System

Abstract

An object of the system according to the embodiment is to change the face of a person appearing during live streaming in real time.SOLUTION: A system includes a video analysis part, a face detection part, a face change part, and a video reconfiguration part. The video analysis unit analyzes a video during live streaming in real time. The face detection unit detects a face of a person appearing in the video analyzed by the video analysis unit. The face changing section changes the face detected by the face detecting section. The image reconstruction unit reconstructs an image including the face changed by the face change 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] With conventional technology, it is difficult to change the faces of people captured during live streaming in real time, which raises the risk of violating portrait rights.

[0005] The system according to the embodiment aims to change the face of a person appearing in a live broadcast in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a video analysis unit, a face detection unit, a face modification unit, and a video reconstruction unit. The video analysis unit analyzes video during live streaming in real time. The face detection unit detects the faces of people appearing in the video analyzed by the video analysis unit. The face modification unit modifies the faces detected by the face detection unit. The video reconstruction unit reconstructs video including the faces modified by the face modification unit. [Effects of the Invention]

[0007] The system according to the embodiment can change the face of a person appearing in a live broadcast in real time. [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 live streaming system according to an embodiment of the present invention analyzes live streaming video in real time and uses a generation AI to change and reconstruct the faces of people who appear in the video. This reduces the risk of portrait rights infringement and minimizes the appearance of incongruity in the video.

[0029] A live streaming system according to an embodiment includes a video analysis unit, a face detection unit, a face modification unit, and a video reconstruction unit. The video analysis unit analyzes video during live streaming in real time. For example, the video analysis unit analyzes a video stream to detect the movement of a person in the video. The video analysis unit can also analyze real-time video to identify background information in the video. The video analysis unit can also analyze video based on frame rate and latency. For example, the video analysis unit analyzes video at 30 frames per second to minimize latency. The face detection unit detects the face of a person captured in the video analyzed by the video analysis unit. For example, the face detection unit detects faces in the video using a face recognition algorithm. The face detection unit can also combine multiple algorithms to improve detection accuracy. The face detection unit can also detect faces based on specific movements or behavioral patterns. For example, the face detection unit detects a hand waving motion and recognizes the face at the moment. The face modification unit modifies the detected face. For example, the face modification unit generates a new face using image generation AI to replace the original face. The face modification unit can also maintain a natural appearance by modifying facial features. The face modification unit can also modify the face by applying a filter. For example, the face modification unit can modify the facial contours and adjust the position of the eyes and mouth. The video reconstruction unit reconstructs a video including the modified face. For example, the video reconstruction unit generates a video using a reconstruction algorithm. The video reconstruction unit can also reconstruct the video at a high resolution to maintain video quality. The video reconstruction unit can also reconstruct the video in real time to minimize delay. For example, the video reconstruction unit can track the modified facial movements and expressions in real time to generate a natural video. This allows the live streaming system according to the embodiment to reduce the risk of infringement of portrait rights and minimize the awkwardness of the video. For example, the output unit provides the reconstructed video to viewers through a live streaming platform. The reconstructed video can also be saved using a recording function. The reconstructed video can also be edited later using video editing software.

[0030] The video analysis unit analyzes the movements and behavioral patterns of people in the video, allowing the face detection unit to detect faces only when specific actions are performed. The video analysis unit detects faces only when a person in the video performs a specific gesture, for example. Face recognition is triggered by actions such as waving or jumping. The video analysis unit also detects faces only when a person in the video enters a specific area. For example, it recognizes a face and starts analysis the moment the person enters a specific zone. The video analysis unit also detects faces only when a person in the video is holding a specific object. For example, it prioritizes recognition of the face of a person holding a camera or smartphone. This allows unnecessary face detection to be prevented by detecting faces only when specific actions are performed.

[0031] The video analysis unit analyzes background information within the video, allowing the face detection unit to detect faces only in specific locations and situations. For example, the video analysis unit detects faces only when the background within the video is a specific landmark or building. For example, it performs face recognition at famous tourist spots and event venues. The video analysis unit also detects faces only when the background within the video was shot during a specific time of day. For example, it performs face recognition on video taken at night or in the evening. The video analysis unit also detects faces only when the background within the video is under specific weather conditions. For example, it performs face recognition on video taken on a rainy or snowy day. In this way, unnecessary face detection can be prevented by detecting faces only in specific locations and situations.

[0032] The video analysis unit also analyzes audio data in the video, and the face detection unit can detect faces when specific sounds or conversations are included. The video analysis unit, for example, analyzes audio data in the video and detects faces when specific keywords are included. For example, facial recognition is performed using words such as "hello" or "thank you" as triggers. The video analysis unit also analyzes audio data in the video and detects faces when a specific volume level is exceeded. For example, it prioritizes recognition of the faces of people speaking loudly. The video analysis unit also analyzes audio data in the video and recognizes faces when a specific audio pattern is detected. For example, facial recognition is performed using laughter or applause as triggers. This makes it possible to prevent unnecessary face detection by detecting faces only when specific sounds or conversations are included.

[0033] The video analysis unit recognizes objects in the video, and the face detection unit can detect a face when a specific object is captured in the video. For example, the video analysis unit detects a face when a specific object (e.g., a car or a motorcycle) is captured in the video. For example, it performs face recognition the moment a car license plate is captured. The video analysis unit also detects a face when a specific object (e.g., a signboard or advertisement) is captured in the video. For example, it performs face recognition the moment a specific advertising signboard is captured. The video analysis unit also detects a face when a specific object (e.g., an animal or pet) is captured in the video. For example, it performs face recognition the moment a dog or cat is captured. In this way, unnecessary face detection can be prevented by detecting a face only when a specific object is captured.

[0034] The face modification unit can modify the detected face to make it look more natural by changing the age and gender of the face. The face modification unit, for example, modifies the age of the detected face to make it look younger or older. For example, a face in its twenties is modified to look like a face in its sixties. The face modification unit can also modify the detected face to make it look more natural by changing a male face to look like a female face. For example, a male face is modified to look like a female face. The face modification unit can also simultaneously modify the age and gender of the detected face to make it look more natural. For example, a young male face is modified to look like an older female face. In this way, by changing the age and gender, it is possible to modify the face to look more natural.

[0035] The face modification unit can modify the facial expression of a detected face to change it into a face with a different emotion. For example, the face modification unit modifies the facial expression of a detected face to a smiling face. For example, it modifies an expressionless face to a smiling face. The face modification unit can also modify the facial expression of a detected face to a surprised face. For example, it modifies an expressionless face to a surprised face. The face modification unit can also modify the facial expression of a detected face to an angry face. For example, it modifies an expressionless face to an angry face. In this way, by modifying the facial expression, it is possible to modify a face to have a different emotion.

[0036] The face modification unit can change the detected face into the face of an animated character or an animal. For example, the face modification unit changes the detected face into the face of an animated character, such as a popular anime character. The face modification unit also changes the detected face into the face of an animal, such as a dog or cat. The face modification unit also changes the detected face into the face of a fantasy character, such as a dragon or a fairy. In this way, by changing the face into the face of an animated character or an animal, visually appealing images can be provided.

[0037] The face modification unit can modify the detected face into the face of a historical figure or a famous person. For example, the face modification unit modifies the detected face into the face of a historical figure. For example, the face modification unit modifies the detected face into the face of Napoleon or Cleopatra. The face modification unit also modifies the detected face into the face of a famous person. For example, the face modification unit modifies the detected face into the face of a movie star or a musician. The face modification unit also modifies the detected face into the face of an athlete. For example, the face modification unit modifies the detected face into the face of an Olympic athlete or a soccer player. In this way, by modifying the face into the face of a historical figure or a famous person, visually appealing images can be provided.

[0038] The video reconstruction unit can track the changed facial movements and expressions in real time to generate natural-looking video. The video reconstruction unit, for example, tracks the changed facial movements in real time to generate natural-looking video that matches the original facial movements. For example, changes in facial direction and expression are reflected in real time. The video reconstruction unit also tracks the changed facial expression in real time to generate natural-looking video that matches the original expression. For example, smiling and surprised expressions are reflected in real time. The video reconstruction unit also tracks the changed facial movements and expressions simultaneously to generate natural-looking video that matches the original facial movements and expression. For example, changes in facial direction and expression are reflected in real time. In this way, natural-looking video can be generated by tracking the changed facial movements and expressions in real time.

[0039] The image reconstructor adjusts the skin color and hairstyle of the modified face, thereby achieving a more natural appearance. The image reconstructor, for example, adjusts the skin color of the modified face to match the original face. For example, it selects and changes a color that is close to the skin color of the original face. The image reconstructor also adjusts the hairstyle of the modified face to match the original face. For example, it selects and changes a style that is close to the hairstyle of the original face. The image reconstructor also simultaneously adjusts the skin color and hairstyle of the modified face, thereby achieving a more natural appearance. For example, it changes the skin color and hairstyle to match the features of the original face. In this way, by adjusting the skin color and hairstyle, a more natural appearance can be achieved.

[0040] The image reconstruction unit can convert the image including the modified face into a 3D model and generate a three-dimensional image. The image reconstruction unit, for example, converts the modified face into a 3D model and generates a three-dimensional image. For example, the facial shape and facial expression are reproduced as a 3D model. The image reconstruction unit also converts the entire image including the modified face into a 3D model and generates a three-dimensional image. For example, the background and other people are also reproduced as a 3D model. The image reconstruction unit also reflects the modified facial movements and expressions in the 3D model and generates a three-dimensional image in real time. For example, changes in facial direction and facial expression are reflected in the 3D model. In this way, three-dimensional images can be generated by converting into a 3D model.

[0041] The video reconstructor animates the video including the modified face to generate visually appealing video. The video reconstructor, for example, converts the modified face into an animated character to generate visually appealing video. For example, it changes the face into a popular anime character. The video reconstructor also animates the entire video including the modified face to generate visually appealing video. For example, it also changes the background and other characters in an animation style. The video reconstructor also animates the movement and facial expression of the modified face to generate visually appealing video in real time. For example, it reflects changes in facial direction and facial expression in the animation style. In this way, visually appealing video can be generated by animation.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The live streaming system can further include an audio analysis unit. The audio analysis unit can analyze audio data during live streaming in real time and detect specific keywords and audio patterns. For example, the audio analysis unit can notify the video analysis unit the moment a specific keyword (e.g., "hello" or "thank you") is uttered, which can be used as a trigger for the face detection unit to detect a face. The audio analysis unit can also detect specific audio patterns (e.g., laughter or applause) and use that as a trigger for the face detection unit to detect a face at that moment. This makes it possible to utilize audio data for more accurate face detection.

[0044] The live streaming system can further include a gesture recognition unit. The gesture recognition unit analyzes gestures of people in the video in real time, and when a specific gesture is made, it can be used as a trigger for the face detection unit to detect a face. For example, the gesture recognition unit can detect actions such as waving a hand, jumping, or pointing a finger, and use that as a trigger for the face detection unit to detect a face at that moment. The gesture recognition unit can also detect a specific gesture combination (e.g., waving a hand followed by jumping), and use that as a trigger for the face detection unit to detect a face at that moment. This makes it possible to utilize gestures to achieve more accurate face detection.

[0045] The live streaming system can further include an object recognition unit. The object recognition unit analyzes specific objects in the video in real time, and can be used as a trigger for the face detection unit to detect a face when the specific object is captured. For example, the object recognition unit can notify the face detection unit the moment a vehicle such as a car or motorcycle is captured, and can be used as a trigger to detect a face. The object recognition unit can also notify the face detection unit the moment a specific object such as a signboard or advertisement is captured, and can be used as a trigger to detect a face. This makes it possible to utilize objects to enable more accurate face detection.

[0046] The live streaming system can further include a background recognition unit. The background recognition unit analyzes background information within the video in real time, and can be used as a trigger for the face detection unit to detect a face when a specific background is captured. For example, the background recognition unit can notify the face detection unit the moment a specific landmark, such as a famous tourist spot or event venue, is captured, and use this as a trigger to detect a face. The background recognition unit can also notify the face detection unit of video captured during specific time periods (e.g., nighttime, evening) or weather conditions (e.g., rainy, snowy days), and use this as a trigger to detect a face. This makes it possible to utilize background information to enable more accurate face detection.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The video analysis unit analyzes the live video in real time. For example, it analyzes the video stream to identify the movements of people in the video and background information. It also analyzes the video based on frame rate and latency, analyzing 30 frames per second video to minimize latency. Step 2: The face detection unit detects faces from the video analyzed by the video analysis unit. For example, it can use a facial recognition algorithm to detect faces in the video, or it can combine multiple algorithms to improve detection accuracy. It can also detect faces based on specific movements or behavioral patterns. Step 3: The face modification unit modifies the detected face. For example, it uses image generation AI to generate a new face to replace the original face. It can also modify facial features to maintain a natural look and apply filters to modify the face. For example, it can change the facial contours and adjust the position of the eyes and mouth. Step 4: The video reconstructor reconstructs the video containing the modified face. For example, it uses a reconstruction algorithm to generate the video and reconstruct it at high resolution. It also reconstructs the video in real time to minimize delay. For example, it tracks the movements and expressions of the modified face in real time to generate a natural video.

[0049] (Example 2) A live streaming system according to an embodiment of the present invention analyzes live streaming video in real time and uses a generation AI to change and reconstruct the faces of people who appear in the video. This reduces the risk of portrait rights infringement and minimizes the appearance of incongruity in the video.

[0050] A live streaming system according to an embodiment includes a video analysis unit, a face detection unit, a face modification unit, and a video reconstruction unit. The video analysis unit analyzes video during live streaming in real time. For example, the video analysis unit analyzes a video stream to detect the movement of a person in the video. The video analysis unit can also analyze real-time video to identify background information in the video. The video analysis unit can also analyze video based on frame rate and latency. For example, the video analysis unit analyzes video at 30 frames per second to minimize latency. The face detection unit detects the face of a person captured in the video analyzed by the video analysis unit. For example, the face detection unit detects faces in the video using a face recognition algorithm. The face detection unit can also combine multiple algorithms to improve detection accuracy. The face detection unit can also detect faces based on specific movements or behavioral patterns. For example, the face detection unit detects a hand waving motion and recognizes the face at the moment. The face modification unit modifies the detected face. For example, the face modification unit generates a new face using image generation AI to replace the original face. The face modification unit can also maintain a natural appearance by modifying facial features. The face modification unit can also modify the face by applying a filter. For example, the face modification unit can modify the facial contours and adjust the position of the eyes and mouth. The video reconstruction unit reconstructs a video including the modified face. For example, the video reconstruction unit generates a video using a reconstruction algorithm. The video reconstruction unit can also reconstruct the video at a high resolution to maintain video quality. The video reconstruction unit can also reconstruct the video in real time to minimize delay. For example, the video reconstruction unit can track the modified facial movements and expressions in real time to generate a natural video. This allows the live streaming system according to the embodiment to reduce the risk of infringement of portrait rights and minimize the awkwardness of the video. For example, the output unit provides the reconstructed video to viewers through a live streaming platform. The reconstructed video can also be saved using a recording function. The reconstructed video can also be edited later using video editing software.

[0051] The video analysis unit analyzes the movements and behavioral patterns of people in the video, allowing the face detection unit to detect faces only when specific actions are performed. The video analysis unit detects faces only when a person in the video performs a specific gesture, for example. Face recognition is triggered by actions such as waving or jumping. The video analysis unit also detects faces only when a person in the video enters a specific area. For example, it recognizes a face and starts analysis the moment the person enters a specific zone. The video analysis unit also detects faces only when a person in the video is holding a specific object. For example, it prioritizes recognition of the face of a person holding a camera or smartphone. This allows unnecessary face detection to be prevented by detecting faces only when specific actions are performed.

[0052] The video analysis unit analyzes background information within the video, allowing the face detection unit to detect faces only in specific locations and situations. For example, the video analysis unit detects faces only when the background within the video is a specific landmark or building. For example, it performs face recognition at famous tourist spots and event venues. The video analysis unit also detects faces only when the background within the video was shot during a specific time of day. For example, it performs face recognition on video taken at night or in the evening. The video analysis unit also detects faces only when the background within the video is under specific weather conditions. For example, it performs face recognition on video taken on a rainy or snowy day. In this way, unnecessary face detection can be prevented by detecting faces only in specific locations and situations.

[0053] The video analysis unit uses the emotion estimation function to analyze the emotional state of a person in the video, allowing the face detection unit to detect a face only when a specific emotion is displayed. The video analysis unit, for example, detects a face only when a person in the video smiles. For example, face recognition is performed using the moment of a smile as a trigger. The video analysis unit also detects a face only when a person in the video shows a surprised expression. For example, a surprised expression is detected and face recognition is performed at that moment. The video analysis unit also detects a face only when a person in the video shows an angry expression. For example, face recognition is performed at the moment the emotion of anger is displayed. In this way, unnecessary face detection can be prevented by detecting a face only when a specific emotion is displayed.

[0054] The video analysis unit also analyzes audio data in the video, and the face detection unit can detect faces when specific sounds or conversations are included. The video analysis unit, for example, analyzes audio data in the video and detects faces when specific keywords are included. For example, facial recognition is performed using words such as "hello" or "thank you" as triggers. The video analysis unit also analyzes audio data in the video and detects faces when a specific volume level is exceeded. For example, it prioritizes recognition of the faces of people speaking loudly. The video analysis unit also analyzes audio data in the video and recognizes faces when a specific audio pattern is detected. For example, facial recognition is performed using laughter or applause as triggers. This makes it possible to prevent unnecessary face detection by detecting faces only when specific sounds or conversations are included.

[0055] The video analysis unit recognizes objects in the video, and the face detection unit can detect a face when a specific object is captured in the video. For example, the video analysis unit detects a face when a specific object (e.g., a car or a motorcycle) is captured in the video. For example, it performs face recognition the moment a car license plate is captured. The video analysis unit also detects a face when a specific object (e.g., a signboard or advertisement) is captured in the video. For example, it performs face recognition the moment a specific advertising signboard is captured. The video analysis unit also detects a face when a specific object (e.g., an animal or pet) is captured in the video. For example, it performs face recognition the moment a dog or cat is captured. In this way, unnecessary face detection can be prevented by detecting a face only when a specific object is captured.

[0056] The face modification unit can modify the detected face to make it look more natural by changing the age and gender of the face. The face modification unit, for example, modifies the age of the detected face to make it look younger or older. For example, a face in its twenties is modified to look like a face in its sixties. The face modification unit can also modify the detected face to make it look more natural by changing a male face to look like a female face. For example, a male face is modified to look like a female face. The face modification unit can also simultaneously modify the age and gender of the detected face to make it look more natural. For example, a young male face is modified to look like an older female face. In this way, by changing the age and gender, it is possible to modify the face to look more natural.

[0057] The face modification unit can modify the facial expression of a detected face to change it into a face with a different emotion. For example, the face modification unit modifies the facial expression of a detected face to a smiling face. For example, it modifies an expressionless face to a smiling face. The face modification unit can also modify the facial expression of a detected face to a surprised face. For example, it modifies an expressionless face to a surprised face. The face modification unit can also modify the facial expression of a detected face to an angry face. For example, it modifies an expressionless face to an angry face. In this way, by modifying the facial expression, it is possible to modify a face to have a different emotion.

[0058] The face modification unit can use the emotion estimation function to modify the face to match the emotion of the viewer. For example, when the viewer shows a smiling emotion, the face modification unit modifies the detected face to a smiling expression. For example, the face is modified to a smiling expression to match the emotion of the viewer. Furthermore, when the viewer shows a surprised emotion, the face modification unit modifies the detected face to a surprised expression. For example, the face is modified to a surprised expression to match the emotion of the viewer. Furthermore, when the viewer shows an angry emotion, the face modification unit modifies the detected face to an angry expression. For example, the face is modified to an angry expression to match the emotion of the viewer. This makes it possible to modify the face to match the emotion of the viewer.

[0059] The face modification unit can change the detected face into the face of an animated character or an animal. For example, the face modification unit changes the detected face into the face of an animated character, such as a popular anime character. The face modification unit also changes the detected face into the face of an animal, such as a dog or cat. The face modification unit also changes the detected face into the face of a fantasy character, such as a dragon or a fairy. In this way, by changing the face into the face of an animated character or an animal, visually appealing images can be provided.

[0060] The face modification unit can modify the detected face into the face of a historical figure or a famous person. For example, the face modification unit modifies the detected face into the face of a historical figure. For example, the face modification unit modifies the detected face into the face of Napoleon or Cleopatra. The face modification unit also modifies the detected face into the face of a famous person. For example, the face modification unit modifies the detected face into the face of a movie star or a musician. The face modification unit also modifies the detected face into the face of an athlete. For example, the face modification unit modifies the detected face into the face of an Olympic athlete or a soccer player. In this way, by modifying the face into the face of a historical figure or a famous person, visually appealing images can be provided.

[0061] The video reconstruction unit can track the changed facial movements and expressions in real time to generate natural-looking video. The video reconstruction unit, for example, tracks the changed facial movements in real time to generate natural-looking video that matches the original facial movements. For example, changes in facial direction and expression are reflected in real time. The video reconstruction unit also tracks the changed facial expression in real time to generate natural-looking video that matches the original expression. For example, smiling and surprised expressions are reflected in real time. The video reconstruction unit also tracks the changed facial movements and expressions simultaneously to generate natural-looking video that matches the original facial movements and expression. For example, changes in facial direction and expression are reflected in real time. In this way, natural-looking video can be generated by tracking the changed facial movements and expressions in real time.

[0062] The image reconstructor adjusts the skin color and hairstyle of the modified face, thereby achieving a more natural appearance. The image reconstructor, for example, adjusts the skin color of the modified face to match the original face. For example, it selects and changes a color that is close to the skin color of the original face. The image reconstructor also adjusts the hairstyle of the modified face to match the original face. For example, it selects and changes a style that is close to the hairstyle of the original face. The image reconstructor also simultaneously adjusts the skin color and hairstyle of the modified face, thereby achieving a more natural appearance. For example, it changes the skin color and hairstyle to match the features of the original face. In this way, by adjusting the skin color and hairstyle, a more natural appearance can be achieved.

[0063] The video reconstructor can use the emotion estimation function to reconstruct video that matches the viewer's emotions. For example, when the viewer shows a smiling emotion, the video reconstructor changes the modified face to a smiling face and reconstructs the video. For example, the face is changed to a smiling face to match the viewer's emotions. Furthermore, when the viewer shows a surprised emotion, the video reconstructor changes the modified face to a surprised expression and reconstructs the video. For example, the face is changed to a surprised expression to match the viewer's emotions. Furthermore, when the viewer shows an angry emotion, the video reconstructor changes the modified face to an angry expression and reconstructs the video. For example, the face is changed to an angry expression to match the viewer's emotions. This makes it possible to reconstruct video that matches the viewer's emotions.

[0064] The image reconstruction unit can convert the image including the modified face into a 3D model and generate a three-dimensional image. The image reconstruction unit, for example, converts the modified face into a 3D model and generates a three-dimensional image. For example, the facial shape and facial expression are reproduced as a 3D model. The image reconstruction unit also converts the entire image including the modified face into a 3D model and generates a three-dimensional image. For example, the background and other people are also reproduced as a 3D model. The image reconstruction unit also reflects the modified facial movements and expressions in the 3D model and generates a three-dimensional image in real time. For example, changes in facial direction and facial expression are reflected in the 3D model. In this way, three-dimensional images can be generated by converting into a 3D model.

[0065] The video reconstructor animates the video including the modified face to generate visually appealing video. The video reconstructor, for example, converts the modified face into an animated character to generate visually appealing video. For example, it changes the face into a popular anime character. The video reconstructor also animates the entire video including the modified face to generate visually appealing video. For example, it also changes the background and other characters in an animation style. The video reconstructor also animates the movement and facial expression of the modified face to generate visually appealing video in real time. For example, it reflects changes in facial direction and facial expression in the animation style. In this way, visually appealing video can be generated by animation.

[0066] The video reconstructor can use the emotion estimation function to reconstruct video that matches the viewer's emotions. For example, when the viewer shows a smiling emotion, the video reconstructor changes the modified face to a smiling face and reconstructs the video. For example, the face is changed to a smiling face to match the viewer's emotions. Furthermore, when the viewer shows a surprised emotion, the video reconstructor changes the modified face to a surprised expression and reconstructs the video. For example, the face is changed to a surprised expression to match the viewer's emotions. Furthermore, when the viewer shows an angry emotion, the video reconstructor changes the modified face to an angry expression and reconstructs the video. For example, the face is changed to an angry expression to match the viewer's emotions. This makes it possible to reconstruct video that matches the viewer's emotions.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The live streaming system can further include an audio analysis unit. The audio analysis unit can analyze audio data during live streaming in real time and detect specific keywords and audio patterns. For example, the audio analysis unit can notify the video analysis unit the moment a specific keyword (e.g., "hello" or "thank you") is uttered, which can be used as a trigger for the face detection unit to detect a face. The audio analysis unit can also detect specific audio patterns (e.g., laughter or applause) and use that as a trigger for the face detection unit to detect a face at that moment. This makes it possible to utilize audio data for more accurate face detection.

[0069] The live streaming system can further include a gesture recognition unit. The gesture recognition unit analyzes gestures of people in the video in real time, and when a specific gesture is made, it can be used as a trigger for the face detection unit to detect a face. For example, the gesture recognition unit can detect actions such as waving a hand, jumping, or pointing a finger, and use that as a trigger for the face detection unit to detect a face at that moment. The gesture recognition unit can also detect a specific gesture combination (e.g., waving a hand followed by jumping), and use that as a trigger for the face detection unit to detect a face at that moment. This makes it possible to utilize gestures to achieve more accurate face detection.

[0070] The live streaming system can further include an object recognition unit. The object recognition unit analyzes specific objects in the video in real time, and can be used as a trigger for the face detection unit to detect a face when the specific object is captured. For example, the object recognition unit can notify the face detection unit the moment a vehicle such as a car or motorcycle is captured, and can be used as a trigger to detect a face. The object recognition unit can also notify the face detection unit the moment a specific object such as a signboard or advertisement is captured, and can be used as a trigger to detect a face. This makes it possible to utilize objects to enable more accurate face detection.

[0071] The live streaming system can further include a background recognition unit. The background recognition unit analyzes background information within the video in real time, and can be used as a trigger for the face detection unit to detect a face when a specific background is captured. For example, the background recognition unit can notify the face detection unit the moment a specific landmark, such as a famous tourist spot or event venue, is captured, and use this as a trigger to detect a face. The background recognition unit can also notify the face detection unit of video captured during specific time periods (e.g., nighttime, evening) or weather conditions (e.g., rainy, snowy days), and use this as a trigger to detect a face. This makes it possible to utilize background information to enable more accurate face detection.

[0072] The live streaming system can further use an emotion estimation function to reconstruct video based on the viewer's emotions. For example, if the viewer expresses a smiling emotion, the video reconstruction unit can change the modified face to a smiling face and reconstruct the video. Also, if the viewer expresses a surprised emotion, the video reconstruction unit can change the modified face to a surprised expression and reconstruct the video. Furthermore, if the viewer expresses an angry emotion, the video reconstruction unit can change the modified face to an angry expression and reconstruct the video. This makes it possible to reconstruct video in accordance with the viewer's emotions, improving the viewing experience.

[0073] The live streaming system can further use an emotion estimation function to modify faces based on the emotional state of a person in the video. For example, if a person in the video shows a smiling emotion, the face modification unit can modify the detected face to a smiling expression. If a person in the video shows a surprised emotion, the face modification unit can modify the detected face to a surprised expression. If a person in the video shows an angry emotion, the face modification unit can modify the detected face to an angry expression. This makes it possible to modify faces to match the emotions of the person in the video, thereby providing more natural-looking video.

[0074] The live streaming system can further use the emotion estimation function to change the background based on the emotional state of the person in the video. For example, if the person in the video shows a smiling emotion, the background can be changed to a bright color or a happy scene. If the person in the video shows a surprised emotion, the background can be changed to a surprising scene. If the person in the video shows an angry emotion, the background can be changed to a dark color or a tense scene. This makes it possible to change the background to match the emotion of the person in the video, thereby providing visually appealing video.

[0075] The live streaming system can further use an emotion estimation function to change the audio based on the viewer's emotions. For example, if the viewer shows a smiling emotion, the audio can be changed to a bright tone or cheerful music. If the viewer shows a surprised emotion, the audio can be changed to a surprising sound effect. If the viewer shows an angry emotion, the audio can be changed to a tense tone or heavy music. This makes it possible to change the audio to match the viewer's emotions, improving the viewing experience.

[0076] The live streaming system can further use an emotion estimation function to apply filters based on the emotional state of the person in the video. For example, if the person in the video shows a smiling emotion, a bright color filter can be applied. If the person in the video shows a surprised emotion, a filter that emphasizes the effect of surprise can be applied. If the person in the video shows an angry emotion, a dark color filter can be applied. This makes it possible to apply filters that match the emotions of the people in the video, thereby providing visually appealing video.

[0077] The live streaming system can further use an emotion estimation function to display subtitles based on the viewer's emotions. For example, if the viewer shows a smile, brighter colored subtitles can be displayed. If the viewer shows a surprise, subtitles that emphasize the effect of surprise can be displayed. If the viewer shows an anger, darker colored subtitles can be displayed. This makes it possible to display subtitles that match the viewer's emotions, improving the viewing experience.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The video analysis unit analyzes the live video in real time. For example, it analyzes the video stream to identify the movements of people in the video and background information. It also analyzes the video based on frame rate and latency, analyzing 30 frames per second video to minimize latency. Step 2: The face detection unit detects faces from the video analyzed by the video analysis unit. For example, it can use a facial recognition algorithm to detect faces in the video, or it can combine multiple algorithms to improve detection accuracy. It can also detect faces based on specific movements or behavioral patterns. Step 3: The face modification unit modifies the detected face. For example, it uses image generation AI to generate a new face to replace the original face. It can also modify facial features to maintain a natural look and apply filters to modify the face. For example, it can change the facial contours and adjust the position of the eyes and mouth. Step 4: The video reconstructor reconstructs the video containing the modified face. For example, it uses a reconstruction algorithm to generate the video and reconstruct it at high resolution. It also reconstructs the video in real time to minimize delay. For example, it tracks the movements and expressions of the modified face in real time to generate a natural video.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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).

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] In the headset type terminal 314, 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 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 specific processing unit 290 using these models.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] In the robot 414, 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 robot 414 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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."

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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]

[0147] 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 video analysis unit that analyzes live streaming video in real time, a face detection unit that detects a face of a person reflected in the video analyzed by the video analysis unit; a face modification unit that modifies the face detected by the face detection unit; an image reconstruction unit that reconstructs an image including the face modified by the face modification unit; A system characterized by:

2. The video analysis unit The face detection unit analyzes the movements and behavior patterns of people in the video and detects faces only when specific actions are performed.

2. The system of claim 1.

3. The video analysis unit The background information in the video is analyzed, and the face detection unit detects faces only in specific locations or situations.

2. The system of claim 1.

4. The video analysis unit The emotional state of a person in the video is analyzed, and the face detection unit detects a face only when the person shows a specific emotion.

2. The system of claim 1.

5. The video analysis unit The audio data in the video is also analyzed, and if a specific sound or conversation is included, the face detection unit detects a face.

2. The system of claim 1.

6. The video analysis unit Object recognition is performed within the video, and when a specific object is captured, the face detection unit detects a face.

2. The system of claim 1.

7. The face change unit By changing the age and gender of the detected face, the face can be changed to look more natural.

2. The system of claim 1.

8. The face change unit Modifying the facial expression of the detected face to change it into a face with a different emotion 2. The system of claim 1.

Images