System

Abstract

An object of the system according to the embodiment is to reduce the influence of the Personal Information Protection Law and promote the use of data by converting moving image data into a deformed moving image.SOLUTION: A system includes a moving image acquisition part, a deformation conversion part, and a storage part. The moving image acquisition unit acquires moving image data. The deformation conversion section converts the moving image data acquired by the moving image acquisition section into a deformed moving image. The storage section stores the deformed moving image generated by the deformation conversion section.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, the storage of video data was susceptible to the effects of the Personal Information Protection Act, which limited the use of the data.

[0005] The system according to the embodiment aims to reduce the impact of the Personal Information Protection Act and promote the use of data by converting video data into deformed video. [Means for solving the problem]

[0006] The system according to the embodiment includes a video acquisition unit, a deformation conversion unit, and a storage unit. The video acquisition unit acquires video data. The deformation conversion unit converts the video data acquired by the video acquisition unit into a deformed video. The storage unit stores the deformed video generated by the deformation conversion unit. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the impact of the Personal Information Protection Act and promote the utilization of data by converting video data into deformed video. [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) The deformed video generation system according to an embodiment of the present invention is a system in which video data is acquired, and a generation AI converts the data into a deformed video and stores it. This reduces the impact of the Personal Information Protection Act, and enables the video data to be safely stored and utilized.

[0029] A deformed video generation system according to an embodiment includes a video acquisition unit, a deformation conversion unit, and a storage unit. The video acquisition unit acquires video data. For example, the video data is collected using a camera or a smartphone. The video acquisition unit can also acquire surveillance camera footage and recorded video of events. For example, surveillance camera footage of a city is acquired in real time. Recorded video of an event can also be acquired in high resolution. The deformation conversion unit converts the video data acquired by the video acquisition unit into a deformed video. For example, the generation AI abstracts information within the video data that can identify an individual and deforms it while retaining necessary information. The generation AI can also use methods such as blurring facial features or leaving only a silhouette. For example, the generation AI blurs facial features to prevent individual identification. By leaving only a silhouette, it is possible to abstract movements and expressions. The storage unit stores the deformed video generated by the deformation conversion unit. For example, the deformed video is stored in cloud storage. The deformed video can also be stored in a distributed storage system. For example, storing data in cloud storage allows multiple image recognition AI services to access the data in real time. Furthermore, storing data in a distributed storage system improves data redundancy and security. This allows the deformed video generation system according to the embodiment to reduce the impact of the Personal Information Protection Act and safely store and utilize video data. For example, deformed videos can be used for image recognition AI services such as face recognition AI and motion analysis AI. Furthermore, using deformed videos makes it possible to maintain the accuracy of image recognition AI services while protecting personal information.

[0030] The video acquisition unit can analyze the movements and facial expressions of the person being filmed in real time, and acquire video data only when a specific movement or facial expression is detected. The video acquisition unit, for example, analyzes in real time the moment when the person being filmed smiles or looks surprised, and automatically acquires video data of that moment. For example, using facial expression recognition technology built into the camera, recording is started when a specific facial expression is detected. The video acquisition unit also analyzes in real time the moment when the person being filmed performs a specific movement (e.g., waving or jumping), and acquires video data only when that movement is detected. For example, using motion recognition technology, recording is started when a specific movement is detected. The video acquisition unit also analyzes in real time the moment when the person being filmed shows a specific emotion (e.g., joy or surprise), and acquires video data only when that emotion is detected. For example, using emotion recognition technology, recording is started when a specific emotion is detected. This enables efficient data collection by acquiring video data only when a specific movement or facial expression is detected.

[0031] The video acquisition unit collects background information of the person being filmed and can dynamically change the video data acquisition conditions based on that information. For example, the video acquisition unit registers the age and gender of the person being filmed in advance and dynamically changes the video data acquisition conditions based on that information. For example, it starts recording only when a person of a specific age group or gender is in the video. The video acquisition unit also registers the occupation and job title of the person being filmed in advance and dynamically changes the video data acquisition conditions based on that information. For example, it starts recording only when a person with a specific occupation or job title is in the video. The video acquisition unit also analyzes the background information of the person being filmed in real time and dynamically changes the video data acquisition conditions based on that information. For example, it starts recording only when the age or gender matches specific conditions. This allows for more appropriate data collection by dynamically changing the video data acquisition conditions based on the background information.

[0032] The video acquisition unit can construct a multimodal dataset by simultaneously collecting audio data and environmental data when acquiring video data. The video acquisition unit, for example, simultaneously collects audio data when acquiring video data, thereby constructing a multimodal dataset. For example, a microphone built into a camera is used to simultaneously record video and audio. The video acquisition unit also simultaneously collects environmental data (e.g., temperature and humidity) when acquiring video data, thereby constructing a multimodal dataset. For example, a sensor built into the camera is used to simultaneously record video and environmental data. The video acquisition unit also simultaneously collects audio data and environmental data when acquiring video data, thereby constructing a multimodal dataset. For example, a microphone or sensor built into the camera is used to simultaneously record video, audio, and environmental data. In this way, by simultaneously collecting audio data and environmental data, a dataset containing richer information can be constructed.

[0033] The video acquisition unit can autonomously acquire video data within a specific area using a drone or a robot. The video acquisition unit develops a system that autonomously acquires video data within a specific area using, for example, a drone. For example, a camera mounted on a drone is used to shoot video while automatically patrolling a specified area. The video acquisition unit also develops a system that autonomously acquires video data within a specific area using a robot. For example, a camera mounted on a robot is used to shoot video while automatically patrolling a specified area. The video acquisition unit also develops a system that autonomously acquires video data within a specific area using a drone or a robot. For example, a camera mounted on a drone or a robot is used to shoot video while automatically patrolling a specified area. This makes it possible to collect data over a wide area by autonomously acquiring video data using a drone or a robot.

[0034] The deformation conversion unit can use generation AI to abstract the movements and facial expressions of people in the video data and generate a deformed video that emphasizes only that particular movement or expression. For example, the deformation conversion unit uses generation AI to abstract a smiling or surprised expression of a person in the video data and generate a deformed video that emphasizes only that expression. For example, to emphasize a smiling or surprised expression, it blurs other parts. The deformation conversion unit also uses generation AI to abstract a specific movement of a person in the video data (e.g., waving or jumping) and generate a deformed video that emphasizes only that movement. For example, to emphasize a waving movement, it blurs other parts. The deformation conversion unit also uses generation AI to abstract facial expressions that show a specific emotion of a person in the video data (e.g., joy or surprise) and generate a deformed video that emphasizes only that emotion. For example, to emphasize a joyful or surprised expression, it blurs other parts. This makes it possible to effectively convey important information by emphasizing specific movements and expressions.

[0035] The deformation conversion unit can use a generation AI to develop a process that automatically removes background information from video data and deforms only the characters. For example, the deformation conversion unit uses a generation AI to develop a process that automatically removes background information from video data and deforms only the characters. For example, the background can be completely removed, leaving only the character's silhouette. The deformation conversion unit also uses a generation AI to develop a process that automatically removes background information from video data and deforms only the characters. For example, the background can be blurred to emphasize the character's features. The deformation conversion unit also uses a generation AI to develop a process that automatically removes background information from video data and deforms only the characters. For example, the background can be abstracted to emphasize the character's movements and facial expressions. By removing the background information, the character's movements and facial expressions can be more clearly emphasized.

[0036] The deformation conversion unit uses a generation AI to convert video data into an animation-style deformed video for use in entertainment. The deformation conversion unit, for example, uses a generation AI to convert video data into an animation-style deformed video for use in entertainment. For example, it deforms a person's movements and facial expressions to make them look like animation. The deformation conversion unit also uses a generation AI to convert video data into an animation-style deformed video for use in entertainment. For example, it deforms the background to make it look like animation, emphasizing the person's movements and facial expressions. The deformation conversion unit also uses a generation AI to convert video data into an animation-style deformed video for use in entertainment. For example, it changes the overall color tone and style to make it look like animation. In this way, the animation-style deformed video can be generated and used in entertainment.

[0037] The deformation conversion unit can use a generation AI to convert video data into a 3D model, enabling use in virtual reality (VR) or augmented reality (AR). The deformation conversion unit, for example, uses a generation AI to convert video data into a 3D model, enabling use in virtual reality (VR). For example, a person's movements and facial expressions are reproduced as a 3D model. The deformation conversion unit also uses a generation AI to convert video data into a 3D model, enabling use in augmented reality (AR). For example, a person's movements and facial expressions are reproduced as a 3D model and displayed in an AR application. The deformation conversion unit also uses a generation AI to convert video data into a 3D model, enabling use in virtual reality (VR) or augmented reality (AR). For example, a person's movements and facial expressions are reproduced as a 3D model, allowing interactive operation in a VR or AR environment. By converting into a 3D model, the data can be used in VR or AR.

[0038] The storage unit may implement a system that encrypts data and strictly manages access permissions when storing deformed videos. For example, the storage unit may implement a system that encrypts data using a strong encryption algorithm such as AES-256 and strictly manages access permissions when storing deformed videos. For example, the encryption key may be securely managed to ensure that only authenticated users have access. The storage unit may also formulate a security policy for encrypting data and managing access permissions when storing deformed videos and apply it to the entire system. For example, the storage unit may implement a mechanism for recording access logs and detecting unauthorized access. The storage unit may also implement dedicated security software for encrypting data and managing access permissions when storing deformed videos. For example, the data may be automatically encrypted and access permissions may be set before being saved in a database. This improves data security through data encryption and access permission management.

[0039] The storage unit can use a distributed storage system to store the deformed videos, thereby improving data redundancy and security. For example, the storage unit uses a distributed storage system to store the deformed videos, thereby improving data redundancy and security. For example, data can be stored in a distributed manner across multiple servers, enabling data restoration in the event of a failure. The storage unit also uses the distributed storage system to distribute the storage location of the deformed videos across multiple geographically distributed data centers, thereby improving security. For example, data can be synchronized between data centers to protect data in the event of a disaster. The storage unit also uses a distributed storage system to store the deformed videos, and introduces a protocol to improve data redundancy and security. For example, distributed data storage and encryption can be combined to enhance security. As a result, data redundancy and security can be improved by using a distributed storage system.

[0040] The storage unit can store the deformed video in cloud storage, allowing multiple image recognition AI services to access it in real time. The storage unit, for example, stores the deformed video in cloud storage, allowing multiple image recognition AI services to access it in real time. For example, a face recognition AI and a motion analysis AI can simultaneously access the deformed video stored in the cloud storage. The storage unit also builds a system using the cloud storage to provide the deformed video to multiple image recognition AI services in real time. For example, the deformed video is streamed on the cloud, and multiple AI services analyze it simultaneously. The storage unit also develops an API for storing the deformed video in cloud storage, allowing multiple image recognition AI services to access it in real time. For example, the deformed video stored in the cloud storage is accessed via the API. As a result, the use of cloud storage allows multiple image recognition AI services to access it in real time.

[0041] The storage unit stores the deformed videos using blockchain technology, thereby preventing data tampering and ensuring traceability. The storage unit, for example, stores the deformed videos using blockchain technology to prevent data tampering and ensure traceability. For example, the hash value of the deformed videos is recorded on the blockchain to prove that no tampering has occurred. The storage unit also uses blockchain technology to distribute storage locations for the deformed videos to prevent data tampering and ensure traceability. For example, data is distributed and stored on multiple nodes to confirm that no tampering has occurred. The storage unit also uses blockchain technology to store the deformed videos and introduces a protocol to prevent data tampering and ensure traceability. For example, metadata for the deformed videos is recorded on the blockchain to prove that no tampering has occurred. In this way, data tampering is prevented and traceability is ensured by using blockchain technology.

[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 video acquisition unit can collect background information about the person being filmed and dynamically change the video data acquisition conditions based on that information. For example, the age and gender of the person being filmed can be registered in advance, and the video data acquisition conditions can be dynamically changed based on that information. For example, recording can start only if a person of a specific age group or gender is in the video. The video acquisition unit can also register the occupation and job title of the person being filmed in advance, and dynamically change the video data acquisition conditions based on that information. For example, recording can start only if a person with a specific occupation or job title is in the video. The video acquisition unit can also analyze the background information of the person being filmed in real time, and dynamically change the video data acquisition conditions based on that information. For example, recording can start only if the age or gender matches specific conditions. This allows for more appropriate data collection by dynamically changing the video data acquisition conditions based on the background information.

[0044] The video acquisition unit can construct a multimodal dataset by simultaneously collecting audio data and environmental data when acquiring video data. For example, audio data can be collected when acquiring video data, thereby constructing a multimodal dataset. For example, a microphone built into a camera can be used to simultaneously record video and audio. The video acquisition unit can also construct a multimodal dataset by simultaneously collecting environmental data (e.g., temperature and humidity) when acquiring video data. For example, a sensor built into the camera can be used to simultaneously record video and environmental data. The video acquisition unit can also construct a multimodal dataset by simultaneously collecting audio data and environmental data when acquiring video data. For example, a microphone or sensor built into the camera can be used to simultaneously record video, audio, and environmental data. In this way, by simultaneously collecting audio data and environmental data, a dataset containing richer information can be constructed.

[0045] The video acquisition unit can autonomously acquire video data within a specific area using a drone or a robot. For example, a system is developed that uses a drone to autonomously acquire video data within a specific area. For example, a camera mounted on a drone is used to automatically patrol a specified area and capture video. The video acquisition unit also develops a system that uses a robot to autonomously acquire video data within a specific area. For example, a camera mounted on a robot is used to automatically patrol a specified area and capture video. The video acquisition unit also develops a system that uses a drone or a robot to autonomously acquire video data within a specific area. For example, a camera mounted on a drone or a robot is used to automatically patrol a specified area and capture video. This makes it possible to collect data over a wide area by autonomously acquiring video data using a drone or a robot.

[0046] The deformation conversion unit can use generation AI to abstract the movements and facial expressions of people in the video data and generate a deformed video that emphasizes only those movements and facial expressions. For example, the generation AI can be used to abstract a smiling or surprised expression of a person in the video data and generate a deformed video that emphasizes only those expressions. For example, to emphasize a smiling or surprised expression, other parts of the image can be blurred. The deformation conversion unit can also use generation AI to abstract a specific movement of a person in the video data (e.g., waving or jumping) and generate a deformed video that emphasizes only that movement. For example, to emphasize a waving movement, other parts can be blurred. The deformation conversion unit can also use generation AI to abstract facial expressions that show a specific emotion of a person in the video data (e.g., joy or surprise) and generate a deformed video that emphasizes only that emotion. For example, to emphasize a joyful or surprised expression, other parts can be blurred. This allows important information to be conveyed effectively by emphasizing specific movements and facial expressions.

[0047] The deformation conversion unit can use generative AI to develop a process that automatically removes background information from video data and deforms only the characters. For example, the deformation conversion unit can use generative AI to develop a process that automatically removes background information from video data and deforms only the characters. For example, the background can be completely removed, leaving only the character's silhouette. The deformation conversion unit can also use generative AI to develop a process that automatically removes background information from video data and deforms only the characters. For example, the background can be blurred to emphasize the character's features. The deformation conversion unit can also use generative AI to develop a process that automatically removes background information from video data and deforms only the characters. For example, the background can be abstracted to emphasize the character's movements and facial expressions. By removing the background information, the character's movements and facial expressions can be more clearly emphasized.

[0048] The deformation conversion unit uses a generation AI to convert video data into an animation-style deformed video for use in entertainment. For example, the generation AI can be used to convert video data into an animation-style deformed video for use in entertainment. For example, the movements and facial expressions of people can be deformed to make them look like animation. The deformation conversion unit also uses a generation AI to convert video data into an animation-style deformed video for use in entertainment. For example, the background can be deformed to make it look like animation to emphasize the movements and facial expressions of people. The deformation conversion unit also uses a generation AI to convert video data into an animation-style deformed video for use in entertainment. For example, the overall color tone and style can be changed to make it look like animation. In this way, an animation-style deformed video can be generated and used in entertainment.

[0049] The deformation conversion unit can use a generation AI to convert video data into a 3D model, enabling use in virtual reality (VR) or augmented reality (AR). For example, the generation AI can be used to convert video data into a 3D model, enabling use in virtual reality (VR). For example, a person's movements and facial expressions can be reproduced as a 3D model. The deformation conversion unit can also use a generation AI to convert video data into a 3D model, enabling use in augmented reality (AR). For example, a person's movements and facial expressions can be reproduced as a 3D model and displayed in an AR application. The deformation conversion unit can also use a generation AI to convert video data into a 3D model, enabling use in virtual reality (VR) or augmented reality (AR). For example, a person's movements and facial expressions can be reproduced as a 3D model, enabling interactive operation in a VR or AR environment. By converting into a 3D model, the data can be used in VR or AR.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The video acquisition unit acquires video data. For example, video data is collected using a camera or a smartphone. The video acquisition unit can also acquire surveillance camera footage and recorded event footage. For example, surveillance camera footage from around town can be acquired in real time. Recorded event footage can also be acquired in high resolution. Step 2: The deformation conversion unit converts the video data acquired by the video acquisition unit into a deformed video. For example, the generation AI abstracts information in the video data that can identify an individual and deforms it while leaving the necessary information. The generation AI can also use methods such as blurring facial features or leaving only a silhouette. For example, the generation AI can blur facial features to make it impossible to identify an individual. Also, by leaving only a silhouette, it can abstract movements and expressions. Step 3: The storage unit stores the deformed video generated by the deformation conversion unit. For example, the deformed video may be stored in cloud storage. Alternatively, the deformed video may be stored in a distributed storage system. For example, storing the video in cloud storage allows multiple image recognition AI services to access the video in real time. Storing the video in a distributed storage system also improves data redundancy and security.

[0052] (Example 2) The deformed video generation system according to an embodiment of the present invention is a system in which video data is acquired, and a generation AI converts the data into a deformed video and stores it. This reduces the impact of the Personal Information Protection Act, and enables the video data to be safely stored and utilized.

[0053] A deformed video generation system according to an embodiment includes a video acquisition unit, a deformation conversion unit, and a storage unit. The video acquisition unit acquires video data. For example, the video data is collected using a camera or a smartphone. The video acquisition unit can also acquire surveillance camera footage and recorded video of events. For example, surveillance camera footage of a city is acquired in real time. Recorded video of an event can also be acquired in high resolution. The deformation conversion unit converts the video data acquired by the video acquisition unit into a deformed video. For example, the generation AI abstracts information within the video data that can identify an individual and deforms it while retaining necessary information. The generation AI can also use methods such as blurring facial features or leaving only a silhouette. For example, the generation AI blurs facial features to prevent individual identification. By leaving only a silhouette, it is possible to abstract movements and expressions. The storage unit stores the deformed video generated by the deformation conversion unit. For example, the deformed video is stored in cloud storage. The deformed video can also be stored in a distributed storage system. For example, storing data in cloud storage allows multiple image recognition AI services to access the data in real time. Furthermore, storing data in a distributed storage system improves data redundancy and security. This allows the deformed video generation system according to the embodiment to reduce the impact of the Personal Information Protection Act and safely store and utilize video data. For example, deformed videos can be used for image recognition AI services such as face recognition AI and motion analysis AI. Furthermore, using deformed videos makes it possible to maintain the accuracy of image recognition AI services while protecting personal information.

[0054] The video acquisition unit can analyze the movements and facial expressions of the person being filmed in real time, and acquire video data only when a specific movement or facial expression is detected. The video acquisition unit, for example, analyzes in real time the moment when the person being filmed smiles or looks surprised, and automatically acquires video data of that moment. For example, using facial expression recognition technology built into the camera, recording is started when a specific facial expression is detected. The video acquisition unit also analyzes in real time the moment when the person being filmed performs a specific movement (e.g., waving or jumping), and acquires video data only when that movement is detected. For example, using motion recognition technology, recording is started when a specific movement is detected. The video acquisition unit also analyzes in real time the moment when the person being filmed shows a specific emotion (e.g., joy or surprise), and acquires video data only when that emotion is detected. For example, using emotion recognition technology, recording is started when a specific emotion is detected. This enables efficient data collection by acquiring video data only when a specific movement or facial expression is detected.

[0055] The video acquisition unit collects background information of the person being filmed and can dynamically change the video data acquisition conditions based on that information. For example, the video acquisition unit registers the age and gender of the person being filmed in advance and dynamically changes the video data acquisition conditions based on that information. For example, it starts recording only when a person of a specific age group or gender is in the video. The video acquisition unit also registers the occupation and job title of the person being filmed in advance and dynamically changes the video data acquisition conditions based on that information. For example, it starts recording only when a person with a specific occupation or job title is in the video. The video acquisition unit also analyzes the background information of the person being filmed in real time and dynamically changes the video data acquisition conditions based on that information. For example, it starts recording only when the age or gender matches specific conditions. This allows for more appropriate data collection by dynamically changing the video data acquisition conditions based on the background information.

[0056] The video acquisition unit can use the emotion estimation function to automatically detect moments when the person being filmed shows a specific emotion and prioritize acquiring video data of those moments. For example, the video acquisition unit detects moments when the person being filmed shows a smile using the emotion estimation function and prioritize acquiring video data of those moments. For example, the video acquisition unit starts recording when a smile is detected using emotion recognition technology built into the camera. The video acquisition unit also detects moments when the person being filmed shows a surprised expression using the emotion estimation function and prioritize acquiring video data of those moments. For example, the video acquisition unit starts recording when a surprised expression is detected using emotion recognition technology built into the camera. The video acquisition unit also detects moments when the person being filmed shows a specific emotion (e.g., joy, surprise) using the emotion estimation function and prioritize acquiring video data of those moments. For example, the video acquisition unit starts recording when a specific emotion is detected using emotion recognition technology. By prioritizing the acquisition of video data of moments when a specific emotion is detected, important moments can be recorded without missing them.

[0057] The video acquisition unit can construct a multimodal dataset by simultaneously collecting audio data and environmental data when acquiring video data. The video acquisition unit, for example, simultaneously collects audio data when acquiring video data, thereby constructing a multimodal dataset. For example, a microphone built into a camera is used to simultaneously record video and audio. The video acquisition unit also simultaneously collects environmental data (e.g., temperature and humidity) when acquiring video data, thereby constructing a multimodal dataset. For example, a sensor built into the camera is used to simultaneously record video and environmental data. The video acquisition unit also simultaneously collects audio data and environmental data when acquiring video data, thereby constructing a multimodal dataset. For example, a microphone or sensor built into the camera is used to simultaneously record video, audio, and environmental data. In this way, by simultaneously collecting audio data and environmental data, a dataset containing richer information can be constructed.

[0058] The video acquisition unit can autonomously acquire video data within a specific area using a drone or a robot. The video acquisition unit develops a system that autonomously acquires video data within a specific area using, for example, a drone. For example, a camera mounted on a drone is used to shoot video while automatically patrolling a specified area. The video acquisition unit also develops a system that autonomously acquires video data within a specific area using a robot. For example, a camera mounted on a robot is used to shoot video while automatically patrolling a specified area. The video acquisition unit also develops a system that autonomously acquires video data within a specific area using a drone or a robot. For example, a camera mounted on a drone or a robot is used to shoot video while automatically patrolling a specified area. This makes it possible to collect data over a wide area by autonomously acquiring video data using a drone or a robot.

[0059] The video acquisition unit can adjust the timing of video data acquisition according to the emotional state of the user using a wearable device equipped with an emotion estimation function. For example, the video acquisition unit adjusts the timing of video data acquisition according to the moment the user smiles using the wearable device equipped with the emotion estimation function. For example, using emotion recognition technology built into the smartwatch, recording is started when a smile is detected. Furthermore, the video acquisition unit adjusts the timing of video data acquisition according to the moment the user makes a surprised expression using the wearable device equipped with the emotion estimation function. For example, using emotion recognition technology built into the smartwatch, recording is started when a surprised expression is detected. Furthermore, the video acquisition unit adjusts the timing of video data acquisition according to the emotional state of the user using the wearable device equipped with the emotion estimation function. For example, using emotion recognition technology built into the smartwatch, recording is started when a specific emotion is detected. In this way, by adjusting the timing of video data acquisition according to the emotional state of the user, important moments can be recorded without missing.

[0060] The deformation conversion unit can use generation AI to abstract the movements and facial expressions of people in the video data and generate a deformed video that emphasizes only that particular movement or expression. For example, the deformation conversion unit uses generation AI to abstract a smiling or surprised expression of a person in the video data and generate a deformed video that emphasizes only that expression. For example, to emphasize a smiling or surprised expression, it blurs other parts. The deformation conversion unit also uses generation AI to abstract a specific movement of a person in the video data (e.g., waving or jumping) and generate a deformed video that emphasizes only that movement. For example, to emphasize a waving movement, it blurs other parts. The deformation conversion unit also uses generation AI to abstract facial expressions that show a specific emotion of a person in the video data (e.g., joy or surprise) and generate a deformed video that emphasizes only that emotion. For example, to emphasize a joyful or surprised expression, it blurs other parts. This makes it possible to effectively convey important information by emphasizing specific movements and expressions.

[0061] The deformation conversion unit can use a generation AI to develop a process that automatically removes background information from video data and deforms only the characters. For example, the deformation conversion unit uses a generation AI to develop a process that automatically removes background information from video data and deforms only the characters. For example, the background can be completely removed, leaving only the character's silhouette. The deformation conversion unit also uses a generation AI to develop a process that automatically removes background information from video data and deforms only the characters. For example, the background can be blurred to emphasize the character's features. The deformation conversion unit also uses a generation AI to develop a process that automatically removes background information from video data and deforms only the characters. For example, the background can be abstracted to emphasize the character's movements and facial expressions. By removing the background information, the character's movements and facial expressions can be more clearly emphasized.

[0062] The deformation conversion unit can use the emotion estimation function to analyze the emotional state of a person in the video data and dynamically adjust the degree of deformation based on the emotion. For example, the deformation conversion unit can use the emotion estimation function to analyze a moment when a person in the video data smiles and dynamically adjust the degree of deformation based on the degree of the smile. For example, if the smile is strong, the smile is emphasized. The deformation conversion unit can also use the emotion estimation function to analyze a moment when a person in the video data shows a surprised expression and dynamically adjust the degree of deformation based on the degree of surprise. For example, if the surprise is strong, the surprised expression is emphasized. The deformation conversion unit can also use the emotion estimation function to analyze a moment when a person in the video data shows a specific emotion (e.g., joy, surprise) and dynamically adjust the degree of deformation based on the degree of the emotion. For example, if the emotion is strong, the emotion is emphasized. In this way, by adjusting the degree of deformation based on the emotion, the emotional expression can be emphasized.

[0063] The deformation conversion unit uses a generation AI to convert video data into an animation-style deformed video for use in entertainment. The deformation conversion unit, for example, uses a generation AI to convert video data into an animation-style deformed video for use in entertainment. For example, it deforms a person's movements and facial expressions to make them look like animation. The deformation conversion unit also uses a generation AI to convert video data into an animation-style deformed video for use in entertainment. For example, it deforms the background to make it look like animation, emphasizing the person's movements and facial expressions. The deformation conversion unit also uses a generation AI to convert video data into an animation-style deformed video for use in entertainment. For example, it changes the overall color tone and style to make it look like animation. In this way, the animation-style deformed video can be generated and used in entertainment.

[0064] The deformation conversion unit can use a generation AI to convert video data into a 3D model, enabling use in virtual reality (VR) or augmented reality (AR). The deformation conversion unit, for example, uses a generation AI to convert video data into a 3D model, enabling use in virtual reality (VR). For example, a person's movements and facial expressions are reproduced as a 3D model. The deformation conversion unit also uses a generation AI to convert video data into a 3D model, enabling use in augmented reality (AR). For example, a person's movements and facial expressions are reproduced as a 3D model and displayed in an AR application. The deformation conversion unit also uses a generation AI to convert video data into a 3D model, enabling use in virtual reality (VR) or augmented reality (AR). For example, a person's movements and facial expressions are reproduced as a 3D model, allowing interactive operation in a VR or AR environment. By converting into a 3D model, the data can be used in VR or AR.

[0065] The deformation conversion unit can use the emotion estimation function to add effects that visually express the emotions of people in the deformed video. For example, the deformation conversion unit uses the emotion estimation function to add an effect that emphasizes the smile the moment a person in the deformed video smiles. For example, it displays a sparkling effect around the smile. The deformation conversion unit also uses the emotion estimation function to add an effect that emphasizes surprise the moment a person in the deformed video looks surprised. For example, it displays a ripple-like effect around the surprised expression. The deformation conversion unit also uses the emotion estimation function to add an effect that visually expresses a particular emotion (e.g., joy, surprise) the moment a person in the deformed video shows that emotion. For example, it displays a fireworks effect in the background when showing the emotion of joy. In this way, adding effects that visually express emotions makes it possible to more effectively communicate emotions.

[0066] The storage unit may implement a system that encrypts data and strictly manages access permissions when storing deformed videos. For example, the storage unit may implement a system that encrypts data using a strong encryption algorithm such as AES-256 and strictly manages access permissions when storing deformed videos. For example, the encryption key may be securely managed to ensure that only authenticated users have access. The storage unit may also formulate a security policy for encrypting data and managing access permissions when storing deformed videos and apply it to the entire system. For example, the storage unit may implement a mechanism for recording access logs and detecting unauthorized access. The storage unit may also implement dedicated security software for encrypting data and managing access permissions when storing deformed videos. For example, the data may be automatically encrypted and access permissions may be set before being saved in a database. This improves data security through data encryption and access permission management.

[0067] The storage unit can use a distributed storage system to store the deformed videos, thereby improving data redundancy and security. For example, the storage unit uses a distributed storage system to store the deformed videos, thereby improving data redundancy and security. For example, data can be stored in a distributed manner across multiple servers, enabling data restoration in the event of a failure. The storage unit also uses the distributed storage system to distribute the storage location of the deformed videos across multiple geographically distributed data centers, thereby improving security. For example, data can be synchronized between data centers to protect data in the event of a disaster. The storage unit also uses a distributed storage system to store the deformed videos, and introduces a protocol to improve data redundancy and security. For example, distributed data storage and encryption can be combined to enhance security. As a result, data redundancy and security can be improved by using a distributed storage system.

[0068] The storage unit uses the emotion estimation function to add metadata according to the emotional state of the user when storing the deformed video, and can utilize this for later search and analysis. For example, the storage unit uses the emotion estimation function to add metadata about the moment the user smiles when storing the deformed video, and utilize this for later search and analysis. For example, the storage unit quantifies the degree of smile and saves it as metadata. The storage unit also uses the emotion estimation function to add metadata about the moment the user makes a surprised expression when storing the deformed video, and utilize this for later search and analysis. For example, the storage unit quantifies the degree of surprise and saves it as metadata. The storage unit also uses the emotion estimation function to add metadata according to the emotional state of the user when storing the deformed video, and utilize this for later search and analysis. For example, the storage unit quantifies the degree of a specific emotion (joy, surprise) and saves it as metadata. In this way, adding metadata according to the emotional state makes later search and analysis easier.

[0069] The storage unit can store the deformed video in cloud storage, allowing multiple image recognition AI services to access it in real time. The storage unit, for example, stores the deformed video in cloud storage, allowing multiple image recognition AI services to access it in real time. For example, a face recognition AI and a motion analysis AI can simultaneously access the deformed video stored in the cloud storage. The storage unit also builds a system using the cloud storage to provide the deformed video to multiple image recognition AI services in real time. For example, the deformed video is streamed on the cloud, and multiple AI services analyze it simultaneously. The storage unit also develops an API for storing the deformed video in cloud storage, allowing multiple image recognition AI services to access it in real time. For example, the deformed video stored in the cloud storage is accessed via the API. As a result, the use of cloud storage allows multiple image recognition AI services to access it in real time.

[0070] The storage unit stores the deformed videos using blockchain technology, thereby preventing data tampering and ensuring traceability. The storage unit, for example, stores the deformed videos using blockchain technology to prevent data tampering and ensure traceability. For example, the hash value of the deformed videos is recorded on the blockchain to prove that no tampering has occurred. The storage unit also uses blockchain technology to distribute storage locations for the deformed videos to prevent data tampering and ensure traceability. For example, data is distributed and stored on multiple nodes to confirm that no tampering has occurred. The storage unit also uses blockchain technology to store the deformed videos and introduces a protocol to prevent data tampering and ensure traceability. For example, metadata for the deformed videos is recorded on the blockchain to prove that no tampering has occurred. In this way, data tampering is prevented and traceability is ensured by using blockchain technology.

[0071] The storage unit can use the emotion estimation function to tag the deformed video based on the user's emotional state when storing the deformed video, thereby improving searchability. For example, the storage unit uses the emotion estimation function to tag the moment the user smiles when storing the deformed video, thereby improving searchability. For example, the storage unit quantifies the degree of smile and saves it as a tag. Furthermore, the storage unit uses the emotion estimation function to tag the moment the user makes a surprised expression when storing the deformed video, thereby improving searchability. For example, the storage unit quantifies the degree of surprise and saves it as a tag. Furthermore, the storage unit uses the emotion estimation function to tag the deformed video based on the user's emotional state when storing the deformed video, thereby improving searchability. For example, the storage unit quantifies the degree of a specific emotion (joy, surprise) and saves it as a tag. In this way, tagging based on the emotional state improves searchability.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The video acquisition unit can analyze the movements and facial expressions of the person being filmed in real time, and acquire video data only when a specific movement or facial expression is detected. For example, the moment when the person being filmed smiles or looks surprised can be analyzed in real time, and video data of that moment can be automatically acquired. For example, using facial expression recognition technology built into the camera, recording can be started when a specific facial expression is detected. The video acquisition unit can also analyze in real time the moment when the person being filmed performs a specific movement (e.g., waving or jumping), and acquire video data only when that movement is detected. For example, using motion recognition technology, recording can be started when a specific movement is detected. The video acquisition unit can also analyze in real time the moment when the person being filmed shows a specific emotion (e.g., joy or surprise), and acquire video data only when that emotion is detected. For example, using emotion recognition technology, recording can be started when a specific emotion is detected. This allows video data to be acquired only when a specific movement or facial expression is detected, enabling efficient data collection.

[0074] The video acquisition unit can collect background information about the person being filmed and dynamically change the video data acquisition conditions based on that information. For example, the age and gender of the person being filmed can be registered in advance, and the video data acquisition conditions can be dynamically changed based on that information. For example, recording can start only if a person of a specific age group or gender is in the video. The video acquisition unit can also register the occupation and job title of the person being filmed in advance, and dynamically change the video data acquisition conditions based on that information. For example, recording can start only if a person with a specific occupation or job title is in the video. The video acquisition unit can also analyze the background information of the person being filmed in real time, and dynamically change the video data acquisition conditions based on that information. For example, recording can start only if the age or gender matches specific conditions. This allows for more appropriate data collection by dynamically changing the video data acquisition conditions based on the background information.

[0075] The video acquisition unit can use the emotion estimation function to automatically detect moments when the person being filmed shows a specific emotion and prioritize acquiring video data of those moments. For example, the emotion estimation function detects moments when the person being filmed shows a smile and prioritizes acquiring video data of those moments. For example, emotion recognition technology built into the camera can be used to start recording when a smile is detected. The video acquisition unit can also use the emotion estimation function to detect moments when the person being filmed shows a surprised expression and prioritize acquiring video data of those moments. For example, emotion recognition technology built into the camera can be used to start recording when a surprised expression is detected. The video acquisition unit can also use the emotion estimation function to detect moments when the person being filmed shows a specific emotion (e.g., joy, surprise) and prioritize acquiring video data of those moments. For example, emotion recognition technology can be used to start recording when a specific emotion is detected. By prioritizing the acquisition of video data of moments when a specific emotion is detected, important moments can be recorded without missing them.

[0076] The video acquisition unit can construct a multimodal dataset by simultaneously collecting audio data and environmental data when acquiring video data. For example, audio data can be collected when acquiring video data, thereby constructing a multimodal dataset. For example, a microphone built into a camera can be used to simultaneously record video and audio. The video acquisition unit can also construct a multimodal dataset by simultaneously collecting environmental data (e.g., temperature and humidity) when acquiring video data. For example, a sensor built into the camera can be used to simultaneously record video and environmental data. The video acquisition unit can also construct a multimodal dataset by simultaneously collecting audio data and environmental data when acquiring video data. For example, a microphone or sensor built into the camera can be used to simultaneously record video, audio, and environmental data. In this way, by simultaneously collecting audio data and environmental data, a dataset containing richer information can be constructed.

[0077] The video acquisition unit can autonomously acquire video data within a specific area using a drone or a robot. For example, a system is developed that uses a drone to autonomously acquire video data within a specific area. For example, a camera mounted on a drone is used to automatically patrol a specified area and capture video. The video acquisition unit also develops a system that uses a robot to autonomously acquire video data within a specific area. For example, a camera mounted on a robot is used to automatically patrol a specified area and capture video. The video acquisition unit also develops a system that uses a drone or a robot to autonomously acquire video data within a specific area. For example, a camera mounted on a drone or a robot is used to automatically patrol a specified area and capture video. This makes it possible to collect data over a wide area by autonomously acquiring video data using a drone or a robot.

[0078] The deformation conversion unit can use generation AI to abstract the movements and facial expressions of people in the video data and generate a deformed video that emphasizes only those movements and facial expressions. For example, the generation AI can be used to abstract a smiling or surprised expression of a person in the video data and generate a deformed video that emphasizes only those expressions. For example, to emphasize a smiling or surprised expression, other parts of the image can be blurred. The deformation conversion unit can also use generation AI to abstract a specific movement of a person in the video data (e.g., waving or jumping) and generate a deformed video that emphasizes only that movement. For example, to emphasize a waving movement, other parts can be blurred. The deformation conversion unit can also use generation AI to abstract facial expressions that show a specific emotion of a person in the video data (e.g., joy or surprise) and generate a deformed video that emphasizes only that emotion. For example, to emphasize a joyful or surprised expression, other parts can be blurred. This allows important information to be conveyed effectively by emphasizing specific movements and facial expressions.

[0079] The deformation conversion unit can use generative AI to develop a process that automatically removes background information from video data and deforms only the characters. For example, the deformation conversion unit can use generative AI to develop a process that automatically removes background information from video data and deforms only the characters. For example, the background can be completely removed, leaving only the character's silhouette. The deformation conversion unit can also use generative AI to develop a process that automatically removes background information from video data and deforms only the characters. For example, the background can be blurred to emphasize the character's features. The deformation conversion unit can also use generative AI to develop a process that automatically removes background information from video data and deforms only the characters. For example, the background can be abstracted to emphasize the character's movements and facial expressions. By removing the background information, the character's movements and facial expressions can be more clearly emphasized.

[0080] The deformation conversion unit can use the emotion estimation function to analyze the emotional state of a person in the video data and dynamically adjust the degree of deformation based on that emotion. For example, the emotion estimation function can be used to analyze a moment when a person in the video data smiles, and the degree of deformation can be dynamically adjusted based on the degree of the smile. For example, if the smile is strong, the smile can be emphasized. The deformation conversion unit can also use the emotion estimation function to analyze a moment when a person in the video data shows a surprised expression, and dynamically adjust the degree of deformation based on the degree of surprise. For example, if the surprise is strong, the surprised expression can be emphasized. The deformation conversion unit can also use the emotion estimation function to analyze a moment when a person in the video data shows a specific emotion (e.g., joy, surprise), and dynamically adjust the degree of deformation based on the degree of that emotion. For example, if the emotion is strong, the emotion can be emphasized. In this way, by adjusting the degree of deformation based on the emotion, the emotional expression can be emphasized.

[0081] The deformation conversion unit uses a generation AI to convert video data into an animation-style deformed video for use in entertainment. For example, the generation AI can be used to convert video data into an animation-style deformed video for use in entertainment. For example, the movements and facial expressions of people can be deformed to make them look like animation. The deformation conversion unit also uses a generation AI to convert video data into an animation-style deformed video for use in entertainment. For example, the background can be deformed to make it look like animation to emphasize the movements and facial expressions of people. The deformation conversion unit also uses a generation AI to convert video data into an animation-style deformed video for use in entertainment. For example, the overall color tone and style can be changed to make it look like animation. In this way, an animation-style deformed video can be generated and used in entertainment.

[0082] The deformation conversion unit can use a generation AI to convert video data into a 3D model, enabling use in virtual reality (VR) or augmented reality (AR). For example, the generation AI can be used to convert video data into a 3D model, enabling use in virtual reality (VR). For example, a person's movements and facial expressions can be reproduced as a 3D model. The deformation conversion unit can also use a generation AI to convert video data into a 3D model, enabling use in augmented reality (AR). For example, a person's movements and facial expressions can be reproduced as a 3D model and displayed in an AR application. The deformation conversion unit can also use a generation AI to convert video data into a 3D model, enabling use in virtual reality (VR) or augmented reality (AR). For example, a person's movements and facial expressions can be reproduced as a 3D model, enabling interactive operation in a VR or AR environment. By converting into a 3D model, the data can be used in VR or AR.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The video acquisition unit acquires video data. For example, video data is collected using a camera or a smartphone. The video acquisition unit can also acquire surveillance camera footage and recorded event footage. For example, surveillance camera footage from around town can be acquired in real time. Recorded event footage can also be acquired in high resolution. Step 2: The deformation conversion unit converts the video data acquired by the video acquisition unit into a deformed video. For example, the generation AI abstracts information in the video data that can identify an individual and deforms it while leaving the necessary information. The generation AI can also use methods such as blurring facial features or leaving only a silhouette. For example, the generation AI can blur facial features to make it impossible to identify an individual. Also, by leaving only a silhouette, it can abstract movements and expressions. Step 3: The storage unit stores the deformed video generated by the deformation conversion unit. For example, the deformed video may be stored in cloud storage. Alternatively, the deformed video may be stored in a distributed storage system. For example, storing the video in cloud storage allows multiple image recognition AI services to access the video in real time. Storing the video in a distributed storage system also improves data redundancy and security.

[0085] 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.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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 AI 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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 AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the 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 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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, in order to avoid confusion and to 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.

[0151] 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]

[0152] 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 acquisition unit that acquires video data; a deformation conversion unit that converts the video data acquired by the video acquisition unit into a deformed video; a storage unit that stores the deformation video generated by the deformation conversion unit. A system characterized by:

2. The video acquisition unit The motions and facial expressions of the person being filmed are analyzed in real time, and the video data is acquired only when a specific motion or facial expression is detected.

2. The system of claim 1.

3. The video acquisition unit Collect background information about the person being filmed, and dynamically change the acquisition conditions for the video data based on that information.

2. The system of claim 1.

4. The video acquisition unit The system automatically detects the moment when the person being filmed shows a specific emotion and prioritizes acquiring the video data of that moment.

2. The system of claim 1.

5. The video acquisition unit When acquiring video data, audio data and environmental data are also collected simultaneously to build a multimodal dataset.

2. The system of claim 1.

Images