System

Abstract

An object of the system according to the embodiment is to accurately track the movement of a subject and perform optimal imaging.SOLUTION: A system according to an embodiment includes a AI smartphone, a video camera platform, and a generation AI. The AI smartphone recognizes the motion of the subject. The generation AI learns the motion of the subject recognized by the AI smartphone. The video camera platform controls the camera based on the motion of the subject learned by the generative AI.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to accurately track the movement of a subject and capture optimal images.

[0005] The system according to the embodiment aims to accurately track the movement of a subject and perform optimal photography. [Means for solving the problem]

[0006] The system according to the embodiment includes an AI smartphone, a video head, and a generation AI. The AI ​​smartphone recognizes the movement of a subject. The generation AI learns the movement of the subject recognized by the AI ​​smartphone. The video head controls the camera based on the movement of the subject learned by the generation AI. [Effects of the Invention]

[0007] The system according to the embodiment can accurately track the movement of a subject and perform optimal photography. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The AI ​​camera system according to an embodiment of the present invention replicates the techniques of professional photographers, allowing anyone to easily capture powerful videos and beautiful still images. By combining an AI smartphone with a video head with robotic control, the system recognizes the subject's movements and automatically controls the camera accordingly. This allows the AI ​​camera system to accurately track and capture any subject, from the fast movements of athletes to animals in nature and even the movement of celestial bodies.

[0029] An AI camera system according to an embodiment includes an AI smartphone, a video head, and a generation AI. The AI ​​smartphone recognizes the movement of a subject. For example, the AI ​​smartphone captures the movements of athletes with a camera and analyzes their movements in real time. The AI ​​smartphone can also recognize and track the movements of animals in nature. Furthermore, the AI ​​smartphone can recognize and track the movements of celestial bodies. The generation AI learns the movement of the subject recognized by the AI ​​smartphone. For example, the generation AI analyzes the movement of the subject using a text generation AI (e.g., LLM) to understand the characteristics of that movement. The generation AI can also learn the movement of the subject using a multimodal generation AI. The generation AI can also extract patterns of the subject's movement and predict that movement. For example, the text generation AI has learned a large amount of movement data and has advanced analytical capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI predicts the subject's future movement based on the movement patterns. The video head controls the camera based on the movement of the subject learned by the generation AI. For example, a video camera head uses motors to control the pan, tilt, and zoom of a camera. A video camera head can also adjust the camera's position to match the movement of a subject. A video camera head can also focus the camera in accordance with the movement of a subject. For example, a video camera head can move a camera at high speed to accurately track the movements of an athlete. A video camera head can track the movements of animals in nature and control the camera accordingly. A video camera head can track the movements of celestial bodies and control the camera accordingly. As a result, the AI ​​camera system according to the embodiment reproduces the skills of professional photographers, allowing anyone to easily capture impressive videos and beautiful still images. For example, the system can be used in a variety of situations, such as sporting events, nature observation, and astronomy observation. This significantly improves the enjoyment and quality of photography.

[0030] AI smartphones can use past motion data to analyze motion patterns and implement algorithms that predict future motion. For example, when learning a subject's motion, an AI smartphone collects past motion data and implements an algorithm that analyzes motion patterns. For example, it analyzes the motion trends of athletes based on past match data. AI smartphones can also use past motion data to predict a subject's future motion. For example, a generative AI analyzes past motion data and simulates future motion. AI smartphones can also predict changes in a subject's motion based on past motion data. For example, it extracts motion patterns and predicts future motion based on those patterns. This allows future motion to be predicted more accurately by using past motion data.

[0031] AI smartphones can use multiple cameras to integrate data from different angles and build more accurate movement models. For example, when recognizing a subject's movement, an AI smartphone uses multiple cameras to collect video data from different angles, and the generative AI integrates that data to build a movement model. For example, it can analyze the movements of athletes from multiple angles. AI smartphones can also use data from different angles to analyze a subject's movement in three dimensions. For example, multiple cameras can be placed to capture the subject's movement in three dimensions. AI smartphones can also build detailed models of a subject's movement based on data from different angles. For example, they can analyze the trajectory of movement and reproduce that movement. This allows for the use of multiple cameras to build more accurate movement models.

[0032] AI smartphones can simultaneously collect audio data and environmental data and perform recognition that takes into account background information about the movement. For example, when recognizing a subject's movement, an AI smartphone simultaneously collects audio data, and the generation AI analyzes that audio data to consider background information about the movement. For example, it analyzes the movements of athletes and the cheers of spectators. AI smartphones can also collect environmental data and consider background information about the movement based on that data. For example, it collects environmental data such as temperature, humidity, and illuminance and analyzes movement based on that data. AI smartphones can also integrate audio data and environmental data to comprehensively analyze background information about the movement. For example, it combines audio data and environmental data to provide detailed background information about the movement. This allows for more accurate recognition of background information about the movement by taking audio data and environmental data into account.

[0033] AI smartphones can target different types of subjects and analyze their movement characteristics. For example, when learning the movement of a subject, an AI smartphone targets different types of subjects (such as plant growth or changes in buildings), and the generation AI analyzes the movement characteristics. For example, it can learn the growth speed and direction of plants and predict future growth. AI smartphones can also target changes in buildings and analyze the patterns of those changes. For example, it can analyze the aging of buildings and the progress of renovations. Furthermore, by targeting different types of subjects, AI smartphones can be used for a wide range of applications. For example, it can analyze the behavior patterns of animals or the movement of celestial bodies. This allows for a wide range of applications by targeting different types of subjects.

[0034] Video heads can optimize motor movement by improving their motion prediction algorithms and predicting the subject's movement in advance. For example, video heads can improve their motion prediction algorithms and optimize motor movement by predicting the subject's movement in advance. For example, they can predict the movements of athletes and adjust camera movement accordingly. Video heads can also use generative AI to analyze past motion data and predict future movements based on that data. For example, they can extract movement patterns and optimize motor movement based on those patterns. Video heads can also predict changes in the subject's movement in real time using motion prediction algorithms. For example, they can adjust the camera position in response to changes in movement. This allows for the optimization of motor movement by improving the motion prediction algorithm.

[0035] A video head can add an interface that allows the user to directly control it using voice instructions or gesture recognition. A video head can add an interface that allows the user to directly control it using voice instructions, for example. For example, a system can be built in which the user controls the camera movement using voice. A video head can also add an interface that allows the user to directly control it using gesture recognition. For example, a system can be built in which the user controls the camera movement using hand movements. A video head can also combine voice instructions and gesture recognition to provide an interface that allows the user to intuitively control the camera. For example, the camera movement can be finely adjusted by combining voice instructions and gestures. This allows the user to intuitively control the camera using voice instructions and gesture recognition.

[0036] Video heads can be equipped with different types of cameras to meet a variety of filming needs. For example, video heads can be equipped with infrared cameras to create systems that meet the needs of filming at night or in dark places. For example, an infrared camera can be used to track the movements of animals at night. Video heads can also be equipped with 360-degree cameras to meet wide-angle filming needs. For example, a 360-degree camera can be used to capture footage in all directions. Video heads can also be interchangeable with different types of cameras, allowing you to switch between cameras depending on the filming scene. For example, you can use an infrared camera and a 360-degree camera depending on the situation. This allows you to install different types of cameras to meet a variety of filming needs.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] When recognizing a subject's movement, an AI smartphone can analyze the subject's speed and direction in real time and use that data to improve the accuracy of its movement predictions. For example, it can analyze the speed and direction of an athlete's movement and predict future movements based on that data. An AI smartphone can also extract movement patterns based on the speed and direction of the subject's movement and predict movements based on those patterns. For example, it can analyze changes in the speed and direction of movement and predict future movements based on those changes. An AI smartphone can also analyze the characteristics of the subject's movement based on the speed and direction of movement and predict movements based on those characteristics. For example, it can analyze the characteristics of the speed and direction of movement and predict future movements based on those characteristics. This improves the accuracy of movement predictions by taking into account the speed and direction of the subject's movement.

[0039] AI smartphones can incorporate algorithms that analyze movement patterns using past movement data and predict future movements. For example, they can analyze movement trends based on a sports player's past match data. AI smartphones can also use past movement data to predict a subject's future movements. For example, a generative AI analyzes past movement data and simulates future movements. AI smartphones can also predict changes in a subject's movement based on past movement data. For example, they can extract movement patterns and predict future movements based on those patterns. This allows for more accurate predictions of future movements by using past movement data.

[0040] AI smartphones can use multiple cameras to integrate data from different angles and build more accurate movement models. For example, they can analyze the movements of athletes from multiple angles. AI smartphones can also use data from different angles to analyze the subject's movements in three dimensions. For example, they can place multiple cameras to capture the subject's movements in three dimensions. AI smartphones can also build detailed models of the subject's movements based on data from different angles. For example, they can analyze the trajectory of movement and reproduce that movement. This allows for the use of multiple cameras to build more accurate movement models.

[0041] AI smartphones can simultaneously collect audio data and environmental data and perform recognition that takes into account background information about movements. For example, they can analyze the movements of athletes and the cheers of spectators. AI smartphones can also collect environmental data and consider background information about movements based on that data. For example, they can collect environmental data such as temperature, humidity, and illuminance and analyze movements based on that data. AI smartphones can also integrate audio data and environmental data to comprehensively analyze background information about movements. For example, they can combine audio data and environmental data to provide detailed background information about movements. This allows for more accurate recognition of background information about movements by taking audio data and environmental data into account.

[0042] AI smartphones can target different types of subjects and analyze their movement characteristics. For example, they can learn the growth rate and direction of plants and predict future growth. AI smartphones can also target changes in buildings and analyze the patterns of those changes. For example, they can analyze the aging of buildings and the progress of renovations. By targeting different types of subjects, AI smartphones can also be used for a wide range of applications. For example, they can analyze the behavior patterns of animals and the movement of celestial bodies. This allows for a wide range of applications by targeting different types of subjects.

[0043] Video heads can optimize motor movement by improving their motion prediction algorithms to predict the subject's movements in advance. For example, they can predict the movements of athletes and adjust camera movement accordingly. Video heads can also use generative AI to analyze past motion data and predict future movements based on that data. For example, they can extract movement patterns and optimize motor movement based on those patterns. Video heads can also predict changes in the subject's movement in real time using motion prediction algorithms. For example, they can adjust the camera position in response to changes in movement. This allows for the optimization of motor movement by improving the motion prediction algorithm.

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

[0045] Step 1: The AI ​​smartphone recognizes the movement of the subject. For example, an AI smartphone can capture the movements of an athlete with a camera and analyze them in real time. An AI smartphone can also recognize the movements of animals in nature and track them. Furthermore, an AI smartphone can recognize the movement of celestial bodies and track them. Step 2: The generative AI learns the subject's movements recognized by the AI ​​smartphone. For example, the generative AI uses text generation AI (e.g., LLM) to analyze the subject's movements and understand their characteristics. The generative AI can also use multimodal generation AI to learn the subject's movements. The generative AI can also extract patterns from the subject's movements and predict their movements. For example, text generation AI has learned large amounts of movement data and has advanced analytical capabilities. Multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generative AI predicts the subject's future movements based on the movement patterns. Step 3: The video head controls the camera based on the subject's movements learned by the generative AI. For example, the video head uses motors to control the pan, tilt, and zoom of the camera. The video head can also adjust the camera's position to match the subject's movements. The video head can also focus the camera according to the subject's movements. For example, the video head moves the camera at high speed to accurately track the movements of an athlete. The video head tracks the movements of animals in nature and controls the camera according to those movements. The video head tracks the movement of celestial bodies and controls the camera according to those movements.

[0046] (Example 2) The AI ​​camera system according to an embodiment of the present invention replicates the techniques of professional photographers, allowing anyone to easily capture powerful videos and beautiful still images. By combining an AI smartphone with a video head with robotic control, the system recognizes the subject's movements and automatically controls the camera accordingly. This allows the AI ​​camera system to accurately track and capture any subject, from the fast movements of athletes to animals in nature and even the movement of celestial bodies.

[0047] An AI camera system according to an embodiment includes an AI smartphone, a video head, and a generation AI. The AI ​​smartphone recognizes the movement of a subject. For example, the AI ​​smartphone captures the movements of athletes with a camera and analyzes their movements in real time. The AI ​​smartphone can also recognize and track the movements of animals in nature. Furthermore, the AI ​​smartphone can recognize and track the movements of celestial bodies. The generation AI learns the movement of the subject recognized by the AI ​​smartphone. For example, the generation AI analyzes the movement of the subject using a text generation AI (e.g., LLM) to understand the characteristics of that movement. The generation AI can also learn the movement of the subject using a multimodal generation AI. The generation AI can also extract patterns of the subject's movement and predict that movement. For example, the text generation AI has learned a large amount of movement data and has advanced analytical capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI predicts the subject's future movement based on the movement patterns. The video head controls the camera based on the movement of the subject learned by the generation AI. For example, a video camera head uses motors to control the pan, tilt, and zoom of a camera. A video camera head can also adjust the camera's position to match the movement of a subject. A video camera head can also focus the camera in accordance with the movement of a subject. For example, a video camera head can move a camera at high speed to accurately track the movements of an athlete. A video camera head can track the movements of animals in nature and control the camera accordingly. A video camera head can track the movements of celestial bodies and control the camera accordingly. As a result, the AI ​​camera system according to the embodiment reproduces the skills of professional photographers, allowing anyone to easily capture impressive videos and beautiful still images. For example, the system can be used in a variety of situations, such as sporting events, nature observation, and astronomy observation. This significantly improves the enjoyment and quality of photography.

[0048] AI smartphones can estimate the emotional state of a subject and improve the accuracy of their movement predictions based on that emotion. For example, when recognizing a subject's movements, an AI smartphone's generation AI infers the emotional state from the subject's facial expressions and movements, and improves the accuracy of their movement predictions based on that emotion. For example, it can sense an athlete's state of tension or concentration and predict changes in their movements. AI smartphones can also analyze the subject's voice to estimate their emotional state. For example, it can analyze the tone and speed of the voice to estimate their emotional state. AI smartphones can also collect the subject's biometric data (heart rate and electrodermal activity) to estimate their emotional state. For example, it can estimate their emotional state based on fluctuations in heart rate. This improves the accuracy of their movement predictions by taking the subject's emotional state into account.

[0049] AI smartphones can use past motion data to analyze motion patterns and implement algorithms that predict future motion. For example, when learning a subject's motion, an AI smartphone collects past motion data and implements an algorithm that analyzes motion patterns. For example, it analyzes the motion trends of athletes based on past match data. AI smartphones can also use past motion data to predict a subject's future motion. For example, a generative AI analyzes past motion data and simulates future motion. AI smartphones can also predict changes in a subject's motion based on past motion data. For example, it extracts motion patterns and predicts future motion based on those patterns. This allows future motion to be predicted more accurately by using past motion data.

[0050] AI smartphones can use multiple cameras to integrate data from different angles and build more accurate movement models. For example, when recognizing a subject's movement, an AI smartphone uses multiple cameras to collect video data from different angles, and the generative AI integrates that data to build a movement model. For example, it can analyze the movements of athletes from multiple angles. AI smartphones can also use data from different angles to analyze a subject's movement in three dimensions. For example, multiple cameras can be placed to capture the subject's movement in three dimensions. AI smartphones can also build detailed models of a subject's movement based on data from different angles. For example, they can analyze the trajectory of movement and reproduce that movement. This allows for the use of multiple cameras to build more accurate movement models.

[0051] AI smartphones can simultaneously collect audio data and environmental data and perform recognition that takes into account background information about the movement. For example, when recognizing a subject's movement, an AI smartphone simultaneously collects audio data, and the generation AI analyzes that audio data to consider background information about the movement. For example, it analyzes the movements of athletes and the cheers of spectators. AI smartphones can also collect environmental data and consider background information about the movement based on that data. For example, it collects environmental data such as temperature, humidity, and illuminance and analyzes movement based on that data. AI smartphones can also integrate audio data and environmental data to comprehensively analyze background information about the movement. For example, it combines audio data and environmental data to provide detailed background information about the movement. This allows for more accurate recognition of background information about the movement by taking audio data and environmental data into account.

[0052] AI smartphones can target different types of subjects and analyze their movement characteristics. For example, when learning the movement of a subject, an AI smartphone targets different types of subjects (such as plant growth or changes in buildings), and the generation AI analyzes the movement characteristics. For example, it can learn the growth speed and direction of plants and predict future growth. AI smartphones can also target changes in buildings and analyze the patterns of those changes. For example, it can analyze the aging of buildings and the progress of renovations. Furthermore, by targeting different types of subjects, AI smartphones can be used for a wide range of applications. For example, it can analyze the behavior patterns of animals or the movement of celestial bodies. This allows for a wide range of applications by targeting different types of subjects.

[0053] AI smartphones can use their emotion estimation function to monitor a user's emotional state in real time and optimize the timing of photo capture. For example, when recognizing a subject's movements, AI smartphones use their emotion estimation function to analyze the user's facial expressions and voice to monitor their emotional state in real time. For example, they can capture and photograph moments when the user is excited. AI smartphones can also collect a user's biometric data and estimate their emotional state based on that data. For example, they can analyze heart rate and electrodermal activity to estimate their emotional state. AI smartphones can also use their emotion estimation function to optimize the timing of photo capture according to the user's emotional state. For example, they can take a photo at the peak of their emotions. This allows them to achieve the optimal timing of photo capture by taking into account the user's emotional state.

[0054] A video camera can take into account the emotional state of the subject and fine-tune movements according to that emotion. For example, a video camera can use generative AI to estimate the emotional state of the subject from their facial expressions and movements, and fine-tune movements according to that emotion. For example, it can adjust camera movement to match the level of tension of an athlete. A video camera can also analyze the subject's voice to estimate their emotional state. For example, it can analyze the tone and speed of their voice to estimate their emotional state. A video camera can also collect biometric data of the subject and estimate their emotional state based on that data. For example, it can estimate their emotional state based on fluctuations in their heart rate. This makes it possible to fine-tune movements by taking into account the emotional state of the subject.

[0055] Video heads can optimize motor movement by improving their motion prediction algorithms and predicting the subject's movement in advance. For example, video heads can improve their motion prediction algorithms and optimize motor movement by predicting the subject's movement in advance. For example, they can predict the movements of athletes and adjust camera movement accordingly. Video heads can also use generative AI to analyze past motion data and predict future movements based on that data. For example, they can extract movement patterns and optimize motor movement based on those patterns. Video heads can also predict changes in the subject's movement in real time using motion prediction algorithms. For example, they can adjust the camera position in response to changes in movement. This allows for the optimization of motor movement by improving the motion prediction algorithm.

[0056] A video head can add an interface that allows the user to directly control it using voice instructions or gesture recognition. A video head can add an interface that allows the user to directly control it using voice instructions, for example. For example, a system can be built in which the user controls the camera movement using voice. A video head can also add an interface that allows the user to directly control it using gesture recognition. For example, a system can be built in which the user controls the camera movement using hand movements. A video head can also combine voice instructions and gesture recognition to provide an interface that allows the user to intuitively control the camera. For example, the camera movement can be finely adjusted by combining voice instructions and gestures. This allows the user to intuitively control the camera using voice instructions and gesture recognition.

[0057] Video heads can be equipped with different types of cameras to meet a variety of filming needs. For example, video heads can be equipped with infrared cameras to create systems that meet the needs of filming at night or in dark places. For example, an infrared camera can be used to track the movements of animals at night. Video heads can also be equipped with 360-degree cameras to meet wide-angle filming needs. For example, a 360-degree camera can be used to capture footage in all directions. Video heads can also be interchangeable with different types of cameras, allowing you to switch between cameras depending on the filming scene. For example, you can use an infrared camera and a 360-degree camera depending on the situation. This allows you to install different types of cameras to meet a variety of filming needs.

[0058] The video head can use an emotion estimation function to monitor a user's emotional state in real time and optimize the timing and angle of shooting. For example, the video head can use the emotion estimation function to analyze a user's facial expressions and voice to monitor the user's emotional state in real time. For example, it can capture a moment when the user is excited and adjust the camera angle. The video head can also collect a user's biometric data and estimate the user's emotional state based on that data. For example, it can analyze heart rate and electrodermal activity to estimate the emotional state. The video head can also use the emotion estimation function to optimize the timing and angle of shooting according to the user's emotional state. For example, it can shoot at the peak of emotion and at the optimal angle. This allows the optimal timing and angle of shooting to be achieved by taking the user's emotional state into consideration.

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

[0060] When recognizing a subject's movement, an AI smartphone can analyze the subject's speed and direction in real time and use that data to improve the accuracy of its movement predictions. For example, it can analyze the speed and direction of an athlete's movement and predict future movements based on that data. An AI smartphone can also extract movement patterns based on the speed and direction of the subject's movement and predict movements based on those patterns. For example, it can analyze changes in the speed and direction of movement and predict future movements based on those changes. An AI smartphone can also analyze the characteristics of the subject's movement based on the speed and direction of movement and predict movements based on those characteristics. For example, it can analyze the characteristics of the speed and direction of movement and predict future movements based on those characteristics. This improves the accuracy of movement predictions by taking into account the speed and direction of the subject's movement.

[0061] AI smartphones can estimate the emotional state of a subject and improve the accuracy of their movement predictions based on that emotion. For example, they can sense the tension and concentration of an athlete and predict changes in their movements. AI smartphones can also analyze the subject's voice to estimate their emotional state. For example, they can analyze the tone and speed of the voice to estimate their emotional state. AI smartphones can also collect the subject's biometric data (heart rate and electrodermal activity) to estimate their emotional state. For example, they can estimate their emotional state based on fluctuations in heart rate. This improves the accuracy of their movement predictions by taking the subject's emotional state into account.

[0062] AI smartphones can incorporate algorithms that analyze movement patterns using past movement data and predict future movements. For example, they can analyze movement trends based on a sports player's past match data. AI smartphones can also use past movement data to predict a subject's future movements. For example, a generative AI analyzes past movement data and simulates future movements. AI smartphones can also predict changes in a subject's movement based on past movement data. For example, they can extract movement patterns and predict future movements based on those patterns. This allows for more accurate predictions of future movements by using past movement data.

[0063] AI smartphones can use multiple cameras to integrate data from different angles and build more accurate movement models. For example, they can analyze the movements of athletes from multiple angles. AI smartphones can also use data from different angles to analyze the subject's movements in three dimensions. For example, they can place multiple cameras to capture the subject's movements in three dimensions. AI smartphones can also build detailed models of the subject's movements based on data from different angles. For example, they can analyze the trajectory of movement and reproduce that movement. This allows for the use of multiple cameras to build more accurate movement models.

[0064] AI smartphones can simultaneously collect audio data and environmental data and perform recognition that takes into account background information about movements. For example, they can analyze the movements of athletes and the cheers of spectators. AI smartphones can also collect environmental data and consider background information about movements based on that data. For example, they can collect environmental data such as temperature, humidity, and illuminance and analyze movements based on that data. AI smartphones can also integrate audio data and environmental data to comprehensively analyze background information about movements. For example, they can combine audio data and environmental data to provide detailed background information about movements. This allows for more accurate recognition of background information about movements by taking audio data and environmental data into account.

[0065] AI smartphones can target different types of subjects and analyze their movement characteristics. For example, they can learn the growth rate and direction of plants and predict future growth. AI smartphones can also target changes in buildings and analyze the patterns of those changes. For example, they can analyze the aging of buildings and the progress of renovations. By targeting different types of subjects, AI smartphones can also be used for a wide range of applications. For example, they can analyze the behavior patterns of animals and the movement of celestial bodies. This allows for a wide range of applications by targeting different types of subjects.

[0066] AI smartphones can use their emotion estimation function to monitor a user's emotional state in real time and optimize the timing of taking photos. For example, they can capture and take photos when the user is excited. AI smartphones can also collect a user's biometric data and estimate their emotional state based on that data. For example, they can analyze heart rate and electrodermal activity to estimate emotional state. AI smartphones can also use their emotion estimation function to optimize the timing of taking photos according to the user's emotional state. For example, they can take photos when the user is at their emotional peak. This allows them to realize the optimal timing of taking photos by taking into account the user's emotional state.

[0067] A video head can take into account the emotional state of the subject and fine-tune movements according to the subject's emotions. For example, it can adjust the camera movement to match the level of tension of an athlete. A video head can also analyze the subject's voice to estimate the subject's emotional state. For example, it can analyze the tone and speed of the voice to estimate the emotional state. A video head can also collect the subject's biometric data and estimate the emotional state based on that data. For example, it can estimate the emotional state based on fluctuations in heart rate. This makes it possible to fine-tune movements by taking into account the subject's emotional state.

[0068] Video heads can optimize motor movement by improving their motion prediction algorithms to predict the subject's movements in advance. For example, they can predict the movements of athletes and adjust camera movement accordingly. Video heads can also use generative AI to analyze past motion data and predict future movements based on that data. For example, they can extract movement patterns and optimize motor movement based on those patterns. Video heads can also predict changes in the subject's movement in real time using motion prediction algorithms. For example, they can adjust the camera position in response to changes in movement. This allows for the optimization of motor movement by improving the motion prediction algorithm.

[0069] The video head can use its emotion estimation function to monitor the user's emotional state in real time and optimize the timing and angle of the shot. For example, it can capture the moment when the user is excited and adjust the camera angle accordingly. The video head can also collect the user's biometric data and estimate the emotional state based on that data. For example, it can analyze the heart rate and electrodermal activity to estimate the emotional state. The video head can also use its emotion estimation function to optimize the timing and angle of the shot according to the user's emotional state. For example, it can take a shot at the peak of the user's emotion and shoot at the optimal angle. This allows the optimal timing and angle of the shot to be achieved by taking the user's emotional state into consideration.

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

[0071] Step 1: The AI ​​smartphone recognizes the movement of the subject. For example, an AI smartphone can capture the movements of an athlete with a camera and analyze them in real time. An AI smartphone can also recognize the movements of animals in nature and track them. Furthermore, an AI smartphone can recognize the movement of celestial bodies and track them. Step 2: The generative AI learns the subject's movements recognized by the AI ​​smartphone. For example, the generative AI uses text generation AI (e.g., LLM) to analyze the subject's movements and understand their characteristics. The generative AI can also use multimodal generation AI to learn the subject's movements. The generative AI can also extract patterns from the subject's movements and predict their movements. For example, text generation AI has learned large amounts of movement data and has advanced analytical capabilities. Multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generative AI predicts the subject's future movements based on the movement patterns. Step 3: The video head controls the camera based on the subject's movements learned by the generative AI. For example, the video head uses motors to control the pan, tilt, and zoom of the camera. The video head can also adjust the camera's position to match the subject's movements. The video head can also focus the camera according to the subject's movements. For example, the video head moves the camera at high speed to accurately track the movements of an athlete. The video head tracks the movements of animals in nature and controls the camera according to those movements. The video head tracks the movement of celestial bodies and controls the camera according to those movements.

[0072] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0073] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0074] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0077] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0078] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0079] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0080] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0081] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0082] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0083] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0084] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0085] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0086] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0087] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0088] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0089] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0092] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0093] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0094] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0095] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0096] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0097] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0098] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0099] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0100] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0101] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0102] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0106] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0113] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0122] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0123] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0124] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0125] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0126] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0127] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0128] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0129] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0130] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0131] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0132] The hardware resource for executing a specific process can be any of the following 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.

[0133] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0134] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0135] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0136] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0137] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0138] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

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

Claims

1. AI smartphone and A video head and Equipped with a generative AI, The AI ​​smartphone is Recognizes the subject's movement and The generated AI is The AI ​​smartphone learns the movement of the subject recognized by the AI ​​smartphone, The video head is Controlling the camera based on the movement of the subject learned by the generation AI A system characterized by:

2. The AI ​​smartphone is Introducing an algorithm that uses past movement data to analyze patterns of said movement and predict future movements.

2. The system of claim 1.

3. The AI ​​smartphone is Collects audio data and environmental data simultaneously to recognize the movement while taking into account the background information.

2. The system of claim 1.

4. The video head is The motion prediction algorithm is improved to predict the subject's motion in advance and optimize the motor movement.

2. The system of claim 1.

5. The AI ​​smartphone is Estimating the emotional state of the subject and improving the accuracy of the prediction of the movement based on the emotion.

2. The system of claim 1.

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