System

Abstract

An object of a system according to an embodiment is to easily generate a portrait from a photograph.SOLUTION: A system includes an acquisition unit, an analysis unit, a generation unit, and a provision unit. The capturing unit captures a photograph. The analyzing unit analyzes the photograph captured by the capturing unit. The generation unit generates a portrait based on the photograph analyzed by the analysis unit. The provision unit provides the portrait generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that the process of generating a portrait from a photograph is time-consuming and difficult to do simply.

[0005] The system according to the embodiment aims to easily generate a portrait from a photograph. [Means for solving the problem]

[0006] A system according to an embodiment includes an acquisition unit, an analysis unit, a generation unit, and a provision unit. The acquisition unit acquires a photograph. The analysis unit analyzes the photograph acquired by the acquisition unit. The generation unit generates a portrait based on the photograph analyzed by the analysis unit. The provision unit provides the portrait generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can easily generate a portrait from a photograph. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A caricature generation system according to an embodiment of the present invention is a system that imports and analyzes a photo, generates a caricature, and provides it. In the caricature generation system, a user imports a photo, and a generation AI analyzes the photo and generates a caricature. This mechanism allows users to easily create caricatures of themselves or others. For example, the caricature generation system allows a user to import a photo. To do this, the user can take a photo using a smartphone or camera, or upload an existing photo. The caricature generation system then analyzes the imported photo using a generation AI. The generation AI recognizes facial features in the photo and generates a caricature based on the recognition. For example, the AI ​​analyzes the position and shape of the eyes, nose, and mouth, and draws a caricature based on the analysis. The generated caricature is provided to the user. The user can download the generated caricature or share it on social media. This allows users to easily create caricatures of themselves or others. The caricature generation system allows users to easily create caricatures of themselves or others. For example, caricatures can be created as souvenirs at events or parties, or as gifts. You can also use it as your profile picture on social media to express your individuality.

[0029] A caricature generation system according to an embodiment includes an import unit, an analysis unit, a generation unit, and a provision unit. The import unit imports a photo. Examples of photos include, but are not limited to, photos taken with a smartphone or a camera, existing photos, etc. The import unit imports, for example, a photo taken with a smartphone into the system. The import unit can also upload existing photos. For example, a user selects an existing photo and uploads it to the system. The analysis unit analyzes the imported photo using a generation AI. For example, the analysis unit recognizes facial features in the photo and performs analysis based on the recognition. For example, the generation AI analyzes the position and shape of the eyes, nose, and mouth and performs analysis based on the recognition. The analysis unit can also analyze facial features in detail using the generation AI. For example, the generation AI analyzes facial contours and facial expressions and performs analysis based on the recognition. The generation unit generates a caricature based on the photo analyzed by the analysis unit. For example, the generation unit generates a caricature based on the facial features in the photo using the generation AI. For example, the generation AI draws a caricature based on the position and shape of the eyes, nose, and mouth. The generation unit can also use a generation AI to generate a caricature that faithfully reproduces facial features. For example, the generation AI draws a caricature based on the contours and facial expressions of the face. The providing unit provides the caricature generated by the generation unit. For example, the providing unit allows a user to download the generated caricature. The providing unit can also have a function to share the generated caricature on a social networking site. For example, a user can send the generated caricature to a friend or use it as a profile picture. In this way, the caricature generation system according to the embodiment can automate a series of processes of importing and analyzing a photo, generating a caricature, and providing it.

[0030] The generation unit can recognize facial features in a photograph and generate a caricature based on the features. The generation unit, for example, uses a generation AI to recognize facial features in a photograph. For example, the generation AI analyzes the position and shape of the eyes, nose, and mouth, and generates a caricature based on that. The generation unit can also use the generation AI to analyze facial features in detail and generate a caricature based on that. For example, the generation AI analyzes facial contours and facial expressions, and draws a caricature based on that. The generation unit can also use the generation AI to generate a caricature that faithfully reproduces facial features. For example, the generation AI draws a caricature based on the position and shape of the eyes, nose, and mouth. This makes it possible to generate a more precise caricature by recognizing facial features in a photograph.

[0031] The providing unit may have a function of downloading the generated portrait. The providing unit, for example, allows the user to download the generated portrait. For example, the providing unit may provide the generated portrait in a file format, which the user can download. The providing unit may also provide the generated portrait in high resolution. For example, the providing unit may provide the generated portrait as a high-resolution image file, which the user can download. In this way, the user can download the generated portrait and keep it on hand.

[0032] The providing unit may have a function of sharing the generated caricature on an SNS. For example, the providing unit may have a function of sharing the generated caricature on an SNS. For example, the providing unit may generate a link for sharing the generated caricature on an SNS, allowing the user to share it. The providing unit may also have a function of directly posting the generated caricature on an SNS. For example, the providing unit may directly post the generated caricature to the user's SNS account. This allows the user to easily share the generated caricature with others by sharing it on an SNS.

[0033] The capture unit can analyze the user's past photo capture history and select an appropriate capture method. The capture unit, for example, analyzes the user's past photo capture history and selects the optimal capture method. For example, the capture unit automatically applies filters and effects that the user has used favorably in the past. The capture unit can also suggest optimal shooting conditions by referring to the time of day and location where the user took photos in the past. The capture unit can also analyze the composition of photos the user has taken in the past and guide the user to take photos with a similar composition. In this way, by selecting the optimal capture method based on the past history, it is possible to capture photos that suit the user's preferences.

[0034] The capture unit can appropriately filter based on the user's current environment when capturing a photo. For example, the capture unit performs filtering based on the user's current environment (lighting, background, etc.) when capturing a photo. For example, if the lighting is low, the capture unit applies a filter that automatically adjusts brightness. Also, if the background is cluttered, the capture unit can apply a filter that blurs the background. Also, if the lighting is too strong, the capture unit can apply a filter that suppresses highlights. In this way, filtering according to the user's environment can be performed to capture optimal photos.

[0035] The capture unit can select an appropriate capture means according to the user's input method when capturing a photo. For example, when capturing a photo, the capture unit selects the optimal capture means according to the user's input method (voice, text, image, etc.). For example, when the user commands "take a photo" by voice, the capture unit activates the camera based on the voice input and takes a photo. Furthermore, when the user inputs "selfie" by text, the capture unit can also activate the front camera and take a photo. Furthermore, when the user uploads an existing image, the capture unit can select that image as the image to be captured. This improves convenience by selecting the optimal capture means according to the user's input method.

[0036] When importing photos, the import unit can prioritize importing relevant photos by taking into account the user's geographical location information. For example, when importing photos, the import unit prioritizes importing highly relevant photos by taking into account the user's geographical location information. For example, when the user is at a tourist spot, the import unit prioritizes importing photos related to that location. Furthermore, when the user is at home, the import unit can also prioritize importing photos of family and pets. Furthermore, when the user is at an event venue, the import unit can also prioritize importing photos related to the event. In this way, highly relevant photos can be prioritized by taking into account the user's geographical location information.

[0037] The capture unit can analyze the user's social media activity and capture related photos when capturing photos. For example, the capture unit analyzes the user's social media activity and captures related photos when capturing photos. For example, the capture unit captures photos related to places where the user has checked in on social media. The capture unit can also analyze the content of the user's social media posts and capture related photos. The capture unit can also capture related photos by referring to the activities of the user's friends on social media. In this way, related photos can be captured by analyzing the user's social media activity.

[0038] The capture unit can adjust the capture method by reflecting the user's past feedback when capturing photos. For example, the capture unit customizes the capture method by reflecting the user's past feedback when capturing photos. For example, the capture unit automatically applies filters and effects that the user previously preferred. The capture unit can also suggest optimal shooting conditions by referring to the time of day and location where the user previously took photos. The capture unit can also analyze the composition of photos the user has taken in the past and guide the user to take photos with a similar composition. In this way, the capture method can be customized by reflecting the user's past feedback.

[0039] The analysis unit can determine the level of detail of the analysis based on the importance of the photo during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the photo during analysis. For example, the analysis unit performs a detailed analysis for a photo of an important event. The analysis unit can also perform a concise analysis for an everyday photo. The analysis unit can also perform an analysis that focuses on a specific person in a photo. In this way, by adjusting the level of detail of the analysis based on the importance of the photo, it is possible to provide appropriate analysis results.

[0040] The analysis unit can use different analysis algorithms depending on the category of the photo during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the photo during analysis. For example, in the case of a portrait photo, the analysis unit applies an algorithm that analyzes facial features in detail. In addition, in the case of a landscape photo, the analysis unit can also apply an algorithm that analyzes background features. In addition, in the case of a group photo, the analysis unit can apply an algorithm that analyzes multiple faces simultaneously. In this way, by applying an analysis algorithm depending on the category of the photo, more accurate analysis results can be provided.

[0041] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit performs a similar analysis based on analysis results that the user has previously preferred. The analysis unit can also adjust the analysis algorithm by referring to feedback provided by the user in the past. The analysis unit can also learn the user's past analysis results and improve the accuracy of the analysis. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0042] The analysis unit can set an analysis priority based on when the photos were taken during analysis. For example, the analysis unit determines an analysis priority based on when the photos were taken during analysis. For example, the analysis unit prioritizes analysis of recently taken photos. The analysis unit can also prioritize analysis of photos from a specific event. The analysis unit can also prioritize analysis of photos taken within a period specified by the user. In this way, by determining an analysis priority based on when the photos were taken, appropriate analysis results can be provided.

[0043] The analysis unit can set the order of analysis based on the relevance of the photos during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of the photos during analysis. For example, the analysis unit continuously analyzes photos of the same event. The analysis unit can also continuously analyze photos that feature the same person. The analysis unit can also continuously analyze photos taken at the same location. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the photos.

[0044] The analysis unit can determine the use of technical terms for the analysis according to the user's level of expertise during analysis. For example, the analysis unit adjusts the use of technical terms for the analysis according to the user's level of expertise during analysis. For example, if the user is an expert, the analysis unit uses detailed technical terms. If the user is a beginner, the analysis unit can also explain the analysis results in simple terms. If the user is an intermediate user, the analysis unit can also use appropriate technical terms. This makes it possible to provide analysis results that are easy to understand by providing analysis results according to the user's level of expertise.

[0045] The generation unit can analyze the facial features in the photograph in detail during generation to generate a more accurate caricature. For example, the generation unit can analyze the facial features in the photograph in detail during generation to generate a more accurate caricature. For example, the generation unit can analyze the shape and position of the eyes in detail and accurately reproduce them. The generation unit can also analyze the shape and position of the nose in detail and accurately reproduce them. The generation unit can also analyze the shape and position of the mouth in detail and accurately reproduce them. In this way, a more accurate caricature can be generated by analyzing the facial features in the photograph in detail.

[0046] The generation unit can improve the accuracy of generation by referring to the user's past caricature generation history during generation. For example, the generation unit can improve the accuracy of generation by referring to the user's past caricature generation history during generation. For example, the generation unit generates a caricature in a similar style based on a style that the user has previously preferred. The generation unit can also adjust the generation algorithm by referring to feedback provided by the user in the past. The generation unit can also learn the user's past caricature generation history and improve the accuracy of generation. In this way, the accuracy of generation can be improved by referring to the user's past caricature generation history.

[0047] The generation unit can adjust the style of the caricature according to the user's preferences when generating the caricature. For example, the generation unit customizes the style of the caricature according to the user's preferences when generating the caricature. For example, the generation unit generates a caricature that reflects the user's preferred color usage. The generation unit can also generate a caricature that reflects the user's preferred touch (realistic, anime-style, etc.). The generation unit can also generate a caricature that reflects the user's preferred background. In this way, satisfaction is improved by generating a caricature in a style that meets the user's preferences.

[0048] The generation unit can generate a background based on the location where the photo was taken at the time of generation. The generation unit, for example, automatically generates a background based on the location where the photo was taken at the time of generation. For example, if the photo was taken in a park, the generation unit generates a caricature with the park scenery as the background. Furthermore, if the photo was taken on a beach, the generation unit can also generate a caricature with the beach scenery as the background. Furthermore, if the photo was taken in a city, the generation unit can also generate a caricature with the city scenery as the background. In this way, by automatically generating a background based on the location where the photo was taken, it is possible to provide a more realistic caricature.

[0049] The generation unit can analyze the user's social media activity at the time of generation and provide a related style. For example, the generation unit can analyze the user's social media activity at the time of generation and suggest a related style. For example, the generation unit can generate a caricature that reflects a filter that the user frequently uses on social media. The generation unit can also analyze the content of the user's social media posts and suggest a related style. The generation unit can also suggest a related style by referring to the activity of the user's friends on social media. In this way, a related style can be suggested by analyzing the user's social media activity.

[0050] The generation unit can adjust the generation method by reflecting the user's past feedback at the time of generation. For example, the generation unit customizes the generation method by reflecting the user's past feedback at the time of generation. For example, the generation unit generates a caricature in a similar style based on a style that the user previously preferred. The generation unit can also adjust the generation algorithm by referring to feedback provided by the user in the past. The generation unit can also learn the user's past feedback and improve the accuracy of generation. In this way, the generation method can be customized by reflecting the user's past feedback.

[0051] The providing unit can select an appropriate providing method by referring to the user's past usage history when providing the content. For example, the providing unit selects the optimal providing method by referring to the user's past usage history when providing the content. For example, if the user has preferred downloading in the past, the providing unit can preferentially display a download link. Furthermore, if the user has preferred sharing on social media in the past, the providing unit can preferentially display a share button. Furthermore, if the user has preferred printing in the past, the providing unit can preferentially display a print option. In this way, the optimal providing method can be selected by referring to the user's past usage history.

[0052] The providing unit can adjust the display content according to the user's current device when providing the content. For example, the providing unit customizes the display content according to the user's current device when providing the content. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a personal computer, the providing unit can also provide a high-resolution display method. This improves convenience by customizing the display content according to the user's current device.

[0053] The providing unit can improve the providing method by reflecting user feedback at the time of providing. For example, the providing unit improves the providing method by reflecting user feedback at the time of providing. For example, the providing unit adjusts the next providing method based on feedback provided by the user regarding the providing method. The providing unit can also select the optimal providing method based on an evaluation provided by the user regarding the providing method. The providing unit can also learn from user feedback and improve the accuracy of the providing method. In this way, the providing method can be improved by reflecting user feedback.

[0054] The providing unit can select an appropriate providing method by taking into consideration the user's geographical location information when providing the information. For example, the providing unit selects the optimal providing method by taking into consideration the user's geographical location information when providing the information. For example, the providing unit may preferentially display a download link when the user is at home. Furthermore, the providing unit may preferentially display a share button on an SNS when the user is out and about. Furthermore, the providing unit may preferentially display a print option when the user is at an event venue. In this way, the optimal providing method can be selected by taking into consideration the user's geographical location information.

[0055] The providing unit can analyze the user's social media activity at the time of providing and provide a related provision method. For example, the providing unit can analyze the user's social media activity at the time of providing and suggest a related provision method. For example, if the user often shares on social media, the providing unit can preferentially display a share button. The providing unit can also analyze the content of the user's posts on social media and suggest a related provision method. The providing unit can also suggest a related provision method by referring to the activity of the user's friends on social media. In this way, it is possible to suggest a related provision method by analyzing the user's social media activity.

[0056] The providing unit can adjust the providing method by reflecting the user's past feedback when providing the information. For example, the providing unit customizes the providing method by reflecting the user's past feedback when providing the information. For example, the providing unit provides the information in a similar method based on a providing method that the user has previously preferred. The providing unit can also adjust the providing method by referring to feedback provided by the user in the past. The providing unit can also learn the user's past feedback and improve the accuracy of the providing method. In this way, the providing method can be customized by reflecting the user's past feedback.

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

[0058] The providing unit can customize the style of the caricature to be provided by referring to the user's past usage history. For example, it can provide a caricature that reflects the color usage and touches that the user has previously preferred. It can also use a similar background based on a background that the user has previously selected. Furthermore, it can also adjust the providing method by referring to feedback provided by the user in the past. In this way, by referring to the user's past usage history, it is possible to provide a caricature that is more satisfying.

[0059] The generation unit can analyze the user's social media activity and suggest related styles. For example, it can generate a caricature that reflects filters the user frequently uses on social media. It can also analyze the content of the user's social media posts and suggest related styles. It can also suggest related styles based on the activity of the user's friends on social media. In this way, it is possible to suggest related styles by analyzing the user's social media activity.

[0060] The capture unit can prioritize capturing highly relevant photos by taking into account the user's geographical location information. For example, if the user is at a tourist spot, photos related to that location can be prioritized. Also, if the user is at home, photos of family and pets can be prioritized. Furthermore, if the user is at an event venue, photos related to the event can be prioritized. In this way, by taking into account the user's geographical location information, highly relevant photos can be prioritized.

[0061] The analysis unit can set analysis priorities based on when the photos were taken. For example, it can prioritize analysis of recently taken photos. It can also prioritize analysis of photos from a specific event. It can also prioritize analysis of photos taken within a period specified by the user. In this way, by setting analysis priorities based on when the photos were taken, it is possible to provide appropriate analysis results.

[0062] The providing unit can adjust the display content according to the user's current device. For example, if the user is using a smartphone, a display method that matches the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a personal computer, a high-resolution display method can be provided. This improves convenience by adjusting the display content according to the user's current device.

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

[0064] Step 1: The import unit imports a photo. Examples of photos include, but are not limited to, photos taken with a smartphone or camera, and existing photos. The import unit imports, for example, a photo taken with a smartphone into the system. The import unit can also upload an existing photo. For example, a user selects an existing photo and uploads it to the system. Step 2: The analysis unit uses the generation AI to analyze the captured photo. The analysis unit, for example, recognizes facial features in the photo and performs analysis based on that. For example, the generation AI analyzes the position and shape of the eyes, nose, and mouth and performs analysis based on that. The analysis unit can also use the generation AI to analyze facial features in detail. For example, the generation AI analyzes the facial contours and facial expressions and performs analysis based on that. Step 3: The generation unit generates a caricature based on the photograph analyzed by the analysis unit. The generation unit uses, for example, a generation AI to generate a caricature based on the facial features in the photograph. For example, the generation AI draws a caricature based on the position and shape of the eyes, nose, and mouth. The generation unit can also use the generation AI to generate a caricature that faithfully reproduces facial features. For example, the generation AI draws a caricature based on the facial contours and facial expressions. Step 4: The providing unit provides the portrait generated by the generating unit. The providing unit, for example, allows the user to download the generated portrait. The providing unit may also have a function for sharing the generated portrait on a social networking site. For example, the user can send the generated portrait to a friend or use it as a profile picture.

[0065] (Example 2) A caricature generation system according to an embodiment of the present invention is a system that imports and analyzes a photo, generates a caricature, and provides it. In the caricature generation system, a user imports a photo, and a generation AI analyzes the photo and generates a caricature. This mechanism allows users to easily create caricatures of themselves or others. For example, the caricature generation system allows a user to import a photo. To do this, the user can take a photo using a smartphone or camera, or upload an existing photo. The caricature generation system then analyzes the imported photo using a generation AI. The generation AI recognizes facial features in the photo and generates a caricature based on the recognition. For example, the AI ​​analyzes the position and shape of the eyes, nose, and mouth, and draws a caricature based on the analysis. The generated caricature is provided to the user. The user can download the generated caricature or share it on social media. This allows users to easily create caricatures of themselves or others. The caricature generation system allows users to easily create caricatures of themselves or others. For example, caricatures can be created as souvenirs at events or parties, or as gifts. You can also use it as your profile picture on social media to express your individuality.

[0066] A caricature generation system according to an embodiment includes an import unit, an analysis unit, a generation unit, and a provision unit. The import unit imports a photo. Examples of photos include, but are not limited to, photos taken with a smartphone or a camera, existing photos, etc. The import unit imports, for example, a photo taken with a smartphone into the system. The import unit can also upload existing photos. For example, a user selects an existing photo and uploads it to the system. The analysis unit analyzes the imported photo using a generation AI. For example, the analysis unit recognizes facial features in the photo and performs analysis based on the recognition. For example, the generation AI analyzes the position and shape of the eyes, nose, and mouth and performs analysis based on the recognition. The analysis unit can also analyze facial features in detail using the generation AI. For example, the generation AI analyzes facial contours and facial expressions and performs analysis based on the recognition. The generation unit generates a caricature based on the photo analyzed by the analysis unit. For example, the generation unit generates a caricature based on the facial features in the photo using the generation AI. For example, the generation AI draws a caricature based on the position and shape of the eyes, nose, and mouth. The generation unit can also use a generation AI to generate a caricature that faithfully reproduces facial features. For example, the generation AI draws a caricature based on the contours and facial expressions of the face. The providing unit provides the caricature generated by the generation unit. For example, the providing unit allows a user to download the generated caricature. The providing unit can also have a function to share the generated caricature on a social networking site. For example, a user can send the generated caricature to a friend or use it as a profile picture. In this way, the caricature generation system according to the embodiment can automate a series of processes of importing and analyzing a photo, generating a caricature, and providing it.

[0067] The generation unit can recognize facial features in a photograph and generate a caricature based on the features. The generation unit, for example, uses a generation AI to recognize facial features in a photograph. For example, the generation AI analyzes the position and shape of the eyes, nose, and mouth, and generates a caricature based on that. The generation unit can also use the generation AI to analyze facial features in detail and generate a caricature based on that. For example, the generation AI analyzes facial contours and facial expressions, and draws a caricature based on that. The generation unit can also use the generation AI to generate a caricature that faithfully reproduces facial features. For example, the generation AI draws a caricature based on the position and shape of the eyes, nose, and mouth. This makes it possible to generate a more precise caricature by recognizing facial features in a photograph.

[0068] The providing unit may have a function of downloading the generated portrait. The providing unit, for example, allows the user to download the generated portrait. For example, the providing unit may provide the generated portrait in a file format, which the user can download. The providing unit may also provide the generated portrait in high resolution. For example, the providing unit may provide the generated portrait as a high-resolution image file, which the user can download. In this way, the user can download the generated portrait and keep it on hand.

[0069] The providing unit may have a function of sharing the generated caricature on an SNS. For example, the providing unit may have a function of sharing the generated caricature on an SNS. For example, the providing unit may generate a link for sharing the generated caricature on an SNS, allowing the user to share it. The providing unit may also have a function of directly posting the generated caricature on an SNS. For example, the providing unit may directly post the generated caricature to the user's SNS account. This allows the user to easily share the generated caricature with others by sharing it on an SNS.

[0070] The capture unit can estimate the user's emotions and appropriately adjust the timing of capturing photos based on the estimated user emotions. The capture unit, for example, estimates the user's emotions and adjusts the timing of capturing photos based on the estimated user emotions. For example, if the user is relaxed, the capture unit delays the capture timing to capture a natural expression. If the user is nervous, the capture unit can capture photos early to complete the capture before the nervousness is evident in the user's facial expression. If the user is enjoying themselves, the capture unit can take continuous photos and select the optimal timing to capture the moment of a smile. This makes it possible to capture photos at the optimal timing depending on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0071] The capture unit can analyze the user's past photo capture history and select an appropriate capture method. The capture unit, for example, analyzes the user's past photo capture history and selects the optimal capture method. For example, the capture unit automatically applies filters and effects that the user has used favorably in the past. The capture unit can also suggest optimal shooting conditions by referring to the time of day and location where the user took photos in the past. The capture unit can also analyze the composition of photos the user has taken in the past and guide the user to take photos with a similar composition. In this way, by selecting the optimal capture method based on the past history, it is possible to capture photos that suit the user's preferences.

[0072] The capture unit can appropriately filter based on the user's current environment when capturing a photo. For example, the capture unit performs filtering based on the user's current environment (lighting, background, etc.) when capturing a photo. For example, if the lighting is low, the capture unit applies a filter that automatically adjusts brightness. Also, if the background is cluttered, the capture unit can apply a filter that blurs the background. Also, if the lighting is too strong, the capture unit can apply a filter that suppresses highlights. In this way, filtering according to the user's environment can be performed to capture optimal photos.

[0073] The capture unit can select an appropriate capture means according to the user's input method when capturing a photo. For example, when capturing a photo, the capture unit selects the optimal capture means according to the user's input method (voice, text, image, etc.). For example, when the user commands "take a photo" by voice, the capture unit activates the camera based on the voice input and takes a photo. Furthermore, when the user inputs "selfie" by text, the capture unit can also activate the front camera and take a photo. Furthermore, when the user uploads an existing image, the capture unit can select that image as the image to be captured. This improves convenience by selecting the optimal capture means according to the user's input method.

[0074] The capture unit can estimate the user's emotions and determine the priority of photos to be captured based on the estimated user emotions. The capture unit, for example, estimates the user's emotions and determines the priority of photos to be captured based on the estimated user emotions. For example, if the user is having fun, the capture unit can prioritize capturing photos of smiling faces. Furthermore, if the user is relaxed, the capture unit can also prioritize capturing photos of natural expressions. Furthermore, if the user is nervous, the capture unit can also prioritize capturing photos of less nervous expressions. In this way, by determining the priority of photos according to the user's emotions, it is possible to capture optimal photos. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] When importing photos, the import unit can prioritize importing relevant photos by taking into account the user's geographical location information. For example, when importing photos, the import unit prioritizes importing highly relevant photos by taking into account the user's geographical location information. For example, when the user is at a tourist spot, the import unit prioritizes importing photos related to that location. Furthermore, when the user is at home, the import unit can also prioritize importing photos of family and pets. Furthermore, when the user is at an event venue, the import unit can also prioritize importing photos related to the event. In this way, highly relevant photos can be prioritized by taking into account the user's geographical location information.

[0076] The capture unit can analyze the user's social media activity and capture related photos when capturing photos. For example, the capture unit analyzes the user's social media activity and captures related photos when capturing photos. For example, the capture unit captures photos related to places where the user has checked in on social media. The capture unit can also analyze the content of the user's social media posts and capture related photos. The capture unit can also capture related photos by referring to the activities of the user's friends on social media. In this way, related photos can be captured by analyzing the user's social media activity.

[0077] The capture unit can adjust the capture method by reflecting the user's past feedback when capturing photos. For example, the capture unit customizes the capture method by reflecting the user's past feedback when capturing photos. For example, the capture unit automatically applies filters and effects that the user previously preferred. The capture unit can also suggest optimal shooting conditions by referring to the time of day and location where the user previously took photos. The capture unit can also analyze the composition of photos the user has taken in the past and guide the user to take photos with a similar composition. In this way, the capture method can be customized by reflecting the user's past feedback.

[0078] The analysis unit can estimate the user's emotion and adjust the way the analysis is presented based on the estimated user's emotion. For example, the analysis unit can estimate the user's emotion and adjust the way the analysis is presented based on the estimated user's emotion. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise analysis results when the user is in a hurry. The analysis unit can also provide visually stimulating analysis results when the user is excited. This allows for adjusting the way the analysis is presented based on the user's emotion, thereby providing more appropriate analysis results. Emotion estimation is achieved 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.

[0079] The analysis unit can determine the level of detail of the analysis based on the importance of the photo during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the photo during analysis. For example, the analysis unit performs a detailed analysis for a photo of an important event. The analysis unit can also perform a concise analysis for an everyday photo. The analysis unit can also perform an analysis that focuses on a specific person in a photo. In this way, by adjusting the level of detail of the analysis based on the importance of the photo, it is possible to provide appropriate analysis results.

[0080] The analysis unit can use different analysis algorithms depending on the category of the photo during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the photo during analysis. For example, in the case of a portrait photo, the analysis unit applies an algorithm that analyzes facial features in detail. In addition, in the case of a landscape photo, the analysis unit can also apply an algorithm that analyzes background features. In addition, in the case of a group photo, the analysis unit can apply an algorithm that analyzes multiple faces simultaneously. In this way, by applying an analysis algorithm depending on the category of the photo, more accurate analysis results can be provided.

[0081] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit performs a similar analysis based on analysis results that the user has previously preferred. The analysis unit can also adjust the analysis algorithm by referring to feedback provided by the user in the past. The analysis unit can also learn the user's past analysis results and improve the accuracy of the analysis. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0082] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can perform a short and to-the-point analysis. If the user is relaxed, the analysis unit can also perform a detailed analysis. If the user is excited, the analysis unit can also perform a visually stimulating analysis. This allows for adjusting the length of the analysis according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved 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.

[0083] The analysis unit can set an analysis priority based on when the photos were taken during analysis. For example, the analysis unit determines an analysis priority based on when the photos were taken during analysis. For example, the analysis unit prioritizes analysis of recently taken photos. The analysis unit can also prioritize analysis of photos from a specific event. The analysis unit can also prioritize analysis of photos taken within a period specified by the user. In this way, by determining an analysis priority based on when the photos were taken, appropriate analysis results can be provided.

[0084] The analysis unit can set the order of analysis based on the relevance of the photos during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of the photos during analysis. For example, the analysis unit continuously analyzes photos of the same event. The analysis unit can also continuously analyze photos that feature the same person. The analysis unit can also continuously analyze photos taken at the same location. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the photos.

[0085] The analysis unit can determine the use of technical terms for the analysis according to the user's level of expertise during analysis. For example, the analysis unit adjusts the use of technical terms for the analysis according to the user's level of expertise during analysis. For example, if the user is an expert, the analysis unit uses detailed technical terms. If the user is a beginner, the analysis unit can also explain the analysis results in simple terms. If the user is an intermediate user, the analysis unit can also use appropriate technical terms. This makes it possible to provide analysis results that are easy to understand by providing analysis results according to the user's level of expertise.

[0086] The generation unit can estimate the user's emotion and adjust the caricature generation method based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the caricature generation method based on the estimated user's emotion. For example, the generation unit generates a caricature with a soft touch when the user is relaxed. Furthermore, the generation unit can generate a caricature using vivid colors when the user is excited. Furthermore, the generation unit can generate a caricature using subdued colors when the user is sad. In this way, by adjusting the caricature generation method according to the user's emotion, a more appropriate caricature can be generated. 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.

[0087] The generation unit can analyze the facial features in the photograph in detail during generation to generate a more accurate caricature. For example, the generation unit can analyze the facial features in the photograph in detail during generation to generate a more accurate caricature. For example, the generation unit can analyze the shape and position of the eyes in detail and accurately reproduce them. The generation unit can also analyze the shape and position of the nose in detail and accurately reproduce them. The generation unit can also analyze the shape and position of the mouth in detail and accurately reproduce them. In this way, a more accurate caricature can be generated by analyzing the facial features in the photograph in detail.

[0088] The generation unit can improve the accuracy of generation by referring to the user's past caricature generation history during generation. For example, the generation unit can improve the accuracy of generation by referring to the user's past caricature generation history during generation. For example, the generation unit generates a caricature in a similar style based on a style that the user has previously preferred. The generation unit can also adjust the generation algorithm by referring to feedback provided by the user in the past. The generation unit can also learn the user's past caricature generation history and improve the accuracy of generation. In this way, the accuracy of generation can be improved by referring to the user's past caricature generation history.

[0089] The generation unit can adjust the style of the caricature according to the user's preferences when generating the caricature. For example, the generation unit customizes the style of the caricature according to the user's preferences when generating the caricature. For example, the generation unit generates a caricature that reflects the user's preferred color usage. The generation unit can also generate a caricature that reflects the user's preferred touch (realistic, anime-style, etc.). The generation unit can also generate a caricature that reflects the user's preferred background. In this way, satisfaction is improved by generating a caricature in a style that meets the user's preferences.

[0090] The generation unit can estimate the user's emotion and adjust the style of the caricature based on the estimated user's emotion. For example, the generation unit can estimate the user's emotion and adjust the style of the caricature based on the estimated user's emotion. For example, if the user is relaxed, the generation unit can generate a caricature with a soft touch. If the user is excited, the generation unit can also generate a caricature using vivid colors. If the user is sad, the generation unit can also generate a caricature using subdued colors. This allows for generating a caricature in a style according to the user's emotion, thereby providing a more appropriate caricature. 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.

[0091] The generation unit can generate a background based on the location where the photo was taken at the time of generation. The generation unit, for example, automatically generates a background based on the location where the photo was taken at the time of generation. For example, if the photo was taken in a park, the generation unit generates a caricature with the park scenery as the background. Furthermore, if the photo was taken on a beach, the generation unit can also generate a caricature with the beach scenery as the background. Furthermore, if the photo was taken in a city, the generation unit can also generate a caricature with the city scenery as the background. In this way, by automatically generating a background based on the location where the photo was taken, it is possible to provide a more realistic caricature.

[0092] The generation unit can analyze the user's social media activity at the time of generation and provide a related style. For example, the generation unit can analyze the user's social media activity at the time of generation and suggest a related style. For example, the generation unit can generate a caricature that reflects a filter that the user frequently uses on social media. The generation unit can also analyze the content of the user's social media posts and suggest a related style. The generation unit can also suggest a related style by referring to the activity of the user's friends on social media. In this way, a related style can be suggested by analyzing the user's social media activity.

[0093] The generation unit can adjust the generation method by reflecting the user's past feedback at the time of generation. For example, the generation unit customizes the generation method by reflecting the user's past feedback at the time of generation. For example, the generation unit generates a caricature in a similar style based on a style that the user previously preferred. The generation unit can also adjust the generation algorithm by referring to feedback provided by the user in the past. The generation unit can also learn the user's past feedback and improve the accuracy of generation. In this way, the generation method can be customized by reflecting the user's past feedback.

[0094] The providing unit can estimate the user's emotion and adjust the display method of the caricature to be provided based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the display method of the caricature to be provided based on the estimated user's emotion. For example, if the user is relaxed, the providing unit can display the caricature in soft colors. If the user is excited, the providing unit can also display the caricature in vivid colors. If the user is sad, the providing unit can also display the caricature in subdued colors. This allows for a more appropriate display by adjusting the display method 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.

[0095] The providing unit can select an appropriate providing method by referring to the user's past usage history when providing the content. For example, the providing unit selects the optimal providing method by referring to the user's past usage history when providing the content. For example, if the user has preferred downloading in the past, the providing unit can preferentially display a download link. Furthermore, if the user has preferred sharing on social media in the past, the providing unit can preferentially display a share button. Furthermore, if the user has preferred printing in the past, the providing unit can preferentially display a print option. In this way, the optimal providing method can be selected by referring to the user's past usage history.

[0096] The providing unit can adjust the display content according to the user's current device when providing the content. For example, the providing unit customizes the display content according to the user's current device when providing the content. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a personal computer, the providing unit can also provide a high-resolution display method. This improves convenience by customizing the display content according to the user's current device.

[0097] The providing unit can improve the providing method by reflecting user feedback at the time of providing. For example, the providing unit improves the providing method by reflecting user feedback at the time of providing. For example, the providing unit adjusts the next providing method based on feedback provided by the user regarding the providing method. The providing unit can also select the optimal providing method based on an evaluation provided by the user regarding the providing method. The providing unit can also learn from user feedback and improve the accuracy of the providing method. In this way, the providing method can be improved by reflecting user feedback.

[0098] The providing unit can estimate the user's emotion and determine the priority of the caricatures to be provided based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and determines the priority of the caricatures to be provided based on the estimated user's emotion. For example, if the user is relaxed, the providing unit can prioritize providing a caricature with a soft touch. Also, if the user is excited, the providing unit can prioritize providing a caricature with vivid colors. Also, if the user is sad, the providing unit can prioritize providing a caricature with subdued colors. In this way, caricatures can be provided in order of priority according to the user's emotion, thereby providing more appropriate caricatures. 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.

[0099] The providing unit can select an appropriate providing method by taking into consideration the user's geographical location information when providing the information. For example, the providing unit selects the optimal providing method by taking into consideration the user's geographical location information when providing the information. For example, the providing unit may preferentially display a download link when the user is at home. Furthermore, the providing unit may preferentially display a share button on an SNS when the user is out and about. Furthermore, the providing unit may preferentially display a print option when the user is at an event venue. In this way, the optimal providing method can be selected by taking into consideration the user's geographical location information.

[0100] The providing unit can analyze the user's social media activity at the time of providing and provide a related provision method. For example, the providing unit can analyze the user's social media activity at the time of providing and suggest a related provision method. For example, if the user often shares on social media, the providing unit can preferentially display a share button. The providing unit can also analyze the content of the user's posts on social media and suggest a related provision method. The providing unit can also suggest a related provision method by referring to the activity of the user's friends on social media. In this way, it is possible to suggest a related provision method by analyzing the user's social media activity.

[0101] The providing unit can adjust the providing method by reflecting the user's past feedback when providing the information. For example, the providing unit customizes the providing method by reflecting the user's past feedback when providing the information. For example, the providing unit provides the information in a similar method based on a providing method that the user has previously preferred. The providing unit can also adjust the providing method by referring to feedback provided by the user in the past. The providing unit can also learn the user's past feedback and improve the accuracy of the providing method. In this way, the providing method can be customized by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned capture unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the capture unit can capture a photograph using the camera 42 of the smart device 14 or the reception device 38. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the captured photograph using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a portrait based on the analyzed photograph. The provision unit can provide the generated portrait to the user using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned capture unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the capture unit can capture a photograph using the camera 42 or microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the captured photograph using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a portrait based on the analyzed photograph. The provision unit can provide the generated portrait to the user using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned capture unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the capture unit can capture a photograph using the camera 42 or microphone 238 of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the captured photograph using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a portrait based on the analyzed photograph. The provision unit can provide the generated portrait to the user using the display 343 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned capture unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the capture unit can capture a photograph using the camera 42 or microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the captured photograph using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a portrait based on the analyzed photograph. The provision unit can provide the generated portrait to the user using the speaker 240 or display device of the robot 414.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user's emotions. For example, if the user is excited, the analysis unit can prioritize analyzing photos that the user is most interested in. If the user is relaxed, the analysis unit can also perform a comprehensive analysis and provide detailed results. Furthermore, if the user is sad, the analysis unit can prioritize analyzing photos that will soothe the user's mood. In this way, by determining the analysis priority according to the user's emotions, more appropriate analysis results can be provided.

[0104] The providing unit can estimate the user's emotion and adjust the style of the caricature to be provided based on the estimated user's emotion. For example, if the user is relaxed, the providing unit can provide a caricature with soft colors. If the user is excited, the providing unit can also provide a caricature with vivid colors. Furthermore, if the user is sad, the providing unit can also provide a caricature with subdued colors. In this way, a caricature can be provided in a style according to the user's emotion, thereby providing a more appropriate caricature.

[0105] The generation unit can estimate the user's emotion and adjust the background of the caricature based on the estimated user's emotion. For example, if the user is relaxed, the generation unit can generate a caricature with a natural landscape as the background. Alternatively, if the user is excited, the generation unit can generate a caricature with an urban landscape as the background. Furthermore, if the user is sad, the generation unit can generate a caricature with a calm landscape as the background. In this way, a more appropriate caricature can be provided by generating a caricature with a background that corresponds to the user's emotion.

[0106] The capture unit can estimate the user's emotions and adjust the photo capture method based on the estimated user's emotions. For example, if the user is relaxed, the capture unit can delay the capture timing to capture a natural expression. If the user is excited, the capture unit can take continuous photos and select the best moment. Furthermore, if the user is sad, the capture unit can capture photos in an environment that will ease the user's mood. In this way, by adjusting the capture method according to the user's emotions, more appropriate photos can be captured.

[0107] The analysis unit can estimate the user's emotions and adjust the level of analysis detail based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results. Furthermore, if the user is excited, the analysis unit can also provide visually stimulating analysis results. In this way, by adjusting the level of analysis detail according to the user's emotions, more appropriate analysis results can be provided.

[0108] The providing unit can customize the style of the caricature to be provided by referring to the user's past usage history. For example, it can provide a caricature that reflects the color usage and touches that the user has previously preferred. It can also use a similar background based on a background that the user has previously selected. Furthermore, it can also adjust the providing method by referring to feedback provided by the user in the past. In this way, by referring to the user's past usage history, it is possible to provide a caricature that is more satisfying.

[0109] The generation unit can analyze the user's social media activity and suggest related styles. For example, it can generate a caricature that reflects filters the user frequently uses on social media. It can also analyze the content of the user's social media posts and suggest related styles. It can also suggest related styles based on the activity of the user's friends on social media. In this way, it is possible to suggest related styles by analyzing the user's social media activity.

[0110] The capture unit can prioritize capturing highly relevant photos by taking into account the user's geographical location information. For example, if the user is at a tourist spot, photos related to that location can be prioritized. Also, if the user is at home, photos of family and pets can be prioritized. Furthermore, if the user is at an event venue, photos related to the event can be prioritized. In this way, by taking into account the user's geographical location information, highly relevant photos can be prioritized.

[0111] The analysis unit can set analysis priorities based on when the photos were taken. For example, it can prioritize analysis of recently taken photos. It can also prioritize analysis of photos from a specific event. It can also prioritize analysis of photos taken within a period specified by the user. In this way, by setting analysis priorities based on when the photos were taken, it is possible to provide appropriate analysis results.

[0112] The providing unit can adjust the display content according to the user's current device. For example, if the user is using a smartphone, a display method that matches the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a personal computer, a high-resolution display method can be provided. This improves convenience by adjusting the display content according to the user's current device.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The import unit imports a photo. Examples of photos include, but are not limited to, photos taken with a smartphone or camera, and existing photos. The import unit imports, for example, a photo taken with a smartphone into the system. The import unit can also upload an existing photo. For example, a user selects an existing photo and uploads it to the system. Step 2: The analysis unit uses the generation AI to analyze the captured photo. The analysis unit, for example, recognizes facial features in the photo and performs analysis based on that. For example, the generation AI analyzes the position and shape of the eyes, nose, and mouth and performs analysis based on that. The analysis unit can also use the generation AI to analyze facial features in detail. For example, the generation AI analyzes the facial contours and facial expressions and performs analysis based on that. Step 3: The generation unit generates a caricature based on the photograph analyzed by the analysis unit. The generation unit uses, for example, a generation AI to generate a caricature based on the facial features in the photograph. For example, the generation AI draws a caricature based on the position and shape of the eyes, nose, and mouth. The generation unit can also use the generation AI to generate a caricature that faithfully reproduces facial features. For example, the generation AI draws a caricature based on the facial contours and facial expressions. Step 4: The providing unit provides the portrait generated by the generating unit. The providing unit, for example, allows the user to download the generated portrait. The providing unit may also have a function for sharing the generated portrait on a social networking site. For example, the user can send the generated portrait to a friend or use it as a profile picture.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] [Explanation of symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an import unit for importing photos; an analysis unit that analyzes the photograph captured by the capture unit; a generation unit that generates a portrait based on the photograph analyzed by the analysis unit; a providing unit that provides the portrait generated by the generating unit; Equipped with A system characterized by:

2. The generation unit Recognize facial features in photos and generate caricatures based on them The system of claim 1 .

3. The providing unit Equipped with the ability to download the generated portrait The system of claim 1 .

4. The providing unit Equipped with a function to share the generated portrait on social media The system of claim 1 .

5. The capture unit is To estimate a user's emotions and appropriately adjust the timing of capturing photos based on the estimated user's emotions. The system of claim 1 .

6. The capture unit is Analyze the user's past photo import history and select the appropriate import method The system of claim 1 .

7. The capture unit is When capturing photos, filter them appropriately based on the user's current environment The system of claim 1 .

8. The capture unit is When importing photos, select the appropriate import method according to the user's input method. The system of claim 1 .

9. The capture unit is Estimate the user's emotions and prioritize the photos to be captured based on the estimated user emotions. The system of claim 1 .

10. The capture unit is When importing photos, the app takes into account the user's geographic location to prioritize relevant photos. The system of claim 1 .

Images