system

Abstract

The system according to this embodiment aims to enable children to safely reach their destination when going out alone. [Solution] The system according to the embodiment comprises a setting unit, a guidance unit, and a camera unit. The setting unit sets the destination. The guidance unit provides directions from the current location to the destination based on the destination set by the setting unit. The camera unit provides camera and recording functions.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a risk that a child gets lost when going out alone, and there is a problem that means for parents to grasp the current location and situation of the child are limited.

[0005] The system according to the embodiment aims to enable a child to safely reach a destination when going out alone.

Means for Solving the Problems

[0006] The system according to the embodiment includes a setting unit, a guiding unit, and a camera unit. The setting unit sets a destination. The guiding unit guides a route from the current location to the destination based on the destination set by the setting unit. The camera unit provides a camera function and a recording function.

Effects of the Invention

[0007] The system according to this embodiment can enable a child to safely reach their destination when going out alone. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The Kids Glasses System according to an embodiment of the present invention is a pair of glasses equipped with a navigation function, camera function, recording function, and UV cut function, designed to prevent elementary school children from getting lost when they are out alone. This Kids Glasses System allows parents to pre-set a destination via an app, and when a child gets lost, they can ask the glasses questions by voice, receiving voice directions from their current location to the destination. For example, if a child asks, "How do I get to 'home' from here?", the system will respond with directions such as, "Cross this traffic light and go straight to the left." Furthermore, it includes a camera and recording function, allowing parents to check accident records and the child's current view via the app. Parents can only view the recordings when the child presses the share button. This allows parents to understand their child's current situation. Additionally, it features a UV cut function to protect the eyes from ultraviolet rays, thus maintaining the child's eye health. As a specific example of use, parents can set destinations such as "home," "cram school," or "after-school program" via the app, and when a child gets lost, they can ask the glasses questions by voice, receiving voice directions from their current location to the destination. Furthermore, the camera and recording functions allow parents to check accident records and the view their child is currently seeing, and the UV-cut function protects the eyes from ultraviolet rays. Thus, the Kids Glasses System of the present invention is a pair of glasses equipped with a navigation function, camera function, recording function, and UV-cut function as a measure against elementary school children getting lost when they are out alone, and provides a mechanism that makes it easier for parents to check on their child's safety. In this way, the Kids Glasses System can provide navigation, camera function, recording function, and UV-cut function as a measure against elementary school children getting lost when they are out alone.

[0029] The Kids Glasses system according to this embodiment comprises a setting unit, a guidance unit, and a camera unit. The setting unit allows parents to set a destination via an app. For example, the setting unit allows parents to set destinations such as "home," "cram school," or "after-school care" via the app. The guidance unit provides directions from the current location to the destination based on the destination set by the setting unit. The guidance unit, for example, uses GPS and AI to determine the current location and calculates the optimal route to the destination. The guidance unit can provide voice guidance. For example, the guidance unit provides voice guidance such as, "Cross this traffic light and go straight to the left." The camera unit provides camera and recording functions. For example, the camera unit can take pictures of the scenery the child is seeing so that parents can review them via the app. The camera unit can also record accidents. For example, the camera unit can record video when the child is involved in an accident so that it can be reviewed later. Thus, the Kids Glasses system according to this embodiment can provide directions, camera functions, and recording functions as a measure to prevent elementary school children from getting lost when they are out alone.

[0030] The settings unit allows parents to set destinations through the app. For example, parents can set destinations such as "home," "cram school," or "after-school care" using the app. Specifically, parents use a dedicated app installed on their smartphone or tablet to select or enter destinations. The app is linked to a map database and can obtain accurate location information based on the address or facility name entered by the parent. Furthermore, the app also has a function to set multiple destinations, allowing users to set multiple routes at once, such as from school to cram school and from cram school to home. The settings unit sends the destination information set by the parent to the Kids Glasses, and the Kids Glasses starts guidance based on that information. The settings unit also has a function that allows parents to change or add destinations in real time. For example, if a parent suddenly needs to change the destination, they can set a new destination through the app and it will be immediately reflected on the Kids Glasses. In this way, the settings unit provides an environment in which parents can flexibly manage their children's movements and watch over them with peace of mind.

[0031] The guidance unit provides directions from the current location to the destination based on the destination set by the setting unit. For example, the guidance unit uses GPS and AI to determine the current location and calculate the optimal route to the destination. Specifically, a GPS module built into the kids' glasses acquires the child's current location in real time, and the AI ​​analyzes this information to calculate the optimal route. The AI ​​utilizes map databases, traffic information, and pedestrian-only route information to select the best route so that the child can reach the destination safely. The guidance unit can also provide voice guidance. For example, the guidance unit may say, "Cross this traffic light and go straight to the left." Voice guidance is given at appropriate times to prevent the child from getting lost, and can be repeated at important points. The guidance unit also has a function to immediately calculate a new route and provide guidance again if the child takes a wrong turn or is in an unexpected location. In this way, the guidance unit supports the child in reaching the destination safely and reliably, and reduces parental anxiety.

[0032] The camera unit provides both camera and recording functions. For example, the camera unit can capture the view a child is seeing, allowing parents to review it via an app. Specifically, a camera built into the kids' glasses captures the child's field of view in real time and transmits the footage to the parent's smartphone or tablet. Parents can check their child's current situation through the app, gaining peace of mind. The camera unit can also record accidents. For example, the camera unit can record footage of an accident involving a child, allowing for later review. The recording function is set to operate automatically under certain conditions; for example, it automatically starts recording when it detects sudden movement or impact. Parents can also manually start recording through the app. The recorded footage is stored on a cloud server for later review and analysis. This allows the camera unit to ensure the child's safety and provide footage that can be used as evidence in the event of an accident. Furthermore, the camera unit can be used not only for parents to monitor their child's behavior, but also as a tool for children to check their surroundings. For example, if a child gets lost, they can communicate their surroundings to their parents through the camera and receive appropriate instructions. This allows the camera unit to provide comprehensive support for children's safety.

[0033] The UV-cut section provides UV protection. The UV-cut section uses, for example, a special lens that blocks ultraviolet rays. The UV-cut section can block ultraviolet wavelengths within a specific range. For example, the UV-cut section blocks ultraviolet rays with wavelengths from 280nm to 400nm. Furthermore, the UV-cut section is designed to maintain its UV-blocking effect for a long period. For example, the UV-cut section is coated with a special coating that maintains its effect even with prolonged use. This allows the UV-cut section to protect the eyes from ultraviolet rays. Some or all of the above-described processes in the UV-cut section may be performed using, for example, AI, or without AI. For example, the UV-cut section can detect the intensity of ultraviolet rays with a sensor, and the AI ​​can select the optimal blocking method.

[0034] The system includes a real-time monitoring unit that allows parents to check on their children in real time. The real-time monitoring unit, for example, transmits the view the child is seeing to the parent in real time. The real-time monitoring unit allows parents to check on their children only when the child presses the share button. For example, the real-time monitoring unit allows parents to check on their children's current situation when the child presses the share button when they feel in danger. The real-time monitoring unit also allows parents to check their child's current location through an app. For example, the real-time monitoring unit sends GPS data to the parent's app and displays the child's current location. This allows parents to check on their child's current situation in real time. Some or all of the above processing in the real-time monitoring unit may be performed using AI, for example, or without AI. For example, the real-time monitoring unit can use AI to analyze the child's camera footage and notify the parent if an anomaly is detected.

[0035] The system includes a privacy restriction unit. This unit, for example, encrypts data to protect a child's privacy. The restriction unit can also restrict the information a parent can access. For example, it can allow a parent to view camera footage only when the child presses the share button. Furthermore, the restriction unit can impose access restrictions. For example, it can allow a parent to access a child's information only during specific time periods. This protects the child's privacy. Some or all of the above processing in the restriction unit may be performed using AI, or not. For example, the restriction unit may use AI to analyze a child's behavior patterns and suggest the optimal restriction method for privacy protection.

[0036] The guidance unit can provide voice guidance. For example, it can provide voice directions to a child who has gotten lost. The guidance unit uses GPS and AI to determine the current location and calculate the optimal route to the destination. For example, the guidance unit can provide voice guidance such as, "Cross this traffic light and go straight to the left." The guidance unit can also provide voice guidance in multiple languages. For example, it can provide voice guidance in languages ​​such as Japanese, English, and Chinese. This allows the guidance unit to provide voice directions. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can use AI to analyze a child's questions and provide voice guidance on the optimal route.

[0037] The camera unit can record accidents. For example, the camera unit can record video when a child is involved in an accident. The camera unit uses a high-resolution camera to record accidents. For example, the camera unit records video in 1080p high resolution. The camera unit can also save the recorded video to the cloud. For example, the camera unit can automatically upload the recorded video to the cloud so that it can be reviewed later. In this way, the camera unit can record accidents. Some or all of the above processing in the camera unit may be performed using AI, for example, or not using AI. For example, the camera unit can use AI to analyze the video and automatically extract important scenes from the accident.

[0038] The configuration unit can analyze the parent's past configuration history and select an appropriate destination setting method. For example, the configuration unit can automatically display destinations that the parent has frequently configured in the past as candidates. The configuration unit can predict and suggest destinations that the parent has configured on specific days of the week or time slots. The configuration unit can also suggest destinations related to specific events based on the parent's past configuration history. In this way, the configuration unit can set the optimal destination based on the parent's past configuration history. Some or all of the above processing in the configuration unit may be performed using AI, for example, or without AI. For example, the configuration unit can input the parent's past configuration data into a generating AI and have the generating AI select the optimal destination setting method.

[0039] The setting unit can filter destinations based on the child's current activities and schedule when setting a destination. For example, if the child is at school, the setting unit will prioritize suggesting destinations close to school. If the child has an extracurricular activity scheduled, the setting unit can prioritize suggesting that location. Furthermore, if the child has free time, the setting unit can suggest destinations such as playgrounds or parks. In this way, the setting unit can suggest appropriate destinations based on the child's activities and schedule. Some or all of the above processing in the setting unit may be performed using AI, for example, or not. For example, the setting unit can input the child's schedule data into a generating AI and have the generating AI suggest the optimal destination.

[0040] The setting unit can prioritize highly relevant destinations when setting a destination, taking into account the child's geographical location. For example, if the child is at school, the setting unit can prioritize destinations close to school. If the child has plans to attend extracurricular activities, the setting unit can prioritize the location of those activities. Furthermore, if the child has free time, the setting unit can prioritize destinations such as playgrounds or parks. In this way, the setting unit can set an appropriate destination based on the child's geographical location. Some or all of the above processing in the setting unit may be performed using AI, for example, or without AI. For example, the setting unit can input the child's GPS data into a generating AI and have the generating AI suggest the optimal destination.

[0041] The settings unit can analyze the parent's social media activity when setting a destination and suggest relevant destinations. For example, the settings unit can suggest places the parent has checked into on social media as candidates. The settings unit can also suggest events or places the parent has shared on social media as destinations. Furthermore, the settings unit can suggest relevant destinations based on the content of the parent's social media posts. In this way, the settings unit can suggest appropriate destinations based on the parent's social media activity. Some or all of the above processing in the settings unit may be performed using AI, for example, or not using AI. For example, the settings unit can input the parent's social media data into a generating AI and have the generating AI suggest the optimal destination.

[0042] The guidance unit can adjust the level of detail in the directions based on the importance of the route. For example, when approaching major intersections or landmarks, the guidance unit provides detailed directions. For simple routes that only require going straight, the guidance unit can provide concise directions. For complex routes, the guidance unit can provide directions step by step. This allows the guidance unit to provide appropriate directions based on the importance of the route. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input route data into a generating AI and have the generating AI adjust the level of detail in the directions based on importance.

[0043] The guidance unit can apply different guidance algorithms depending on the route category during guidance. For example, if the guidance unit is providing directions for pedestrians, it will apply a guidance algorithm specifically for pedestrians. If the guidance unit is providing directions for bicycles, it will apply a guidance algorithm specifically for bicycles. Furthermore, if the guidance unit is providing directions for cars, it will apply a guidance algorithm specifically for cars. This allows the guidance unit to apply an appropriate guidance algorithm according to the route category. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input route data into a generating AI and have the generating AI apply a guidance algorithm according to the category.

[0044] The guidance system can determine the priority of directions based on when the directions were submitted. For example, the guidance system may prioritize directions submitted most recently. It may also prioritize directions submitted in advance. Furthermore, the guidance system may determine the priority of directions based on importance, regardless of when they were submitted. This allows the guidance system to provide appropriate directions based on when the directions were submitted. Some or all of the above processing in the guidance system may be performed using AI, for example, or not. For example, the guidance system may input direction data into a generating AI and have the generating AI determine the priority of directions based on when they were submitted.

[0045] The guidance unit can adjust the order of directions based on the relevance of the route. For example, the guidance unit may prioritize directions to major intersections and landmarks. It may also postpone simple routes that only require going straight. Furthermore, it may prioritize directions that are complex. In this way, the guidance unit can provide an appropriate order of directions based on the relevance of the route. Some or all of the above processing in the guidance unit may be performed using AI, for example, or not using AI. For example, the guidance unit can input route data into a generating AI and have the generating AI adjust the order of directions based on relevance.

[0046] The camera unit can adjust the level of detail in recordings based on the severity of the accident during recording. For example, in the case of a serious accident, the camera unit can record in high resolution and detail. In the case of a minor accident, the camera unit can record in low resolution and concisely. The camera unit can also automatically select the appropriate recording settings depending on the type of accident. This allows the camera unit to provide an appropriate level of detail in recordings based on the severity of the accident. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input accident data into a generating AI and have the generating AI adjust the level of detail in recordings based on the severity.

[0047] The camera unit can apply different recording algorithms depending on the accident category during recording. For example, in the case of a traffic accident, the camera unit can record the vehicle's movement and the status of traffic signals in detail. In the case of a fall, the camera unit can record the surrounding circumstances and the moment of the fall in detail. In the case of a theft, the camera unit can also record the perpetrator's movements and the surrounding circumstances in detail. In this way, the camera unit can provide an appropriate recording algorithm depending on the accident category. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input accident data into a generating AI and apply a category-appropriate recording algorithm to the generating AI.

[0048] The camera unit can determine recording priorities based on the timing of accidents during recording. For example, the camera unit may prioritize recording the most recent accident. The camera unit may also prioritize recording accidents that have been predicted in advance. Furthermore, the camera unit may determine recording priorities based on importance, regardless of the timing of the accident. This allows the camera unit to provide appropriate recording priorities based on the timing of accidents. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input accident data into a generating AI and have the generating AI determine the recording priorities based on the timing of the accidents.

[0049] The camera unit can adjust the recording order based on the relevance of the accidents during recording. For example, the camera unit can prioritize recording serious accidents. The camera unit can postpone recording minor accidents. The camera unit can also automatically select an appropriate recording order depending on the type of accident. This allows the camera unit to provide an appropriate recording order based on the relevance of the accidents. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input accident data into a generating AI and have the generating AI adjust the recording order based on relevance.

[0050] The UV protection unit can analyze a child's past UV exposure history to select the optimal UV protection method. For example, if a child has been exposed to strong UV rays in the past, the UV protection unit can apply strong UV protection. If a child has not been exposed to UV rays in the past, the UV protection unit can apply moderate UV protection. The UV protection unit can also analyze a child's UV exposure history to select the optimal UV protection method. This allows the UV protection unit to provide an appropriate UV protection method based on a child's past UV exposure history. Some or all of the above processing in the UV protection unit may be performed using AI, for example, or without AI. For example, the UV protection unit can input UV exposure data into a generating AI and have the generating AI select the optimal UV protection method.

[0051] The UV protection unit can select the optimal UV protection method by considering the child's geographical location information during UV protection. For example, if the child is in an area where strong ultraviolet radiation is predicted, the UV protection unit can apply strong UV protection. If the child is in an area with low ultraviolet radiation, the UV protection unit can apply moderate UV protection. The UV protection unit can also analyze the child's geographical location information and select the optimal UV protection method. This allows the UV protection unit to provide an appropriate UV protection method based on the child's geographical location information. Some or all of the above processing in the UV protection unit may be performed using AI, for example, or without AI. For example, the UV protection unit can input geographical location data into a generating AI and have the generating AI select the optimal UV protection method.

[0052] The real-time verification unit can select the optimal display method by referring to the parent's past verification history during real-time verification. For example, if the parent has previously preferred to view detailed information, the real-time verification unit can provide detailed information. If the parent has previously preferred to view concise information, the real-time verification unit can provide concise information. The real-time verification unit can also analyze the parent's past verification history and select the optimal display method. This allows the real-time verification unit to provide an appropriate display method based on the parent's past verification history. Some or all of the above processing in the real-time verification unit may be performed using AI, for example, or without AI. For example, the real-time verification unit can input the parent's verification history data into a generating AI and have the generating AI select the optimal display method.

[0053] The real-time verification unit can select the optimal display method by considering the parent's device information during real-time verification. For example, if the parent is using a smartphone, the real-time verification unit can provide a display method that matches the screen size. If the parent is using a tablet, the real-time verification unit can provide a display method optimized for a larger screen. Furthermore, if the parent is using a smartwatch, the real-time verification unit can provide a concise and highly visible display method. In this way, the real-time verification unit can provide an appropriate display method based on the parent's device information. Some or all of the above processing in the real-time verification unit may be performed using AI, for example, or without AI. For example, the real-time verification unit can input the parent's device information into a generating AI and have the generating AI select the optimal display method.

[0054] The real-time verification unit can select the optimal display method by considering the parent's device information during real-time verification. For example, if the parent is using a smartphone, the real-time verification unit can provide a display method that matches the screen size. If the parent is using a tablet, the real-time verification unit can provide a display method optimized for a larger screen. Furthermore, if the parent is using a smartwatch, the real-time verification unit can provide a concise and highly visible display method. In this way, the real-time verification unit can provide an appropriate display method based on the parent's device information. Some or all of the above processing in the real-time verification unit may be performed using AI, for example, or without AI. For example, the real-time verification unit can input the parent's device information into a generating AI and have the generating AI select the optimal display method.

[0055] The confirmation restriction unit can select the optimal restriction method by referring to the parent's past confirmation history when restricting confirmation. For example, if the parent has preferred to confirm detailed information in the past, the confirmation restriction unit can provide detailed information. If the parent has preferred to confirm concise information in the past, the confirmation restriction unit can provide concise information. The confirmation restriction unit can also analyze the parent's past confirmation history and select the optimal restriction method. This allows the confirmation restriction unit to provide an appropriate restriction method based on the parent's past confirmation history. Some or all of the above processing in the confirmation restriction unit may be performed using AI, for example, or without AI. For example, the confirmation restriction unit can input the parent's confirmation history data into a generating AI and have the generating AI select the optimal restriction method.

[0056] The verification and restriction unit can select the optimal restriction method when performing verification and restriction, taking into account the parent's device information. For example, if the parent is using a smartphone, the verification and restriction unit can provide a restriction method that matches the screen size. If the parent is using a tablet, the verification and restriction unit can provide a restriction method optimized for a larger screen. Furthermore, if the parent is using a smartwatch, the verification and restriction unit can provide a simple and highly visible restriction method. In this way, the verification and restriction unit can provide an appropriate restriction method based on the parent's device information. Some or all of the above processing in the verification and restriction unit may be performed using AI, for example, or without AI. For example, the verification and restriction unit can input the parent's device information into a generating AI and have the generating AI select the optimal restriction method.

[0057] The verification and restriction unit can select the optimal restriction method when performing verification and restriction, taking into account the parent's device information. For example, if the parent is using a smartphone, the verification and restriction unit can provide a restriction method that matches the screen size. If the parent is using a tablet, the verification and restriction unit can provide a restriction method optimized for a larger screen. Furthermore, if the parent is using a smartwatch, the verification and restriction unit can provide a simple and highly visible restriction method. In this way, the verification and restriction unit can provide an appropriate restriction method based on the parent's device information. Some or all of the above processing in the verification and restriction unit may be performed using AI, for example, or without AI. For example, the verification and restriction unit can input the parent's device information into a generating AI and have the generating AI select the optimal restriction method.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The Kids Glasses system can also be equipped with a temperature sensor. This sensor can measure the child's body temperature and ambient temperature, and notify parents if an abnormality is detected. For example, it can send an alert to parents if the child's body temperature becomes high. It can also notify parents if the ambient temperature is extremely high or low. Furthermore, the temperature sensor can record the child's body temperature data, which can be used for health management. This allows the temperature sensor to monitor the child's health status in real time.

[0060] The kids' glasses system can also be equipped with a vibration notification unit. This vibration notification unit can alert parents via vibration when a child senses danger. For example, if a child senses danger, they can tap the glasses to send a vibration notification to their parent. Parents can also send vibration notifications to their children via an app to alert them. Furthermore, the vibration notification unit can automatically send a vibration notification when a child enters a specific area. This allows the vibration notification unit to provide a means of ensuring the child's safety.

[0061] The Kids Glasses system can also be equipped with a location history function. This function records a child's past movement history, which parents can review through an app. For example, parents can see which route their child took to school. The location history function can also display where the child was at specific times. Furthermore, the location history function can analyze the child's movement patterns and suggest safe routes. In this way, the location history function can provide information to understand the child's movement and ensure their safety.

[0062] The Kids Glasses system can also be equipped with an emergency call function. This function allows children to notify their parents or emergency contacts if they encounter an emergency. For example, if a child feels in danger, they can press an emergency button to notify their parents. The emergency call function can also automatically transmit the child's location information, enabling a quick response. Furthermore, parents can set up emergency contacts through the app. This allows the emergency call function to provide a means of rapid response to ensure the child's safety.

[0063] The Kids Glasses system can also be equipped with a learning support unit. This unit can provide helpful information to children while they are learning. For example, when a child is doing homework, they can ask the glasses questions about unfamiliar words or problems, and the learning support unit will provide explanations. The learning support unit also allows parents to check their child's learning progress through an app. Furthermore, the learning support unit can record the child's learning history and suggest effective learning methods. In this way, the learning support unit can support children's learning and provide an effective learning environment.

[0064] The Kids Glasses system can also be equipped with a health management unit. This unit can monitor a child's health and notify parents. For example, it can measure a child's heart rate and body temperature and send an alert to parents if an abnormality is detected. The health management unit can also record a child's activity level and support healthy lifestyle habits. Furthermore, the health management unit allows parents to review their child's health data through an app and receive appropriate advice. In this way, the health management unit can provide comprehensive support for a child's health.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: In the setup section, the parent sets the destination through the app. For example, the parent can set destinations such as "home," "cram school," or "after-school care" in the app. Step 2: The guidance unit provides directions from the current location to the destination based on the destination set by the setting unit. The guidance unit uses GPS and AI to determine the current location and calculate the optimal route to the destination. Furthermore, the guidance unit can provide voice guidance, such as, "Cross this traffic light and go straight to the left." Step 3: The camera unit provides camera and recording functions. The camera unit captures the scenery the child is seeing, allowing parents to review it on the app. The camera unit can also record accidents, capturing footage of the child in the event of an accident for later review.

[0067] (Example of form 2) The Kids Glasses System according to an embodiment of the present invention is a pair of glasses equipped with a navigation function, camera function, recording function, and UV cut function, designed to prevent elementary school children from getting lost when they are out alone. This Kids Glasses System allows parents to pre-set a destination via an app, and when a child gets lost, they can ask the glasses questions by voice, receiving voice directions from their current location to the destination. For example, if a child asks, "How do I get to 'home' from here?", the system will respond with directions such as, "Cross this traffic light and go straight to the left." Furthermore, it includes a camera and recording function, allowing parents to check accident records and the child's current view via the app. Parents can only view the recordings when the child presses the share button. This allows parents to understand their child's current situation. Additionally, it features a UV cut function to protect the eyes from ultraviolet rays, thus maintaining the child's eye health. As a specific example of use, parents can set destinations such as "home," "cram school," or "after-school program" via the app, and when a child gets lost, they can ask the glasses questions by voice, receiving voice directions from their current location to the destination. Furthermore, the camera and recording functions allow parents to check accident records and the view their child is currently seeing, and the UV-cut function protects the eyes from ultraviolet rays. Thus, the Kids Glasses System of the present invention is a pair of glasses equipped with a navigation function, camera function, recording function, and UV-cut function as a measure against elementary school children getting lost when they are out alone, and provides a mechanism that makes it easier for parents to check on their child's safety. In this way, the Kids Glasses System can provide navigation, camera function, recording function, and UV-cut function as a measure against elementary school children getting lost when they are out alone.

[0068] The Kids Glasses system according to this embodiment comprises a setting unit, a guidance unit, and a camera unit. The setting unit allows parents to set a destination via an app. For example, the setting unit allows parents to set destinations such as "home," "cram school," or "after-school care" via the app. The guidance unit provides directions from the current location to the destination based on the destination set by the setting unit. The guidance unit, for example, uses GPS and AI to determine the current location and calculates the optimal route to the destination. The guidance unit can provide voice guidance. For example, the guidance unit provides voice guidance such as, "Cross this traffic light and go straight to the left." The camera unit provides camera and recording functions. For example, the camera unit can take pictures of the scenery the child is seeing so that parents can review them via the app. The camera unit can also record accidents. For example, the camera unit can record video when the child is involved in an accident so that it can be reviewed later. Thus, the Kids Glasses system according to this embodiment can provide directions, camera functions, and recording functions as a measure to prevent elementary school children from getting lost when they are out alone.

[0069] The settings unit allows parents to set destinations through the app. For example, parents can set destinations such as "home," "cram school," or "after-school care" using the app. Specifically, parents use a dedicated app installed on their smartphone or tablet to select or enter destinations. The app is linked to a map database and can obtain accurate location information based on the address or facility name entered by the parent. Furthermore, the app also has a function to set multiple destinations, allowing users to set multiple routes at once, such as from school to cram school and from cram school to home. The settings unit sends the destination information set by the parent to the Kids Glasses, and the Kids Glasses starts guidance based on that information. The settings unit also has a function that allows parents to change or add destinations in real time. For example, if a parent suddenly needs to change the destination, they can set a new destination through the app and it will be immediately reflected on the Kids Glasses. In this way, the settings unit provides an environment in which parents can flexibly manage their children's movements and watch over them with peace of mind.

[0070] The guidance unit provides directions from the current location to the destination based on the destination set by the setting unit. For example, the guidance unit uses GPS and AI to determine the current location and calculate the optimal route to the destination. Specifically, a GPS module built into the kids' glasses acquires the child's current location in real time, and the AI ​​analyzes this information to calculate the optimal route. The AI ​​utilizes map databases, traffic information, and pedestrian-only route information to select the best route so that the child can reach the destination safely. The guidance unit can also provide voice guidance. For example, the guidance unit may say, "Cross this traffic light and go straight to the left." Voice guidance is given at appropriate times to prevent the child from getting lost, and can be repeated at important points. The guidance unit also has a function to immediately calculate a new route and provide guidance again if the child takes a wrong turn or is in an unexpected location. In this way, the guidance unit supports the child in reaching the destination safely and reliably, and reduces parental anxiety.

[0071] The camera unit provides both camera and recording functions. For example, the camera unit can capture the view a child is seeing, allowing parents to review it via an app. Specifically, a camera built into the kids' glasses captures the child's field of view in real time and transmits the footage to the parent's smartphone or tablet. Parents can check their child's current situation through the app, gaining peace of mind. The camera unit can also record accidents. For example, the camera unit can record footage of an accident involving a child, allowing for later review. The recording function is set to operate automatically under certain conditions; for example, it automatically starts recording when it detects sudden movement or impact. Parents can also manually start recording through the app. The recorded footage is stored on a cloud server for later review and analysis. This allows the camera unit to ensure the child's safety and provide footage that can be used as evidence in the event of an accident. Furthermore, the camera unit can be used not only for parents to monitor their child's behavior, but also as a tool for children to check their surroundings. For example, if a child gets lost, they can communicate their surroundings to their parents through the camera and receive appropriate instructions. This allows the camera unit to provide comprehensive support for children's safety.

[0072] The UV-cut section provides UV protection. The UV-cut section uses, for example, a special lens that blocks ultraviolet rays. The UV-cut section can block ultraviolet wavelengths within a specific range. For example, the UV-cut section blocks ultraviolet rays with wavelengths from 280nm to 400nm. Furthermore, the UV-cut section is designed to maintain its UV-blocking effect for a long period. For example, the UV-cut section is coated with a special coating that maintains its effect even with prolonged use. This allows the UV-cut section to protect the eyes from ultraviolet rays. Some or all of the above-described processes in the UV-cut section may be performed using, for example, AI, or without AI. For example, the UV-cut section can detect the intensity of ultraviolet rays with a sensor, and the AI ​​can select the optimal blocking method.

[0073] The system includes a real-time monitoring unit that allows parents to check on their children in real time. The real-time monitoring unit, for example, transmits the view the child is seeing to the parent in real time. The real-time monitoring unit allows parents to check on their children only when the child presses the share button. For example, the real-time monitoring unit allows parents to check on their children's current situation when the child presses the share button when they feel in danger. The real-time monitoring unit also allows parents to check their child's current location through an app. For example, the real-time monitoring unit sends GPS data to the parent's app and displays the child's current location. This allows parents to check on their child's current situation in real time. Some or all of the above processing in the real-time monitoring unit may be performed using AI, for example, or without AI. For example, the real-time monitoring unit can use AI to analyze the child's camera footage and notify the parent if an anomaly is detected.

[0074] The system includes a privacy restriction unit. This unit, for example, encrypts data to protect a child's privacy. The restriction unit can also restrict the information a parent can access. For example, it can allow a parent to view camera footage only when the child presses the share button. Furthermore, the restriction unit can impose access restrictions. For example, it can allow a parent to access a child's information only during specific time periods. This protects the child's privacy. Some or all of the above processing in the restriction unit may be performed using AI, or not. For example, the restriction unit may use AI to analyze a child's behavior patterns and suggest the optimal restriction method for privacy protection.

[0075] The guidance unit can provide voice guidance. For example, it can provide voice directions to a child who has gotten lost. The guidance unit uses GPS and AI to determine the current location and calculate the optimal route to the destination. For example, the guidance unit can provide voice guidance such as, "Cross this traffic light and go straight to the left." The guidance unit can also provide voice guidance in multiple languages. For example, it can provide voice guidance in languages ​​such as Japanese, English, and Chinese. This allows the guidance unit to provide voice directions. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can use AI to analyze a child's questions and provide voice guidance on the optimal route.

[0076] The camera unit can record accidents. For example, the camera unit can record video when a child is involved in an accident. The camera unit uses a high-resolution camera to record accidents. For example, the camera unit records video in 1080p high resolution. The camera unit can also save the recorded video to the cloud. For example, the camera unit can automatically upload the recorded video to the cloud so that it can be reviewed later. In this way, the camera unit can record accidents. Some or all of the above processing in the camera unit may be performed using AI, for example, or not using AI. For example, the camera unit can use AI to analyze the video and automatically extract important scenes from the accident.

[0077] The setting unit can estimate the child's emotions and automatically suggest destinations based on the estimated emotions. For example, if the child is feeling anxious, the setting unit will prioritize suggesting safe places such as home or the parent's workplace. If the child is having fun, the setting unit can suggest fun places such as playgrounds or a friend's house. If the child is tired, the setting unit can also suggest places where they can rest, such as nearby rest areas or cafes. In this way, the setting unit can suggest appropriate destinations based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the setting unit may be performed using AI, or not using AI. For example, the setting unit can input the child's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0078] The configuration unit can analyze the parent's past configuration history and select an appropriate destination setting method. For example, the configuration unit can automatically display destinations that the parent has frequently configured in the past as candidates. The configuration unit can predict and suggest destinations that the parent has configured on specific days of the week or time slots. The configuration unit can also suggest destinations related to specific events based on the parent's past configuration history. In this way, the configuration unit can set the optimal destination based on the parent's past configuration history. Some or all of the above processing in the configuration unit may be performed using AI, for example, or without AI. For example, the configuration unit can input the parent's past configuration data into a generating AI and have the generating AI select the optimal destination setting method.

[0079] The setting unit can filter destinations based on the child's current activities and schedule when setting a destination. For example, if the child is at school, the setting unit will prioritize suggesting destinations close to school. If the child has an extracurricular activity scheduled, the setting unit can prioritize suggesting that location. Furthermore, if the child has free time, the setting unit can suggest destinations such as playgrounds or parks. In this way, the setting unit can suggest appropriate destinations based on the child's activities and schedule. Some or all of the above processing in the setting unit may be performed using AI, for example, or not. For example, the setting unit can input the child's schedule data into a generating AI and have the generating AI suggest the optimal destination.

[0080] The setting unit can estimate the child's emotions and determine the priority of destinations based on the estimated emotions. For example, if the child is feeling anxious, the setting unit can prioritize safe places such as home or the parent's workplace. If the child is having fun, the setting unit can prioritize fun places such as playgrounds or friends' houses. If the child is tired, the setting unit can prioritize resting places such as nearby rest areas or cafes. In this way, the setting unit can determine the priority of destinations based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the setting unit may be performed using AI, or not using AI. For example, the setting unit can input the child's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0081] The setting unit can prioritize highly relevant destinations when setting a destination, taking into account the child's geographical location. For example, if the child is at school, the setting unit can prioritize destinations close to school. If the child has plans to attend extracurricular activities, the setting unit can prioritize the location of those activities. Furthermore, if the child has free time, the setting unit can prioritize destinations such as playgrounds or parks. In this way, the setting unit can set an appropriate destination based on the child's geographical location. Some or all of the above processing in the setting unit may be performed using AI, for example, or without AI. For example, the setting unit can input the child's GPS data into a generating AI and have the generating AI suggest the optimal destination.

[0082] The settings unit can analyze the parent's social media activity when setting a destination and suggest relevant destinations. For example, the settings unit can suggest places the parent has checked into on social media as candidates. The settings unit can also suggest events or places the parent has shared on social media as destinations. Furthermore, the settings unit can suggest relevant destinations based on the content of the parent's social media posts. In this way, the settings unit can suggest appropriate destinations based on the parent's social media activity. Some or all of the above processing in the settings unit may be performed using AI, for example, or not using AI. For example, the settings unit can input the parent's social media data into a generating AI and have the generating AI suggest the optimal destination.

[0083] The guidance unit can estimate a child's emotions and adjust the way it delivers guidance based on those emotions. For example, if a child is feeling anxious, the guidance unit can deliver guidance in a gentle and slow voice. If a child is having fun, the guidance unit can deliver guidance in a cheerful and energetic voice. If a child is in a hurry, the guidance unit can deliver guidance in a concise and quick manner. In this way, the guidance unit can provide appropriate guidance based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guidance unit may be performed using AI, for example, or not using AI. For example, the guidance unit can input a child's voice data into a generative AI and have the generative AI perform emotion estimation.

[0084] The guidance unit can adjust the level of detail in the directions based on the importance of the route. For example, when approaching major intersections or landmarks, the guidance unit provides detailed directions. For simple routes that only require going straight, the guidance unit can provide concise directions. For complex routes, the guidance unit can provide directions step by step. This allows the guidance unit to provide appropriate directions based on the importance of the route. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input route data into a generating AI and have the generating AI adjust the level of detail in the directions based on importance.

[0085] The guidance unit can apply different guidance algorithms depending on the route category during guidance. For example, if the guidance unit is providing directions for pedestrians, it will apply a guidance algorithm specifically for pedestrians. If the guidance unit is providing directions for bicycles, it will apply a guidance algorithm specifically for bicycles. Furthermore, if the guidance unit is providing directions for cars, it will apply a guidance algorithm specifically for cars. This allows the guidance unit to apply an appropriate guidance algorithm according to the route category. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input route data into a generating AI and have the generating AI apply a guidance algorithm according to the category.

[0086] The guidance unit can estimate a child's emotions and adjust the length of the guidance based on the estimated emotions. For example, if a child is feeling anxious, the guidance unit can provide detailed guidance to reassure them. If a child is having fun, the guidance unit can provide concise guidance and allow them to act freely. Furthermore, if a child is in a hurry, the guidance unit can provide concise guidance emphasizing the shortest route. This allows the guidance unit to provide an appropriate length of guidance based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guidance unit may be performed using AI, or not. For example, the guidance unit can input a child's voice data into a generative AI and have the generative AI perform emotion estimation.

[0087] The guidance system can determine the priority of directions based on when the directions were submitted. For example, the guidance system may prioritize directions submitted most recently. It may also prioritize directions submitted in advance. Furthermore, the guidance system may determine the priority of directions based on importance, regardless of when they were submitted. This allows the guidance system to provide appropriate directions based on when the directions were submitted. Some or all of the above processing in the guidance system may be performed using AI, for example, or not. For example, the guidance system may input direction data into a generating AI and have the generating AI determine the priority of directions based on when they were submitted.

[0088] The guidance unit can adjust the order of directions based on the relevance of the route. For example, the guidance unit may prioritize directions to major intersections and landmarks. It may also postpone simple routes that only require going straight. Furthermore, it may prioritize directions that are complex. In this way, the guidance unit can provide an appropriate order of directions based on the relevance of the route. Some or all of the above processing in the guidance unit may be performed using AI, for example, or not using AI. For example, the guidance unit can input route data into a generating AI and have the generating AI adjust the order of directions based on relevance.

[0089] The camera unit can estimate a child's emotions and adjust its shooting method based on the estimated emotions. For example, if a child is feeling anxious, the camera unit can capture the surrounding environment in detail with a wide-angle lens. If a child is having fun, the camera unit can focus on a specific object. Furthermore, if a child is in a hurry, the camera unit can capture important scenes quickly. This allows the camera unit to provide an appropriate shooting method based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the camera unit may be performed using AI, or not. For example, the camera unit can input the child's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0090] The camera unit can adjust the level of detail in recordings based on the severity of the accident during recording. For example, in the case of a serious accident, the camera unit can record in high resolution and detail. In the case of a minor accident, the camera unit can record in low resolution and concisely. The camera unit can also automatically select the appropriate recording settings depending on the type of accident. This allows the camera unit to provide an appropriate level of detail in recordings based on the severity of the accident. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input accident data into a generating AI and have the generating AI adjust the level of detail in recordings based on the severity.

[0091] The camera unit can apply different recording algorithms depending on the accident category during recording. For example, in the case of a traffic accident, the camera unit can record the vehicle's movement and the status of traffic signals in detail. In the case of a fall, the camera unit can record the surrounding circumstances and the moment of the fall in detail. In the case of a theft, the camera unit can also record the perpetrator's movements and the surrounding circumstances in detail. In this way, the camera unit can provide an appropriate recording algorithm depending on the accident category. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input accident data into a generating AI and apply a category-appropriate recording algorithm to the generating AI.

[0092] The camera unit can estimate the child's emotions and adjust the recording length based on the estimated emotions. For example, if the child is feeling anxious, the camera unit can record for a longer duration to preserve a detailed record. If the child is having fun, the camera unit can record for a shorter duration, capturing only the important scenes. Furthermore, if the child is in a hurry, the camera unit can record important scenes in a shorter timeframe. This allows the camera unit to provide an appropriate recording length based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the camera unit may be performed using AI, or not. For example, the camera unit can input the child's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0093] The camera unit can determine recording priorities based on the timing of accidents during recording. For example, the camera unit may prioritize recording the most recent accident. The camera unit may also prioritize recording accidents that have been predicted in advance. Furthermore, the camera unit may determine recording priorities based on importance, regardless of the timing of the accident. This allows the camera unit to provide appropriate recording priorities based on the timing of accidents. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input accident data into a generating AI and have the generating AI determine the recording priorities based on the timing of the accidents.

[0094] The camera unit can adjust the recording order based on the relevance of the accidents during recording. For example, the camera unit can prioritize recording serious accidents. The camera unit can postpone recording minor accidents. The camera unit can also automatically select an appropriate recording order depending on the type of accident. This allows the camera unit to provide an appropriate recording order based on the relevance of the accidents. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input accident data into a generating AI and have the generating AI adjust the recording order based on relevance.

[0095] The UV protection unit can estimate a child's emotions and adjust the intensity of UV protection based on the estimated emotions. For example, if a child is feeling anxious, the UV protection unit can apply strong UV protection to provide a sense of security. If a child is having fun, the UV protection unit can apply moderate UV protection to maintain comfort. Furthermore, if a child is in a hurry, the UV protection unit can quickly apply UV protection to ensure clear vision. Thus, the UV protection unit can provide appropriate UV protection intensity based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the UV protection unit may be performed using AI, or not. For example, the UV protection unit can input child facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0096] The UV protection unit can analyze a child's past UV exposure history to select the optimal UV protection method. For example, if a child has been exposed to strong UV rays in the past, the UV protection unit can apply strong UV protection. If a child has not been exposed to UV rays in the past, the UV protection unit can apply moderate UV protection. The UV protection unit can also analyze a child's UV exposure history to select the optimal UV protection method. This allows the UV protection unit to provide an appropriate UV protection method based on a child's past UV exposure history. Some or all of the above processing in the UV protection unit may be performed using AI, for example, or without AI. For example, the UV protection unit can input UV exposure data into a generating AI and have the generating AI select the optimal UV protection method.

[0097] The UV protection unit can estimate a child's emotions and determine the priority of UV protection based on the estimated emotions. For example, if a child is feeling anxious, the UV protection unit will apply strong UV protection as its top priority. If a child is having fun, the UV protection unit can prioritize applying moderate UV protection. Furthermore, if a child is in a hurry, the UV protection unit can apply UV protection quickly. This allows the UV protection unit to provide appropriate UV protection priorities based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the UV protection unit may be performed using AI, or not. For example, the UV protection unit can input child facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0098] The UV protection unit can select the optimal UV protection method by considering the child's geographical location information during UV protection. For example, if the child is in an area where strong ultraviolet radiation is predicted, the UV protection unit can apply strong UV protection. If the child is in an area with low ultraviolet radiation, the UV protection unit can apply moderate UV protection. The UV protection unit can also analyze the child's geographical location information and select the optimal UV protection method. This allows the UV protection unit to provide an appropriate UV protection method based on the child's geographical location information. Some or all of the above processing in the UV protection unit may be performed using AI, for example, or without AI. For example, the UV protection unit can input geographical location data into a generating AI and have the generating AI select the optimal UV protection method.

[0099] The real-time monitoring unit can estimate a child's emotions and adjust the display method of the real-time monitoring based on the estimated emotions. For example, if the child is feeling anxious, the real-time monitoring unit can provide detailed information to the parent to reassure them. If the child is having fun, the real-time monitoring unit can provide concise information to the parent to reassure them. Also, if the child is in a hurry, the real-time monitoring unit can provide information quickly to the parent to reassure them. In this way, the real-time monitoring unit can provide an appropriate display method of the real-time monitoring based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the real-time monitoring unit may be performed using AI, for example, or without AI. For example, the real-time monitoring unit can input the child's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0100] The real-time verification unit can select the optimal display method by referring to the parent's past verification history during real-time verification. For example, if the parent has previously preferred to view detailed information, the real-time verification unit can provide detailed information. If the parent has previously preferred to view concise information, the real-time verification unit can provide concise information. The real-time verification unit can also analyze the parent's past verification history and select the optimal display method. This allows the real-time verification unit to provide an appropriate display method based on the parent's past verification history. Some or all of the above processing in the real-time verification unit may be performed using AI, for example, or without AI. For example, the real-time verification unit can input the parent's verification history data into a generating AI and have the generating AI select the optimal display method.

[0101] The real-time verification unit can select the optimal display method by considering the parent's device information during real-time verification. For example, if the parent is using a smartphone, the real-time verification unit can provide a display method that matches the screen size. If the parent is using a tablet, the real-time verification unit can provide a display method optimized for a larger screen. Furthermore, if the parent is using a smartwatch, the real-time verification unit can provide a concise and highly visible display method. In this way, the real-time verification unit can provide an appropriate display method based on the parent's device information. Some or all of the above processing in the real-time verification unit may be performed using AI, for example, or without AI. For example, the real-time verification unit can input the parent's device information into a generating AI and have the generating AI select the optimal display method.

[0102] The real-time monitoring unit can estimate a child's emotions and determine the priority of real-time monitoring based on the estimated emotions. For example, if a child is feeling anxious, the real-time monitoring unit will provide the parent with detailed information as a top priority. If a child is having fun, the real-time monitoring unit can prioritize providing concise information. Furthermore, if a child is in a hurry, the real-time monitoring unit can provide information quickly. This allows the real-time monitoring unit to provide appropriate real-time monitoring priorities based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the real-time monitoring unit may be performed using AI, or not. For example, the real-time monitoring unit can input the child's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0103] The real-time verification unit can select the optimal display method by considering the parent's device information during real-time verification. For example, if the parent is using a smartphone, the real-time verification unit can provide a display method that matches the screen size. If the parent is using a tablet, the real-time verification unit can provide a display method optimized for a larger screen. Furthermore, if the parent is using a smartwatch, the real-time verification unit can provide a concise and highly visible display method. In this way, the real-time verification unit can provide an appropriate display method based on the parent's device information. Some or all of the above processing in the real-time verification unit may be performed using AI, for example, or without AI. For example, the real-time verification unit can input the parent's device information into a generating AI and have the generating AI select the optimal display method.

[0104] The confirmation and restriction unit can estimate the child's emotions and adjust the confirmation and restriction method based on the estimated emotions. For example, if the child is feeling anxious, the confirmation and restriction unit can provide the parent with detailed information to reassure them. If the child is having fun, the confirmation and restriction unit can provide concise information to reassure the parent. Also, if the child is in a hurry, the confirmation and restriction unit can provide information quickly to reassure the parent. In this way, the confirmation and restriction unit can provide an appropriate confirmation and restriction method based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the confirmation and restriction unit may be performed using AI, for example, or not using AI. For example, the confirmation and restriction unit can input the child's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0105] The confirmation restriction unit can select the optimal restriction method by referring to the parent's past confirmation history when restricting confirmation. For example, if the parent has preferred to confirm detailed information in the past, the confirmation restriction unit can provide detailed information. If the parent has preferred to confirm concise information in the past, the confirmation restriction unit can provide concise information. The confirmation restriction unit can also analyze the parent's past confirmation history and select the optimal restriction method. This allows the confirmation restriction unit to provide an appropriate restriction method based on the parent's past confirmation history. Some or all of the above processing in the confirmation restriction unit may be performed using AI, for example, or without AI. For example, the confirmation restriction unit can input the parent's confirmation history data into a generating AI and have the generating AI select the optimal restriction method.

[0106] The verification and restriction unit can select the optimal restriction method when performing verification and restriction, taking into account the parent's device information. For example, if the parent is using a smartphone, the verification and restriction unit can provide a restriction method that matches the screen size. If the parent is using a tablet, the verification and restriction unit can provide a restriction method optimized for a larger screen. Furthermore, if the parent is using a smartwatch, the verification and restriction unit can provide a simple and highly visible restriction method. In this way, the verification and restriction unit can provide an appropriate restriction method based on the parent's device information. Some or all of the above processing in the verification and restriction unit may be performed using AI, for example, or without AI. For example, the verification and restriction unit can input the parent's device information into a generating AI and have the generating AI select the optimal restriction method.

[0107] The confirmation and restriction unit can estimate the child's emotions and determine the priority of confirmation and restriction based on the estimated emotions. For example, if the child is feeling anxious, the confirmation and restriction unit will provide the parent with detailed information as a top priority. If the child is having fun, the confirmation and restriction unit can prioritize providing concise information. The confirmation and restriction unit can also provide information quickly if the child is in a hurry. In this way, the confirmation and restriction unit can provide appropriate priority of confirmation and restriction based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the confirmation and restriction unit may be performed using AI, for example, or not using AI. For example, the confirmation and restriction unit can input the child's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0108] The verification and restriction unit can select the optimal restriction method when performing verification and restriction, taking into account the parent's device information. For example, if the parent is using a smartphone, the verification and restriction unit can provide a restriction method that matches the screen size. If the parent is using a tablet, the verification and restriction unit can provide a restriction method optimized for a larger screen. Furthermore, if the parent is using a smartwatch, the verification and restriction unit can provide a simple and highly visible restriction method. In this way, the verification and restriction unit can provide an appropriate restriction method based on the parent's device information. Some or all of the above processing in the verification and restriction unit may be performed using AI, for example, or without AI. For example, the verification and restriction unit can input the parent's device information into a generating AI and have the generating AI select the optimal restriction method.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The Kids Glasses system can also be equipped with a voice recognition unit. The voice recognition unit can recognize a child's voice and perform actions based on specific commands. For example, if a child says "Take a picture," the camera unit will automatically take a picture. Also, if a child says "Give me directions," the guidance unit can provide voice directions from the current location to the destination. Furthermore, the voice recognition unit can analyze the tone and speed of the child's voice and estimate their emotions. This allows the voice recognition unit to perform appropriate actions based on the child's voice.

[0111] The Kids Glasses system can also be equipped with a temperature sensor. This sensor can measure the child's body temperature and ambient temperature, and notify parents if an abnormality is detected. For example, it can send an alert to parents if the child's body temperature becomes high. It can also notify parents if the ambient temperature is extremely high or low. Furthermore, the temperature sensor can record the child's body temperature data, which can be used for health management. This allows the temperature sensor to monitor the child's health status in real time.

[0112] The Kids Glasses system can also be equipped with a music playback unit. This unit can play music to help children relax or have fun. For example, if a child is feeling anxious, it can play relaxing music. Conversely, if a child is having fun, it can play upbeat music. Furthermore, parents can select music suitable for their child through an app. This allows the music playback unit to provide appropriate music based on the child's emotions.

[0113] The kids' glasses system can also be equipped with a vibration notification unit. This vibration notification unit can alert parents via vibration when a child senses danger. For example, if a child senses danger, they can tap the glasses to send a vibration notification to their parent. Parents can also send vibration notifications to their children via an app to alert them. Furthermore, the vibration notification unit can automatically send a vibration notification when a child enters a specific area. This allows the vibration notification unit to provide a means of ensuring the child's safety.

[0114] The Kids Glasses system can also be equipped with a location history function. This function records a child's past movement history, which parents can review through an app. For example, parents can see which route their child took to school. The location history function can also display where the child was at specific times. Furthermore, the location history function can analyze the child's movement patterns and suggest safe routes. In this way, the location history function can provide information to understand the child's movement and ensure their safety.

[0115] The Kids Glasses system can also be equipped with an emergency call function. This function allows children to notify their parents or emergency contacts if they encounter an emergency. For example, if a child feels in danger, they can press an emergency button to notify their parents. The emergency call function can also automatically transmit the child's location information, enabling a quick response. Furthermore, parents can set up emergency contacts through the app. This allows the emergency call function to provide a means of rapid response to ensure the child's safety.

[0116] The Kids Glasses system can also be equipped with a learning support unit. This unit can provide helpful information to children while they are learning. For example, when a child is doing homework, they can ask the glasses questions about unfamiliar words or problems, and the learning support unit will provide explanations. The learning support unit also allows parents to check their child's learning progress through an app. Furthermore, the learning support unit can record the child's learning history and suggest effective learning methods. In this way, the learning support unit can support children's learning and provide an effective learning environment.

[0117] The Kids Glasses system can also be equipped with an emotion sharing unit. This unit can share a child's emotions with parents in real time. For example, if a child is feeling anxious, a notification is sent to the parent. Also, if a child is having fun, that emotion can be shared with the parent. Furthermore, the emotion sharing unit can also allow parents to send messages through the app that correspond to the child's emotions. In this way, the emotion sharing unit can understand the child's emotions in real time and promote communication between parents and children.

[0118] The Kids Glasses system can also be equipped with a health management unit. This unit can monitor a child's health and notify parents. For example, it can measure a child's heart rate and body temperature and send an alert to parents if an abnormality is detected. The health management unit can also record a child's activity level and support healthy lifestyle habits. Furthermore, the health management unit allows parents to review their child's health data through an app and receive appropriate advice. In this way, the health management unit can provide comprehensive support for a child's health.

[0119] The Kids Glasses system can also be equipped with an emotion analysis unit. This unit can analyze a child's emotions in detail and provide feedback to parents. For example, it can analyze a child's facial expressions and tone of voice to detect changes in their emotions. The emotion analysis unit can also accumulate data on the child's emotions and understand long-term emotional trends. Furthermore, the emotion analysis unit can allow parents to review their child's emotional data through an app and receive advice on how to respond appropriately. In this way, the emotion analysis unit can provide information that allows for a detailed understanding of a child's emotions and deepens communication between parents and children.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: In the setup section, the parent sets the destination through the app. For example, the parent can set destinations such as "home," "cram school," or "after-school care" in the app. Step 2: The guidance unit provides directions from the current location to the destination based on the destination set by the setting unit. The guidance unit uses GPS and AI to determine the current location and calculate the optimal route to the destination. Furthermore, the guidance unit can provide voice guidance, such as, "Cross this traffic light and go straight to the left." Step 3: The camera unit provides camera and recording functions. The camera unit captures the scenery the child is seeing, allowing parents to review it on the app. The camera unit can also record accidents, capturing footage of the child in the event of an accident for later review.

[0122] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0123] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0124] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0125] For example, the setting unit is implemented in either the data processing unit 12 or the smart device 14. For example, the identification processing unit 290 of the data processing unit 12 processes the parent to set a destination through the app. The guidance unit is implemented, for example, by the control unit 46A of the smart device 14, which uses GPS and AI to determine the current location and provides voice guidance. The camera unit takes pictures of the scenery the child is seeing using the camera 42 of the smart device 14, and allows the parent to check them on the app. The UV cut unit is implemented, for example, by applying a special coating to the lens of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0128] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0129] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0130] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0132] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0133] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0134] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0135] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0136] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0138] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0140] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] For example, the setting unit is implemented in either the data processing unit 12 or the smart glasses 214. For example, the identification processing unit 290 of the data processing unit 12 processes the parent to set a destination through the app. The guidance unit is implemented, for example, by the control unit 46A of the smart glasses 214, which uses GPS and AI to determine the current location and provides voice guidance. The camera unit takes pictures of the scenery the child is seeing using the camera 42 of the smart glasses 214, and allows the parent to check them on the app. The UV cut unit is implemented, for example, by applying a special coating to the lenses of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] For example, the setting unit is implemented in either the data processing unit 12 or the headset terminal 314. For example, the identification processing unit 290 of the data processing unit 12 processes the parent to set a destination through the app. The guidance unit is implemented, for example, by the control unit 46A of the headset terminal 314, which uses GPS and AI to determine the current location and provides voice guidance. The camera unit, for example, uses the camera 42 of the headset terminal 314 to photograph the scenery the child is seeing, and allows the parent to check it on the app. The UV cut unit is implemented, for example, by applying a special coating to the lens of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] As shown in Figure 7, the 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.

[0160] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0161] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0162] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0164] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0165] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0166] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0167] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0168] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0169] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0170] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0171] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0172] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0173] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0174] For example, the setting unit is implemented in either the data processing unit 12 or the robot 414. For example, the identification processing unit 290 of the data processing unit 12 processes the parent to set a destination via the app. The guidance unit is implemented, for example, by the control unit 46A of the robot 414, which uses GPS and AI to determine the current location and provides voice guidance. The camera unit, for example, uses the camera 42 of the robot 414 to photograph the scenery the child is seeing, allowing the parent to check it on the app. The UV cut unit is implemented, for example, by applying a special coating to the lens of the robot 414. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0175] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0176] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0177] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0178] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0179] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0180] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0181] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0182] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0183] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0184] 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.

[0185] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0186] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0187] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0188] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0189] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0190] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0191] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0192] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0193] (Note 1) A setting unit for setting the destination, A guidance unit that provides directions from the current location to the destination based on the destination set by the aforementioned setting unit, It features a camera unit that provides camera and recording functions. A system characterized by the following features. (Note 2) It features a UV-cut section that provides UV protection. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a real-time monitoring unit so that parents can check in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) Equipped with a privacy restriction section for verification. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned guide section is Provide voice guidance The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned camera unit is Record the accident The system described in Appendix 1, characterized by the features described herein. (Note 7) The setting unit is, It estimates a child's emotions and automatically suggests a destination based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The setting unit is, Analyze the parent's past setting history and select the appropriate destination setting method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The setting unit is, When setting a destination, filtering is performed based on the child's current activities and schedule. The system described in Appendix 1, characterized by the features described herein. (Note 10) The setting unit is, The system estimates the child's emotions and determines the priority of destinations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The setting unit is, When setting a destination, the system prioritizes highly relevant destinations by considering the child's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The setting unit is, When setting a destination, the app analyzes the parents' social media activity and suggests relevant destinations. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned guide section is The system estimates the child's emotions and adjusts the way instructions are presented based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned guide section is When providing directions, the level of detail in the directions is adjusted based on the importance of the route. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned guide section is When providing directions, different directions are applied depending on the route category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned guide section is The system estimates the child's emotions and adjusts the length of the guidance based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned guide section is When providing directions, we will determine the priority of the directions based on when the directions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned guide section is When giving directions, adjust the order of directions based on the relevance of the route. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned camera unit is The system estimates the child's emotions and adjusts the camera's shooting method based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned camera unit is During recording, the level of detail of the recording is adjusted based on the severity of the accident. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned camera unit is During recording, different recording algorithms are applied depending on the category of the accident. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned camera unit is The system estimates the child's emotions and adjusts the length of the video based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned camera unit is During filming, prioritize recording based on when the accident occurred. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned camera unit is During filming, the order of recordings is adjusted based on the relevance of the incident. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned UV-cut portion is The system estimates the child's emotions and adjusts the UV protection intensity based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned UV-cut portion is When providing UV protection, the optimal UV protection method is selected by analyzing the child's past UV exposure history. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned UV-cut portion is The system estimates the child's emotions and determines the priority of UV protection based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned UV-cut portion is When using UV protection, the optimal UV protection method is selected considering the child's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 29) The real-time verification unit described above is: The system estimates the child's emotions and adjusts the real-time display method based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 30) The real-time verification unit described above is: During real-time monitoring, the system selects the optimal display method by referring to the parent's past monitoring history. The system described in Appendix 3, characterized by the features described herein. (Note 31) The real-time verification unit described above is: During real-time monitoring, the optimal display method is selected by considering the parent device information. The system described in Appendix 3, characterized by the features described herein. (Note 32) The real-time verification unit described above is: The system estimates the child's emotions and prioritizes real-time monitoring based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 33) The real-time verification unit described above is: During real-time monitoring, the optimal display method is selected by considering the parent device information. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned confirmation limiting unit is The system estimates the child's emotions and adjusts the confirmation and restriction methods based on the estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 35) The aforementioned confirmation limiting unit is When restricting access, the system will refer to the parent's past access history to select the most appropriate restriction method. The system described in Appendix 4, characterized by the features described herein. (Note 36) The aforementioned confirmation limiting unit is When setting restrictions, the system selects the most appropriate restriction method by considering the parent's device information. The system described in Appendix 4, characterized by the features described herein. (Note 37) The aforementioned confirmation limiting unit is The system estimates the child's emotions and determines the priority of confirmation restrictions based on the estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 38) The aforementioned confirmation limiting unit is When setting restrictions, the system selects the most appropriate restriction method by considering the parent's device information. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A setting unit for setting the destination, A guidance unit that provides directions from the current location to the destination based on the destination set by the aforementioned setting unit, It features a camera unit that provides camera and recording functions. A system characterized by the following features.

2. It features a UV-cut section that provides UV protection. The system according to feature 1.

3. It includes a real-time monitoring unit so that parents can check in real time. The system according to feature 1.

4. Equipped with a privacy restriction section for verification. The system according to feature 1.

5. The aforementioned guide section is Provide voice guidance The system according to feature 1.

6. The aforementioned camera unit is Record the accident The system according to feature 1.

7. The setting unit is, It estimates a child's emotions and automatically suggests a destination based on those estimated emotions. The system according to feature 1.

8. The setting unit is, Analyze the parent's past setting history and select the appropriate destination setting method. The system according to feature 1.

9. The setting unit is, When setting a destination, filtering is performed based on the child's current activities and schedule. The system according to feature 1.

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