System

Abstract

An object of a system according to an embodiment is to make it possible to grasp the daily state of a child left in a nursery school or a kindergarten in detail.SOLUTION: A system includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects a video of a camera installed in a nursery school. The analysis unit analyzes the video collected by the collection unit. The generation part creates a diary on the basis of the result analyzed by the analysis part. The provision unit provides the diary created by the generation unit to the protector.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to obtain detailed information about the daily activities of children in daycare centers and kindergartens.

[0005] The system according to the embodiment aims to provide detailed information on the daily activities of children left at a nursery school or kindergarten. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects video footage from cameras installed in the nursery school. The analysis unit analyzes the video footage collected by the collection unit. The generation unit creates a diary based on the results of the analysis by the analysis unit. The provision unit provides the diary created by the generation unit to parents. [Effects of the Invention]

[0007] The system according to the embodiment can provide detailed information on the daily life of children left at daycare centers or kindergartens. [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 allows parents of children at daycare centers or kindergartens to learn in detail about their children's daily activities. This system collects video footage from cameras installed in the daycare center, and a generation AI analyzes the footage to recognize the children's behavior, facial expressions, and play activities to understand how each child spent their time. Based on the analysis results, the generation AI then creates a diary with videos, photos, and text. This diary is then provided to parents, who can view it on their smartphones or computers. For example, a camera installed in the daycare center captures video footage of the children, which is then input into the generation AI. The generation AI then recognizes the children's behavior, facial expressions, and play activities to understand how each child spent their time. Based on the analysis results, the generation AI then creates a diary with videos, photos, and text. For example, it records videos and photos of children playing with blocks or with friends, and summarizes the day's events in text. This diary is then shared with parents, who can view it on their smartphones or computers. This allows parents to learn in detail about their children's daily activities, providing peace of mind. It also reduces the burden on childcare workers, allowing them to spend more time interacting directly with children. This allows parents who leave their children at daycare centers and kindergartens to know in detail how their children are doing every day, giving them peace of mind. It also reduces the burden on childcare workers, allowing them to spend more time interacting directly with children.

[0029] A nursery school information provision system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects video footage from cameras installed in the nursery school. For example, the collection unit can collect video footage from multiple cameras within the nursery school. The collection unit can also set the quality, format, and collection range of the video footage. For example, the collection unit can adjust the camera resolution and frame rate to collect optimal video footage. The collection unit can also set the collection frequency and collection time period. For example, the collection unit can be configured to collect video footage during a specific time period. The analysis unit analyzes the video footage collected by the collection unit. The analysis unit recognizes children's behavior, facial expressions, and play content using, for example, a generation AI. The generation AI receives video footage as input, analyzes the children's behavior and facial expressions, and outputs the results. For example, the generation AI can recognize children playing with blocks or playing with friends. The generation AI can also analyze children's facial expressions and recognize emotions such as smiling and crying. The generation unit creates a diary based on the results of the analysis by the analysis unit. The generation unit creates a diary using, for example, a generation AI with videos, photos, and text. Based on the analysis results, the generation AI records the children's behavior, expressions, and play content, and summarizes the day's events in text. For example, the generation AI records videos and photos of children playing with blocks. The generation AI can also write text about children reading picture books. The providing unit provides the diary created by the generation unit to parents. The providing unit can provide the diary via, for example, a smartphone or a PC. The providing unit can send the diary to the parents' device so that the parents can check it. For example, the providing unit can send the diary by email. The providing unit can also store the diary on the cloud so that parents can access it at any time. As a result, the nursery school information providing system according to the embodiment can analyze video footage from cameras installed in nurseries, record children's daily activities in detail, and provide the diary to parents.

[0030] The nursery school information provision system further includes a security unit that implements specific security measures for handling video data and sharing it with parents. The security unit implements appropriate security measures for handling video data and sharing it with parents. The security unit protects the video data using, for example, data encryption technology. For example, the security unit can encrypt the video data in real time when collecting it. The security unit can also encrypt the video data when storing it, thereby ensuring data security. The security unit can also strengthen access control when sharing data with parents. For example, the security unit can perform password authentication when parents access the diary. The security unit can also introduce two-factor authentication and require an additional authentication method when parents access the diary. In this way, the security unit can ensure data security by implementing appropriate security measures for handling video data and sharing it with parents.

[0031] The nursery school information provision system further includes an algorithm section that describes in detail the algorithms used by the generation AI. The algorithm section specifically describes the algorithms used by the generation AI. The algorithm section can use, for example, machine learning algorithms or natural language processing algorithms. For example, the algorithm section describes in detail the algorithms used by the generation AI when analyzing video. For example, the generation AI can analyze video using deep learning technology and recognize children's behavior and facial expressions. The algorithm section also describes the algorithms used by the generation AI when creating a diary. For example, the generation AI can generate text using natural language processing technology and describe children's behavior and events in writing. In this way, the algorithm section can improve the accuracy and reliability of the system by specifically describing the algorithms used by the generation AI.

[0032] The collection unit can perform filtering to focus on a specific child when collecting video. For example, the collection unit can automatically focus when a specific child enters the camera's field of view. The collection unit can also prioritize collecting video of a child designated by a parent. Furthermore, the collection unit can dynamically adjust the camera focus according to the child's movements. In this way, by focusing on a specific child, video that is of interest to the parent can be prioritized and collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0033] When collecting video, the collection unit can set a collection range based on a specific area within the nursery school. For example, the collection unit can prioritize collecting activities in the playground. The collection unit can also periodically collect footage from the dining area. Furthermore, the collection unit can focus on collecting footage from areas within the nursery school where specific events are held. In this way, by adjusting the collection range based on a specific area within the nursery school, it is possible to prioritize the collection of footage from important areas. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0034] When collecting video, the collection unit can adjust the collection frequency based on the child's activity level. For example, if the child is active, the collection unit can collect video more frequently. Alternatively, if the child is quiet, the collection unit can reduce the collection frequency. Furthermore, the collection unit can dynamically adjust the collection frequency when the child's activity changes. This allows collection without missing important moments by changing the collection frequency according to the child's activity level. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without using AI.

[0035] When collecting video, the collection unit can set a collection method based on the weather and time of day at the nursery school. For example, the collection unit can prioritize collecting indoor activities when it is raining. The collection unit can also prioritize collecting outdoor activities when it is sunny. Furthermore, the collection unit can adjust the area and activity content to be collected depending on the time of day. In this way, by adjusting the collection method based on the weather and time of day at the nursery school, appropriate video can be collected. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0036] The collection unit can measure the degree of involvement of the childcare worker by collecting the video including the activities of the childcare worker. For example, the collection unit collects footage of the childcare worker playing with children. The collection unit can also collect footage of the childcare worker instructing children. Furthermore, the collection unit can also collect footage of the childcare worker ensuring the safety of children. In this way, by collecting footage including the activities of the childcare worker, the degree of involvement of the childcare worker can be evaluated. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0037] When collecting video, the collection unit can prioritize collecting specific events or activities based on a parent's request. For example, the collection unit prioritizes collecting video of an athletic meet requested by a parent. The collection unit can also prioritize collecting video of a birthday party requested by a parent. The collection unit can also prioritize collecting video of a specific play activity requested by a parent. This makes it possible to meet the needs of parents by prioritizing the collection of specific events or activities based on parental requests. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without using AI.

[0038] During analysis, the analysis unit can analyze a child's behavioral patterns over the long term and identify abnormal behavior. For example, the analysis unit can analyze a child's behavioral patterns over several weeks to detect abnormal behavior. The analysis unit can also analyze a child's behavioral patterns on a monthly basis to detect abnormal behavior. Furthermore, the analysis unit can analyze a child's behavioral patterns on a yearly basis to detect abnormal behavior. In this way, by analyzing a child's behavioral patterns over the long term, abnormal behavior can be detected early. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0039] During analysis, the analysis unit can read emotions from the child's facial expressions and reflect them in the diary. For example, the analysis unit can detect a child's smiling face and write "having fun" in the diary. The analysis unit can also detect a child's crying face and write "sad" in the diary. Furthermore, the analysis unit can detect a child's surprised face and write "surprised" in the diary. In this way, by reading emotions from the child's facial expressions, more detailed information can be reflected in the diary. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0040] During the analysis, the analysis unit can classify the content of the child's play in detail and measure the time spent on each activity. For example, the analysis unit records the time spent by the child playing with blocks. The analysis unit can also record the time spent by the child reading picture books. Furthermore, the analysis unit can record the time spent by the child playing outside. In this way, by classifying the content of the child's play in detail and recording the time spent on each activity, specific information can be provided in the diary. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0041] During the analysis, the analysis unit can analyze the environmental sounds of the nursery school and analyze factors that affect children's activities. The analysis unit can, for example, identify factors that affect children's concentration from the environmental sounds. The analysis unit can also identify factors that affect children's relaxation levels from the environmental sounds. Furthermore, the analysis unit can also identify factors that affect children's activity levels from the environmental sounds. In this way, by analyzing the environmental sounds of the nursery school, factors that affect children's activities can be identified. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI.

[0042] The analysis unit can monitor the child's health condition during analysis. For example, the analysis unit can analyze the frequency of the child's coughing to monitor the health condition. The analysis unit can also analyze the frequency of the child's sneezing to monitor the health condition. Furthermore, the analysis unit can analyze changes in the child's body temperature to monitor the health condition. In this way, by monitoring the child's health condition, abnormalities can be detected early. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0043] The analysis unit can measure the child's social interactions during the analysis. For example, the analysis unit can analyze the time the child spends playing with friends and evaluate the social interactions. The analysis unit can also analyze how the child cooperates with friends and evaluate the social interactions. Furthermore, the analysis unit can analyze how the child competes with friends and evaluate the social interactions. In this way, by evaluating the child's social interactions, it is possible to understand the child's social development. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0044] The generation unit can automatically select highlights of the child's activities when generating the diary. For example, the generation unit selects moments when the child is particularly enjoying themselves as highlights. The generation unit can also select moments when the child acquires a new skill as highlights. Furthermore, the generation unit can also select moments when the child cooperates with a friend as highlights. In this way, by automatically selecting highlights of the child's activities, a diary that emphasizes important moments can be provided. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0045] When generating the diary, the generation unit can track the child's growth record over the long term and compare it with past data. The generation unit, for example, tracks the child's growth record on a monthly basis and compares it with past data. The generation unit can also track the child's growth record on a yearly basis and compare it with past data. Furthermore, the generation unit can track the child's growth record on a weekly basis and compare it with past data. In this way, by tracking the child's growth record over the long term and comparing it with past data, the progress of growth can be understood. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0046] The generation unit can automatically insert comments from the childcare worker when generating the diary. For example, the generation unit allows the childcare worker to add comments about the child's activities. The generation unit can also allow the childcare worker to add comments about the child's growth. Furthermore, the generation unit can also allow the childcare worker to add comments about the child's social interactions. In this way, by automatically adding the childcare worker's comments, it is possible to add a professional perspective to the diary. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0047] The generation unit can insert educational advice related to the child's activities when generating the diary. For example, if a child is playing with blocks, the generation unit can add advice to improve spatial awareness. Furthermore, if a child is reading a picture book, the generation unit can add advice to improve reading comprehension. Furthermore, if a child is playing outside, the generation unit can add advice to improve motor skills. In this way, by adding educational advice related to the child's activities, it is possible to provide useful information to parents. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0048] When generating the diary, the generation unit can suggest recommended play and learning activities at home based on the child's activities. For example, if the child is playing with blocks, the generation unit can suggest ideas for block play at home. Furthermore, if the child is reading a picture book, the generation unit can also suggest ideas for reading activities at home. Furthermore, if the child is playing outside, the generation unit can also suggest ideas for physical play at home. This makes it possible to support childcare at home by suggesting play and learning activities at home based on the child's activities. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0049] When generating the diary, the generation unit can automatically select photos and videos related to the child's activities. For example, the generation unit automatically selects photos of the child playing with blocks. The generation unit can also automatically select videos of the child reading picture books. Furthermore, the generation unit can automatically select photos and videos of the child playing outside. In this way, by automatically selecting photos and videos related to the child's activities, visual information can be added to the diary. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0050] When providing a diary, the providing unit can select the optimal timing for providing the diary by referring to the guardian's past browsing history. For example, the providing unit provides the diary during a time period when the guardian frequently browsed the diary in the past. Furthermore, if the guardian tends to browse the diary on a specific day of the week, the providing unit can also provide the diary on that day. Furthermore, the providing unit can analyze the guardian's past browsing history and select the optimal timing for providing the diary. In this way, the diary can be provided at the optimal timing by referring to the guardian's past browsing history. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0051] When providing the diary, the providing unit can provide it in an optimal format taking into account the device information of the guardian. For example, if the guardian uses a smartphone, the providing unit can provide the diary in a format optimized for the smartphone. Furthermore, if the guardian uses a tablet, the providing unit can also provide the diary in a format optimized for the tablet. Furthermore, if the guardian uses a personal computer, the providing unit can also provide the diary in a format optimized for the personal computer. In this way, the diary can be provided in an optimal format by taking into account the device information of the guardian. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0052] The providing unit can improve the providing method by reflecting the parent's feedback when providing the diary. The providing unit can, for example, improve the content of the diary based on the parent's feedback. The providing unit can also improve the timing of providing the diary based on the parent's feedback. Furthermore, the providing unit can also improve the format of the diary based on the parent's feedback. In this way, the providing method can be continuously improved by reflecting the parent's feedback. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or without using AI.

[0053] When providing the diary, the providing unit can deliver a diary in multiple languages ​​according to the language setting of the guardian. The providing unit automatically sets the language of the diary based on, for example, the language setting of the guardian's device. The providing unit can also provide a language switching function if the guardian uses multiple languages. Furthermore, if the guardian selects a specific language, the providing unit can also provide the diary in that language. In this way, by providing a diary in multiple languages ​​according to the guardian's language setting, information can be provided across language barriers. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0054] The providing unit can select the optimal timing for providing the diary based on the guardian's schedule when providing the diary. The providing unit, for example, references the guardian's calendar information to select the optimal timing for providing the diary. The providing unit can also adjust the timing for providing the diary based on the guardian's work schedule. Furthermore, the providing unit can also adjust the timing for providing the diary based on the guardian's home schedule. In this way, by selecting the optimal timing for providing the diary based on the guardian's schedule, the diary can be provided at a time that is convenient for the guardian. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0055] The providing unit can additionally deliver related information based on the guardian's areas of interest when providing the diary. For example, the providing unit can additionally provide educational information that the guardian is interested in to the diary. The providing unit can also additionally provide health information that the guardian is interested in to the diary. Furthermore, the providing unit can also additionally provide event information that the guardian is interested in to the diary. In this way, by providing additional related information based on the guardian's areas of interest, it is possible to provide useful information to the guardian. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0056] The security unit can encrypt data when handling video data. For example, the security unit performs encryption in real time when collecting video data. The security unit can also perform encryption when storing video data. Furthermore, the security unit can also perform encryption when sharing video data. In this way, by encrypting video data, the security of the data can be ensured. Some or all of the above-mentioned processing in the security unit may be performed using AI, for example, or may be performed without using AI.

[0057] The security unit can strengthen access control when sharing data with parents. For example, the security unit performs password authentication when a parent accesses a diary. The security unit can also implement two-factor authentication to require an additional authentication method when a parent accesses a diary. Furthermore, the security unit can set IP address restrictions and allow access only from specific IP addresses. This strengthens access control and improves security when sharing data with parents. Some or all of the above-described processing in the security unit may be performed using AI, for example, or may be performed without using AI.

[0058] The security unit can periodically back up data when handling video data. For example, the security unit backs up video data daily. The security unit can also back up video data weekly. Furthermore, the security unit can also back up video data monthly. By periodically backing up data, data loss can be prevented. Some or all of the above-described processing in the security unit may be performed using AI, for example, or may be performed without using AI.

[0059] The security unit can implement two-factor authentication when sharing data with a guardian. For example, the security unit performs SMS authentication when a guardian accesses a diary. The security unit can also perform email authentication when a guardian accesses a diary. Furthermore, the security unit can also use an authentication app when a guardian accesses a diary. By introducing two-factor authentication, security can be strengthened when sharing data with a guardian. Some or all of the above-described processing in the security unit can be performed using AI, for example, or without using AI.

[0060] The algorithm unit can perform optimization by referring to past data when learning the algorithm. The algorithm unit can, for example, improve the accuracy of the algorithm based on past data. The algorithm unit can also optimize the performance of the algorithm based on past data. Furthermore, the algorithm unit can improve the learning speed of the algorithm based on past data. In this way, by performing optimization by referring to past data, the accuracy and performance of the algorithm can be improved. Some or all of the above-mentioned processing in the algorithm unit can be performed, for example, using AI or without using AI.

[0061] The algorithm unit can improve the accuracy of the algorithm by reflecting parental feedback when the algorithm is executed. For example, the algorithm unit can improve the accuracy of the algorithm based on parental feedback. The algorithm unit can also optimize the performance of the algorithm based on parental feedback. Furthermore, the algorithm unit can improve the learning speed of the algorithm based on parental feedback. In this way, the accuracy of the algorithm can be continuously improved by reflecting parental feedback. Some or all of the above-mentioned processing in the algorithm unit may be performed using AI, for example, or may be performed without using AI.

[0062] The algorithm unit can improve accuracy by integrating information from different data sources when learning the algorithm. For example, the algorithm unit can improve the accuracy of the algorithm by integrating not only camera footage from the nursery school but also observation records from nursery teachers. The algorithm unit can also improve the accuracy of the algorithm by integrating feedback information from parents. Furthermore, the algorithm unit can improve the accuracy of the algorithm by integrating data from other nursery schools. In this way, the accuracy of the algorithm can be improved by integrating information from different data sources. Some or all of the above-mentioned processing in the algorithm unit may be performed using AI, for example, or may be performed without using AI.

[0063] The algorithm unit can adjust the algorithm based on the parent's areas of interest when executing the algorithm. For example, if the parent is interested in education, the algorithm unit can prioritize providing information related to education. Furthermore, if the parent is interested in health, the algorithm unit can prioritize providing information related to health. Furthermore, if the parent is interested in social interactions, the algorithm unit can prioritize providing information about playing with friends. In this way, by customizing based on the parent's areas of interest, it is possible to provide useful information to the parent. Some or all of the above-mentioned processing in the algorithm unit may be performed using AI, for example, or may be performed without using AI.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The nursery school information providing system may further include a message sending unit that enables parents to send specific messages to their children. The message sending unit allows parents to send messages of encouragement or support to their children via a smartphone or computer, for example. The message sending unit also allows parents to send messages for specific events or activities. Furthermore, the message sending unit also allows parents to send real-time feedback on their children's behavior and achievements. This allows parents to strengthen communication with their children and show their interest and affection for them.

[0066] The nursery school information system can further include an interactive function that allows parents to respond to their children's activities in real time. The interactive function allows, for example, parents to send comments in real time while watching their children's activities. The interactive function also allows parents to send reactions to their children's activities. Furthermore, the interactive function also allows parents to send questions about their children's activities and nursery teachers to answer those questions. This allows parents to be actively involved in their children's activities and deepen communication with their children.

[0067] The nursery school information providing system may further include an evaluation unit that allows parents to evaluate their children's activities. The evaluation unit, for example, allows parents to give star ratings to their children's activities. The evaluation unit also allows parents to add comments to their children's activities. The evaluation unit also allows parents to provide feedback to their children's activities. This allows parents to evaluate their children's activities and provide feedback to nursery teachers.

[0068] The nursery school information providing system may further include an advice unit that provides advice to parents regarding their child's activities. For example, the advice unit may provide educational advice to parents regarding their child's activities. The advice unit may also provide health advice to parents regarding their child's activities. Furthermore, the advice unit may also provide social interaction advice to parents regarding their child's activities. This allows parents to provide useful advice to their child's activities and support their child's growth.

[0069] The nursery school information providing system may further include a goal setting unit that allows parents to set goals for their children's activities. The goal setting unit, for example, allows parents to set short-term goals for their children's activities. The goal setting unit also allows parents to set medium-term goals for their children's activities. The goal setting unit also allows parents to set long-term goals for their children's activities. This allows parents to set specific goals for their children's growth and support their children's growth.

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

[0071] Step 1: The collection unit collects video footage from cameras installed in the nursery school. The collection unit can collect video footage from multiple cameras within the nursery school, and can set the video quality, format, and collection range. For example, it can adjust the camera resolution and frame rate to collect optimal video footage. It is also possible to set the collection frequency and collection time period, and it can be set to collect video footage during specific time periods. Step 2: The analysis unit analyzes the video collected by the collection unit. The analysis unit uses the generation AI to recognize the children's behavior, facial expressions, and content of play. The generation AI receives the video as input, analyzes the children's behavior and facial expressions, and outputs the results. For example, it can recognize how children are playing with blocks or how they are playing with friends. It can also analyze the children's facial expressions and recognize emotions such as smiling or crying. Step 3: The generation unit creates a diary based on the results of the analysis by the analysis unit. The generation unit uses generation AI to create a diary with videos, photos, and text. Based on the analysis results, the generation AI records the children's actions, expressions, and play content, and summarizes the events of the day in text. For example, it can record video and take photos of a child playing with blocks. It can also record text of a child reading a picture book. Step 4: The providing unit provides the diary created by the generating unit to the guardian. The providing unit can provide the diary via a smartphone or a PC. The providing unit sends the diary to the guardian's device so that the guardian can check the diary. For example, the diary can be sent by email. The diary can also be stored on the cloud so that the guardian can access it at any time.

[0072] (Example 2) A system according to an embodiment of the present invention allows parents of children at daycare centers or kindergartens to learn in detail about their children's daily activities. This system collects video footage from cameras installed in the daycare center, and a generation AI analyzes the footage to recognize the children's behavior, facial expressions, and play activities to understand how each child spent their time. Based on the analysis results, the generation AI then creates a diary with videos, photos, and text. This diary is then provided to parents, who can view it on their smartphones or computers. For example, a camera installed in the daycare center captures video footage of the children, which is then input into the generation AI. The generation AI then recognizes the children's behavior, facial expressions, and play activities to understand how each child spent their time. Based on the analysis results, the generation AI then creates a diary with videos, photos, and text. For example, it records videos and photos of children playing with blocks or with friends, and summarizes the day's events in text. This diary is then shared with parents, who can view it on their smartphones or computers. This allows parents to learn in detail about their children's daily activities, providing peace of mind. It also reduces the burden on childcare workers, allowing them to spend more time interacting directly with children. This allows parents who leave their children at daycare centers and kindergartens to know in detail how their children are doing every day, giving them peace of mind. It also reduces the burden on childcare workers, allowing them to spend more time interacting directly with children.

[0073] A nursery school information provision system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects video footage from cameras installed in the nursery school. For example, the collection unit can collect video footage from multiple cameras within the nursery school. The collection unit can also set the quality, format, and collection range of the video footage. For example, the collection unit can adjust the camera resolution and frame rate to collect optimal video footage. The collection unit can also set the collection frequency and collection time period. For example, the collection unit can be configured to collect video footage during a specific time period. The analysis unit analyzes the video footage collected by the collection unit. The analysis unit recognizes children's behavior, facial expressions, and play content using, for example, a generation AI. The generation AI receives video footage as input, analyzes the children's behavior and facial expressions, and outputs the results. For example, the generation AI can recognize children playing with blocks or playing with friends. The generation AI can also analyze children's facial expressions and recognize emotions such as smiling and crying. The generation unit creates a diary based on the results of the analysis by the analysis unit. The generation unit creates a diary using, for example, a generation AI with videos, photos, and text. Based on the analysis results, the generation AI records the children's behavior, expressions, and play content, and summarizes the day's events in text. For example, the generation AI records videos and photos of children playing with blocks. The generation AI can also write text about children reading picture books. The providing unit provides the diary created by the generation unit to parents. The providing unit can provide the diary via, for example, a smartphone or a PC. The providing unit can send the diary to the parents' device so that the parents can check it. For example, the providing unit can send the diary by email. The providing unit can also store the diary on the cloud so that parents can access it at any time. As a result, the nursery school information providing system according to the embodiment can analyze video footage from cameras installed in nurseries, record children's daily activities in detail, and provide the diary to parents.

[0074] The nursery school information provision system further includes a security unit that implements specific security measures for handling video data and sharing it with parents. The security unit implements appropriate security measures for handling video data and sharing it with parents. The security unit protects the video data using, for example, data encryption technology. For example, the security unit can encrypt the video data in real time when collecting it. The security unit can also encrypt the video data when storing it, thereby ensuring data security. The security unit can also strengthen access control when sharing data with parents. For example, the security unit can perform password authentication when parents access the diary. The security unit can also introduce two-factor authentication and require an additional authentication method when parents access the diary. In this way, the security unit can ensure data security by implementing appropriate security measures for handling video data and sharing it with parents.

[0075] The nursery school information provision system further includes an algorithm section that describes in detail the algorithms used by the generation AI. The algorithm section specifically describes the algorithms used by the generation AI. The algorithm section can use, for example, machine learning algorithms or natural language processing algorithms. For example, the algorithm section describes in detail the algorithms used by the generation AI when analyzing video. For example, the generation AI can analyze video using deep learning technology and recognize children's behavior and facial expressions. The algorithm section also describes the algorithms used by the generation AI when creating a diary. For example, the generation AI can generate text using natural language processing technology and describe children's behavior and events in writing. In this way, the algorithm section can improve the accuracy and reliability of the system by specifically describing the algorithms used by the generation AI.

[0076] The 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 anxious, the collection unit can collect videos frequently to provide a sense of security. Alternatively, if the user is relaxed, the collection unit can collect videos at a normal collection frequency. Furthermore, if the user is busy, the collection unit can collect and notify only important events. This allows for more appropriate video collection by adjusting the timing of video collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0077] The collection unit can perform filtering to focus on a specific child when collecting video. For example, the collection unit can automatically focus when a specific child enters the camera's field of view. The collection unit can also prioritize collecting video of a child designated by a parent. Furthermore, the collection unit can dynamically adjust the camera focus according to the child's movements. In this way, by focusing on a specific child, video that is of interest to the parent can be prioritized and collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0078] When collecting video, the collection unit can set a collection range based on a specific area within the nursery school. For example, the collection unit can prioritize collecting activities in the playground. The collection unit can also periodically collect footage from the dining area. Furthermore, the collection unit can focus on collecting footage from areas within the nursery school where specific events are held. In this way, by adjusting the collection range based on a specific area within the nursery school, it is possible to prioritize the collection of footage from important areas. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0079] When collecting video, the collection unit can adjust the collection frequency based on the child's activity level. For example, if the child is active, the collection unit can collect video more frequently. Alternatively, if the child is quiet, the collection unit can reduce the collection frequency. Furthermore, the collection unit can dynamically adjust the collection frequency when the child's activity changes. This allows collection without missing important moments by changing the collection frequency according to the child's activity level. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without using AI.

[0080] The collection unit can estimate the user's emotions and determine the priority of videos to be collected based on the estimated user emotions. For example, if the user is feeling anxious, the collection unit can prioritize collecting videos of children. Also, if the user is relaxed, the collection unit can collect videos with a normal priority. Furthermore, if the user is interested in a particular event, the collection unit can prioritize collecting videos of that event. In this way, by determining the priority of videos based on the user's emotions, important videos can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0081] When collecting video, the collection unit can set a collection method based on the weather and time of day at the nursery school. For example, the collection unit can prioritize collecting indoor activities when it is raining. The collection unit can also prioritize collecting outdoor activities when it is sunny. Furthermore, the collection unit can adjust the area and activity content to be collected depending on the time of day. In this way, by adjusting the collection method based on the weather and time of day at the nursery school, appropriate video can be collected. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0082] The collection unit can measure the degree of involvement of the childcare worker by collecting the video including the activities of the childcare worker. For example, the collection unit collects footage of the childcare worker playing with children. The collection unit can also collect footage of the childcare worker instructing children. Furthermore, the collection unit can also collect footage of the childcare worker ensuring the safety of children. In this way, by collecting footage including the activities of the childcare worker, the degree of involvement of the childcare worker can be evaluated. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0083] When collecting video, the collection unit can prioritize collecting specific events or activities based on a parent's request. For example, the collection unit prioritizes collecting video of an athletic meet requested by a parent. The collection unit can also prioritize collecting video of a birthday party requested by a parent. The collection unit can also prioritize collecting video of a specific play activity requested by a parent. This makes it possible to meet the needs of parents by prioritizing the collection of specific events or activities based on parental requests. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without using AI.

[0084] The analysis unit can estimate the user's emotions and adjust the way the analysis results are presented based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can provide detailed analysis results. Furthermore, if the user is relaxed, the analysis unit can provide concise analysis results. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. By adjusting the way the analysis results are presented based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0085] During analysis, the analysis unit can analyze a child's behavioral patterns over the long term and identify abnormal behavior. For example, the analysis unit can analyze a child's behavioral patterns over several weeks to detect abnormal behavior. The analysis unit can also analyze a child's behavioral patterns on a monthly basis to detect abnormal behavior. Furthermore, the analysis unit can analyze a child's behavioral patterns on a yearly basis to detect abnormal behavior. In this way, by analyzing a child's behavioral patterns over the long term, abnormal behavior can be detected early. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0086] During analysis, the analysis unit can read emotions from the child's facial expressions and reflect them in the diary. For example, the analysis unit can detect a child's smiling face and write "having fun" in the diary. The analysis unit can also detect a child's crying face and write "sad" in the diary. Furthermore, the analysis unit can detect a child's surprised face and write "surprised" in the diary. In this way, by reading emotions from the child's facial expressions, more detailed information can be reflected in the diary. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0087] During the analysis, the analysis unit can classify the content of the child's play in detail and measure the time spent on each activity. For example, the analysis unit records the time spent by the child playing with blocks. The analysis unit can also record the time spent by the child reading picture books. Furthermore, the analysis unit can record the time spent by the child playing outside. In this way, by classifying the content of the child's play in detail and recording the time spent on each activity, specific information can be provided in the diary. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0088] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can prioritize providing important analysis results. Also, if the user is relaxed, the analysis unit can provide analysis results with normal priority. Furthermore, if the user is interested in a particular event, the analysis unit can prioritize providing analysis results related to that event. In this way, by prioritizing the analysis results based on the user's emotions, important analysis results can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0089] During the analysis, the analysis unit can analyze the environmental sounds of the nursery school and analyze factors that affect children's activities. The analysis unit can, for example, identify factors that affect children's concentration from the environmental sounds. The analysis unit can also identify factors that affect children's relaxation levels from the environmental sounds. Furthermore, the analysis unit can also identify factors that affect children's activity levels from the environmental sounds. In this way, by analyzing the environmental sounds of the nursery school, factors that affect children's activities can be identified. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI.

[0090] The analysis unit can monitor the child's health condition during analysis. For example, the analysis unit can analyze the frequency of the child's coughing to monitor the health condition. The analysis unit can also analyze the frequency of the child's sneezing to monitor the health condition. Furthermore, the analysis unit can analyze changes in the child's body temperature to monitor the health condition. In this way, by monitoring the child's health condition, abnormalities can be detected early. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0091] The analysis unit can measure the child's social interactions during the analysis. For example, the analysis unit can analyze the time the child spends playing with friends and evaluate the social interactions. The analysis unit can also analyze how the child cooperates with friends and evaluate the social interactions. Furthermore, the analysis unit can analyze how the child competes with friends and evaluate the social interactions. In this way, by evaluating the child's social interactions, it is possible to understand the child's social development. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0092] The generation unit can estimate the user's emotions and adjust the way the diary is expressed based on the estimated user emotions. For example, if the user is feeling anxious, the generation unit can generate a detailed diary. Furthermore, if the user is relaxed, the generation unit can also generate a concise diary. Furthermore, if the user is excited, the generation unit can also generate a visually appealing diary. By adjusting the way the diary is expressed based on the user's emotions, it is possible to provide a diary that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0093] The generation unit can automatically select highlights of the child's activities when generating the diary. For example, the generation unit selects moments when the child is particularly enjoying themselves as highlights. The generation unit can also select moments when the child acquires a new skill as highlights. Furthermore, the generation unit can also select moments when the child cooperates with a friend as highlights. In this way, by automatically selecting highlights of the child's activities, a diary that emphasizes important moments can be provided. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0094] When generating the diary, the generation unit can track the child's growth record over the long term and compare it with past data. The generation unit, for example, tracks the child's growth record on a monthly basis and compares it with past data. The generation unit can also track the child's growth record on a yearly basis and compare it with past data. Furthermore, the generation unit can track the child's growth record on a weekly basis and compare it with past data. In this way, by tracking the child's growth record over the long term and comparing it with past data, the progress of growth can be understood. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0095] The generation unit can automatically insert comments from the childcare worker when generating the diary. For example, the generation unit allows the childcare worker to add comments about the child's activities. The generation unit can also allow the childcare worker to add comments about the child's growth. Furthermore, the generation unit can also allow the childcare worker to add comments about the child's social interactions. In this way, by automatically adding the childcare worker's comments, it is possible to add a professional perspective to the diary. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0096] The generation unit can estimate the user's emotions and adjust the length of the diary entry based on the estimated user emotions. For example, if the user is feeling anxious, the generation unit can generate a detailed diary entry. If the user is feeling relaxed, the generation unit can also generate a concise diary entry. Furthermore, if the user is excited, the generation unit can also generate a visually appealing diary entry. By adjusting the length of the diary entry based on the user's emotions, it is possible to provide a diary entry with an optimal amount of information for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0097] The generation unit can insert educational advice related to the child's activities when generating the diary. For example, if a child is playing with blocks, the generation unit can add advice to improve spatial awareness. Furthermore, if a child is reading a picture book, the generation unit can add advice to improve reading comprehension. Furthermore, if a child is playing outside, the generation unit can add advice to improve motor skills. In this way, by adding educational advice related to the child's activities, it is possible to provide useful information to parents. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0098] When generating the diary, the generation unit can suggest recommended play and learning activities at home based on the child's activities. For example, if the child is playing with blocks, the generation unit can suggest ideas for block play at home. Furthermore, if the child is reading a picture book, the generation unit can also suggest ideas for reading activities at home. Furthermore, if the child is playing outside, the generation unit can also suggest ideas for physical play at home. This makes it possible to support childcare at home by suggesting play and learning activities at home based on the child's activities. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0099] When generating the diary, the generation unit can automatically select photos and videos related to the child's activities. For example, the generation unit automatically selects photos of the child playing with blocks. The generation unit can also automatically select videos of the child reading picture books. Furthermore, the generation unit can automatically select photos and videos of the child playing outside. In this way, by automatically selecting photos and videos related to the child's activities, visual information can be added to the diary. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.

[0100] The providing unit can estimate the user's emotions and adjust the way the diary is provided based on the estimated user's emotions. For example, if the user is feeling anxious, the providing unit can provide a detailed diary. Furthermore, if the user is relaxed, the providing unit can also provide a concise diary. Furthermore, if the user is excited, the providing unit can also provide a visually appealing diary. In this way, by adjusting the way the diary is provided based on the user's emotions, the diary can be provided in an optimal way for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0101] When providing a diary, the providing unit can select the optimal timing for providing the diary by referring to the guardian's past browsing history. For example, the providing unit provides the diary during a time period when the guardian frequently browsed the diary in the past. Furthermore, if the guardian tends to browse the diary on a specific day of the week, the providing unit can also provide the diary on that day. Furthermore, the providing unit can analyze the guardian's past browsing history and select the optimal timing for providing the diary. In this way, the diary can be provided at the optimal timing by referring to the guardian's past browsing history. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0102] When providing the diary, the providing unit can provide it in an optimal format taking into account the device information of the guardian. For example, if the guardian uses a smartphone, the providing unit can provide the diary in a format optimized for the smartphone. Furthermore, if the guardian uses a tablet, the providing unit can also provide the diary in a format optimized for the tablet. Furthermore, if the guardian uses a personal computer, the providing unit can also provide the diary in a format optimized for the personal computer. In this way, the diary can be provided in an optimal format by taking into account the device information of the guardian. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0103] The providing unit can improve the providing method by reflecting the parent's feedback when providing the diary. The providing unit can, for example, improve the content of the diary based on the parent's feedback. The providing unit can also improve the timing of providing the diary based on the parent's feedback. Furthermore, the providing unit can also improve the format of the diary based on the parent's feedback. In this way, the providing method can be continuously improved by reflecting the parent's feedback. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or without using AI.

[0104] The providing unit can estimate the user's emotions and adjust the frequency of providing the diary entries based on the estimated user emotions. For example, if the user is feeling anxious, the providing unit can provide the diary entries frequently. Furthermore, if the user is relaxed, the providing unit can also provide the diary entries at a normal frequency. Furthermore, if the user is excited, the providing unit can also frequently provide diaries about specific events. In this way, by adjusting the frequency of providing the diary entries based on the user's emotions, the diary entries can be provided at an optimal frequency for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0105] When providing the diary, the providing unit can deliver a diary in multiple languages ​​according to the language setting of the guardian. The providing unit automatically sets the language of the diary based on, for example, the language setting of the guardian's device. The providing unit can also provide a language switching function if the guardian uses multiple languages. Furthermore, if the guardian selects a specific language, the providing unit can also provide the diary in that language. In this way, by providing a diary in multiple languages ​​according to the guardian's language setting, information can be provided across language barriers. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0106] The providing unit can select the optimal timing for providing the diary based on the guardian's schedule when providing the diary. The providing unit, for example, references the guardian's calendar information to select the optimal timing for providing the diary. The providing unit can also adjust the timing for providing the diary based on the guardian's work schedule. Furthermore, the providing unit can also adjust the timing for providing the diary based on the guardian's home schedule. In this way, by selecting the optimal timing for providing the diary based on the guardian's schedule, the diary can be provided at a time that is convenient for the guardian. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0107] The providing unit can additionally deliver related information based on the guardian's areas of interest when providing the diary. For example, the providing unit can additionally provide educational information that the guardian is interested in to the diary. The providing unit can also additionally provide health information that the guardian is interested in to the diary. Furthermore, the providing unit can also additionally provide event information that the guardian is interested in to the diary. In this way, by providing additional related information based on the guardian's areas of interest, it is possible to provide useful information to the guardian. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0108] The security unit can estimate the user's emotions and adjust the security level based on the estimated user emotions. For example, if the user feels anxious, the security unit sets the security level high. The security unit can also set a normal security level if the user feels relaxed. Furthermore, the security unit can also set a high security level for specific data if the user feels excited. This allows appropriate security measures to be taken by adjusting the security level based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0109] The security unit can encrypt data when handling video data. For example, the security unit performs encryption in real time when collecting video data. The security unit can also perform encryption when storing video data. Furthermore, the security unit can also perform encryption when sharing video data. In this way, by encrypting video data, the security of the data can be ensured. Some or all of the above-mentioned processing in the security unit may be performed using AI, for example, or may be performed without using AI.

[0110] The security unit can strengthen access control when sharing data with parents. For example, the security unit performs password authentication when a parent accesses a diary. The security unit can also implement two-factor authentication to require an additional authentication method when a parent accesses a diary. Furthermore, the security unit can set IP address restrictions and allow access only from specific IP addresses. This strengthens access control and improves security when sharing data with parents. Some or all of the above-described processing in the security unit may be performed using AI, for example, or may be performed without using AI.

[0111] The security unit can estimate the user's emotions and set the frequency of security notifications based on the estimated user emotions. For example, if the user feels anxious, the security unit can provide security notifications more frequently. If the user feels relaxed, the security unit can also provide security notifications at a normal frequency. Furthermore, if the user feels excited, the security unit can provide security notifications related to specific events more frequently. In this way, by adjusting the frequency of security notifications based on the user's emotions, security notifications can be provided at an appropriate frequency. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0112] The security unit can periodically back up data when handling video data. For example, the security unit backs up video data daily. The security unit can also back up video data weekly. Furthermore, the security unit can also back up video data monthly. By periodically backing up data, data loss can be prevented. Some or all of the above-described processing in the security unit may be performed using AI, for example, or may be performed without using AI.

[0113] The security unit can implement two-factor authentication when sharing data with a guardian. For example, the security unit performs SMS authentication when a guardian accesses a diary. The security unit can also perform email authentication when a guardian accesses a diary. Furthermore, the security unit can also use an authentication app when a guardian accesses a diary. By introducing two-factor authentication, security can be strengthened when sharing data with a guardian. Some or all of the above-described processing in the security unit can be performed using AI, for example, or without using AI.

[0114] The algorithm unit can estimate the user's emotions and adjust the algorithm parameters based on the estimated user emotions. For example, if the user is feeling anxious, the algorithm unit can adjust the algorithm parameters to perform a detailed analysis. Furthermore, if the user is relaxed, the algorithm unit can also adjust the algorithm parameters to perform a normal analysis. Furthermore, if the user is excited, the algorithm unit can also adjust the algorithm parameters to perform a visually appealing analysis. Thus, by adjusting the algorithm parameters based on the user's emotions, the accuracy of the analysis results can be improved. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0115] The algorithm unit can perform optimization by referring to past data when learning the algorithm. The algorithm unit can, for example, improve the accuracy of the algorithm based on past data. The algorithm unit can also optimize the performance of the algorithm based on past data. Furthermore, the algorithm unit can improve the learning speed of the algorithm based on past data. In this way, by performing optimization by referring to past data, the accuracy and performance of the algorithm can be improved. Some or all of the above-mentioned processing in the algorithm unit can be performed, for example, using AI or without using AI.

[0116] The algorithm unit can improve the accuracy of the algorithm by reflecting parental feedback when the algorithm is executed. For example, the algorithm unit can improve the accuracy of the algorithm based on parental feedback. The algorithm unit can also optimize the performance of the algorithm based on parental feedback. Furthermore, the algorithm unit can improve the learning speed of the algorithm based on parental feedback. In this way, the accuracy of the algorithm can be continuously improved by reflecting parental feedback. Some or all of the above-mentioned processing in the algorithm unit may be performed using AI, for example, or may be performed without using AI.

[0117] The algorithm unit can estimate the user's emotions and set the execution frequency of the algorithm based on the estimated user's emotions. For example, if the user is feeling anxious, the algorithm unit can execute the algorithm frequently. Also, if the user is relaxed, the algorithm unit can execute the algorithm at a normal frequency. Furthermore, if the user is excited, the algorithm unit can execute an algorithm related to a specific event frequently. In this way, by adjusting the execution frequency of the algorithm based on the user's emotions, the algorithm can be executed at an appropriate frequency. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0118] The algorithm unit can improve accuracy by integrating information from different data sources when learning the algorithm. For example, the algorithm unit can improve the accuracy of the algorithm by integrating not only camera footage from the nursery school but also observation records from nursery teachers. The algorithm unit can also improve the accuracy of the algorithm by integrating feedback information from parents. Furthermore, the algorithm unit can improve the accuracy of the algorithm by integrating data from other nursery schools. In this way, the accuracy of the algorithm can be improved by integrating information from different data sources. Some or all of the above-mentioned processing in the algorithm unit may be performed using AI, for example, or may be performed without using AI.

[0119] The algorithm unit can adjust the algorithm based on the parent's areas of interest when executing the algorithm. For example, if the parent is interested in education, the algorithm unit can prioritize providing information related to education. Furthermore, if the parent is interested in health, the algorithm unit can prioritize providing information related to health. Furthermore, if the parent is interested in social interactions, the algorithm unit can prioritize providing information about playing with friends. In this way, by customizing based on the parent's areas of interest, it is possible to provide useful information to the parent. Some or all of the above-mentioned processing in the algorithm unit may be performed using AI, for example, or may be performed without using AI. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, provision unit, and security 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 collection unit collects video of the nursery school using the camera 42 of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the video using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a diary based on the analysis results. The provision unit is realized by the control unit 46A of the smart device 14 and provides the diary to the parent. The security unit is realized by the specific processing unit 290 of the data processing device 12 and encrypts and controls access to the video data. The algorithm unit is realized by the specific processing unit 290 of the data processing device 12 and describes the algorithm used by the generation AI. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, provision unit, and security 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 collection unit collects video images of the nursery school using the camera 42 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the video images using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a diary based on the analysis results. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides the diary to the guardian. The security unit is realized by the specific processing unit 290 of the data processing device 12 and performs encryption and access control of the video data. The algorithm unit is realized by the specific processing unit 290 of the data processing device 12 and describes the algorithm used by the generation AI. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, provision unit, and security 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 collection unit collects video of the nursery school using the camera 42 of the headset-type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the video using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a diary based on the analysis results. The provision unit is realized by the control unit 46A of the headset-type terminal 314 and provides the diary to the guardian. The security unit is realized by the specific processing unit 290 of the data processing device 12 and performs encryption and access control of the video data. The algorithm unit is realized by the specific processing unit 290 of the data processing device 12 and describes the algorithm used by the generation AI. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, provision unit, and security unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects video of the nursery school using the camera 42 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the video using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a diary based on the analysis results. The provision unit is realized by the control unit 46A of the robot 414 and provides the diary to the parent. The security unit is realized by the specific processing unit 290 of the data processing device 12 and performs encryption and access control of the video data. The algorithm unit is realized by the specific processing unit 290 of the data processing device 12 and describes the algorithm used by the generation AI.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The nursery school information providing system may further include a message sending unit that enables parents to send specific messages to their children. The message sending unit allows parents to send messages of encouragement or support to their children via a smartphone or computer, for example. The message sending unit also allows parents to send messages for specific events or activities. Furthermore, the message sending unit also allows parents to send real-time feedback on their children's behavior and achievements. This allows parents to strengthen communication with their children and show their interest and affection for them.

[0122] The nursery school information system can further include an interactive function that allows parents to respond to their children's activities in real time. The interactive function allows, for example, parents to send comments in real time while watching their children's activities. The interactive function also allows parents to send reactions to their children's activities. Furthermore, the interactive function also allows parents to send questions about their children's activities and nursery teachers to answer those questions. This allows parents to be actively involved in their children's activities and deepen communication with their children.

[0123] The nursery school information providing system may further include an evaluation unit that allows parents to evaluate their children's activities. The evaluation unit, for example, allows parents to give star ratings to their children's activities. The evaluation unit also allows parents to add comments to their children's activities. The evaluation unit also allows parents to provide feedback to their children's activities. This allows parents to evaluate their children's activities and provide feedback to nursery teachers.

[0124] The nursery school information providing system may further include an advice unit that provides advice to parents regarding their child's activities. For example, the advice unit may provide educational advice to parents regarding their child's activities. The advice unit may also provide health advice to parents regarding their child's activities. Furthermore, the advice unit may also provide social interaction advice to parents regarding their child's activities. This allows parents to provide useful advice to their child's activities and support their child's growth.

[0125] The nursery school information providing system may further include a goal setting unit that allows parents to set goals for their children's activities. The goal setting unit, for example, allows parents to set short-term goals for their children's activities. The goal setting unit also allows parents to set medium-term goals for their children's activities. The goal setting unit also allows parents to set long-term goals for their children's activities. This allows parents to set specific goals for their children's growth and support their children's growth.

[0126] The nursery school information providing system may further include an emotion expression unit that allows a guardian to express emotions regarding their child's activities. The emotion expression unit allows, for example, a guardian to express emotions such as joy or surprise regarding their child's activities. The emotion expression unit also allows a guardian to express emotions such as support or encouragement regarding their child's activities. Furthermore, the emotion expression unit also allows a guardian to express emotions such as gratitude or excitement regarding their child's activities. This allows a guardian to express their emotions regarding their child's activities and deepen their bond with their child.

[0127] The nursery school information providing system may further include an emotion sharing unit that allows parents to share their emotions regarding their child's activities. The emotion sharing unit, for example, allows parents to share their emotions regarding their child's activities with other parents. The emotion sharing unit may also allow parents to share their emotions regarding their child's activities with childcare workers. Furthermore, the emotion sharing unit may also allow parents to share their emotions regarding their child's activities with other family members. This allows parents to share their emotions regarding their child's activities and deepen empathy with other people.

[0128] The nursery school information providing system may further include an emotion recording unit that allows parents to record their feelings regarding their child's activities. The emotion recording unit may, for example, record the emotions parents feel regarding their child's activities in diary format. The emotion recording unit may also record the emotions parents feel regarding their child's activities along with photos and videos. Furthermore, the emotion recording unit may also record the emotions parents feel regarding their child's activities as voice messages. This allows parents to record the emotions they feel regarding their child's activities and look back on them later.

[0129] The nursery school information providing system may further include an emotion analysis unit that analyzes the emotions felt by parents regarding their children's activities. The emotion analysis unit can, for example, analyze the emotions felt by parents regarding their children's activities and grasp changes in emotions. The emotion analysis unit can also visualize the emotions felt by parents regarding their children's activities in the form of graphs or charts. Furthermore, the emotion analysis unit can compare the emotions felt by parents regarding their children's activities with those of other parents. This allows parents to analyze the emotions felt regarding their children's activities and grasp changes in emotions.

[0130] The nursery school information providing system may further include an emotion prediction unit that predicts the emotions that a guardian may feel in response to a child's activity. The emotion prediction unit may, for example, predict the emotions that a guardian may feel in response to a child's activity. The emotion prediction unit may also suggest an appropriate response based on the emotions that a guardian may feel in response to a child's activity. The emotion prediction unit may also provide advice to a childcare worker based on the emotions that a guardian may feel in response to a child's activity. This allows a guardian to predict the emotions that a guardian may feel in response to a child's activity and take an appropriate response.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The collection unit collects video footage from cameras installed in the nursery school. The collection unit can collect video footage from multiple cameras within the nursery school, and can set the video quality, format, and collection range. For example, it can adjust the camera resolution and frame rate to collect optimal video footage. It is also possible to set the collection frequency and collection time period, and it can be set to collect video footage during specific time periods. Step 2: The analysis unit analyzes the video collected by the collection unit. The analysis unit uses the generation AI to recognize the children's behavior, facial expressions, and content of play. The generation AI receives the video as input, analyzes the children's behavior and facial expressions, and outputs the results. For example, it can recognize how children are playing with blocks or how they are playing with friends. It can also analyze the children's facial expressions and recognize emotions such as smiling or crying. Step 3: The generation unit creates a diary based on the results of the analysis by the analysis unit. The generation unit uses generation AI to create a diary with videos, photos, and text. Based on the analysis results, the generation AI records the children's actions, expressions, and play content, and summarizes the events of the day in text. For example, it can record video and take photos of a child playing with blocks. It can also record text of a child reading a picture book. Step 4: The providing unit provides the diary created by the generating unit to the guardian. The providing unit can provide the diary via a smartphone or a PC. The providing unit sends the diary to the guardian's device so that the guardian can check the diary. For example, the diary can be sent by email. The diary can also be stored on the cloud so that the guardian can access it at any time.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Explanation of symbols]

[0205] 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 collection unit that collects images from cameras installed in the nursery school; an analysis unit that analyzes the video collected by the collection unit; a creation unit that creates a diary based on the results of the analysis by the analysis unit; a providing unit that provides the diary created by the creating unit to the guardian. A system characterized by:

2. The company will also have a security department that will implement specific security measures regarding the handling of video data and how it is shared with parents.

2. The system of claim 1.

3. Further, an algorithm section is provided that details the algorithm used by the generating AI.

2. The system of claim 1.

4. The collecting unit Estimate the user's emotions and adjust the timing of video collection based on the estimated user emotions.

2. The system of claim 1.

5. The collecting unit When collecting footage, filtering is used to focus on specific children.

2. The system of claim 1.

6. The collecting unit When collecting footage, set the collection range based on a specific area within the nursery 2. The system of claim 1.

7. The collecting unit When collecting footage, adjust the frequency of collection based on the child's activity level 2. The system of claim 1.

8. The collecting unit Estimate the user's emotions and prioritize the videos to be collected based on the estimated user emotions.

2. The system of claim 1.

9. The collecting unit When collecting footage, set collection methods based on the weather and time of day at the nursery 2. The system of claim 1.

Images