System

Abstract

An object of a system according to an embodiment is to reduce a burden on a nursery teacher and enable a parent to grasp a state of a child in more detail.SOLUTION: A system includes a data collection unit, an analysis unit, a generation unit, and a distribution unit. The data collection unit collects a moving image captured by a fixed-point camera. The analysis unit analyzes the moving image collected by the data collection unit and identifies the child using a face authentication technique. The generation unit generates a digest moving image for each child identified by the analysis unit. The distribution unit distributes the digest moving image generated by the generation unit to each household.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is a heavy burden for childcare workers to create contact books for a large number of children, and parents are unable to fully understand how their children are doing.

[0005] The system according to the embodiment aims to reduce the burden on childcare workers and enable parents to understand the state of their children in more detail. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, a generation unit, and a distribution unit. The data collection unit collects videos captured by fixed cameras. The analysis unit analyzes the videos collected by the data collection unit and identifies children using facial recognition technology. The generation unit generates a summary video for each child identified by the analysis unit. The distribution unit distributes the summary video generated by the generation unit to each household. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the burden on childcare workers and enable parents to understand the state of their children in more detail. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A system according to an embodiment of the present invention is a system for solving problems faced by both childcare workers and families using the daycare center. This system captures videos of children's activities at the daycare center and uses a generation AI to generate summary videos for each child, which are then distributed to each family. For example, fixed cameras are installed in classrooms at the daycare center, continuously capturing the children's activities throughout the day. The captured videos are then input into a generation AI, which uses facial recognition technology to identify the children and generate summary videos for each child. The generated summary videos are distributed to each family. This reduces the burden on childcare workers of creating communication notebooks, allowing families using the daycare center to learn more about their children's activities and reduce their anxiety. This system reduces the burden on childcare workers of creating communication notebooks and allows families using the daycare center to learn more about their children's activities. For example, families can feel more secure by seeing their children playing happily and eating well.

[0029] A childcare support system according to an embodiment includes a data collection unit, an analysis unit, a generation unit, and a distribution unit. The data collection unit collects videos captured by a fixed camera. For example, the data collection unit can continuously capture children's daily activities using fixed cameras or PTZ cameras installed in classrooms. The data collection unit can also set the resolution and frame rate of the collected videos. The analysis unit analyzes the videos collected by the data collection unit and identifies children using facial recognition technology. For example, the analysis unit can identify children in the videos using facial recognition technology based on deep learning. The analysis unit can also identify children's faces using feature point extraction technology. The generation unit uses a generation AI to generate a summary video for each child identified by the analysis unit. For example, the generation unit can use a deep learning-based generative model to extract footage of a specific child playing or eating and compile it into a short summary video. The generation unit can also generate the summary video using a rule-based generation method. The distribution unit distributes the summary video generated by the generation unit to each household. For example, the distribution unit can distribute the generated summary video to each household via email or an app. The distribution unit can also set the timing and method of distribution. This reduces the burden on childcare workers of creating a contact list, and allows families using the nursery school to learn in detail how their children are doing. For example, families can feel reassured by seeing how their children are playing energetically and eating well.

[0030] The analysis unit can identify children using facial recognition technology. The analysis unit can identify children in videos using, for example, facial recognition technology that uses deep learning. For example, the analysis unit can identify children's faces with high accuracy using a deep learning model. The analysis unit can also identify children's faces using feature point extraction technology. For example, the analysis unit extracts facial feature points and identifies children based on the extracted feature points. This allows children to be accurately identified using facial recognition technology.

[0031] The generation unit can generate summary videos for each child using a generation AI. The generation unit, for example, uses a generation model using deep learning to extract scenes of a specific child playing or eating and compiles them into a short summary video. For example, the generation unit inputs a prompt to the generation AI such as "Please summarize the scenes of a specific child playing," and the generation AI generates a summary video based on that content. The generation unit can also generate summary videos using a rule-based generation method. For example, the generation unit generates summary videos based on specific rules. This makes the generation of summary videos more efficient by using the generation AI.

[0032] The distribution unit can distribute the generated summary video to each household. For example, the distribution unit distributes the generated summary video to each household via email or an app. For example, the distribution unit sends the generated summary video by email so that each household can watch the video. The distribution unit can also distribute the summary video through a dedicated app. For example, the distribution unit distributes the summary video to each household via an app so that each household can watch the video via the app. In this way, by distributing the summary video to each household, families who use the nursery school can know in detail how their children are doing.

[0033] The data collection unit can collect videos captured by fixed cameras. For example, the data collection unit continuously captures children's daily activities using fixed cameras and PTZ cameras installed in classrooms. For example, the data collection unit can capture the entire classroom using fixed cameras. The data collection unit can also zoom in and capture a specific area using a PTZ camera. Furthermore, the data collection unit can set the resolution and frame rate of the collected videos. For example, the data collection unit can collect videos at high resolution to provide detailed images. In this way, by collecting videos captured by fixed cameras, children's activities can be continuously recorded.

[0034] The generation unit can extract footage of a specific child playing or eating and compile it into a short summary video. The generation unit can, for example, extract footage of a specific child playing and compile it into a short summary video. For example, the generation unit can extract footage of playing with blocks or playing in the sandbox and compile it into a summary video. The generation unit can also extract footage of a specific child eating and compile it into a short summary video. For example, the generation unit can extract footage of lunch or snack time and compile it into a summary video. In this way, by compiling footage of a specific child as a short summary video, families who use the nursery school can learn more about their child's activities.

[0035] The data collection unit can select the type of video to collect based on a specific event. For example, the data collection unit collects footage of children eating during mealtimes. For example, the data collection unit collects footage of children eating during lunch and snack times. The data collection unit can also collect footage of children playing during playtimes. For example, the data collection unit collects footage of children playing with blocks or in the sandbox. The data collection unit can also collect footage of children taking a nap during naptimes. For example, the data collection unit collects footage of children sleeping during naptimes. In this way, by selecting the type of video based on a specific event, important moments can be effectively collected.

[0036] The data collection unit can automatically adjust the position and angle of the camera to collect high-quality video. For example, the data collection unit points the camera at an area where children are playing. For example, the data collection unit uses a PTZ camera to point the camera at an area where children are playing. The data collection unit can also adjust the camera to capture the top of the table during mealtimes. For example, the data collection unit adjusts the camera to capture the top of the table during mealtimes. The data collection unit can also adjust the camera to capture children sleeping during naptimes. For example, the data collection unit adjusts the camera to capture children sleeping during naptimes. In this way, optimal video can be collected by automatically adjusting the position and angle of the camera.

[0037] The data collection unit can simultaneously collect audio data and analyze children's voices and environmental sounds. The data collection unit collects, for example, children's laughter and talking. For example, the data collection unit collects children's laughter and talking using a microphone installed in the classroom. The data collection unit can also collect and analyze environmental sounds in the classroom. For example, the data collection unit collects and analyzes environmental sounds in the classroom. The data collection unit can also collect instructions and comments from childcare workers. For example, the data collection unit collects and analyzes instructions and comments from childcare workers. In this way, by collecting and analyzing audio data, it is possible to understand the state of children in more detail.

[0038] The data collection unit can adjust the timing of video collection in consideration of the activity status of the childcare worker. For example, the data collection unit refrains from collecting videos during hours when the childcare worker is busy. For example, the data collection unit refrains from collecting videos during hours when the childcare worker is in class or playing with children. The data collection unit can also increase the amount of video collection during hours when the childcare worker is playing with children. For example, the data collection unit increases the amount of video collection during hours when the childcare worker is playing with children. The data collection unit can also reduce the amount of video collection during hours when the childcare worker is on a break. For example, the data collection unit reduces the amount of video collection during hours when the childcare worker is on a break. In this way, videos can be collected at an appropriate time by considering the activity status of the childcare worker.

[0039] The data collection unit can change the content of the videos to be collected in consideration of the external environment of the nursery school. For example, the data collection unit collects indoor activities on rainy days. For example, the data collection unit collects footage of indoor play and activities on rainy days. The data collection unit can also collect outdoor activities on sunny days. For example, the data collection unit collects footage of outdoor play and activities on sunny days. The data collection unit can also collect seasonal events (e.g., sports days, birthday parties). For example, the data collection unit collects footage of sports days and birthday parties. This makes it possible to collect videos with appropriate content by taking the external environment into consideration.

[0040] The data collection unit can improve the collection method by reflecting feedback from the childcare worker. For example, when a childcare worker requests to collect videos of a specific child, the data collection unit prioritizes collecting videos of that child. For example, when a childcare worker requests to collect videos of a specific child, the data collection unit prioritizes collecting videos of that child. Furthermore, when a childcare worker requests to collect videos from a specific time period, the data collection unit can also prioritize collecting videos from that time period. For example, when a childcare worker requests to collect videos from a specific time period, the data collection unit prioritizes collecting videos from that time period. Furthermore, when a childcare worker requests to collect videos of a specific activity, the data collection unit can also prioritize collecting videos of that activity. For example, when a childcare worker requests to collect videos of a specific activity, the data collection unit prioritizes collecting videos of that activity. In this way, the collection method can be optimized by reflecting feedback from childcare workers.

[0041] The analysis unit can be added with a function of learning a child's behavioral patterns and detecting abnormal behavior. For example, if a child behaves differently from usual, the analysis unit detects this as abnormal behavior. For example, the analysis unit learns a child's behavioral patterns and detects this differently from usual as abnormal behavior. The analysis unit can also detect abnormal behavior if a child behaves abnormally during a specific time period. For example, the analysis unit detects abnormal behavior if a child behaves abnormally during a specific time period. The analysis unit can also detect abnormal behavior if a child behaves abnormally in a specific location. For example, the analysis unit detects abnormal behavior if a child behaves abnormally in a specific location. In this way, by learning a child's behavioral patterns and detecting abnormal behavior, abnormal behavior can be discovered early.

[0042] The analysis unit can analyze the child's facial expressions and movements to estimate the emotions and health state. The analysis unit, for example, analyzes the child's smiling or crying face to estimate the emotions. For example, the analysis unit analyzes the child's facial expressions and estimates the emotions based on the smiling or crying face. The analysis unit can also analyze the child's movements to estimate the health state. For example, the analysis unit analyzes the child's movements to estimate the health state. The analysis unit can also combine the child's facial expressions and movements to estimate the overall emotions and health state. For example, the analysis unit combines the child's facial expressions and movements to estimate the overall emotions and health state. In this way, the emotions and health state can be estimated by analyzing the child's facial expressions and movements.

[0043] The analysis unit can integrate multiple camera images to perform a more detailed analysis. The analysis unit, for example, integrates multiple camera images to perform a detailed analysis of the child's behavior. For example, the analysis unit integrates multiple camera images to perform a detailed analysis of the child's behavior. The analysis unit can also integrate multiple camera images to perform a detailed analysis of the child's facial expressions. For example, the analysis unit integrates multiple camera images to perform a detailed analysis of the child's facial expressions. The analysis unit can also integrate multiple camera images to perform a detailed analysis of the child's movements. For example, the analysis unit integrates multiple camera images to perform a detailed analysis of the child's movements. In this way, by integrating multiple camera images, a detailed analysis becomes possible.

[0044] The analysis unit can analyze the association with the child's behavior by referring to the activity data of the childcare worker. The analysis unit, for example, analyzes the child's behavior during a time period when the childcare worker is performing a specific activity. For example, the analysis unit analyzes the child's behavior during a time period when the childcare worker is performing a specific activity. The analysis unit can also analyze the child's behavior during a time period when the childcare worker is giving specific instructions. For example, the analysis unit analyzes the child's behavior during a time period when the childcare worker is giving specific instructions. The analysis unit can also analyze the child's behavior during a time period when the childcare worker is performing an activity at a specific location. For example, the analysis unit analyzes the child's behavior during a time period when the childcare worker is performing an activity at a specific location. In this way, the association with the child's behavior can be analyzed by referring to the activity data of the childcare worker.

[0045] The analysis unit can strengthen the analysis of specific time periods by taking into account the schedule information of the nursery school. For example, the analysis unit analyzes in detail how the child eats during mealtimes. For example, the analysis unit analyzes in detail how the child eats during mealtimes. The analysis unit can also analyze in detail how the child plays during playtimes. For example, the analysis unit analyzes in detail how the child plays during playtimes. The analysis unit can also analyze in detail how the child takes a nap during naptimes. For example, the analysis unit analyzes in detail how the child takes a nap during naptimes. In this way, the analysis of specific time periods can be strengthened by taking into account the schedule information of the nursery school.

[0046] The analysis unit can improve the analysis algorithm by reflecting feedback from the childcare worker. For example, when a childcare worker requests a detailed analysis of the behavior of a specific child, the analysis unit prioritizes analyzing the behavior of the child. For example, when a childcare worker requests a detailed analysis of the behavior of a specific child, the analysis unit prioritizes analyzing the behavior of the child. Furthermore, when a childcare worker requests a detailed analysis of the behavior during a specific time period, the analysis unit can also prioritize analyzing the behavior during that time period. For example, when a childcare worker requests a detailed analysis of the behavior during a specific time period, the analysis unit prioritizes analyzing the behavior during that time period. Furthermore, when a childcare worker requests a detailed analysis of the behavior of a specific activity, the analysis unit can also prioritize analyzing the behavior of that activity. For example, when a childcare worker requests a detailed analysis of the behavior of a specific activity, the analysis unit prioritizes analyzing the behavior of that activity. In this way, the analysis algorithm can be optimized by reflecting feedback from childcare workers.

[0047] The generation unit can highlight important moments of the child. For example, the generation unit highlights the moment when the child takes their first steps. For example, the generation unit includes the moment when the child takes their first steps in the summary video to highlight it. The generation unit can also highlight the child participating in a special event (e.g., a birthday party). For example, the generation unit includes the child participating in the birthday party in the summary video to highlight it. The generation unit can also highlight the child playing with new friends. For example, the generation unit includes the child playing with new friends in the summary video to highlight it. In this way, by highlighting important moments of the child, a valuable video can be provided to parents.

[0048] The generation unit can combine videos of multiple children to summarize group activities. The generation unit, for example, summarizes a video of multiple children playing together. For example, the generation unit includes in the summary video a video of multiple children playing with blocks or playing in the sandbox together. The generation unit can also summarize a video of multiple children eating together. For example, the generation unit includes in the summary video a video of multiple children having lunch or a snack together. The generation unit can also summarize a video of multiple children taking a nap together. For example, the generation unit includes in the summary video a video of multiple children taking a nap together. In this way, the group activity can be effectively summarized by combining videos of multiple children.

[0049] The generation unit can analyze the audio data to include children's voices and comments from the childcare worker. The generation unit, for example, includes children's laughter and talking in the summary video. For example, the generation unit analyzes children's laughter and talking and includes them in the summary video. The generation unit can also include instructions and comments from the childcare worker in the summary video. For example, the generation unit analyzes instructions and comments from the childcare worker and includes them in the summary video. The generation unit can also include environmental sounds in a classroom in the summary video. For example, the generation unit analyzes environmental sounds in a classroom and includes them in the summary video. In this way, by including audio data, a more realistic summary video can be provided.

[0050] The generation unit can emphasize the involvement of the childcare worker by referring to the activity data of the childcare worker. The generation unit, for example, emphasizes the childcare worker playing with the children. For example, the generation unit includes in the summary video the childcare worker playing with the children to emphasize the scene. The generation unit can also emphasize the childcare worker feeding the children. For example, the generation unit includes in the summary video the childcare worker feeding the children to emphasize the scene. The generation unit can also emphasize the childcare worker putting the children to bed. For example, the generation unit includes in the summary video the childcare worker putting the children to bed to emphasize the scene. In this way, by emphasizing the involvement of the childcare worker, it is possible to provide a video that is valuable to parents.

[0051] The generation unit can summarize specific events at the nursery school. For example, the generation unit summarizes a birthday party. For example, the generation unit includes the birthday party in the summary video. The generation unit can also summarize a sports day. For example, the generation unit includes the sports day in the summary video. The generation unit can also summarize seasonal events (e.g., a Christmas party or a Halloween party). For example, the generation unit includes the Christmas party or the Halloween party in the summary video. In this way, by summarizing specific events, valuable videos can be provided to parents.

[0052] The generation unit can customize the summarization method by reflecting feedback from the childcare worker. For example, if the childcare worker requests that the appearance of a particular child be emphasized, the generation unit includes the appearance of the child in the summary video. For example, if the childcare worker requests that the appearance of a particular child be emphasized, the generation unit includes the appearance of the child in the summary video. Furthermore, if the childcare worker requests that the appearance of a particular time period be emphasized, the generation unit can also include the appearance of that time period in the summary video. For example, if the childcare worker requests that the appearance of a particular time period be emphasized, the generation unit can also include the appearance of that time period in the summary video. Furthermore, if the childcare worker requests that the appearance of a particular activity be emphasized, the generation unit can also include the appearance of that activity in the summary video. For example, if the childcare worker requests that the appearance of a particular activity be emphasized, the generation unit can include the appearance of that activity in the summary video. In this way, the summarization method can be optimized by reflecting feedback from the childcare worker.

[0053] The distribution unit can select an appropriate distribution method in consideration of the communication environment of each home. For example, the distribution unit distributes a high-quality summarized video to a home with a good communication environment. For example, the distribution unit distributes a high-quality summarized video to a home with a good communication environment. The distribution unit can also distribute a low-quality summarized video to a home with an unstable communication environment. For example, the distribution unit distributes a low-quality summarized video to a home with an unstable communication environment. The distribution unit can also distribute an audio-only summary to a home with a very poor communication environment. For example, the distribution unit distributes an audio-only summary to a home with a very poor communication environment. In this way, smooth video distribution is possible by selecting a distribution method according to the communication environment.

[0054] The distribution unit can analyze the viewing history of the video to improve the content of the next distribution. The distribution unit, for example, reflects content of particular interest based on the viewing history in the next distribution. For example, the distribution unit analyzes the viewing history and reflects content of particular interest in the next distribution. The distribution unit can also reflect content with a long viewing time based on the viewing history in the next distribution. For example, the distribution unit analyzes the viewing history and reflects content with a long viewing time in the next distribution. The distribution unit can also reflect content with a high viewing frequency based on the viewing history in the next distribution. For example, the distribution unit analyzes the viewing history and reflects content with a high viewing frequency in the next distribution. In this way, the content of the next distribution can be optimized by analyzing the viewing history.

[0055] The distribution unit can collect parental feedback and improve the content of the distribution. For example, if a parent requests to see more of a specific content, the distribution unit reflects that content in the next distribution. For example, if a parent requests to see more of a specific content, the distribution unit reflects that content in the next distribution. Furthermore, if a parent requests distribution at a specific time period, the distribution unit can distribute the content at that time period. For example, if a parent requests distribution at a specific time period, the distribution unit distributes the content at that time period. Furthermore, if a parent requests distribution in a specific format (e.g., short clips, long videos), the distribution unit can distribute the content in that format. For example, if a parent requests short clips, the distribution unit distributes the content in that format. In this way, the distribution content can be optimized by collecting parental feedback.

[0056] The distribution unit can select an appropriate distribution time by taking into consideration schedule information of the guardian. For example, the distribution unit distributes during a time period when the guardian returns home from work. For example, the distribution unit distributes during a time period when the guardian returns home from work. The distribution unit can also distribute during a time period when the guardian spends time with their child. For example, the distribution unit distributes during a time period when the guardian spends time with their child. The distribution unit can also distribute during a time period when the guardian is relaxing. For example, the distribution unit distributes during a time period when the guardian is relaxing. In this way, the optimal distribution time can be selected by taking into consideration schedule information of the guardian.

[0057] The distribution unit can select the optimal distribution format by taking into account the device information of the parent / guardian. For example, if the parent / guardian is using a smartphone, the distribution unit distributes the video in a format optimized for the smartphone. For example, if the parent / guardian is using a smartphone, the distribution unit distributes the video in a format optimized for the smartphone. Furthermore, if the parent / guardian is using a tablet, the distribution unit can distribute the video in a format optimized for the tablet. For example, if the parent / guardian is using a tablet, the distribution unit can distribute the video in a format optimized for the tablet. Furthermore, if the parent / guardian is using a personal computer, the distribution unit can distribute the video in a format optimized for the personal computer. For example, if the parent / guardian is using a personal computer, the distribution unit distributes the video in a format optimized for the personal computer. In this way, by taking into account the device information of the parent / guardian, the video can be distributed in the optimal format.

[0058] The distribution unit can customize the distribution method by reflecting parental feedback. For example, if a parent requests distribution at a specific time period, the distribution unit distributes at that time period. For example, if a parent requests distribution at a specific time period, the distribution unit distributes at that time period. Furthermore, if a parent requests a specific format (e.g., short clips, long videos), the distribution unit can distribute in that format. For example, if a parent requests short clips, the distribution unit distributes in that format. Furthermore, if a parent requests to see more of a specific content, the distribution unit can reflect that content in the next distribution. For example, if a parent requests to see more of a specific content, the distribution unit reflects that content in the next distribution. In this way, the distribution method can be optimized by reflecting parental feedback.

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

[0060] The childcare support system can further include a voice recognition unit. The voice recognition unit can analyze conversations between childcare workers and children and extract important information. For example, the voice recognition unit can analyze scenes in which childcare workers are giving instructions to children and save the content of those instructions as text data. The voice recognition unit can also analyze children's conversations and detect specific keywords (e.g., words asking for help). Furthermore, the voice recognition unit can improve the accuracy of the analysis based on feedback from childcare workers. This makes it possible to use voice data to understand in detail the instructions from childcare workers and the content of children's conversations.

[0061] The analysis unit can further include a child's movement analysis function. The movement analysis function can analyze a child's movements in real time and detect abnormal movements. For example, the analysis unit can detect abnormalities when a child falls or repeats a specific movement. The analysis unit can also learn a child's movement patterns and issue an alert if the movement deviates from normal movements. Furthermore, the analysis unit can improve the movement analysis algorithm based on feedback from childcare workers. This ensures the safety of children and enables early detection of abnormal movements.

[0062] The distribution unit can further customize the content of distribution based on the user's viewing history. For example, the distribution unit can analyze the content of videos that the user has viewed in the past and optimize the content of the next distribution. For example, if the user frequently watches videos of a particular child, the distribution unit can prioritize the distribution of videos of that child. Also, if the user watches videos during a particular time period, the distribution unit can also adjust the distribution to suit that time period. Furthermore, the distribution unit can improve the distribution method based on user feedback. In this way, by utilizing the user's viewing history, more personalized distribution is possible.

[0063] The data collection unit may further include an environmental sensor. The environmental sensor may collect environmental information such as temperature, humidity, and illuminance in the classroom and provide it to the analysis unit. For example, the data collection unit may issue an alert if the temperature in the classroom is too high. The data collection unit may also automatically activate a humidifier if the humidity is too low. Furthermore, the data collection unit may adjust the lighting if the illuminance is inappropriate. This allows the classroom environment to be optimized and provides a comfortable environment for children.

[0064] The generation unit can further generate a summary video based on the activity data of the childcare worker. For example, scenes in which the childcare worker is providing special care to a particular child can be included in the summary video. Scenes in which the childcare worker is playing with the children can also be emphasized. Furthermore, scenes in which the childcare worker is instructing the children can also be included in the summary video. This allows the activities of the childcare worker to be recorded in detail, and a summary video that emphasizes the involvement of the childcare worker can be provided to parents.

[0065] The data collection unit can further collect health data of the child. For example, the data collection unit can monitor the child's body temperature and heart rate and issue an alert if an abnormality is detected. The data collection unit can also analyze the child's sleep patterns and provide an appropriate sleeping environment. Furthermore, the data collection unit can record the child's diet and manage nutritional balance. This allows for a detailed understanding of the child's health condition and the provision of appropriate care.

[0066] The analysis unit can further analyze the child's learning data. For example, the analysis unit can analyze what kind of play or activity the child is interested in and provide the results to the childcare worker. The analysis unit can also monitor the child's learning progress and suggest appropriate teaching methods. Furthermore, the analysis unit can create an individual learning plan based on the child's learning data. This allows for a detailed understanding of the child's learning situation and enables instruction to be provided according to individual needs.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The data collection unit collects video footage taken by fixed cameras. For example, fixed or PTZ cameras installed in classrooms can be used to continuously capture the children's daily activities. It is also possible to set the resolution and frame rate of the collected video. Step 2: The analysis unit analyzes the videos collected by the data collection unit and identifies children using facial recognition technology. For example, facial recognition technology using deep learning and feature point extraction technology can be used to identify children in the videos. Step 3: The generator generates a summary video for each child identified by the analyzer. For example, a deep learning-based generative model or rule-based generation method can be used to extract footage of a specific child playing or eating, and then compile it into a short summary video. Step 4: The distribution unit distributes the summary video generated by the generation unit to each household. For example, the generated summary video can be distributed to each household via email or an app. The timing and method of distribution can also be set.

[0069] (Example 2) A system according to an embodiment of the present invention is a system for solving problems faced by both childcare workers and families using the daycare center. This system captures videos of children's activities at the daycare center and uses a generation AI to generate summary videos for each child, which are then distributed to each family. For example, fixed cameras are installed in classrooms at the daycare center, continuously capturing the children's activities throughout the day. The captured videos are then input into a generation AI, which uses facial recognition technology to identify the children and generate summary videos for each child. The generated summary videos are distributed to each family. This reduces the burden on childcare workers of creating communication notebooks, allowing families using the daycare center to learn more about their children's activities and reduce their anxiety. This system reduces the burden on childcare workers of creating communication notebooks and allows families using the daycare center to learn more about their children's activities. For example, families can feel more secure by seeing their children playing happily and eating well.

[0070] A childcare support system according to an embodiment includes a data collection unit, an analysis unit, a generation unit, and a distribution unit. The data collection unit collects videos captured by a fixed camera. For example, the data collection unit can continuously capture children's daily activities using fixed cameras or PTZ cameras installed in classrooms. The data collection unit can also set the resolution and frame rate of the collected videos. The analysis unit analyzes the videos collected by the data collection unit and identifies children using facial recognition technology. For example, the analysis unit can identify children in the videos using facial recognition technology based on deep learning. The analysis unit can also identify children's faces using feature point extraction technology. The generation unit uses a generation AI to generate a summary video for each child identified by the analysis unit. For example, the generation unit can use a deep learning-based generative model to extract footage of a specific child playing or eating and compile it into a short summary video. The generation unit can also generate the summary video using a rule-based generation method. The distribution unit distributes the summary video generated by the generation unit to each household. For example, the distribution unit can distribute the generated summary video to each household via email or an app. The distribution unit can also set the timing and method of distribution. This reduces the burden on childcare workers of creating a contact list, and allows families using the nursery school to learn in detail how their children are doing. For example, families can feel reassured by seeing how their children are playing energetically and eating well.

[0071] The analysis unit can identify children using facial recognition technology. The analysis unit can identify children in videos using, for example, facial recognition technology that uses deep learning. For example, the analysis unit can identify children's faces with high accuracy using a deep learning model. The analysis unit can also identify children's faces using feature point extraction technology. For example, the analysis unit extracts facial feature points and identifies children based on the extracted feature points. This allows children to be accurately identified using facial recognition technology.

[0072] The generation unit can generate summary videos for each child using a generation AI. The generation unit, for example, uses a generation model using deep learning to extract scenes of a specific child playing or eating and compiles them into a short summary video. For example, the generation unit inputs a prompt to the generation AI such as "Please summarize the scenes of a specific child playing," and the generation AI generates a summary video based on that content. The generation unit can also generate summary videos using a rule-based generation method. For example, the generation unit generates summary videos based on specific rules. This makes the generation of summary videos more efficient by using the generation AI.

[0073] The distribution unit can distribute the generated summary video to each household. For example, the distribution unit distributes the generated summary video to each household via email or an app. For example, the distribution unit sends the generated summary video by email so that each household can watch the video. The distribution unit can also distribute the summary video through a dedicated app. For example, the distribution unit distributes the summary video to each household via an app so that each household can watch the video via the app. In this way, by distributing the summary video to each household, families who use the nursery school can know in detail how their children are doing.

[0074] The data collection unit can collect videos captured by fixed cameras. For example, the data collection unit continuously captures children's daily activities using fixed cameras and PTZ cameras installed in classrooms. For example, the data collection unit can capture the entire classroom using fixed cameras. The data collection unit can also zoom in and capture a specific area using a PTZ camera. Furthermore, the data collection unit can set the resolution and frame rate of the collected videos. For example, the data collection unit can collect videos at high resolution to provide detailed images. In this way, by collecting videos captured by fixed cameras, children's activities can be continuously recorded.

[0075] The generation unit can extract footage of a specific child playing or eating and compile it into a short summary video. The generation unit can, for example, extract footage of a specific child playing and compile it into a short summary video. For example, the generation unit can extract footage of playing with blocks or playing in the sandbox and compile it into a summary video. The generation unit can also extract footage of a specific child eating and compile it into a short summary video. For example, the generation unit can extract footage of lunch or snack time and compile it into a summary video. In this way, by compiling footage of a specific child as a short summary video, families who use the nursery school can learn more about their child's activities.

[0076] The data collection unit can estimate the user's emotions and adjust the timing of video collection based on the estimated user emotions. For example, if the user is feeling stressed, the data collection unit can increase the timing of video collection to provide a sense of security. For example, the data collection unit can analyze the user's facial expressions and increase the frequency of video collection if it determines that the user is feeling stressed. The data collection unit can also set the timing of video collection to normal if the user is relaxed. For example, the data collection unit can analyze the user's voice and maintain the normal collection timing if it determines that the user is relaxed. The data collection unit can also adjust the timing to collect only important moments if the user is in a hurry. For example, the data collection unit can analyze the user's behavior and collect only important moments if it determines that the user is in a hurry. In this way, by adjusting the timing of video collection according to the user's emotions, videos can be collected at more appropriate times.

[0077] The data collection unit can select the type of video to collect based on a specific event. For example, the data collection unit collects footage of children eating during mealtimes. For example, the data collection unit collects footage of children eating during lunch and snack times. The data collection unit can also collect footage of children playing during playtimes. For example, the data collection unit collects footage of children playing with blocks or in the sandbox. The data collection unit can also collect footage of children taking a nap during naptimes. For example, the data collection unit collects footage of children sleeping during naptimes. In this way, by selecting the type of video based on a specific event, important moments can be effectively collected.

[0078] The data collection unit can automatically adjust the position and angle of the camera to collect high-quality video. For example, the data collection unit points the camera at an area where children are playing. For example, the data collection unit uses a PTZ camera to point the camera at an area where children are playing. The data collection unit can also adjust the camera to capture the top of the table during mealtimes. For example, the data collection unit adjusts the camera to capture the top of the table during mealtimes. The data collection unit can also adjust the camera to capture children sleeping during naptimes. For example, the data collection unit adjusts the camera to capture children sleeping during naptimes. In this way, optimal video can be collected by automatically adjusting the position and angle of the camera.

[0079] The data collection unit can simultaneously collect audio data and analyze children's voices and environmental sounds. The data collection unit collects, for example, children's laughter and talking. For example, the data collection unit collects children's laughter and talking using a microphone installed in the classroom. The data collection unit can also collect and analyze environmental sounds in the classroom. For example, the data collection unit collects and analyzes environmental sounds in the classroom. The data collection unit can also collect instructions and comments from childcare workers. For example, the data collection unit collects and analyzes instructions and comments from childcare workers. In this way, by collecting and analyzing audio data, it is possible to understand the state of children in more detail.

[0080] The data collection unit can estimate the user's emotions and determine the priority of videos to be collected based on the estimated user's emotions. For example, if the user is feeling anxious, the data collection unit prioritizes collecting videos of children smiling and having fun. For example, if the data collection unit analyzes the user's facial expressions and determines that the user is feeling anxious, it prioritizes collecting videos of children smiling and having fun. The data collection unit can also maintain the normal collection order if the user is relaxed. For example, if the data collection unit analyzes the user's voice and determines that the user is relaxed, it maintains the normal collection order. The data collection unit can also prioritize collecting only important moments if the user is in a hurry. For example, if the data collection unit analyzes the user's behavior and determines that the user is in a hurry, it prioritizes collecting only important moments. In this way, by determining the priority of videos based on the user's emotions, important moments can be collected preferentially.

[0081] The data collection unit can adjust the timing of video collection in consideration of the activity status of the childcare worker. For example, the data collection unit refrains from collecting videos during hours when the childcare worker is busy. For example, the data collection unit refrains from collecting videos during hours when the childcare worker is in class or playing with children. The data collection unit can also increase the amount of video collection during hours when the childcare worker is playing with children. For example, the data collection unit increases the amount of video collection during hours when the childcare worker is playing with children. The data collection unit can also reduce the amount of video collection during hours when the childcare worker is on a break. For example, the data collection unit reduces the amount of video collection during hours when the childcare worker is on a break. In this way, videos can be collected at an appropriate time by considering the activity status of the childcare worker.

[0082] The data collection unit can change the content of the videos to be collected in consideration of the external environment of the nursery school. For example, the data collection unit collects indoor activities on rainy days. For example, the data collection unit collects footage of indoor play and activities on rainy days. The data collection unit can also collect outdoor activities on sunny days. For example, the data collection unit collects footage of outdoor play and activities on sunny days. The data collection unit can also collect seasonal events (e.g., sports days, birthday parties). For example, the data collection unit collects footage of sports days and birthday parties. This makes it possible to collect videos with appropriate content by taking the external environment into consideration.

[0083] The data collection unit can improve the collection method by reflecting feedback from the childcare worker. For example, when a childcare worker requests to collect videos of a specific child, the data collection unit prioritizes collecting videos of that child. For example, when a childcare worker requests to collect videos of a specific child, the data collection unit prioritizes collecting videos of that child. Furthermore, when a childcare worker requests to collect videos from a specific time period, the data collection unit can also prioritize collecting videos from that time period. For example, when a childcare worker requests to collect videos from a specific time period, the data collection unit prioritizes collecting videos from that time period. Furthermore, when a childcare worker requests to collect videos of a specific activity, the data collection unit can also prioritize collecting videos of that activity. For example, when a childcare worker requests to collect videos of a specific activity, the data collection unit prioritizes collecting videos of that activity. In this way, the collection method can be optimized by reflecting feedback from childcare workers.

[0084] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit increases the accuracy of the analysis to provide more detailed information. For example, if the analysis unit analyzes the user's facial expression and determines that the user is feeling anxious, it increases the accuracy of the analysis to provide more detailed information. The analysis unit can also maintain normal analysis accuracy if the user is relaxed. For example, if the analysis unit analyzes the user's voice and determines that the user is relaxed, it maintains normal analysis accuracy. The analysis unit can also analyze only important information if the user is in a hurry. For example, if the analysis unit analyzes the user's behavior and determines that the user is in a hurry, it analyzes only important information. In this way, by adjusting the accuracy of the analysis based on the user's emotions, more appropriate analysis results can be provided.

[0085] The analysis unit can be added with a function of learning a child's behavioral patterns and detecting abnormal behavior. For example, if a child behaves differently from usual, the analysis unit detects this as abnormal behavior. For example, the analysis unit learns a child's behavioral patterns and detects this differently from usual as abnormal behavior. The analysis unit can also detect abnormal behavior if a child behaves abnormally during a specific time period. For example, the analysis unit detects abnormal behavior if a child behaves abnormally during a specific time period. The analysis unit can also detect abnormal behavior if a child behaves abnormally in a specific location. For example, the analysis unit detects abnormal behavior if a child behaves abnormally in a specific location. In this way, by learning a child's behavioral patterns and detecting abnormal behavior, abnormal behavior can be discovered early.

[0086] The analysis unit can analyze the child's facial expressions and movements to estimate the emotions and health state. The analysis unit, for example, analyzes the child's smiling or crying face to estimate the emotions. For example, the analysis unit analyzes the child's facial expressions and estimates the emotions based on the smiling or crying face. The analysis unit can also analyze the child's movements to estimate the health state. For example, the analysis unit analyzes the child's movements to estimate the health state. The analysis unit can also combine the child's facial expressions and movements to estimate the overall emotions and health state. For example, the analysis unit combines the child's facial expressions and movements to estimate the overall emotions and health state. In this way, the emotions and health state can be estimated by analyzing the child's facial expressions and movements.

[0087] The analysis unit can integrate multiple camera images to perform a more detailed analysis. The analysis unit, for example, integrates multiple camera images to perform a detailed analysis of the child's behavior. For example, the analysis unit integrates multiple camera images to perform a detailed analysis of the child's behavior. The analysis unit can also integrate multiple camera images to perform a detailed analysis of the child's facial expressions. For example, the analysis unit integrates multiple camera images to perform a detailed analysis of the child's facial expressions. The analysis unit can also integrate multiple camera images to perform a detailed analysis of the child's movements. For example, the analysis unit integrates multiple camera images to perform a detailed analysis of the child's movements. In this way, by integrating multiple camera images, a detailed analysis becomes possible.

[0088] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit displays detailed analysis results. For example, the analysis unit analyzes the user's facial expressions and determines that the user is feeling anxious, and displays detailed analysis results. The analysis unit can also display normal analysis results if the user is relaxed. For example, the analysis unit analyzes the user's voice and determines that the user is relaxed, and displays normal analysis results. The analysis unit can also display analysis results that emphasize the main points if the user is in a hurry. For example, the analysis unit analyzes the user's behavior and determines that the user is in a hurry, and displays analysis results that emphasize the main points. In this way, by adjusting the display method of the analysis results based on the user's emotions, more appropriate information can be provided.

[0089] The analysis unit can analyze the association with the child's behavior by referring to the activity data of the childcare worker. The analysis unit, for example, analyzes the child's behavior during a time period when the childcare worker is performing a specific activity. For example, the analysis unit analyzes the child's behavior during a time period when the childcare worker is performing a specific activity. The analysis unit can also analyze the child's behavior during a time period when the childcare worker is giving specific instructions. For example, the analysis unit analyzes the child's behavior during a time period when the childcare worker is giving specific instructions. The analysis unit can also analyze the child's behavior during a time period when the childcare worker is performing an activity at a specific location. For example, the analysis unit analyzes the child's behavior during a time period when the childcare worker is performing an activity at a specific location. In this way, the association with the child's behavior can be analyzed by referring to the activity data of the childcare worker.

[0090] The analysis unit can strengthen the analysis of specific time periods by taking into account the schedule information of the nursery school. For example, the analysis unit analyzes in detail how the child eats during mealtimes. For example, the analysis unit analyzes in detail how the child eats during mealtimes. The analysis unit can also analyze in detail how the child plays during playtimes. For example, the analysis unit analyzes in detail how the child plays during playtimes. The analysis unit can also analyze in detail how the child takes a nap during naptimes. For example, the analysis unit analyzes in detail how the child takes a nap during naptimes. In this way, the analysis of specific time periods can be strengthened by taking into account the schedule information of the nursery school.

[0091] The analysis unit can improve the analysis algorithm by reflecting feedback from the childcare worker. For example, when a childcare worker requests a detailed analysis of the behavior of a specific child, the analysis unit prioritizes analyzing the behavior of the child. For example, when a childcare worker requests a detailed analysis of the behavior of a specific child, the analysis unit prioritizes analyzing the behavior of the child. Furthermore, when a childcare worker requests a detailed analysis of the behavior during a specific time period, the analysis unit can also prioritize analyzing the behavior during that time period. For example, when a childcare worker requests a detailed analysis of the behavior during a specific time period, the analysis unit prioritizes analyzing the behavior during that time period. Furthermore, when a childcare worker requests a detailed analysis of the behavior of a specific activity, the analysis unit can also prioritize analyzing the behavior of that activity. For example, when a childcare worker requests a detailed analysis of the behavior of a specific activity, the analysis unit prioritizes analyzing the behavior of that activity. In this way, the analysis algorithm can be optimized by reflecting feedback from childcare workers.

[0092] The generation unit can estimate the user's emotions and adjust the presentation method of the summary video based on the estimated user's emotions. For example, if the user is feeling anxious, the generation unit generates a detailed summary video. For example, the generation unit analyzes the user's facial expression and generates a detailed summary video if it determines that the user is feeling anxious. The generation unit can also generate a normal summary video if the user is relaxed. For example, the generation unit analyzes the user's voice and generates a normal summary video if it determines that the user is relaxed. The generation unit can also generate a summary video that includes only important moments if the user is in a hurry. For example, the generation unit analyzes the user's behavior and generates a summary video that includes only important moments if it determines that the user is in a hurry. This makes it possible to provide a more appropriate video by adjusting the presentation method of the summary video based on the user's emotions.

[0093] The generation unit can highlight important moments of the child. For example, the generation unit highlights the moment when the child takes their first steps. For example, the generation unit includes the moment when the child takes their first steps in the summary video to highlight it. The generation unit can also highlight the child participating in a special event (e.g., a birthday party). For example, the generation unit includes the child participating in the birthday party in the summary video to highlight it. The generation unit can also highlight the child playing with new friends. For example, the generation unit includes the child playing with new friends in the summary video to highlight it. In this way, by highlighting important moments of the child, a valuable video can be provided to parents.

[0094] The generation unit can combine videos of multiple children to summarize group activities. The generation unit, for example, summarizes a video of multiple children playing together. For example, the generation unit includes in the summary video a video of multiple children playing with blocks or playing in the sandbox together. The generation unit can also summarize a video of multiple children eating together. For example, the generation unit includes in the summary video a video of multiple children having lunch or a snack together. The generation unit can also summarize a video of multiple children taking a nap together. For example, the generation unit includes in the summary video a video of multiple children taking a nap together. In this way, the group activity can be effectively summarized by combining videos of multiple children.

[0095] The generation unit can analyze the audio data to include children's voices and comments from the childcare worker. The generation unit, for example, includes children's laughter and talking in the summary video. For example, the generation unit analyzes children's laughter and talking and includes them in the summary video. The generation unit can also include instructions and comments from the childcare worker in the summary video. For example, the generation unit analyzes instructions and comments from the childcare worker and includes them in the summary video. The generation unit can also include environmental sounds in a classroom in the summary video. For example, the generation unit analyzes environmental sounds in a classroom and includes them in the summary video. In this way, by including audio data, a more realistic summary video can be provided.

[0096] The generation unit can estimate the user's emotions and adjust the length of the summary video based on the estimated user's emotions. For example, if the user is feeling anxious, the generation unit generates a longer summary video. For example, if the generation unit analyzes the user's facial expression and determines that the user is feeling anxious, it generates a longer summary video. The generation unit can also generate a summary video of normal length if the user is relaxed. For example, if the generation unit analyzes the user's voice and determines that the user is relaxed, it generates a summary video of normal length. The generation unit can also generate a shorter summary video if the user is in a hurry. For example, if the generation unit analyzes the user's behavior and determines that the user is in a hurry, it generates a shorter summary video. In this way, by adjusting the length of the summary video based on the user's emotions, it is possible to provide a more appropriate video.

[0097] The generation unit can emphasize the involvement of the childcare worker by referring to the activity data of the childcare worker. The generation unit, for example, emphasizes the childcare worker playing with the children. For example, the generation unit includes in the summary video the childcare worker playing with the children to emphasize the scene. The generation unit can also emphasize the childcare worker feeding the children. For example, the generation unit includes in the summary video the childcare worker feeding the children to emphasize the scene. The generation unit can also emphasize the childcare worker putting the children to bed. For example, the generation unit includes in the summary video the childcare worker putting the children to bed to emphasize the scene. In this way, by emphasizing the involvement of the childcare worker, it is possible to provide a video that is valuable to parents.

[0098] The generation unit can summarize specific events at the nursery school. For example, the generation unit summarizes a birthday party. For example, the generation unit includes the birthday party in the summary video. The generation unit can also summarize a sports day. For example, the generation unit includes the sports day in the summary video. The generation unit can also summarize seasonal events (e.g., a Christmas party or a Halloween party). For example, the generation unit includes the Christmas party or the Halloween party in the summary video. In this way, by summarizing specific events, valuable videos can be provided to parents.

[0099] The generation unit can customize the summarization method by reflecting feedback from the childcare worker. For example, if the childcare worker requests that the appearance of a particular child be emphasized, the generation unit includes the appearance of the child in the summary video. For example, if the childcare worker requests that the appearance of a particular child be emphasized, the generation unit includes the appearance of the child in the summary video. Furthermore, if the childcare worker requests that the appearance of a particular time period be emphasized, the generation unit can also include the appearance of that time period in the summary video. For example, if the childcare worker requests that the appearance of a particular time period be emphasized, the generation unit can also include the appearance of that time period in the summary video. Furthermore, if the childcare worker requests that the appearance of a particular activity be emphasized, the generation unit can also include the appearance of that activity in the summary video. For example, if the childcare worker requests that the appearance of a particular activity be emphasized, the generation unit can include the appearance of that activity in the summary video. In this way, the summarization method can be optimized by reflecting feedback from the childcare worker.

[0100] The distribution unit can estimate the user's emotions and adjust the distribution timing based on the estimated user's emotions. For example, if the user is feeling anxious, the distribution unit distributes a summary video earlier. For example, if the distribution unit analyzes the user's facial expression and determines that the user is feeling anxious, the distribution unit distributes the summary video earlier. The distribution unit can also maintain the normal distribution timing if the user is relaxed. For example, if the distribution unit analyzes the user's voice and determines that the user is relaxed, the distribution unit maintains the normal distribution timing. The distribution unit can also distribute a summary video containing only important moments earlier if the user is in a hurry. For example, if the distribution unit analyzes the user's behavior and determines that the user is in a hurry, the distribution unit distributes a summary video containing only important moments earlier. In this way, by adjusting the distribution timing based on the user's emotions, it is possible to distribute videos at more appropriate times.

[0101] The distribution unit can select an appropriate distribution method in consideration of the communication environment of each home. For example, the distribution unit distributes a high-quality summarized video to a home with a good communication environment. For example, the distribution unit distributes a high-quality summarized video to a home with a good communication environment. The distribution unit can also distribute a low-quality summarized video to a home with an unstable communication environment. For example, the distribution unit distributes a low-quality summarized video to a home with an unstable communication environment. The distribution unit can also distribute an audio-only summary to a home with a very poor communication environment. For example, the distribution unit distributes an audio-only summary to a home with a very poor communication environment. In this way, smooth video distribution is possible by selecting a distribution method according to the communication environment.

[0102] The distribution unit can analyze the viewing history of the video to improve the content of the next distribution. The distribution unit, for example, reflects content of particular interest based on the viewing history in the next distribution. For example, the distribution unit analyzes the viewing history and reflects content of particular interest in the next distribution. The distribution unit can also reflect content with a long viewing time based on the viewing history in the next distribution. For example, the distribution unit analyzes the viewing history and reflects content with a long viewing time in the next distribution. The distribution unit can also reflect content with a high viewing frequency based on the viewing history in the next distribution. For example, the distribution unit analyzes the viewing history and reflects content with a high viewing frequency in the next distribution. In this way, the content of the next distribution can be optimized by analyzing the viewing history.

[0103] The distribution unit can collect parental feedback and improve the content of the distribution. For example, if a parent requests to see more of a specific content, the distribution unit reflects that content in the next distribution. For example, if a parent requests to see more of a specific content, the distribution unit reflects that content in the next distribution. Furthermore, if a parent requests distribution at a specific time period, the distribution unit can distribute the content at that time period. For example, if a parent requests distribution at a specific time period, the distribution unit distributes the content at that time period. Furthermore, if a parent requests distribution in a specific format (e.g., short clips, long videos), the distribution unit can distribute the content in that format. For example, if a parent requests short clips, the distribution unit distributes the content in that format. In this way, the distribution content can be optimized by collecting parental feedback.

[0104] The distribution unit can estimate the user's emotions and adjust the order of videos to be distributed based on the estimated user's emotions. For example, if the user is feeling anxious, the distribution unit first distributes content that gives a sense of security. For example, if the distribution unit analyzes the user's facial expression and determines that the user is feeling anxious, it first distributes content that gives a sense of security. Furthermore, if the user is relaxed, the distribution unit can distribute videos in a normal order. For example, if the distribution unit analyzes the user's voice and determines that the user is relaxed, it distributes videos in a normal order. Furthermore, if the user is in a hurry, the distribution unit can distribute important moments first. For example, if the distribution unit analyzes the user's behavior and determines that the user is in a hurry, it distributes important moments first. In this way, by adjusting the order of videos based on the user's emotions, it is possible to distribute videos in a more appropriate order.

[0105] The distribution unit can select an appropriate distribution time by taking into consideration schedule information of the guardian. For example, the distribution unit distributes during a time period when the guardian returns home from work. For example, the distribution unit distributes during a time period when the guardian returns home from work. The distribution unit can also distribute during a time period when the guardian spends time with their child. For example, the distribution unit distributes during a time period when the guardian spends time with their child. The distribution unit can also distribute during a time period when the guardian is relaxing. For example, the distribution unit distributes during a time period when the guardian is relaxing. In this way, the optimal distribution time can be selected by taking into consideration schedule information of the guardian.

[0106] The distribution unit can select the optimal distribution format by taking into account the device information of the parent / guardian. For example, if the parent / guardian is using a smartphone, the distribution unit distributes the video in a format optimized for the smartphone. For example, if the parent / guardian is using a smartphone, the distribution unit distributes the video in a format optimized for the smartphone. Furthermore, if the parent / guardian is using a tablet, the distribution unit can distribute the video in a format optimized for the tablet. For example, if the parent / guardian is using a tablet, the distribution unit can distribute the video in a format optimized for the tablet. Furthermore, if the parent / guardian is using a personal computer, the distribution unit can distribute the video in a format optimized for the personal computer. For example, if the parent / guardian is using a personal computer, the distribution unit distributes the video in a format optimized for the personal computer. In this way, by taking into account the device information of the parent / guardian, the video can be distributed in the optimal format.

[0107] The distribution unit can customize the distribution method by reflecting parental feedback. For example, if a parent requests distribution at a specific time period, the distribution unit distributes at that time period. For example, if a parent requests distribution at a specific time period, the distribution unit distributes at that time period. Furthermore, if a parent requests a specific format (e.g., short clips, long videos), the distribution unit can distribute in that format. For example, if a parent requests short clips, the distribution unit distributes in that format. Furthermore, if a parent requests to see more of a specific content, the distribution unit can reflect that content in the next distribution. For example, if a parent requests to see more of a specific content, the distribution unit reflects that content in the next distribution. In this way, the distribution method can be optimized by reflecting parental feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the data collection unit, analysis unit, generation unit, and distribution unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the data collection unit captures images of the children using the camera 42 and microphone 38B of the smart device 14, and the captured images are collected by the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected videos and identifies the children using facial recognition technology. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates a summary video using a generation AI. The distribution unit, realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, distributes the generated summary video to each household. === Hard Collateral 1-2 === Each of the multiple elements, including the data collection unit, analysis unit, generation unit, and distribution unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the data collection unit captures images of the children using the camera 42 and microphone 238 of the smart glasses 214, and the captured images are collected by the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected videos and identifies the children using facial recognition technology. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates a summary video using a generation AI. The distribution unit, realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, distributes the generated summary video to each household. === Hard Collateral 1-3 === Each of the multiple elements including the data collection unit, analysis unit, generation unit, and distribution unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the data collection unit captures images of the children using the camera 42 and microphone 238 of the headset-type terminal 314, and the captured images are collected by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected videos, and identifies the children using facial recognition technology. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a summary video using a generation AI. The distribution unit is realized, for example, by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12, and distributes the generated summary video to each household. === Hard Collateral 1-4 === Each of the multiple elements including the data collection unit, analysis unit, generation unit, and distribution unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the data collection unit captures images of the children using the camera 42 and microphone 238 of the robot 414, and the captured images are collected by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected videos, and identifies the children using facial recognition technology. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a summary video using a generation AI. The distribution unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and distributes the generated summary video to each household.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] The childcare support system can further include a voice recognition unit. The voice recognition unit can analyze conversations between childcare workers and children and extract important information. For example, the voice recognition unit can analyze scenes in which childcare workers are giving instructions to children and save the content of those instructions as text data. The voice recognition unit can also analyze children's conversations and detect specific keywords (e.g., words asking for help). Furthermore, the voice recognition unit can improve the accuracy of the analysis based on feedback from childcare workers. This makes it possible to use voice data to understand in detail the instructions from childcare workers and the content of children's conversations.

[0110] The analysis unit can further include a child's movement analysis function. The movement analysis function can analyze a child's movements in real time and detect abnormal movements. For example, the analysis unit can detect abnormalities when a child falls or repeats a specific movement. The analysis unit can also learn a child's movement patterns and issue an alert if the movement deviates from normal movements. Furthermore, the analysis unit can improve the movement analysis algorithm based on feedback from childcare workers. This ensures the safety of children and enables early detection of abnormal movements.

[0111] The generation unit can further include an emotion analysis function. The emotion analysis function can analyze a child's facial expressions and tone of voice to estimate their emotions. For example, the generation unit can detect scenes in which a child is laughing and include those scenes in the summary video. The generation unit can also detect scenes in which a child is crying and include those scenes in the summary video. Furthermore, the generation unit can display the child's emotional changes in chronological order. This allows for a detailed understanding of the child's emotions and provides parents with a more emotionally rich summary video.

[0112] The distribution unit can further customize the content of distribution based on the user's viewing history. For example, the distribution unit can analyze the content of videos that the user has viewed in the past and optimize the content of the next distribution. For example, if the user frequently watches videos of a particular child, the distribution unit can prioritize the distribution of videos of that child. Also, if the user watches videos during a particular time period, the distribution unit can also adjust the distribution to suit that time period. Furthermore, the distribution unit can improve the distribution method based on user feedback. In this way, by utilizing the user's viewing history, more personalized distribution is possible.

[0113] The data collection unit may further include an environmental sensor. The environmental sensor may collect environmental information such as temperature, humidity, and illuminance in the classroom and provide it to the analysis unit. For example, the data collection unit may issue an alert if the temperature in the classroom is too high. The data collection unit may also automatically activate a humidifier if the humidity is too low. Furthermore, the data collection unit may adjust the lighting if the illuminance is inappropriate. This allows the classroom environment to be optimized and provides a comfortable environment for children.

[0114] The generation unit can further generate a summary video based on the activity data of the childcare worker. For example, scenes in which the childcare worker is providing special care to a particular child can be included in the summary video. Scenes in which the childcare worker is playing with the children can also be emphasized. Furthermore, scenes in which the childcare worker is instructing the children can also be included in the summary video. This allows the activities of the childcare worker to be recorded in detail, and a summary video that emphasizes the involvement of the childcare worker can be provided to parents.

[0115] The data collection unit can further collect health data of the child. For example, the data collection unit can monitor the child's body temperature and heart rate and issue an alert if an abnormality is detected. The data collection unit can also analyze the child's sleep patterns and provide an appropriate sleeping environment. Furthermore, the data collection unit can record the child's diet and manage nutritional balance. This allows for a detailed understanding of the child's health condition and the provision of appropriate care.

[0116] The analysis unit can further analyze the child's learning data. For example, the analysis unit can analyze what kind of play or activity the child is interested in and provide the results to the childcare worker. The analysis unit can also monitor the child's learning progress and suggest appropriate teaching methods. Furthermore, the analysis unit can create an individual learning plan based on the child's learning data. This allows for a detailed understanding of the child's learning situation and enables instruction to be provided according to individual needs.

[0117] The distribution unit can further estimate the user's emotions and customize the content of the distribution based on the estimated emotions. For example, if the user is feeling stressed, videos with relaxing content can be preferentially distributed. Also, if the user is happy, videos with content that will further enhance that emotion can be distributed. Furthermore, if the user is sad, videos with comforting content can be distributed. This makes it possible to provide a more personalized experience by providing content that matches the user's emotions.

[0118] The analysis unit can further estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is feeling anxious, detailed analysis results can be displayed. If the user is relaxed, concise analysis results can be displayed. Furthermore, if the user is in a hurry, analysis results that focus on the main points can be displayed. This allows for more appropriate information to be provided by providing a display method of the analysis results that suits the user's emotions.

[0119] The processing flow of the second embodiment will be briefly explained below.

[0120] Step 1: The data collection unit collects video footage taken by fixed cameras. For example, fixed or PTZ cameras installed in classrooms can be used to continuously capture the children's daily activities. It is also possible to set the resolution and frame rate of the collected video. Step 2: The analysis unit analyzes the videos collected by the data collection unit and identifies children using facial recognition technology. For example, facial recognition technology using deep learning and feature point extraction technology can be used to identify children in the videos. Step 3: The generator generates a summary video for each child identified by the analyzer. For example, a deep learning-based generative model or rule-based generation method can be used to extract footage of a specific child playing or eating, and then compile it into a short summary video. Step 4: The distribution unit distributes the summary video generated by the generation unit to each household. For example, the generated summary video can be distributed to each household via email or an app. The timing and method of distribution can also be set.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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 identification processing unit 290 using these models.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification 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 the same process as the identification processing unit 290 using these models.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] [Explanation of symbols]

[0193] 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 data collection unit that collects videos taken by fixed cameras; an analysis unit that analyzes the video collected by the data collection unit and identifies the child using face recognition technology; a generation unit that generates a summary video for each child identified by the analysis unit; a distribution unit that distributes the summary video generated by the generation unit to each home. A system characterized by:

2. The analysis unit Identifying children using facial recognition technology 2. The system of claim 1.

3. The generation unit Generate summary videos for each child using generative AI 2. The system of claim 1.

4. The distribution unit The generated summary video is distributed to each household.

2. The system of claim 1.

5. The data collection unit Collecting videos taken by fixed cameras 2. The system of claim 1.

6. The generation unit Extract specific scenes of children playing (e.g., playing with blocks, playing in the sandbox) or eating (e.g., lunch, snacks) and compile them into short summary videos.

2. The system of claim 1.

7. The data collection unit Estimate user emotions and adjust video collection timing based on the estimated user emotions.

2. The system of claim 1.

8. The data collection unit Select the type of video to collect based on specific events 2. The system of claim 1.

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