System

Abstract

An object of the system according to the embodiment is to make the self-expression of the user more realistic by making the photograph look worse on purpose.SOLUTION: A system according to an embodiment includes a photograph acquiring unit, a generation AI processing unit, and an inverse decorating unit. The photograph acquisition unit acquires a photograph from a user. The generation AI processing unit analyzes the photograph acquired by the photograph acquiring unit. The reverse embellishment processing unit performs processing for intentionally making the photograph analyzed by the generation AI processing unit look worse.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 focused on making photos look beautiful, and has the problem of not being able to meet the need to intentionally make photos look bad.

[0005] The system according to the embodiment aims to make users' self-expression more realistic by intentionally making their photos look bad. [Means for solving the problem]

[0006] The system according to the embodiment includes a photo acquisition unit, a generation AI processing unit, and a reverse image processing unit. The photo acquisition unit acquires a photo from a user. The generation AI processing unit analyzes the photo acquired by the photo acquisition unit. The reverse image processing unit processes the photo analyzed by the generation AI processing unit to intentionally make it look worse. [Effects of the Invention]

[0007] The system according to the embodiment can make a user's self-expression more realistic by intentionally making the photo look bad. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The Reverse Camera & Photo Editor according to an embodiment of the present invention is a system that uses generative AI to process photos to intentionally make them look worse. This allows users to express themselves more realistically on social media and free them from the pressure of the need for approval and perfectionism.

[0029] The reverse-image camera and photo editor according to the embodiment includes a photo acquisition unit, a generation AI processing unit, and a reverse-image processing unit. The photo acquisition unit acquires photos from a user. For example, the photo acquisition unit can acquire photos directly from a camera device. The photo acquisition unit can also acquire photos by a user uploading a file. The generation AI processing unit analyzes the photos acquired by the photo acquisition unit. For example, the generation AI processing unit can analyze the content of the photo using image recognition technology. The generation AI processing unit can also extract features of the photo using an AI algorithm. The reverse-image processing unit processes the photo analyzed by the generation AI processing unit to intentionally make it look worse. For example, the reverse-image processing unit can change the color tone of the photo to make it look worse. The reverse-image processing unit can also reduce the resolution of the photo to make it look worse. The reverse-image processing unit can also add noise to the photo to make it look worse. This allows the user to intentionally process the photo to make it look worse.

[0030] The generative AI processing unit can change the overall atmosphere of a photo by analyzing the background of the photo and adding or removing specific objects. The generative AI processing unit, for example, uses background segmentation technology to analyze the background of the photo. For example, the generative AI processing unit divides the background of the photo and analyzes each segment. The generative AI processing unit can also detect specific objects using object detection technology. For example, the generative AI processing unit detects objects such as people, animals, and buildings in the photo. The generative AI processing unit can also change the overall atmosphere of the photo by adding or removing detected objects. For example, the generative AI processing unit can add dust or dirt to the background to create a dirty impression. The generative AI processing unit can also change the atmosphere of the photo by removing specific objects from the background. This allows the overall atmosphere of the photo to be changed by analyzing the background of the photo and adding or removing specific objects.

[0031] The generation AI processing unit can analyze a user's past posting history and perform consistent reverse photo editing. The generation AI processing unit, for example, uses text analysis technology to analyze a user's past posting history. For example, the generation AI processing unit analyzes the content of a user's past postings and extracts specific themes or styles. The generation AI processing unit can also analyze a user's past posted images using image analysis technology. For example, the generation AI processing unit analyzes filters and effects used in past posted images and applies the same filters and effects to new posts. The generation AI processing unit can also perform consistent reverse photo editing based on a user's past posting history. For example, the generation AI processing unit applies the reverse photo editing filter used in past posts to new photos. This allows the user's past posting history to be analyzed and consistent reverse photo editing to be performed.

[0032] The generation AI processing unit can perform reverse processing on the video. For example, the generation AI processing unit selects a specific frame of the video and applies reverse processing to that frame. For example, the generation AI processing unit adds dust or dirt to a specific scene in the video. The generation AI processing unit can also apply reverse processing to the entire video. For example, the generation AI processing unit can create a reverse processing effect by changing the color tone of the video. The generation AI processing unit can also create a reverse processing effect by lowering the resolution of the video. The generation AI processing unit can also create a reverse processing effect by adding noise to the video. This makes it possible to process the video in reverse.

[0033] The generation AI processing unit can perform reverse reflection processing in real time, providing a reverse reflection effect to viewers during live streaming. The generation AI processing unit, for example, uses real-time processing technology to perform reverse reflection processing in real time during live streaming. For example, the generation AI processing unit analyzes the video during live streaming in real time and applies reverse reflection processing. The generation AI processing unit can also use technology to minimize delay. For example, the generation AI processing unit uses a high-speed algorithm to improve the video processing speed. The generation AI processing unit can also provide a reverse reflection effect to viewers during live streaming. For example, the generation AI processing unit can create a reverse reflection effect by adding dust or dirt to the video during live streaming. The generation AI processing unit can also create a reverse reflection effect by changing the color tone of the video during live streaming. This allows a reverse reflection effect to be provided in real time during live streaming.

[0034] The generation AI processing unit can analyze the content of a user's posts and provide advice for maintaining consistency in self-expression. The generation AI processing unit, for example, uses text analysis technology to analyze the content of a user's posts. For example, the generation AI processing unit analyzes the content of a user's posts and extracts a specific theme or style. The generation AI processing unit can also analyze the images posted by the user using image analysis technology. For example, the generation AI processing unit analyzes filters and effects on the posted images and applies the same filters and effects to new posts. The generation AI processing unit can also provide advice for maintaining consistency in self-expression based on the content of a user's posts. For example, the generation AI processing unit suggests applying filters and effects used in past posts to new posts. The generation AI processing unit can also suggest maintaining a specific theme or style. This makes it possible to provide advice for maintaining consistency in self-expression.

[0035] The generation AI processing unit can analyze comments and feedback on user posts and automatically filter out negative comments. The generation AI processing unit, for example, uses text mining technology to analyze comments and feedback on user posts. For example, the generation AI processing unit analyzes the content of the comments and feedback and detects negative comments. The generation AI processing unit can also analyze the emotions of the comments and feedback using sentiment analysis technology. For example, the generation AI processing unit calculates a sentiment score for the comments and feedback and detects negative comments. The generation AI processing unit can also detect negative comments using keyword filtering technology. For example, the generation AI processing unit detects comments that include offensive language or unpleasant expressions. The generation AI processing unit can also automatically filter out detected negative comments. For example, the generation AI processing unit deletes negative comments. The generation AI processing unit can also hide negative comments. This allows negative comments to be automatically filtered out.

[0036] The generation AI processing unit can provide customizable templates to support user self-expression. For example, the generation AI processing unit analyzes the content of a user's post and provides a post template based on a specific theme. For example, the generation AI processing unit suggests templates tailored to themes such as travel, food, and fashion. The generation AI processing unit can also provide customizable templates to support user self-expression. For example, the generation AI processing unit can provide design templates to allow users to freely customize them. The generation AI processing unit can also provide text templates to allow users to easily create post content. The generation AI processing unit can also suggest an optimal template based on the content of a user's post. In this way, customizable templates can be provided to support user self-expression.

[0037] The generative AI processing unit can analyze the content of a user's posts and make them applicable to self-expression on other social media platforms. The generative AI processing unit, for example, uses text analysis technology to analyze the content of a user's posts. For example, the generative AI processing unit analyzes the content of a user's posts and extracts a specific theme or style. The generative AI processing unit can also analyze the images posted by the user using image analysis technology. For example, the generative AI processing unit analyzes the filters and effects of the posted images and optimizes them for other social media platforms. The generative AI processing unit can also make them applicable to self-expression on other social media platforms based on the content of a user's posts. For example, the generative AI processing unit optimizes Instagram posts for Twitter. For example, it adjusts the image size and caption length. The generative AI processing unit can also optimize Facebook posts for LinkedIn. For example, it adjusts the content to be more business-oriented. This makes it applicable to self-expression on other social media platforms.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The reverse photo camera & photo editor may further include a music synchronization unit. The music synchronization unit can apply reverse photo effects to photos and videos in sync with music selected by the user. For example, the color tone of the photo can be changed in sync with the tempo of the music. Also, the video frames can be switched in sync with the rhythm of the music. Furthermore, the strength of the reverse photo effect can be adjusted based on the emotional tone of the music. This allows for a reverse photo effect that is synchronized with the music.

[0040] The reverse photo camera & photo editor can further include a weather information acquisition unit. The weather information acquisition unit can acquire current weather information and perform reverse photo processing based on that information. For example, on rainy days, a raindrop effect can be added to photos. On cloudy days, the color tone of photos can be darkened. Furthermore, on sunny days, strong light reflections can be added to photos. This makes it possible to provide reverse photo effects based on weather information.

[0041] The reverse photo camera & photo editor can further include a location information acquisition unit. The location information acquisition unit can acquire the user's current location and perform reverse photo processing based on that information. For example, in urban areas, it can add the shadows of buildings to photos. In nature, it can also add tree and grass effects to photos. Furthermore, it can add sand and wave effects to photos at the beach. This makes it possible to provide reverse photo effects based on location information.

[0042] The reverse photo camera & photo editor can further include a time information acquisition unit. The time information acquisition unit can acquire current time information and perform reverse photo processing based on that information. For example, it can add dark tones to photos at night, add an orange filter to photos in the evening, or add a soft light effect to photos in the morning. This makes it possible to provide reverse photo effects based on time information.

[0043] The reverse photo camera & photo editor can further include a user hobby information acquisition unit. The hobby information acquisition unit can perform reverse photo processing based on the user's hobbies and interests. For example, for a user who likes music, music-related effects can be added. Also, for a user who likes sports, sports-related effects can be added. Furthermore, for a user who likes art, art-style filters can be added. This makes it possible to provide reverse photo effects based on the user's hobbies and interests.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The photo acquisition unit acquires a photo from a user. For example, the photo can be acquired directly from a camera device. Alternatively, the photo can be acquired by the user uploading a file. Step 2: The generation AI processing unit analyzes the photo acquired by the photo acquisition unit. For example, it can analyze the content of the photo using image recognition technology and extract features of the photo using an AI algorithm. Step 3: The reverse image processing unit processes the photo analyzed by the generation AI processing unit to intentionally make it look worse. For example, it can make the photo look worse by changing the color tone of the photo, reducing the resolution, or adding noise.

[0046] (Example 2) The Reverse Camera & Photo Editor according to an embodiment of the present invention is a system that uses generative AI to process photos to intentionally make them look worse. This allows users to express themselves more realistically on social media and free them from the pressure of the need for approval and perfectionism.

[0047] The reverse-image camera and photo editor according to the embodiment includes a photo acquisition unit, a generation AI processing unit, and a reverse-image processing unit. The photo acquisition unit acquires photos from a user. For example, the photo acquisition unit can acquire photos directly from a camera device. The photo acquisition unit can also acquire photos by a user uploading a file. The generation AI processing unit analyzes the photos acquired by the photo acquisition unit. For example, the generation AI processing unit can analyze the content of the photo using image recognition technology. The generation AI processing unit can also extract features of the photo using an AI algorithm. The reverse-image processing unit processes the photo analyzed by the generation AI processing unit to intentionally make it look worse. For example, the reverse-image processing unit can change the color tone of the photo to make it look worse. The reverse-image processing unit can also reduce the resolution of the photo to make it look worse. The reverse-image processing unit can also add noise to the photo to make it look worse. This allows the user to intentionally process the photo to make it look worse.

[0048] The generation AI processing unit can estimate the user's emotional state and perform reverse image processing according to that emotion. The generation AI processing unit, for example, uses facial expression recognition technology to estimate the user's emotional state. For example, the generation AI processing unit analyzes the user's facial expression to estimate the emotional state. The generation AI processing unit can also estimate the user's emotional state using voice analysis technology. For example, the generation AI processing unit analyzes the tone and speed of the user's voice to estimate the emotional state. The generation AI processing unit can also estimate the user's emotional state using text analysis technology. For example, the generation AI processing unit analyzes the content of text entered by the user to estimate the emotional state. This makes it possible to perform reverse image processing according to the user's emotional state.

[0049] The generative AI processing unit can change the overall atmosphere of a photo by analyzing the background of the photo and adding or removing specific objects. The generative AI processing unit, for example, uses background segmentation technology to analyze the background of the photo. For example, the generative AI processing unit divides the background of the photo and analyzes each segment. The generative AI processing unit can also detect specific objects using object detection technology. For example, the generative AI processing unit detects objects such as people, animals, and buildings in the photo. The generative AI processing unit can also change the overall atmosphere of the photo by adding or removing detected objects. For example, the generative AI processing unit can add dust or dirt to the background to create a dirty impression. The generative AI processing unit can also change the atmosphere of the photo by removing specific objects from the background. This allows the overall atmosphere of the photo to be changed by analyzing the background of the photo and adding or removing specific objects.

[0050] The generation AI processing unit can analyze a user's past posting history and perform consistent reverse photo editing. The generation AI processing unit, for example, uses text analysis technology to analyze a user's past posting history. For example, the generation AI processing unit analyzes the content of a user's past postings and extracts specific themes or styles. The generation AI processing unit can also analyze a user's past posted images using image analysis technology. For example, the generation AI processing unit analyzes filters and effects used in past posted images and applies the same filters and effects to new posts. The generation AI processing unit can also perform consistent reverse photo editing based on a user's past posting history. For example, the generation AI processing unit applies the reverse photo editing filter used in past posts to new photos. This allows the user's past posting history to be analyzed and consistent reverse photo editing to be performed.

[0051] The generation AI processing unit can perform reverse processing on the video. For example, the generation AI processing unit selects a specific frame of the video and applies reverse processing to that frame. For example, the generation AI processing unit adds dust or dirt to a specific scene in the video. The generation AI processing unit can also apply reverse processing to the entire video. For example, the generation AI processing unit can create a reverse processing effect by changing the color tone of the video. The generation AI processing unit can also create a reverse processing effect by lowering the resolution of the video. The generation AI processing unit can also create a reverse processing effect by adding noise to the video. This makes it possible to process the video in reverse.

[0052] The generation AI processing unit can perform reverse reflection processing in real time, providing a reverse reflection effect to viewers during live streaming. The generation AI processing unit, for example, uses real-time processing technology to perform reverse reflection processing in real time during live streaming. For example, the generation AI processing unit analyzes the video during live streaming in real time and applies reverse reflection processing. The generation AI processing unit can also use technology to minimize delay. For example, the generation AI processing unit uses a high-speed algorithm to improve the video processing speed. The generation AI processing unit can also provide a reverse reflection effect to viewers during live streaming. For example, the generation AI processing unit can create a reverse reflection effect by adding dust or dirt to the video during live streaming. The generation AI processing unit can also create a reverse reflection effect by changing the color tone of the video during live streaming. This allows a reverse reflection effect to be provided in real time during live streaming.

[0053] The generation AI processing unit can use the emotion estimation function to analyze the emotion a user has when taking a photo in real time and suggest a reverse photo effect based on that emotion. The generation AI processing unit, for example, uses the emotion estimation function to analyze the emotion a user has when taking a photo in real time. For example, the generation AI processing unit analyzes the user's facial expression to estimate their emotional state. The generation AI processing unit can also analyze the tone and speed of the user's voice to estimate their emotional state. The generation AI processing unit can also suggest a reverse photo effect based on the emotion a user has when taking a photo. For example, if the user is feeling stressed, the generation AI processing unit can suggest a reverse photo effect with a relaxed atmosphere. If the user is happy, the generation AI processing unit can also suggest a reverse photo effect with bright colors. If the user is sad, the generation AI processing unit can also suggest a reverse photo effect with calm colors. This makes it possible to suggest a reverse photo effect based on the emotion a user has when taking a photo.

[0054] The generation AI processing unit can analyze the content of a user's posts and provide advice for maintaining consistency in self-expression. The generation AI processing unit, for example, uses text analysis technology to analyze the content of a user's posts. For example, the generation AI processing unit analyzes the content of a user's posts and extracts a specific theme or style. The generation AI processing unit can also analyze the images posted by the user using image analysis technology. For example, the generation AI processing unit analyzes filters and effects on the posted images and applies the same filters and effects to new posts. The generation AI processing unit can also provide advice for maintaining consistency in self-expression based on the content of a user's posts. For example, the generation AI processing unit suggests applying filters and effects used in past posts to new posts. The generation AI processing unit can also suggest maintaining a specific theme or style. This makes it possible to provide advice for maintaining consistency in self-expression.

[0055] The generation AI processing unit can analyze comments and feedback on user posts and automatically filter out negative comments. The generation AI processing unit, for example, uses text mining technology to analyze comments and feedback on user posts. For example, the generation AI processing unit analyzes the content of the comments and feedback and detects negative comments. The generation AI processing unit can also analyze the emotions of the comments and feedback using sentiment analysis technology. For example, the generation AI processing unit calculates a sentiment score for the comments and feedback and detects negative comments. The generation AI processing unit can also detect negative comments using keyword filtering technology. For example, the generation AI processing unit detects comments that include offensive language or unpleasant expressions. The generation AI processing unit can also automatically filter out detected negative comments. For example, the generation AI processing unit deletes negative comments. The generation AI processing unit can also hide negative comments. This allows negative comments to be automatically filtered out.

[0056] The generation AI processing unit can provide customizable templates to support user self-expression. For example, the generation AI processing unit analyzes the content of a user's post and provides a post template based on a specific theme. For example, the generation AI processing unit suggests templates tailored to themes such as travel, food, and fashion. The generation AI processing unit can also provide customizable templates to support user self-expression. For example, the generation AI processing unit can provide design templates to allow users to freely customize them. The generation AI processing unit can also provide text templates to allow users to easily create post content. The generation AI processing unit can also suggest an optimal template based on the content of a user's post. In this way, customizable templates can be provided to support user self-expression.

[0057] The generative AI processing unit can analyze the content of a user's posts and make them applicable to self-expression on other social media platforms. The generative AI processing unit, for example, uses text analysis technology to analyze the content of a user's posts. For example, the generative AI processing unit analyzes the content of a user's posts and extracts a specific theme or style. The generative AI processing unit can also analyze the images posted by the user using image analysis technology. For example, the generative AI processing unit analyzes the filters and effects of the posted images and optimizes them for other social media platforms. The generative AI processing unit can also make them applicable to self-expression on other social media platforms based on the content of a user's posts. For example, the generative AI processing unit optimizes Instagram posts for Twitter. For example, it adjusts the image size and caption length. The generative AI processing unit can also optimize Facebook posts for LinkedIn. For example, it adjusts the content to be more business-oriented. This makes it applicable to self-expression on other social media platforms.

[0058] The generation AI processing unit can use the emotion estimation function to analyze the emotion a user expresses when posting and adjust the content of the post based on that emotion. The generation AI processing unit, for example, uses the emotion estimation function to analyze the emotion a user expresses when posting. For example, the generation AI processing unit analyzes the user's facial expression to estimate the user's emotional state. The generation AI processing unit can also analyze the tone and speed of the user's voice to estimate the user's emotional state. The generation AI processing unit can also analyze the content of text entered by the user to estimate the user's emotional state. The generation AI processing unit can also adjust the content of the post based on the user's emotion when posting. For example, if the user is feeling sad, the generation AI processing unit can suggest positive content. If the user is feeling stressed, the generation AI processing unit can also suggest relaxing content. If the user is happy, the generation AI processing unit can also suggest cheerful content. This makes it possible to adjust the content of the post based on the user's emotion when posting.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The reverse photo camera & photo editor may further include a music synchronization unit. The music synchronization unit can apply reverse photo effects to photos and videos in sync with music selected by the user. For example, the color tone of the photo can be changed in sync with the tempo of the music. Also, the video frames can be switched in sync with the rhythm of the music. Furthermore, the strength of the reverse photo effect can be adjusted based on the emotional tone of the music. This allows for a reverse photo effect that is synchronized with the music.

[0061] The reverse photo camera & photo editor can further include a weather information acquisition unit. The weather information acquisition unit can acquire current weather information and perform reverse photo processing based on that information. For example, on rainy days, a raindrop effect can be added to photos. On cloudy days, the color tone of photos can be darkened. Furthermore, on sunny days, strong light reflections can be added to photos. This makes it possible to provide reverse photo effects based on weather information.

[0062] The reverse photo camera & photo editor can further include a location information acquisition unit. The location information acquisition unit can acquire the user's current location and perform reverse photo processing based on that information. For example, in urban areas, it can add the shadows of buildings to photos. In nature, it can also add tree and grass effects to photos. Furthermore, it can add sand and wave effects to photos at the beach. This makes it possible to provide reverse photo effects based on location information.

[0063] The reverse photo camera & photo editor can further include a time information acquisition unit. The time information acquisition unit can acquire current time information and perform reverse photo processing based on that information. For example, it can add dark tones to photos at night, add an orange filter to photos in the evening, or add a soft light effect to photos in the morning. This makes it possible to provide reverse photo effects based on time information.

[0064] The reverse photo camera & photo editor can further include a user hobby information acquisition unit. The hobby information acquisition unit can perform reverse photo processing based on the user's hobbies and interests. For example, for a user who likes music, music-related effects can be added. Also, for a user who likes sports, sports-related effects can be added. Furthermore, for a user who likes art, art-style filters can be added. This makes it possible to provide reverse photo effects based on the user's hobbies and interests.

[0065] The Reverse Photo Camera & Photo Editor can also estimate the user's emotions and adjust the intensity of the reverse photo effect based on the user's emotions. For example, if the user is feeling stressed, the intensity of the reverse photo effect can be reduced. If the user is feeling relaxed, the intensity of the reverse photo effect can be increased. Furthermore, if the user is excited, the intensity of the reverse photo effect can be adjusted to a medium level. This makes it possible to provide a reverse photo effect based on the user's emotions.

[0066] The Reverse Photo Camera & Photo Editor can also estimate the user's emotions and suggest types of reverse photo effects based on those emotions. For example, if the user is sad, it can suggest reverse photo effects with bright colors. If the user is happy, it can suggest reverse photo effects with calm colors. Furthermore, if the user is angry, it can suggest reverse photo effects with calm colors. This makes it possible to suggest reverse photo effects based on the user's emotions.

[0067] The Reverse Photo Camera & Photo Editor can also estimate the user's emotions and select a reverse photo filter based on those emotions. For example, if the user is relaxed, a soft-toned filter can be selected. If the user is excited, a vibrant-toned filter can be selected. Furthermore, if the user is tired, a calm-toned filter can be selected. This makes it possible to select a reverse photo filter based on the user's emotions.

[0068] The Reverse Photo Camera & Photo Editor can also estimate the user's emotions and select a reverse photo effect based on that emotion. For example, if the user is happy, a bright effect can be selected. If the user is sad, a dark effect can be selected. Furthermore, if the user is angry, a calm effect can be selected. This makes it possible to select a reverse photo effect based on the user's emotions.

[0069] The Reverse Photo Camera & Photo Editor can also estimate the user's emotions and suggest reverse photo templates based on those emotions. For example, if the user is relaxed, a template with a relaxed atmosphere can be suggested. If the user is excited, a template with an energetic atmosphere can be suggested. Furthermore, if the user is tired, a template with a calm atmosphere can be suggested. This makes it possible to suggest reverse photo templates based on the user's emotions.

[0070] The processing flow of the second embodiment will be briefly explained below.

[0071] Step 1: The photo acquisition unit acquires a photo from a user. For example, the photo can be acquired directly from a camera device. Alternatively, the photo can be acquired by the user uploading a file. Step 2: The generation AI processing unit analyzes the photo acquired by the photo acquisition unit. For example, it can analyze the content of the photo using image recognition technology and extract features of the photo using an AI algorithm. Step 3: The reverse image processing unit processes the photo analyzed by the generation AI processing unit to intentionally make it look worse. For example, it can make the photo look worse by changing the color tone of the photo, reducing the resolution, or adding noise.

[0072] 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.

[0073] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0074] 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.

[0075] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0076] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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).

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0085] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0091] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0098] 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.

[0099] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0100] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0106] 7, the 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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."

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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. [Explanation of symbols]

[0139] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a photo acquisition unit that acquires a photo from a user; a generation AI processing unit that analyzes the photograph acquired by the photograph acquisition unit; and a reverse processing unit that processes the photograph analyzed by the generation AI processing unit to intentionally make it look worse. A system characterized by:

2. The generation AI processing unit: Estimate the user's emotional state and apply reverse image processing according to that emotion 2. The system of claim 1.

3. The generation AI processing unit: Analyze the background of your photo and change the overall atmosphere by adding or removing specific objects 2. The system of claim 1.

4. The generation AI processing unit: Apply reverse video effects 2. The system of claim 1.

5. The generation AI processing unit: Real-time reverse video processing to provide a reverse video effect for viewers during live streaming 2. The system of claim 1.

6. The generation AI processing unit: Analyze comments and feedback on user posts and automatically filter out negative comments 2. The system of claim 1.

7. The generation AI processing unit: Providing customizable templates to support user self-expression 2. The system of claim 1.

8. The generation AI processing unit: Using emotion estimation, the system analyzes the emotions users express when posting and adjusts the content of posts based on those emotions.

2. The system of claim 1.

Images