unit can apply edge smoothing technology to achieve a natural finish. In addition, if the edges of the processed image are jagged, the processing unit can also apply edge smoothing technology to smooth them. Furthermore, if the edges of the processed image are blurred, the processing unit can also apply edge smoothing technology to make them clear. In this way, by smoothing the edges of the processed image, a natural finish can be achieved.

[0104] The processing unit can estimate the user's emotions and adjust the display method of the processed image based on the estimated user's emotions. For example, if the user is relaxed, the processing unit displays the processed image in a normal display method. Furthermore, if the user is nervous, the processing unit can provide a simple, highly visible display method. Furthermore, if the user is excited, the processing unit can provide a visually stimulating display method. In this way, visibility is improved by adjusting the display method of the processed image according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0105] The processing unit can select a blur pattern or style when blurring the scenery reflected in the eyes. For example, the processing unit applies Gaussian blur when blurring the scenery reflected in the eyes. The processing unit can also apply motion blur when blurring the scenery reflected in the eyes. Furthermore, the processing unit can also apply lens blur when blurring the scenery reflected in the eyes. In this way, by selecting a blur pattern or style, the scenery reflected in the eyes can be blurred appropriately.

[0106] The processing unit can customize the scenery according to the user's preferences when replacing the scenery reflected in the eyes with another scenery. For example, the processing unit pre-registers the scenery that the user likes and replaces it with that scenery. The processing unit can also customize the scenery to match the user's preferred color or style. Furthermore, the processing unit can customize the scenery to match the user's preferred season or time of day. This allows for more satisfying image processing by customizing the scenery according to the user's preferences.

[0107] The processing unit can adjust the overall balance by using a technique for adjusting the color tone of the processed image. For example, if the color tone of the processed image is unnatural, the processing unit applies a color tone adjustment technique to make it natural. In addition, if the color tone of the processed image is dark, the processing unit can adjust the brightness to adjust the overall balance. Furthermore, if the color tone of the processed image is too vivid, the processing unit can adjust the saturation to adjust the overall balance. In this way, by adjusting the color tone of the processed image, the overall balance can be adjusted. === Hard Collateral 1-1 === Each of the multiple elements including the selection unit, detection unit, analysis unit, and processing unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the smart device 14 and is used when a user selects an image to post on an SNS. The detection unit detects the pupil portion of the selected image using the camera 42 of the smart device 14. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the scenery reflected in the detected pupil. The processing unit is realized by the identification processing unit 290 of the data processing device 12 and processes the pupil portion based on the analyzed scenery information. The selection unit has a function of estimating the user's emotion and adjusting the timing of image selection based on the estimated emotion. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned selection unit, detection unit, analysis unit, and processing unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the smart glasses 214 and is used when a user selects an image to post on an SNS. The detection unit detects the pupil portion of the selected image using the camera 42 of the smart glasses 214. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the scenery reflected in the detected pupil. The processing unit is realized by the identification processing unit 290 of the data processing device 12 and processes the pupil portion based on the analyzed scenery information. The selection unit has a function of estimating the user's emotion and adjusting the timing of image selection based on the estimated emotion. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned selection unit, detection unit, analysis unit, and processing unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the headset type terminal 314 and is used when a user selects an image to post on an SNS. The detection unit detects the pupil portion of the selected image using the camera 42 of the headset type terminal 314. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the scenery reflected in the detected pupil. The processing unit is realized by the identification processing unit 290 of the data processing device 12 and processes the pupil portion based on the analyzed scenery information. The selection unit has a function of estimating the user's emotion and adjusting the timing of image selection based on the estimated emotion. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned selection unit, detection unit, analysis unit, and processing unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the robot 414 and is used when a user selects an image to post on an SNS. The detection unit detects the pupil portion of the selected image using the camera 42 of the robot 414. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the scenery reflected in the detected pupil. The processing unit is realized by the identification processing unit 290 of the data processing device 12 and processes the pupil portion based on the analyzed scenery information. The selection unit has a function of estimating the user's emotion and adjusting the timing of image selection based on the estimated emotion.

[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 analysis unit can estimate the user's emotions and adjust the level of detail of the analysis results based on the estimated user emotions. For example, if the user is relaxed, the analysis results can be displayed in detail, and if the user is excited, the analysis results can be displayed in a concise manner. Also, if the user is feeling stressed, the analysis results can be displayed in a visually easy-to-understand manner. This allows the provision of optimal information to the user by adjusting the level of detail of the analysis results according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0110] The providing unit can estimate the user's emotions and suggest image processing based on the estimated user's emotions. For example, if the user is relaxed, image processing of natural scenery can be suggested, and if the user is excited, image processing of action scenes can be suggested. Also, if the user is sad, soothing image processing can be suggested. This makes it possible to suggest optimal image processing according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0111] The providing unit can estimate the user's emotions and collect feedback on image processing based on the estimated user emotions. For example, if the user is relaxed, detailed feedback can be requested, and if the user is excited, brief feedback can be requested. Also, if the user is feeling stressed, feedback collection can be temporarily suspended. This enables optimal feedback collection according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0112] The providing unit can estimate the user's emotions and adjust the method of sharing the image processing results based on the estimated user's emotions. For example, if the user is relaxed, detailed results can be shared, and if the user is excited, brief results can be shared. Also, if the user is feeling stressed, the sharing of results can be temporarily suspended. This enables optimal result sharing 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 can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0113] The providing unit can estimate the user's emotions and automatically adjust image processing settings based on the estimated user's emotions. For example, if the user is relaxed, natural processing settings can be applied, and if the user is excited, vivid processing settings can be applied. Also, if the user is stressed, gentle processing settings can be applied. This enables optimal image processing settings to be set according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0114] The providing unit can analyze the user's past behavior history and suggest the optimal image processing method. For example, it can prioritize suggesting processing methods that the user has used favorably in the past. It can also exclude from suggestions processing methods that the user has avoided in the past. It can also suggest processing methods that the user has given high ratings to in the past. In this way, it is possible to suggest the optimal image processing method by analyzing the user's past behavior history.

[0115] The providing unit can analyze the user's current activity status and suggest the optimal image processing method. For example, if the user is currently traveling, a travel-related processing method can be suggested. If the user is currently working, a work-related processing method can be suggested. Furthermore, if the user is currently relaxing, a relaxation-related processing method can be suggested. In this way, the optimal image processing method can be suggested by analyzing the user's current activity status.

[0116] The providing unit can analyze the user's social media activity and suggest the optimal image processing method. For example, it can suggest processing methods related to images shared by the user on social media. It can also suggest processing methods related to images shared by the user's friends. It can also suggest processing methods related to images that the user has "liked" on social media. In this way, it is possible to suggest the optimal image processing method by analyzing the user's social media activity.

[0117] The providing unit can propose an optimal image processing method by taking into consideration the user's geographical location information. For example, it can propose a processing method related to the user's current location. It can also propose a processing method related to places the user has visited in the past. It can also propose a processing method related to places the user plans to visit in the future. In this way, it is possible to propose an optimal image processing method by taking into consideration the user's geographical location information.

[0118] The providing unit can suggest the optimal image processing method depending on the user's input method. For example, if the user uses voice input, the providing unit can suggest a processing method based on a voice command. If the user uses text input, the providing unit can also suggest a processing method based on keywords. Furthermore, if the user uses image input, the providing unit can perform a similar image search and suggest related processing methods. This makes it easier to process images by suggesting the optimal image processing method depending on the user's input method.

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

[0120] Step 1: The selection unit selects an image to be posted to an SNS. When a user selects an image to be posted to an SNS, the user does not need to pay particular attention to the pupil area. Step 2: The detection unit detects the pupil area of ​​the image selected by the selection unit. The detection unit identifies the pupil area by combining face recognition technology and brightness / hue analysis. Step 3: The analysis unit analyzes the scene reflected in the pupil detected by the detection unit. The analysis unit analyzes the scene reflected in the pupil using object recognition technology and scene analysis technology. Step 4: The processing unit processes the pupil area based on the scenery information analyzed by the analysis unit. The processing unit blurs the scenery reflected in the pupil to reduce the risk of it being read by others. It can also replace the scenery reflected in the pupil with a different scenery.

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

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

[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 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 type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

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

[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 selection section for selecting an image to post to SNS; a detection unit that detects a pupil portion of the image selected by the selection unit; an analysis unit that analyzes a scene reflected in the pupil detected by the detection unit; a processing unit that processes the pupil portion based on the scenery information analyzed by the analysis unit; A system characterized by:

2. The detection unit Combining face recognition technology with brightness and hue analysis to detect pupils 2. The system of claim 1.

3. The analysis unit Analyzing the scenery reflected in the eyes using object recognition and scene analysis technologies 2. The system of claim 1.

4. The processing unit is Blur the scenery reflected in the eyes 2. The system of claim 1.

5. The processing unit is Performs a process to replace the scenery reflected in the eyes with another scenery 2. The system of claim 1.

6. The processing unit is Uses techniques to smooth the edges of the processed image 2. The system of claim 1.

7. The processing unit is Use a technique to adjust the color tone of the processed image 2. The system of claim 1.

8. The selection unit Estimate the user's emotions and adjust the timing of image selection based on the estimated user emotions.

2. The system of claim 1.

Images