System

Abstract

An object of the system according to the embodiment is to allow a child-care household to efficiently record memories and create a moving image as desired.SOLUTION: A system according to an embodiment includes a high image quality camera, a face recognition AI, a smartphone application, and a generation AI. The high-image-quality camera is a fixed camera that captures the entire scene. The face recognition AI analyzes a video captured by a high-quality camera and automatically creates a moving image for each child. The smartphone application transmits contents of a moving image desired by the user. The generation AI edits the video on the basis of the desire transmitted by the smartphone application.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback that it is time-consuming for families with children to record their memories, making it difficult to create videos efficiently.

[0005] The system according to the embodiment aims to enable families with children to efficiently record memories and create videos that meet their wishes. [Means for solving the problem]

[0006] The system according to the embodiment comprises a high-definition camera, a facial recognition AI, a smartphone app, and a generation AI. The high-definition camera is a fixed camera that captures the entire scene. The facial recognition AI analyzes the footage captured by the high-definition camera and automatically creates videos for each child. The smartphone app communicates the content of the video the user desires. The generation AI edits the video based on the preferences communicated via the smartphone app. [Effects of the Invention]

[0007] The system according to the embodiment allows families with children to efficiently record memories and create videos according to their wishes. [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 memory recording system according to the embodiment of the present invention uses a high-resolution fixed camera to capture the entire scene, and facial recognition AI automatically creates videos for each child. Users can simply communicate their wishes to a smartphone app to create videos that meet their needs. This allows the memory recording system to use AI to take over the effort of recording memories for families with children.

[0029] A memory recording system according to an embodiment includes a high-definition camera, a facial recognition AI, a smartphone app, and a generation AI. The high-definition camera captures the entire scene. For example, it can capture the entire scene at events such as sports days and birthday parties. The high-definition camera also covers a wide area and can record children's movements in detail. The facial recognition AI analyzes the footage captured by the high-definition camera and automatically creates videos for each child. For example, the facial recognition AI can identify faces in the footage and generate videos focused on specific children. The facial recognition AI can also generate videos based on the video data and the children's facial information. The smartphone app allows users to communicate their desired video content. For example, users can input preferences such as "cut out unnecessary scenes," "slow motion," "create a digest version," and "publish on social media." The generation AI edits the video based on the preferences communicated via the smartphone app. For example, the generation AI automatically cuts out unnecessary scenes, converts the video to slow motion, creates a digest version, and prepares it for publishing on social media. This allows the memory recording system according to an embodiment to use AI to take the hassle of recording memories for families with children. For example, videos of school sports days and birthday parties can be easily edited and shared with family and friends. Furthermore, facial recognition AI can be used to automatically create videos focusing on specific children, allowing parents to record important moments without missing them.

[0030] High-definition cameras can be combined with 360-degree cameras to simultaneously record omnidirectional video, allowing users to later play back the video from any viewpoint. High-definition cameras, for example, can be combined with 360-degree cameras to simultaneously record omnidirectional video. This allows users to later play back the video from any viewpoint. For example, a 360-degree camera can capture the entire view of a sports day and then track the movements of a specific child. A 360-degree camera can also be used to record the entire view of an event, allowing users to later freely change the viewpoint. For example, a 360-degree camera can be used to capture video of a birthday party and then check the expressions of all the family members. A system can also be built by combining high-definition cameras and 360-degree cameras to record omnidirectional video and then play back the video from any viewpoint. For example, a 360-degree camera can capture the entire view of a sports day and then track the movements of a specific event or child. This allows omnidirectional video to be recorded and then played back from any viewpoint.

[0031] High-definition cameras are equipped with a voice recognition function, which can automatically mark important scenes when triggered by specific sounds. For example, high-definition cameras are equipped with a voice recognition function, which can automatically mark important scenes when triggered by specific sounds (such as a child's laughter or someone calling their child's name). This makes it easy to find important scenes later. We will also add a voice recognition function to the camera and build a system that automatically marks scenes when specific sounds are detected. For example, the system can detect the voice of a parent calling their child's name at a sports day and automatically mark that scene. We will also develop a camera system that automatically marks important scenes when triggered by specific sounds using the voice recognition function. For example, the system can detect a child's laughter at a birthday party and automatically mark that scene. This makes it possible to automatically mark important scenes when specific sounds are detected.

[0032] A high-resolution camera can be mounted on a drone to automatically capture aerial footage of an event, allowing for a wide-ranging recording of the entire event. A high-resolution camera can be mounted on a drone, for example, to automatically capture aerial footage of an event. This allows for a wider-ranging recording of the entire event. For example, the entire view of an athletic meet can be captured from above, allowing for a grasp of the overall movement. A system can also be constructed that mounts a high-resolution camera on a drone to automatically capture aerial footage of an event. For example, the entire view of a birthday party can be captured from above, allowing for a wide-ranging recording of the entire event. A system can also be developed that mounts a high-resolution camera on a drone to automatically capture aerial footage of an event. For example, the entire view of an athletic meet can be captured from above, allowing for a grasp of the overall movement. This allows for a wider-ranging recording of the entire event.

[0033] High-resolution cameras can be equipped with temperature sensors to automatically adjust shooting settings in response to changes in the environment. High-resolution cameras can be equipped with temperature sensors to automatically adjust shooting settings in response to changes in the environment. For example, the exposure can be adjusted in hot weather, and settings can be made to reduce battery consumption in cold climates. We will also use temperature sensors to build camera systems that automatically adjust shooting settings in response to changes in the environment. For example, the camera's cooling function can be strengthened on hot days, and settings can be made to reduce battery consumption on cold days. We will also develop systems that add temperature sensors to cameras to automatically adjust shooting settings in response to changes in the environment. For example, the exposure can be adjusted in hot weather, and settings can be made to reduce battery consumption in cold climates. This makes it possible to automatically adjust shooting settings in response to changes in the environment.

[0034] Facial recognition AI can be integrated with motion recognition AI to detect specific actions and create videos. For example, facial recognition AI can be integrated with motion recognition AI to detect specific actions (e.g., running, jumping) and create videos. This makes it possible to create videos that highlight a child's specific actions. We will also use motion recognition AI to build a system that detects specific actions and creates videos centered around those actions. For example, we will detect a scene where a child is running at a sports day and create a video centered around that scene. We will also combine facial recognition AI and motion recognition AI to develop a system that detects specific actions and creates a video centered around that action. For example, we will detect a scene where a child is jumping at a birthday party and create a video centered around that scene. This makes it possible to detect specific actions and create videos.

[0035] Facial recognition AI can track multiple children simultaneously and record group activities. For example, facial recognition AI can be added with the ability to track multiple children simultaneously and record group activities. This makes it possible to combine scenes of multiple children playing together into a single video. We will also build a system that records group activities using facial recognition AI that tracks multiple children simultaneously. For example, we will record scenes of multiple children competing together at a sports day. We will also develop a system that records group activities by adding the ability to track multiple children simultaneously to facial recognition AI. For example, we will record scenes of multiple children playing together at a birthday party. This makes it possible to track multiple children simultaneously and record group activities.

[0036] Facial recognition AI can be enhanced with a voice recognition function, making it possible to create videos triggered by a child's voice. For example, facial recognition AI can be enhanced with a voice recognition function, making it possible to create videos centered around scenes where children are talking. We will also use the voice recognition function to build a system that creates videos triggered by a child's voice. For example, it can detect scenes where children are cheering at a sports day and create a video centered around that scene. We will also combine facial recognition AI with a voice recognition function to develop a system that creates videos triggered by a child's voice. For example, it can detect scenes where children are singing at a birthday party and create a video centered around that scene. This makes it possible to create videos triggered by a child's voice.

[0037] A voice input function will be added to the smartphone app, allowing users to create videos simply by stating their wishes aloud. For example, a voice input function will be added to the smartphone app, allowing users to create videos simply by stating their wishes aloud. This allows users to easily communicate their wishes. Furthermore, using the voice input function, a system will be developed in which users can create videos simply by stating their wishes aloud. For example, requests such as "cut out unnecessary scenes" or "slow motion" will be input by voice. Furthermore, a voice input function will be added to the smartphone app, allowing users to create videos simply by stating their wishes aloud. For example, requests such as "create a digest version" or "publish on SNS" will be input by voice. This allows users to create videos simply by stating their wishes aloud.

[0038] A scene recognition function can be added to a smartphone app, allowing it to automatically detect and edit specific scenes. For example, a scene recognition function can be added to a smartphone app to automatically detect and edit specific scenes (for example, the appearance of a birthday cake). This makes it easy to edit important scenes. We will also use the scene recognition function to build a system that automatically detects specific scenes and edits videos around those scenes. For example, we can detect a scene where a child crosses the finish line at a sports day and edit a video around that scene. We will also add a scene recognition function to a smartphone app to develop a system that automatically detects and edits specific scenes. For example, we can detect the scene where a cake appears at a birthday party and edit a video around that scene. This makes it easy to detect and edit specific scenes.

[0039] A smartphone app can be equipped with an AR function to add effects to videos in real time. For example, an AR function can be added to a smartphone app to add effects to videos in real time. This allows for adding visual effects to videos. A system can also be built using the AR function to add effects to videos in real time. For example, balloon and firework effects can be added to videos of birthday parties. A system can also be developed to add an AR function to a smartphone app to add effects to videos in real time. For example, cheering messages and scoreboard effects can be added to videos of sports days. This allows for adding effects to videos in real time.

[0040] A smartphone app can be equipped with a multi-user collaboration function, allowing the whole family to edit together. For example, a smartphone app can be equipped with a multi-user collaboration function, allowing the whole family to edit together. This allows the whole family to edit memorable videos together. A system can also be built using the collaboration function, allowing multiple users to edit videos simultaneously. For example, all family members can participate in editing videos from their own devices. A smartphone app can also be equipped with a multi-user collaboration function, allowing the whole family to edit together. For example, all family members can create videos that reflect their own opinions. This allows the whole family to edit together.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] For example, a high-resolution camera can be equipped with a nighttime shooting mode, allowing it to record clear images even in dark places. This makes it possible to record high-quality images even in scenes such as nighttime events and camping. Furthermore, a system can be constructed that automatically adjusts exposure and sensitivity when shooting in dark places using the nighttime shooting mode. For example, it can clearly record images of nighttime sports events and fireworks displays. Furthermore, a camera system can be developed that automatically adjusts exposure and sensitivity when shooting in dark places by adding a nighttime shooting mode. For example, it can clearly record images of nighttime birthday parties and campfires. This makes it possible to record high-quality images even in dark places.

[0043] For example, a time lapse function can be added to a high-resolution camera, allowing long events to be played back in a short time. This allows the entire flow of the event to be grasped in a short time. Furthermore, a system can be constructed using the time lapse function to play back long events in a short time. For example, it is possible to play back the entire process of a sports day from preparation to completion in a short time. Furthermore, a camera system can be developed that adds a time lapse function to play back long events in a short time. For example, it is possible to play back the entire process of a birthday party from preparation to completion in a short time. This allows long events to be played back in a short time.

[0044] High-resolution cameras, for example, have enhanced image stabilization capabilities, making it possible to record stable video even in scenes with a lot of movement. This makes it possible to record high-quality video even in scenes with a lot of movement, such as athletic meets and sporting events. Furthermore, systems are being developed that have enhanced image stabilization capabilities and record stable video even in scenes with a lot of movement. For example, scenes of children running at athletic meets can be stably recorded. Furthermore, camera systems are being developed that have enhanced image stabilization capabilities and record stable video even in scenes with a lot of movement. For example, scenes of children playing at birthday parties can be stably recorded. This makes it possible to record stable video even in scenes with a lot of movement.

[0045] A high-resolution camera can be mounted on a drone, for example, to automatically capture footage of an event from above. This allows the entire situation to be recorded over a wider area. For example, the entire view of an athletic meet can be captured from above, allowing the overall movements to be grasped. A system can also be constructed that mounts a high-resolution camera on a drone and automatically captures footage of an event from above. For example, the entire view of a birthday party can be captured from above, allowing the entire situation to be recorded. A system can also be developed that mounts a high-resolution camera on a drone and automatically captures footage of an event from above, allowing the entire situation to be recorded over a wider area. For example, the entire view of an athletic meet can be captured from above, allowing the overall movements to be grasped. This allows the entire situation to be automatically captured from above, allowing the entire situation to be recorded over a wider area.

[0046] High-resolution cameras, for example, can be equipped with temperature sensors to automatically adjust shooting settings in response to changes in the environment. For example, the exposure can be adjusted in hot weather, and settings can be made to reduce battery consumption in cold climates. We can also use temperature sensors to build camera systems that automatically adjust shooting settings in response to changes in the environment. For example, the camera's cooling function can be strengthened on hot days, and settings can be made to reduce battery consumption on cold days. We can also add temperature sensors to cameras to develop systems that automatically adjust shooting settings in response to changes in the environment. For example, the exposure can be adjusted in hot weather, and settings can be made to reduce battery consumption in cold climates. This makes it possible to automatically adjust shooting settings in response to changes in the environment.

[0047] For example, facial recognition AI can be incorporated into motion recognition AI to detect specific actions (e.g., running, jumping) and create videos. This makes it possible to create videos that highlight a child's specific actions. We can also use motion recognition AI to build a system that detects specific actions and creates videos centered around those actions. For example, we can detect a scene where a child is running at a sports day and create a video centered around that scene. We can also combine facial recognition AI and motion recognition AI to develop a system that detects specific actions and creates a video centered around that action. For example, we can detect a scene where a child is jumping at a birthday party and create a video centered around that scene. This makes it possible to detect specific actions and create videos.

[0048] For example, facial recognition AI can be equipped with the ability to simultaneously track multiple children and record group activities. This makes it possible to combine scenes of multiple children playing together into a single video. We will also use facial recognition AI to simultaneously track multiple children and develop a system to record group activities. For example, we will record scenes of multiple children competing together at a sports day. We will also add the ability to simultaneously track multiple children to facial recognition AI and develop a system to record group activities. For example, we will record scenes of multiple children playing together at a birthday party. This makes it possible to simultaneously track multiple children and record group activities.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: High-definition cameras capture the entire scene. For example, they can capture the entire scene at events like sports days and birthday parties. High-definition cameras also cover a wide area and can record the children's movements in detail. Step 2: Facial recognition AI analyzes the footage captured by the high-definition camera and automatically creates videos for each child. For example, facial recognition AI can identify faces in the footage and generate videos focused on specific children. Facial recognition AI can also generate videos based on the video data and the child's facial information. Step 3: The smartphone app allows users to specify their desired video content, such as whether they want unnecessary scenes cut out, slow motion, a digest version created, or published on social media. Step 4: The AI ​​then edits the video based on the user's preferences, such as automatically cutting out unnecessary scenes, converting the video to slow motion, creating a digest version, and preparing the video for posting on social media.

[0051] (Example 2) The memory recording system according to the embodiment of the present invention uses a high-resolution fixed camera to capture the entire scene, and facial recognition AI automatically creates videos for each child. Users can simply communicate their wishes to a smartphone app to create videos that meet their needs. This allows the memory recording system to use AI to take over the effort of recording memories for families with children.

[0052] A memory recording system according to an embodiment includes a high-definition camera, a facial recognition AI, a smartphone app, and a generation AI. The high-definition camera captures the entire scene. For example, it can capture the entire scene at events such as sports days and birthday parties. The high-definition camera also covers a wide area and can record children's movements in detail. The facial recognition AI analyzes the footage captured by the high-definition camera and automatically creates videos for each child. For example, the facial recognition AI can identify faces in the footage and generate videos focused on specific children. The facial recognition AI can also generate videos based on the video data and the children's facial information. The smartphone app allows users to communicate their desired video content. For example, users can input preferences such as "cut out unnecessary scenes," "slow motion," "create a digest version," and "publish on social media." The generation AI edits the video based on the preferences communicated via the smartphone app. For example, the generation AI automatically cuts out unnecessary scenes, converts the video to slow motion, creates a digest version, and prepares it for publishing on social media. This allows the memory recording system according to an embodiment to use AI to take the hassle of recording memories for families with children. For example, videos of school sports days and birthday parties can be easily edited and shared with family and friends. Furthermore, facial recognition AI can be used to automatically create videos focusing on specific children, allowing parents to record important moments without missing them.

[0053] High-definition cameras can be combined with 360-degree cameras to simultaneously record omnidirectional video, allowing users to later play back the video from any viewpoint. High-definition cameras, for example, can be combined with 360-degree cameras to simultaneously record omnidirectional video. This allows users to later play back the video from any viewpoint. For example, a 360-degree camera can capture the entire view of a sports day and then track the movements of a specific child. A 360-degree camera can also be used to record the entire view of an event, allowing users to later freely change the viewpoint. For example, a 360-degree camera can be used to capture video of a birthday party and then check the expressions of all the family members. A system can also be built by combining high-definition cameras and 360-degree cameras to record omnidirectional video and then play back the video from any viewpoint. For example, a 360-degree camera can capture the entire view of a sports day and then track the movements of a specific event or child. This allows omnidirectional video to be recorded and then played back from any viewpoint.

[0054] High-definition cameras are equipped with a voice recognition function, which can automatically mark important scenes when triggered by specific sounds. For example, high-definition cameras are equipped with a voice recognition function, which can automatically mark important scenes when triggered by specific sounds (such as a child's laughter or someone calling their child's name). This makes it easy to find important scenes later. We will also add a voice recognition function to the camera and build a system that automatically marks scenes when specific sounds are detected. For example, the system can detect the voice of a parent calling their child's name at a sports day and automatically mark that scene. We will also develop a camera system that automatically marks important scenes when triggered by specific sounds using the voice recognition function. For example, the system can detect a child's laughter at a birthday party and automatically mark that scene. This makes it possible to automatically mark important scenes when specific sounds are detected.

[0055] A high-resolution camera is equipped with an emotion estimation function, which can analyze a child's facial expression in real time and automatically record emotionally charged moments as highlights. A high-resolution camera, for example, is equipped with an emotion estimation function and analyzes a child's facial expression in real time while being filmed. By automatically recording particularly emotionally charged moments as highlights, moving scenes can be easily found later. Furthermore, a system is constructed that uses the emotion estimation function to analyze a child's facial expression in real time while being filmed and automatically record particularly emotionally charged moments. For example, a joyful expression when a child crosses the finish line at a sports day can be recorded as a highlight. Furthermore, a camera system is developed that analyzes a child's facial expression in real time while being filmed and automatically record emotionally charged moments as highlights. For example, a surprised expression when a child opens a present at a birthday party can be recorded. This makes it possible to automatically record emotionally charged moments as highlights.

[0056] A high-resolution camera can be mounted on a drone to automatically capture aerial footage of an event, allowing for a wide-ranging recording of the entire event. A high-resolution camera can be mounted on a drone, for example, to automatically capture aerial footage of an event. This allows for a wider-ranging recording of the entire event. For example, the entire view of an athletic meet can be captured from above, allowing for a grasp of the overall movement. A system can also be constructed that mounts a high-resolution camera on a drone to automatically capture aerial footage of an event. For example, the entire view of a birthday party can be captured from above, allowing for a wide-ranging recording of the entire event. A system can also be developed that mounts a high-resolution camera on a drone to automatically capture aerial footage of an event. For example, the entire view of an athletic meet can be captured from above, allowing for a grasp of the overall movement. This allows for a wider-ranging recording of the entire event.

[0057] High-resolution cameras can be equipped with temperature sensors to automatically adjust shooting settings in response to changes in the environment. High-resolution cameras can be equipped with temperature sensors to automatically adjust shooting settings in response to changes in the environment. For example, the exposure can be adjusted in hot weather, and settings can be made to reduce battery consumption in cold climates. We will also use temperature sensors to build camera systems that automatically adjust shooting settings in response to changes in the environment. For example, the camera's cooling function can be strengthened on hot days, and settings can be made to reduce battery consumption on cold days. We will also develop systems that add temperature sensors to cameras to automatically adjust shooting settings in response to changes in the environment. For example, the exposure can be adjusted in hot weather, and settings can be made to reduce battery consumption in cold climates. This makes it possible to automatically adjust shooting settings in response to changes in the environment.

[0058] A high-definition camera is equipped with an emotion estimation function, which can analyze parents' emotions and automatically select and record touching moments. A high-definition camera, for example, is equipped with an emotion estimation function and analyzes parents' emotions while filming. By automatically selecting and recording particularly touching moments, it becomes easy to find touching scenes later. In addition, a system is constructed that uses the emotion estimation function to analyze parents' emotions while filming and automatically select and record particularly touching moments. For example, moments when parents are moved at a sports day are recorded as highlights. In addition, a camera system is developed that analyzes parents' emotions in real time while filming and automatically selects and records touching moments. For example, moments when parents are moved at a birthday party are recorded. This makes it possible to analyze parents' emotions and automatically select and record touching moments.

[0059] Facial recognition AI can be integrated with motion recognition AI to detect specific actions and create videos. For example, facial recognition AI can be integrated with motion recognition AI to detect specific actions (e.g., running, jumping) and create videos. This makes it possible to create videos that highlight a child's specific actions. We will also use motion recognition AI to build a system that detects specific actions and creates videos centered around those actions. For example, we will detect a scene where a child is running at a sports day and create a video centered around that scene. We will also combine facial recognition AI and motion recognition AI to develop a system that detects specific actions and creates a video centered around that action. For example, we will detect a scene where a child is jumping at a birthday party and create a video centered around that scene. This makes it possible to detect specific actions and create videos.

[0060] Facial recognition AI can track multiple children simultaneously and record group activities. For example, facial recognition AI can be added with the ability to track multiple children simultaneously and record group activities. This makes it possible to combine scenes of multiple children playing together into a single video. We will also build a system that records group activities using facial recognition AI that tracks multiple children simultaneously. For example, we will record scenes of multiple children competing together at a sports day. We will also develop a system that records group activities by adding the ability to track multiple children simultaneously to facial recognition AI. For example, we will record scenes of multiple children playing together at a birthday party. This makes it possible to track multiple children simultaneously and record group activities.

[0061] By adding an emotion estimation function to facial recognition AI, it is possible to identify the moment when a child's emotions are at their highest and edit the video around that moment. For example, by adding an emotion estimation function to facial recognition AI, it is possible to identify the moment when a child's emotions are at their highest. By editing the video around that moment, it is possible to create a video that emphasizes moving scenes. In addition, a system can be built that uses the emotion estimation function to identify the moment when a child's emotions are at their highest and edit the video around that moment. For example, a video can be edited around the expression of joy when a child crosses the finish line at a sports day. In addition, by combining facial recognition AI with the emotion estimation function, a system can be developed that identifies the moment when a child's emotions are at their highest and edits the video around that moment. For example, a video can be edited around the expression of surprise when a child opens a present at a birthday party. This makes it possible to identify the moment when a child's emotions are at their highest and edit the video around that moment.

[0062] Facial recognition AI can be enhanced with a voice recognition function, making it possible to create videos triggered by a child's voice. For example, facial recognition AI can be enhanced with a voice recognition function, making it possible to create videos centered around scenes where children are talking. We will also use the voice recognition function to build a system that creates videos triggered by a child's voice. For example, it can detect scenes where children are cheering at a sports day and create a video centered around that scene. We will also combine facial recognition AI with a voice recognition function to develop a system that creates videos triggered by a child's voice. For example, it can detect scenes where children are singing at a birthday party and create a video centered around that scene. This makes it possible to create videos triggered by a child's voice.

[0063] By adding an emotion estimation function to facial recognition AI, it is possible to identify the moment when a parent's emotions were strongest and edit the video around that moment. For example, by adding an emotion estimation function to facial recognition AI, it is possible to identify the moment when a parent's emotions were strongest. By editing the video around that moment, it is possible to create a video that emphasizes moving scenes. Furthermore, by using the emotion estimation function, a system will be built that identifies the moment when a parent's emotions were strongest and edits the video around that moment. For example, a video will be edited around the moment when a parent was moved at a sports day. Furthermore, by combining facial recognition AI with the emotion estimation function, a system will be developed that identifies the moment when a parent's emotions were strongest and edits the video around that moment. For example, a video will be edited around the moment when a parent was moved at a birthday party. This makes it possible to identify the moment when a parent's emotions were strongest and edit the video around that moment.

[0064] A voice input function will be added to the smartphone app, allowing users to create videos simply by stating their wishes aloud. For example, a voice input function will be added to the smartphone app, allowing users to create videos simply by stating their wishes aloud. This allows users to easily communicate their wishes. Furthermore, using the voice input function, a system will be developed in which users can create videos simply by stating their wishes aloud. For example, requests such as "cut out unnecessary scenes" or "slow motion" will be input by voice. Furthermore, a voice input function will be added to the smartphone app, allowing users to create videos simply by stating their wishes aloud. For example, requests such as "create a digest version" or "publish on SNS" will be input by voice. This allows users to create videos simply by stating their wishes aloud.

[0065] A scene recognition function can be added to a smartphone app, allowing it to automatically detect and edit specific scenes. For example, a scene recognition function can be added to a smartphone app to automatically detect and edit specific scenes (for example, the appearance of a birthday cake). This makes it easy to edit important scenes. We will also use the scene recognition function to build a system that automatically detects specific scenes and edits videos around those scenes. For example, we can detect a scene where a child crosses the finish line at a sports day and edit a video around that scene. We will also add a scene recognition function to a smartphone app to develop a system that automatically detects and edits specific scenes. For example, we can detect the scene where a cake appears at a birthday party and edit a video around that scene. This makes it easy to detect and edit specific scenes.

[0066] An emotion estimation function is added to a smartphone app, which can automatically adjust the video editing style based on the user's emotions. For example, an emotion estimation function is added to a smartphone app, which can automatically adjust the video editing style based on the user's emotions. This makes it possible to create videos that match the user's emotions. Furthermore, a system is constructed that uses the emotion estimation function to automatically adjust the video editing style based on the user's emotions. For example, an editing style that emphasizes moving scenes is selected. Furthermore, an emotion estimation function is added to a smartphone app, and a system is developed that automatically adjusts the video editing style based on the user's emotions. For example, an editing style that emphasizes happy scenes is selected. This makes it possible to automatically adjust the video editing style based on the user's emotions.

[0067] A smartphone app can be equipped with an AR function to add effects to videos in real time. For example, an AR function can be added to a smartphone app to add effects to videos in real time. This allows for adding visual effects to videos. A system can also be built using the AR function to add effects to videos in real time. For example, balloon and firework effects can be added to videos of birthday parties. A system can also be developed to add an AR function to a smartphone app to add effects to videos in real time. For example, cheering messages and scoreboard effects can be added to videos of sports days. This allows for adding effects to videos in real time.

[0068] A smartphone app can be equipped with a multi-user collaboration function, allowing the whole family to edit together. For example, a smartphone app can be equipped with a multi-user collaboration function, allowing the whole family to edit together. This allows the whole family to edit memorable videos together. A system can also be built using the collaboration function, allowing multiple users to edit videos simultaneously. For example, all family members can participate in editing videos from their own devices. A smartphone app can also be equipped with a multi-user collaboration function, allowing the whole family to edit together. For example, all family members can create videos that reflect their own opinions. This allows the whole family to edit together.

[0069] An emotion estimation function is added to a smartphone app, which can analyze the emotions of all family members and automatically select and edit moving scenes. For example, an emotion estimation function is added to a smartphone app to analyze the emotions of all family members. By automatically selecting and editing the most moving scenes, it is possible to create moving videos. Furthermore, a system is developed that uses the emotion estimation function to analyze the emotions of all family members and automatically select and edit the most moving scenes. For example, a video is edited focusing on the moments at a sports day when the whole family was moved. Furthermore, an emotion estimation function is added to a smartphone app to develop a system that analyzes the emotions of all family members and automatically selects and edits the most moving scenes. For example, a video is edited focusing on the moments at a birthday party when the whole family was moved. This allows the emotions of all family members to be analyzed and the most moving scenes to be automatically selected and edited.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] For example, a high-resolution camera can be equipped with a nighttime shooting mode, allowing it to record clear images even in dark places. This makes it possible to record high-quality images even in scenes such as nighttime events and camping. Furthermore, a system can be constructed that automatically adjusts exposure and sensitivity when shooting in dark places using the nighttime shooting mode. For example, it can clearly record images of nighttime sports events and fireworks displays. Furthermore, a camera system can be developed that automatically adjusts exposure and sensitivity when shooting in dark places by adding a nighttime shooting mode. For example, it can clearly record images of nighttime birthday parties and campfires. This makes it possible to record high-quality images even in dark places.

[0072] For example, a time lapse function can be added to a high-resolution camera, allowing long events to be played back in a short time. This allows the entire flow of the event to be grasped in a short time. Furthermore, a system can be constructed using the time lapse function to play back long events in a short time. For example, it is possible to play back the entire process of a sports day from preparation to completion in a short time. Furthermore, a camera system can be developed that adds a time lapse function to play back long events in a short time. For example, it is possible to play back the entire process of a birthday party from preparation to completion in a short time. This allows long events to be played back in a short time.

[0073] High-resolution cameras, for example, have enhanced image stabilization capabilities, making it possible to record stable video even in scenes with a lot of movement. This makes it possible to record high-quality video even in scenes with a lot of movement, such as athletic meets and sporting events. Furthermore, systems are being developed that have enhanced image stabilization capabilities and record stable video even in scenes with a lot of movement. For example, scenes of children running at athletic meets can be stably recorded. Furthermore, camera systems are being developed that have enhanced image stabilization capabilities and record stable video even in scenes with a lot of movement. For example, scenes of children playing at birthday parties can be stably recorded. This makes it possible to record stable video even in scenes with a lot of movement.

[0074] High-resolution cameras, for example, are equipped with an emotion estimation function that can analyze a child's facial expression in real time and automatically record moments of heightened emotion as highlights. This makes it easy to find touching scenes later. Furthermore, a system will be developed that uses the emotion estimation function to analyze a child's facial expression in real time while being filmed and automatically record moments of heightened emotion. For example, the expression of joy the moment a child crosses the finish line at a sports day will be recorded as a highlight. Furthermore, a camera system will be developed that analyzes a child's facial expression in real time while being filmed and automatically record moments of heightened emotion as highlights. For example, the expression of surprise the moment a child opens a present at a birthday party will be recorded. This will make it possible to automatically record moments of heightened emotion as highlights.

[0075] A high-resolution camera can be mounted on a drone, for example, to automatically capture footage of an event from above. This allows the entire situation to be recorded over a wider area. For example, the entire view of an athletic meet can be captured from above, allowing the overall movements to be grasped. A system can also be constructed that mounts a high-resolution camera on a drone and automatically captures footage of an event from above. For example, the entire view of a birthday party can be captured from above, allowing the entire situation to be recorded. A system can also be developed that mounts a high-resolution camera on a drone and automatically captures footage of an event from above, allowing the entire situation to be recorded over a wider area. For example, the entire view of an athletic meet can be captured from above, allowing the overall movements to be grasped. This allows the entire situation to be automatically captured from above, allowing the entire situation to be recorded over a wider area.

[0076] High-resolution cameras, for example, can be equipped with temperature sensors to automatically adjust shooting settings in response to changes in the environment. For example, the exposure can be adjusted in hot weather, and settings can be made to reduce battery consumption in cold climates. We can also use temperature sensors to build camera systems that automatically adjust shooting settings in response to changes in the environment. For example, the camera's cooling function can be strengthened on hot days, and settings can be made to reduce battery consumption on cold days. We can also add temperature sensors to cameras to develop systems that automatically adjust shooting settings in response to changes in the environment. For example, the exposure can be adjusted in hot weather, and settings can be made to reduce battery consumption in cold climates. This makes it possible to automatically adjust shooting settings in response to changes in the environment.

[0077] For example, a high-resolution camera could be equipped with an emotion estimation function that analyzes the emotions of parents while filming. By automatically selecting and recording particularly touching moments, it would be easy to find touching scenes later. Furthermore, a system could be constructed that uses the emotion estimation function to analyze the emotions of parents while filming and automatically select and record particularly touching moments. For example, moments when parents are moved at a sports day could be recorded as highlights. Furthermore, a camera system could be developed that analyzes the emotions of parents while filming in real time and automatically selects and records touching moments. For example, it could record moments when parents are moved at a birthday party. This would allow parents' emotions to be analyzed and touching moments to be automatically selected and recorded.

[0078] For example, facial recognition AI can be incorporated into motion recognition AI to detect specific actions (e.g., running, jumping) and create videos. This makes it possible to create videos that highlight a child's specific actions. We can also use motion recognition AI to build a system that detects specific actions and creates videos centered around those actions. For example, we can detect a scene where a child is running at a sports day and create a video centered around that scene. We can also combine facial recognition AI and motion recognition AI to develop a system that detects specific actions and creates a video centered around that action. For example, we can detect a scene where a child is jumping at a birthday party and create a video centered around that scene. This makes it possible to detect specific actions and create videos.

[0079] For example, facial recognition AI can be equipped with the ability to simultaneously track multiple children and record group activities. This makes it possible to combine scenes of multiple children playing together into a single video. We will also use facial recognition AI to simultaneously track multiple children and develop a system to record group activities. For example, we will record scenes of multiple children competing together at a sports day. We will also add the ability to simultaneously track multiple children to facial recognition AI and develop a system to record group activities. For example, we will record scenes of multiple children playing together at a birthday party. This makes it possible to simultaneously track multiple children and record group activities.

[0080] For example, facial recognition AI can be enhanced with an emotion estimation function to identify the moment when a child's emotions are at their highest. By editing the video around that moment, it is possible to create a video that emphasizes moving scenes. Furthermore, a system can be built that uses the emotion estimation function to identify the moment when a child's emotions are at their highest and edit the video around that moment. For example, a video can be edited around the expression of joy when a child crosses the finish line at a sports day. Furthermore, by combining facial recognition AI with the emotion estimation function, a system can be developed that identifies the moment when a child's emotions are at their highest and edits the video around that moment. For example, a video can be edited around the expression of surprise when a child opens a present at a birthday party. This makes it possible to identify the moment when a child's emotions are at their highest and edit the video around that moment.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: High-definition cameras capture the entire scene. For example, they can capture the entire scene at events like sports days and birthday parties. High-definition cameras also cover a wide area and can record the children's movements in detail. Step 2: Facial recognition AI analyzes the footage captured by the high-definition camera and automatically creates videos for each child. For example, facial recognition AI can identify faces in the footage and generate videos focused on specific children. Facial recognition AI can also generate videos based on the video data and the child's facial information. Step 3: The smartphone app allows users to specify their desired video content, such as whether they want unnecessary scenes cut out, slow motion, a digest version created, or published on social media. Step 4: The AI ​​then edits the video based on the user's preferences, such as automatically cutting out unnecessary scenes, converting the video to slow motion, creating a digest version, and preparing the video for posting on social media.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] 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]

[0150] 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 high-definition camera that captures the entire landscape with a fixed camera, A facial recognition AI that analyzes the images captured by the high-definition camera and automatically creates videos for each child. A smartphone app that lets users express their desired video content, A generation AI that edits videos based on the wishes conveyed by the smartphone app. A system characterized by:

2. The high-definition camera is Adding a 360-degree camera to simultaneously record video in all directions allows you to play back the video from any viewpoint later. The system of claim 1 .

3. The high-definition camera is Equipped with voice recognition function, it can automatically mark important scenes when triggered by specific sounds. The system of claim 1 .

4. The high-definition camera is Equipped with an emotion estimation function, it analyzes the child's facial expressions in real time and automatically records moments when emotions are heightened as highlights. The system of claim 1 .

5. The high-definition camera is By mounting it on a drone and automatically capturing aerial footage of the event, the entire event can be recorded over a wide area. The system of claim 1 .

6. The high-definition camera is Add a temperature sensor to automatically adjust shooting settings according to environmental changes. The system of claim 1 .

7. The high-definition camera is Equipped with an emotion estimation function, it analyzes the parent's emotions and automatically selects and records touching moments. The system of claim 1 .

8. The face recognition AI is Introducing motion recognition AI to detect specific movements and create videos The system of claim 1 .

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