system

Abstract

The system according to the embodiment aims to ensure the safety of children when they go to and from school. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, a monitoring unit, and a notification unit. The reception unit accepts input of routes to and from school. The analysis unit analyzes the route accepted by the reception unit and identifies dangerous points. The proposal unit proposes a safe route based on the dangerous points identified by the analysis unit. The monitoring unit monitors the location information of the children. The notification unit detects and notifies of abnormalities.

Description

[Technical Field]

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

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

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

[0004] Conventional technology lacks effective means to ensure the safety of children on their way to and from school, and there is room for improvement.

[0005] The system according to the embodiment aims to ensure the safety of children when they go to and from school. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, a monitoring unit, and a notification unit. The reception unit accepts input of routes to and from school. The analysis unit analyzes the route accepted by the reception unit and identifies dangerous points. The proposal unit proposes a safe route based on the dangerous points identified by the analysis unit. The monitoring unit monitors the location information of the children. The notification unit detects and notifies of abnormalities. [Effects of the Invention]

[0007] The system according to the embodiment can ensure the safety of children when they go to and from school. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An embodiment of the present invention is an after-school care system that reduces the risk of traffic accidents for first- and second-grade elementary school children on their way to and from school. The system requires parents or guardians to input their children's routes to and from school in advance, and AI analyzes the routes to identify risk points, such as dangerous intersections and busy roads. Based on this information, the AI ​​suggests safe routes for children and provides real-time navigation. Furthermore, the system constantly monitors children's location information and immediately notifies parents or guardians if any abnormal movements or unplanned route changes are detected. The AI ​​also analyzes traffic signal changes and the movement of surrounding vehicles in real time to instruct children to cross the street at the appropriate time. For example, if a child is about to run a red light, the AI ​​will immediately issue a warning to warn the child. Furthermore, the AI ​​is configured to pay special attention in areas with high traffic accident rates, helping children to safely go to and from school. This allows the after-school care system to ensure children's safety even in situations where it is difficult for parents or guardians to directly supervise them. This will enable the school protection system to keep children safe on their way to and from school and eliminate the term "the terrible sevens" altogether.

[0029] The school child protection system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, a monitoring unit, and a notification unit. The reception unit accepts input of routes to and from school. Examples of routes to and from school include, but are not limited to, routes from school to home and routes during specific time periods. The reception unit allows, for example, a parent or guardian to input the route using a smartphone or a computer. The reception unit can also accept routes using voice input or image input. The analysis unit analyzes the route accepted by the reception unit and identifies danger points. The danger points are identified based on, for example, criteria such as the frequency of traffic accidents, traffic volume, and the presence or absence of traffic lights, but are not limited to, examples. The analysis unit identifies danger points based on, for example, past traffic accident data. The analysis unit can also analyze changes in traffic lights and the movement of surrounding vehicles in real time. The proposal unit proposes a safe route based on the danger points identified by the analysis unit. The safe route is proposed based on, for example, criteria such as roads with low traffic volume and roads with many traffic lights, but are not limited to, examples. The proposal unit notifies, for example, parents or guardians of the safe route. The suggestion unit can also provide real-time navigation for the children. The monitoring unit monitors the children's location information. The location information is obtained using, for example, but not limited to, technologies such as GPS or beacons. The monitoring unit, for example, monitors the children's location information in real time and detects abnormal movements or unplanned route changes. The notification unit immediately notifies parents or guardians when an abnormality is detected. The notification is performed using, for example, but not limited to, a smartphone app, email, SMS, or other means. As a result, the school child protection system according to the embodiment can ensure the safety of children on their way to and from school and reduce the risk of traffic accidents.

[0030] The school child protection system includes an analysis unit that analyzes traffic signal changes and the movement of surrounding vehicles. The analysis unit analyzes traffic signal changes and the movement of surrounding vehicles. Traffic signal changes are analyzed based on, for example, but not limited to, criteria such as changes in signal color and signal cycle. For example, the analysis unit detects signal color changes in real time and analyzes the signal cycle. The analysis unit also analyzes the movement of surrounding vehicles. The movement of surrounding vehicles is analyzed based on, for example, but not limited to, criteria such as vehicle speed, direction of travel, and following distance. For example, the analysis unit detects vehicle speed in real time and analyzes the direction of travel and following distance. This allows for analysis of traffic signals and vehicle movement to provide safer routes. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit may perform analysis using an AI model that inputs traffic signal changes and the movement of surrounding vehicles and outputs analysis results.

[0031] The school child protection system includes an instruction unit that instructs children when to cross the street. The instruction unit instructs children when to cross the street. The timing to cross is instructed based on, for example, a timing based on a traffic light change or a timing based on vehicle movement, but is not limited to these examples. The instruction unit instructs children when to cross the street based on, for example, a traffic light change. The instruction unit can also instruct children when to cross the street based on vehicle movement. This reduces the risk of traffic accidents by instructing children to cross the street at the appropriate time. Some or all of the above-described processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can give instructions using an AI model that inputs traffic light changes and vehicle movement and outputs the timing to cross the street.

[0032] The school child protection system includes a warning unit that issues a warning when there is a possibility of red light ignition. The warning unit issues a warning when there is a possibility of red light ignition. The possibility of red light ignition is detected based on criteria such as, but not limited to, the relationship between the color of the traffic light and the movement of vehicles, the movement of pedestrians, etc. The warning unit, for example, analyzes the relationship between the color of the traffic light and the movement of vehicles to detect the possibility of red light ignition. The warning unit can also analyze the movement of pedestrians to detect the possibility of red light ignition. This prevents red light ignition and reduces the risk of traffic accidents. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can issue a warning using an AI model that inputs the color of the traffic light, the movement of vehicles, and the movement of pedestrians, and outputs the possibility of red light ignition.

[0033] The suggestion unit is configured to pay attention to areas where traffic accidents frequently occur. The suggestion unit is configured to pay attention to areas where traffic accidents frequently occur. Areas where traffic accidents frequently occur are identified based on criteria such as, for example, past accident data, traffic volume, and road structure, but are not limited to these examples. The suggestion unit identifies areas where traffic accidents frequently occur based on, for example, past accident data. The suggestion unit can also analyze traffic volume and road structure to identify areas where traffic accidents frequently occur. This improves safety in areas where traffic accidents frequently occur. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can perform analysis using an AI model that inputs past accident data, traffic volume, and road structure, and outputs areas where traffic accidents frequently occur.

[0034] The monitoring unit monitors the children's location information in real time and can detect abnormal movements or unplanned route changes. The monitoring unit monitors the children's location information in real time and can detect abnormal movements or unplanned route changes. Abnormal movements are detected based on criteria such as, but not limited to, unplanned route changes or stoppages for a certain period of time. For example, the monitoring unit compares the location information with a pre-set route to detect unplanned route changes. The monitoring unit can also monitor changes in location information to detect stoppages for a certain period of time. This allows for immediate detection of abnormal movements or unplanned route changes and rapid response. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can perform monitoring using an AI model that inputs location information and outputs abnormal movements or unplanned route changes.

[0035] The notification unit can immediately notify a parent or guardian when an abnormality is detected. The notification unit immediately notifies a parent or guardian when an abnormality is detected. An abnormality is detected based on criteria such as, for example, an unplanned route change or a stop for a certain period of time or longer, but is not limited to such examples. For example, the notification unit notifies a parent or guardian when an unplanned route change is detected. The notification unit can also notify a parent or guardian when a stop for a certain period of time or longer is detected. This makes it possible to quickly notify a parent or guardian when an abnormality occurs and prompt them to take action. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can perform notification using an AI model that inputs an abnormality and outputs a notification.

[0036] The reception unit can analyze the parent's or guardian's past route input history and suggest an appropriate input method. The reception unit can analyze the parent's or guardian's past route input history and suggest an appropriate input method. The past route input history is analyzed based on criteria such as, for example, the frequency of route input, the time period, and the type of route, but these examples are not limited to these. The reception unit can, for example, automatically display routes that the parent or guardian has frequently input in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the parent or guardian has used in the past. Furthermore, the reception unit can predict and suggest routes to be used during specific time periods based on the parent's or guardian's past input history. For example, the reception unit can suggest an optimal route based on routes that the parent or guardian has frequently input in the past. The reception unit can also suggest an optimal input method based on the input method the parent or guardian has used in the past. In this way, the analysis of the past route input history can suggest an optimal input method for the parent or guardian. Some or all of the above-described processing by the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can perform analysis using an AI model that takes past route input history as input and outputs an appropriate input method.

[0037] The reception unit can filter route input based on the parent's or guardian's current living situation and areas of interest. The reception unit can filter route input based on the parent's or guardian's current living situation and areas of interest. The current living situation can be filtered based on criteria such as, but not limited to, work schedule and family situation. The areas of interest can be filtered based on criteria such as, but not limited to, hobbies and places of interest. For example, if the parent or guardian is busy, the reception unit can provide a simple route input option. Furthermore, if the parent or guardian is interested in a specific area, the reception unit can prioritize displaying routes related to that area. Furthermore, the reception unit can suggest an optimal route input method depending on the parent's or guardian's living situation. For example, if the parent or guardian is busy, the reception unit can quickly input a route using a simple interface. Furthermore, if the parent or guardian is interested in a specific area, the reception unit can prioritize displaying routes related to that area. This allows filtering based on the parent's or guardian's living situation and areas of interest, enabling more appropriate route input. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can perform filtering using an AI model that takes the living conditions and areas of interest of parents or guardians as input and outputs the filtering results.

[0038] The reception unit can select an appropriate input means depending on the parent or guardian's input method when inputting a route. The reception unit can select an appropriate input means depending on the parent or guardian's input method when inputting a route. The input method can be selected based on criteria such as, but not limited to, voice input, text input, or image input. For example, if the parent or guardian prefers voice input, the reception unit can preferentially provide a voice input option. Also, if the parent or guardian prefers text input, the reception unit can preferentially provide a text input option. Furthermore, if the parent or guardian prefers image input, the reception unit can preferentially provide an image input option. For example, if the parent or guardian prefers voice input, the reception unit can provide a voice input option and perform route input using voice recognition technology. Also, if the parent or guardian prefers text input, the reception unit can provide a text input option and perform route input using a text input interface. This facilitates route input by selecting the optimal input means depending on the parent or guardian's input method. Some or all of the above-described processing in the reception unit can be performed using, for example, AI or without AI. For example, the reception unit can make a selection using an AI model that takes the input method of a parent or guardian as input and outputs an appropriate input means.

[0039] When inputting a route, the reception unit can prioritize inputting a highly relevant route by taking into account the geographical location information of the parent or guardian. When inputting a route, the reception unit prioritizes inputting a highly relevant route by taking into account the geographical location information of the parent or guardian. The geographical location information is considered based on criteria such as, for example, the current location and past movement history, but is not limited to such examples. For example, the reception unit prioritizes suggesting a route close to the current location of the parent or guardian. The reception unit can also suggest a route based on places frequently visited by the parent or guardian. Furthermore, the reception unit can also suggest an optimal route based on the geographical location information of the parent or guardian. For example, the reception unit prioritizes suggesting a route close to the current location of the parent or guardian. The reception unit can also suggest a route based on places frequently visited by the parent or guardian. In this way, by taking the geographical location information of the parent or guardian into account, a highly relevant route can be preferentially input. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can consider the geographical location information of the parent or guardian using an AI model that inputs the geographical location information of the parent or guardian and outputs a highly relevant route.

[0040] The reception unit can analyze the social media activity of the parent or guardian when the route is input and suggest a related route. The reception unit can analyze the social media activity of the parent or guardian when the route is input and suggest a related route. Social media activity is analyzed based on criteria such as, but not limited to, the content of posts, location information, and friendships. The reception unit can, for example, suggest a route based on the locations where the parent or guardian has checked in on social media. The reception unit can also analyze the content of the parent's or guardian's social media posts and suggest a related route. The reception unit can also suggest a related route by referring to the activity of the parent's or guardian's friends on social media. For example, the reception unit can suggest an optimal route based on the locations where the parent or guardian has checked in on social media. The reception unit can also suggest an optimal route based on the content of the parent's or guardian's social media posts. In this way, a related route can be suggested by analyzing the social media activity of the parent or guardian. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can perform analysis using an AI model that inputs the social media activity of the parent or guardian and outputs a related route.

[0041] The reception unit can customize the input method by reflecting past feedback from the parent or guardian when inputting a route. The reception unit customizes the input method by reflecting past feedback from the parent or guardian when inputting a route. The past feedback is reflected based on criteria such as, for example, the content of the evaluation and areas for improvement, but is not limited to such examples. The reception unit, for example, suggests an optimal input method based on feedback previously provided by the parent or guardian. The reception unit can also customize the input interface by reflecting past feedback from the parent or guardian. Furthermore, the reception unit can optimize the input procedure based on past feedback from the parent or guardian. For example, the reception unit suggests an optimal input method based on feedback previously provided by the parent or guardian. The reception unit can also customize the input interface by reflecting past feedback from the parent or guardian. In this way, the input method can be customized by reflecting past feedback from the parent or guardian. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can use an AI model that uses past feedback from the parent or guardian as input and customizes the input method.

[0042] The analysis unit can improve the accuracy of identifying dangerous points by referring to past accident data during analysis. The analysis unit can improve the accuracy of identifying dangerous points by referring to past accident data during analysis. The past accident data is referenced based on criteria such as, for example, accident frequency, accident type, and accident cause, but is not limited to these examples. The analysis unit can identify dangerous points based on, for example, past traffic accident data. The analysis unit can also analyze past accident data to identify dangerous points in specific time periods or locations. The analysis unit can also improve the accuracy of identifying dangerous points by referring to past accident data. For example, dangerous points are identified based on past traffic accident data. The analysis unit can also analyze past accident data to identify dangerous points in specific time periods or locations. Thus, by referring to past accident data, the accuracy of identifying dangerous points is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform reference using an AI model that inputs past accident data and outputs dangerous points.

[0043] The analysis unit can apply different analysis algorithms depending on the route category during analysis. The analysis unit can apply different analysis algorithms depending on the route category during analysis. Route categories are classified based on criteria such as, for example, main roads, residential areas, and areas around schools, but are not limited to these examples. For example, the analysis unit can apply a specific analysis algorithm to routes around schools. The analysis unit can also apply a different analysis algorithm to routes in residential areas. Furthermore, the analysis unit can apply yet another analysis algorithm to routes in commercial areas. For example, a specific analysis algorithm can be applied to routes around schools. A different analysis algorithm can also be applied to routes in residential areas. This enables more accurate analysis by applying an analysis algorithm depending on the route category. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can apply an AI model that inputs the route category and outputs an analysis algorithm.

[0044] The analysis unit can improve the accuracy of the analysis by referring to the parent's or guardian's past route analysis results during analysis. The analysis unit improves the accuracy of the analysis by referring to the parent's or guardian's past route analysis results during analysis. The past route analysis results are referenced based on criteria such as, for example, the accuracy of the analysis results and the type of analysis results, but are not limited to these examples. The analysis unit improves the accuracy of the analysis, for example, based on route analysis results previously provided by the parent or guardian. The analysis unit can also identify dangerous points by referring to the parent's or guardian's past route analysis results. Furthermore, the analysis unit can adjust the analysis algorithm by referring to the parent's or guardian's past route analysis results. For example, the analysis accuracy is improved based on the parent's or guardian's past route analysis results. The analysis unit can also identify dangerous points by referring to the parent's or guardian's past route analysis results. In this way, the accuracy of the analysis is improved by referring to the parent's or guardian's past route analysis results. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI or without AI. For example, the analysis unit can input the results of a parent's or guardian's past route analysis and use an AI model to improve the accuracy of the analysis.

[0045] The analysis unit can determine the priority of dangerous points based on the time of route submission during analysis. The analysis unit can determine the priority of dangerous points based on the time of route submission during analysis. The time of route submission is considered based on criteria such as, but not limited to, the time of day when the route was submitted or the day of the week when the route was submitted. The analysis unit can also determine the priority of dangerous points based on, for example, the time of day when the route was submitted. The analysis unit can also determine the priority of dangerous points based on the day of the week when the route was submitted. The analysis unit can also determine the priority of dangerous points based on the season when the route was submitted. For example, the analysis unit can determine the priority of dangerous points based on the time of day when the route was submitted. The analysis unit can also determine the priority of dangerous points based on the day of the week when the route was submitted. This enables more appropriate analysis by determining the priority of dangerous points based on the time of route submission. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can make its determination using an AI model that inputs the time of route submission and outputs the priority of dangerous points.

[0046] The analysis unit can adjust the order of danger points based on the route relevance during analysis. The analysis unit can adjust the order of danger points based on the route relevance during analysis. The route relevance is adjusted based on criteria such as, but not limited to, the degree of route overlap and the route distance. For example, the analysis unit prioritizes the analysis of the most relevant danger point based on the route relevance. The analysis unit can also adjust the order of danger points based on the route relevance. Furthermore, the analysis unit can optimize the analysis order of danger points based on the route relevance. For example, the analysis unit prioritizes the analysis of the most relevant danger point based on the route relevance. The analysis unit can also adjust the order of danger points based on the route relevance. This enables more accurate analysis by adjusting the order of danger points based on the route relevance. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can make the adjustment using an AI model that inputs the route relevance and outputs the order of danger points.

[0047] The analysis unit can adjust the use of technical terms in the analysis according to the parent's or guardian's level of expertise during analysis. The analysis unit can adjust the use of technical terms in the analysis according to the parent's or guardian's level of expertise during analysis. The level of expertise is adjusted based on criteria such as, but not limited to, the frequency of use of technical terms and the level of detail in the explanation. For example, the analysis unit can provide detailed analysis results if the parent or guardian has technical expertise. Alternatively, the analysis unit can provide concise analysis results if the parent or guardian does not have technical expertise. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the parent's or guardian's level of expertise. For example, detailed analysis results are provided if the parent or guardian has technical expertise. Alternatively, concise analysis results are provided if the parent or guardian does not have technical expertise. In this way, by adjusting the technical terms in the analysis according to the parent's or guardian's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can make adjustments using an AI model that inputs the parent's or guardian's level of expertise and outputs the way the analysis results are presented.

[0048] The suggestion unit can adjust the level of detail of the proposal based on the importance of the route when making the proposal. The suggestion unit adjusts the level of detail of the proposal based on the importance of the route when making the proposal. The importance of the route is evaluated based on criteria such as, but not limited to, the number of dangerous points and a safety rating. For example, the suggestion unit makes a detailed proposal for an important route. The suggestion unit can also make a concise proposal for a low-importance route. Furthermore, the suggestion unit can adjust the level of detail of the proposal based on the importance of the route. For example, the suggestion unit makes a detailed proposal for an important route. The suggestion unit can also make a concise proposal for a low-importance route. In this way, adjusting the level of detail of the proposal based on the importance of the route enables more appropriate proposals. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can make the adjustment using an AI model that inputs the importance of the route and outputs the level of detail of the proposal.

[0049] The suggestion unit can apply different suggestion algorithms depending on the route category when proposing a route. The suggestion unit can apply different suggestion algorithms depending on the route category when proposing a route. Route categories are classified based on criteria such as, for example, main roads, residential areas, and areas around schools, but are not limited to these examples. The suggestion unit can apply a specific suggestion algorithm to routes around schools. The suggestion unit can also apply a different suggestion algorithm to routes in residential areas. The suggestion unit can also apply a different suggestion algorithm to routes in commercial areas. For example, the suggestion unit can apply a specific suggestion algorithm to routes around schools. The suggestion unit can also apply a different suggestion algorithm to routes in residential areas. This allows for more appropriate suggestions by applying a suggestion algorithm depending on the route category. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can perform the application using an AI model that inputs the route category and outputs a suggestion algorithm.

[0050] The suggestion unit can improve the accuracy of the suggestion by referring to the parent's or guardian's past suggestion results when making a suggestion. The suggestion unit improves the accuracy of the suggestion by referring to the parent's or guardian's past suggestion results when making a suggestion. The past suggestion results are referenced based on criteria such as, for example, the success rate of the suggestion, the content of the suggestion, etc., but are not limited to these examples. The suggestion unit improves the accuracy of the suggestion, for example, based on the suggestion results provided in the past by the parent or guardian. The suggestion unit can also make optimal suggestions by referring to the parent's or guardian's past suggestion results. Furthermore, the suggestion unit can adjust the suggestion algorithm by referring to the parent's or guardian's past suggestion results. For example, the suggestion unit improves the accuracy of the suggestion based on the parent's or guardian's past suggestion results. The suggestion unit can also make optimal suggestions by referring to the parent's or guardian's past suggestion results. In this way, the accuracy of the suggestion is improved by referring to the parent's or guardian's past suggestion results. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can use the parent's or guardian's past suggestion results as input and perform reference using an AI model that improves the accuracy of the suggestion.

[0051] The suggestion unit may determine the priority of the proposal based on the time of submission of the route when the proposal is made. The suggestion unit may determine the priority of the proposal based on the time of submission of the route when the proposal is made. The time of submission of the route is considered based on criteria such as, but not limited to, the time of submission or the day of the week when the route was submitted. The suggestion unit may determine the priority of the proposal based on, for example, the time of submission of the route. The suggestion unit may also determine the priority of the proposal based on the day of the week when the route was submitted. The suggestion unit may also determine the priority of the proposal based on the season when the route was submitted. For example, the suggestion unit may determine the priority of the proposal based on the time of submission of the route. The suggestion unit may also determine the priority of the proposal based on the day of the week when the route was submitted. This enables more appropriate suggestions by determining the priority of the proposal based on the time of submission of the route. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may make a decision using an AI model that inputs the time of submission of the route and outputs the priority of the proposal.

[0052] The suggestion unit can adjust the order of proposals based on the relevance of the routes when making a proposal. The suggestion unit can adjust the order of proposals based on the relevance of the routes when making a proposal. The route relevance is adjusted based on criteria such as, but not limited to, the degree of overlap of the routes and the distance of the routes. For example, the suggestion unit prioritizes the most relevant proposal based on the relevance of the routes. The suggestion unit can also adjust the order of proposals based on the relevance of the routes. Furthermore, the suggestion unit can optimize the order of proposals based on the relevance of the routes. For example, the most relevant proposal is prioritized based on the relevance of the routes. The suggestion unit can also adjust the order of proposals based on the relevance of the routes. As a result, adjusting the order of proposals based on the relevance of the routes enables more appropriate proposals. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can make the adjustment using an AI model that inputs the relevance of the routes and outputs the order of proposals.

[0053] The suggestion unit can adjust the use of technical terms in the proposal according to the parent's or guardian's level of expertise. The suggestion unit can adjust the use of technical terms in the proposal according to the parent's or guardian's level of expertise. The level of expertise can be adjusted based on criteria such as, for example, the frequency of use of technical terms and the level of detail in the explanation, but is not limited to these examples. For example, if the parent or guardian has technical expertise, the suggestion unit can make a detailed proposal. Alternatively, if the parent or guardian does not have technical expertise, the suggestion unit can make a concise proposal. Furthermore, the suggestion unit can adjust the way the proposal is expressed according to the parent's or guardian's level of expertise. For example, if the parent or guardian has technical expertise, the suggestion unit can make a detailed proposal. Alternatively, if the parent or guardian does not have technical expertise, the suggestion unit can make a concise proposal. In this way, adjusting the technical terms in the proposal according to the parent's or guardian's level of expertise enables the proposal to be more easily understood. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can make adjustments using an AI model that inputs the parent's or guardian's level of expertise and outputs the way the proposal is expressed.

[0054] The monitoring unit can improve the accuracy of monitoring by referring to past location information data during monitoring. The monitoring unit can improve the accuracy of monitoring by referring to past location information data during monitoring. The past location information data is referenced based on criteria such as, for example, the accuracy of the location information and the frequency of location information updates, but examples are not limited to these. The monitoring unit can improve the accuracy of monitoring based on, for example, past location information data. The monitoring unit can also analyze past location information data to improve the accuracy of monitoring at specific time periods or locations. The monitoring unit can also improve the accuracy of monitoring by referring to past location information data. For example, the monitoring unit can improve the accuracy of monitoring based on past location information data. The monitoring unit can also analyze past location information data to improve the accuracy of monitoring at specific time periods or locations. In this way, the accuracy of monitoring is improved by referring to past location information data. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input past location information data and perform reference using an AI model that improves the accuracy of monitoring.

[0055] The monitoring unit can apply different monitoring algorithms depending on the route category during monitoring. The monitoring unit applies different monitoring algorithms depending on the route category during monitoring. Route categories are classified based on criteria such as, for example, main roads, residential areas, and areas around schools, but are not limited to these examples. For example, the monitoring unit can apply a specific monitoring algorithm to routes around schools. The monitoring unit can also apply a different monitoring algorithm to routes in residential areas. Furthermore, the monitoring unit can apply yet another monitoring algorithm to routes in commercial areas. For example, a specific monitoring algorithm can be applied to routes around schools. A different monitoring algorithm can also be applied to routes in residential areas. This enables more appropriate monitoring by applying a monitoring algorithm depending on the route category. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can apply the monitoring algorithm using an AI model that inputs the route category and outputs the monitoring algorithm.

[0056] The monitoring unit can improve the accuracy of monitoring by referring to the parent's or guardian's past monitoring results during monitoring. The monitoring unit can improve the accuracy of monitoring by referring to the parent's or guardian's past monitoring results during monitoring. The past monitoring results are referenced based on criteria such as, for example, monitoring accuracy and monitoring content, but are not limited to these examples. The monitoring unit can improve the accuracy of monitoring, for example, based on monitoring results previously provided by the parent or guardian. The monitoring unit can also improve the accuracy of monitoring by referring to the parent's or guardian's past monitoring results. Furthermore, the monitoring unit can adjust the monitoring algorithm by referring to the parent's or guardian's past monitoring results. For example, the monitoring unit can improve the accuracy of monitoring based on the parent's or guardian's past monitoring results. The monitoring unit can also improve the accuracy of monitoring by referring to the parent's or guardian's past monitoring results. In this way, the accuracy of monitoring is improved by referring to the parent's or guardian's past monitoring results. Some or all of the above-described processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the parent's or guardian's past monitoring results and perform reference using an AI model that improves the accuracy of monitoring.

[0057] The monitoring unit can determine the monitoring priority based on the time when the location information was submitted during monitoring. The monitoring unit can determine the monitoring priority based on the time when the location information was submitted during monitoring. The time when the location information was submitted is considered based on criteria such as, but not limited to, the time of day when the location information was submitted or the day of the week when the location information was submitted. The monitoring unit can also determine the monitoring priority based on, for example, the time of day when the location information was submitted. The monitoring unit can also determine the monitoring priority based on the day of the week when the location information was submitted. Furthermore, the monitoring unit can determine the monitoring priority based on the season when the location information was submitted. For example, the monitoring unit can determine the monitoring priority based on the time of day when the location information was submitted. The monitoring unit can also determine the monitoring priority based on the day of the week when the location information was submitted. This enables more appropriate monitoring by determining the monitoring priority based on the time when the location information was submitted. Some or all of the above-described processing by the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can make its determination using an AI model that inputs the time when the location information was submitted and outputs the monitoring priority.

[0058] The monitoring unit can adjust the monitoring order based on the relevance of the location information during monitoring. The monitoring unit can adjust the monitoring order based on the relevance of the location information during monitoring. The relevance of the location information is adjusted based on criteria such as, for example, the degree of overlap of the location information and the distance between the location information, but is not limited to these examples. For example, the monitoring unit prioritizes monitoring the most relevant point based on the relevance of the location information. The monitoring unit can also adjust the monitoring order based on the relevance of the location information. Furthermore, the monitoring unit can optimize the monitoring order based on the relevance of the location information. For example, the monitoring unit prioritizes monitoring the most relevant point based on the relevance of the location information. The monitoring unit can also adjust the monitoring order based on the relevance of the location information. As a result, adjusting the monitoring order based on the relevance of the location information enables more appropriate monitoring. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can make the adjustment using an AI model that inputs the relevance of the location information and outputs the monitoring order.

[0059] The monitoring unit can adjust the use of monitoring terminology according to the parent's or guardian's level of expertise during monitoring. The monitoring unit can adjust the use of monitoring terminology according to the parent's or guardian's level of expertise during monitoring. The level of expertise is adjusted based on criteria such as, but not limited to, the frequency of use of terminology and the level of detail in the explanation. For example, the monitoring unit can provide detailed monitoring results if the parent or guardian has specialized knowledge. The monitoring unit can also provide concise monitoring results if the parent or guardian does not have specialized knowledge. Furthermore, the monitoring unit can adjust the way the monitoring results are presented according to the parent's or guardian's level of expertise. For example, the monitoring unit can provide detailed monitoring results if the parent or guardian has specialized knowledge. The monitoring unit can also provide concise monitoring results if the parent or guardian does not have specialized knowledge. In this way, by adjusting the monitoring terminology according to the parent's or guardian's level of expertise, it is possible to provide monitoring results that are easier to understand. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can make adjustments using an AI model that inputs the parent's or guardian's level of expertise and outputs the way the monitoring results are presented.

[0060] The notification unit can adjust the level of detail of the notification based on the importance of the anomaly at the time of notification. The notification unit adjusts the level of detail of the notification based on the importance of the anomaly at the time of notification. The importance of the anomaly is evaluated based on criteria such as, for example, the type of anomaly and the frequency of occurrence of the anomaly, but is not limited to such examples. For example, the notification unit provides a detailed notification in the case of a significant anomaly. The notification unit can also provide a brief notification in the case of a low-importance anomaly. Furthermore, the notification unit can adjust the level of detail of the notification based on the importance of the anomaly. For example, a detailed notification in the case of a significant anomaly. The notification unit can also provide a brief notification in the case of a low-importance anomaly. In this way, adjusting the level of detail of the notification based on the importance of the anomaly enables more appropriate notification. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can make the adjustment using an AI model that inputs the importance of the anomaly and outputs the level of detail of the notification.

[0061] The notification unit can apply different notification algorithms depending on the category of the abnormality when issuing a notification. The notification unit can apply different notification algorithms depending on the category of the abnormality when issuing a notification. The categories of abnormalities are classified based on criteria such as, for example, a traffic accident, a lost child, an unplanned route change, etc., but are not limited to these examples. For example, the notification unit can apply a specific notification algorithm to an abnormality related to a traffic signal. The notification unit can also apply a different notification algorithm to an abnormality related to vehicle movement. Furthermore, the notification unit can apply yet another notification algorithm to an abnormality related to pedestrian movement. For example, a specific notification algorithm can be applied to an abnormality related to a traffic signal. A different notification algorithm can also be applied to an abnormality related to vehicle movement. This enables more appropriate notification by applying a notification algorithm depending on the category of the abnormality. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can apply the notification algorithm using an AI model that inputs the category of the abnormality and outputs the notification algorithm.

[0062] The notification unit can improve the accuracy of notifications by referring to the parent's or guardian's past notification results when providing notifications. The notification unit can improve the accuracy of notifications by referring to the parent's or guardian's past notification results when providing notifications. Past notification results are referenced based on criteria such as, but not limited to, the success rate of notifications and the content of notifications. The notification unit can improve the accuracy of notifications, for example, based on notification results provided by the parent or guardian in the past. The notification unit can also provide optimal notifications by referring to the parent's or guardian's past notification results. The notification unit can also adjust the notification algorithm by referring to the parent's or guardian's past notification results. For example, the notification unit can improve the accuracy of notifications based on the parent's or guardian's past notification results. The notification unit can also provide optimal notifications by referring to the parent's or guardian's past notification results. In this way, the accuracy of notifications is improved by referring to the parent's or guardian's past notification results. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without AI. For example, the notification unit can input the parent's or guardian's past notification results and perform reference using an AI model that improves the accuracy of notifications.

[0063] The notification unit can determine the priority of notifications based on the time of occurrence of the abnormality at the time of notification. The notification unit determines the priority of notifications based on the time of occurrence of the abnormality at the time of notification. The time of occurrence of the abnormality is considered based on criteria such as, for example, the time period in which the abnormality occurred and the day of the week in which the abnormality occurred, but is not limited to these examples. The notification unit can determine the priority of notifications based on, for example, the time period in which the abnormality occurred. The notification unit can also determine the priority of notifications based on the day of the week in which the abnormality occurred. Furthermore, the notification unit can determine the priority of notifications based on the season in which the abnormality occurred. For example, the notification unit can determine the priority of notifications based on the time period in which the abnormality occurred. The notification unit can also determine the priority of notifications based on the day of the week in which the abnormality occurred. This enables more appropriate notifications by determining the priority of notifications based on the time of occurrence of the abnormality. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can make its determination using an AI model that inputs the time of occurrence of the abnormality and outputs the priority of notifications.

[0064] The notification unit can adjust the order of notifications based on the relevance of the anomalies when issuing notifications. The notification unit adjusts the order of notifications based on the relevance of the anomalies when issuing notifications. The relevance of the anomalies is adjusted based on criteria such as, for example, the degree of overlap of the anomalies and the type of anomaly, but is not limited to these examples. For example, the notification unit prioritizes the most relevant notification based on the relevance of the anomalies. The notification unit can also adjust the order of notifications based on the relevance of the anomalies. Furthermore, the notification unit can optimize the order of notifications based on the relevance of the anomalies. For example, the most relevant notification is prioritized based on the relevance of the anomalies. The notification unit can also adjust the order of notifications based on the relevance of the anomalies. As a result, adjusting the order of notifications based on the relevance of the anomalies enables more appropriate notifications. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can make the adjustment using an AI model that inputs the relevance of the anomalies and outputs the order of notifications.

[0065] The notification unit can adjust the use of technical terms in the notification according to the parent's or guardian's level of expertise. The notification unit can adjust the use of technical terms in the notification according to the parent's or guardian's level of expertise. The level of expertise can be adjusted based on criteria such as, for example, the frequency of use of technical terms and the level of detail in the explanation, but is not limited to these examples. For example, the notification unit can provide a detailed notification if the parent or guardian has technical expertise. The notification unit can also provide a concise notification if the parent or guardian does not have technical expertise. Furthermore, the notification unit can adjust the way the notification is expressed according to the parent's or guardian's level of expertise. For example, the notification unit can provide a detailed notification if the parent or guardian has technical expertise. The notification unit can also provide a concise notification if the parent or guardian does not have technical expertise. In this way, adjusting the technical terms in the notification according to the parent's or guardian's level of expertise enables the notification to be more easily understood. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or without AI. For example, the notification unit can make the adjustment using an AI model that inputs the parent's or guardian's level of expertise and outputs the way the notification is expressed.

[0066] The instruction unit can improve the accuracy of the crossing instruction by referring to past traffic signal data when issuing a crossing instruction. The instruction unit can improve the accuracy of the crossing instruction by referring to past traffic signal data when issuing a crossing instruction. The past traffic signal data is referenced based on, for example, criteria such as signal change patterns and signal cycles, but is not limited to such examples. The instruction unit can improve the accuracy of the crossing instruction based on, for example, past traffic signal data. The instruction unit can also analyze past traffic signal data to improve the accuracy of the crossing instruction at a specific time period or location. The instruction unit can also improve the accuracy of the crossing instruction by referring to past traffic signal data. For example, the accuracy of the crossing instruction can be improved based on past traffic signal data. The accuracy of the crossing instruction can also be improved at a specific time period or location by analyzing past traffic signal data. In this way, the accuracy of the crossing instruction is improved by referring to past traffic signal data. Some or all of the above-described processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can input past traffic signal data and perform reference using an AI model that improves the accuracy of the crossing instruction.

[0067] The instruction unit can apply different instruction algorithms depending on traffic conditions when issuing a crossing instruction. The instruction unit applies different instruction algorithms depending on traffic conditions when issuing a crossing instruction. Traffic conditions are analyzed based on criteria such as, but not limited to, vehicle speed, vehicle density, and the number of pedestrians. For example, the instruction unit applies a specific instruction algorithm when traffic volume is heavy. The instruction unit can also apply a different instruction algorithm when traffic volume is light. Furthermore, the instruction unit can apply an optimal instruction algorithm depending on traffic conditions. For example, a specific instruction algorithm is applied when traffic volume is heavy. A different instruction algorithm can also be applied when traffic volume is light. This enables more appropriate crossing instructions to be issued by applying an instruction algorithm depending on the traffic conditions. Some or all of the above-described processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can apply an AI model that inputs traffic conditions and outputs an instruction algorithm.

[0068] The instruction unit can improve the accuracy of instructions when instructing the child to cross the street by referring to past instructions from the parent or guardian. The instruction unit improves the accuracy of instructions when instructing the child to cross the street by referring to past instructions from the parent or guardian. Past instruction results are referenced based on criteria such as, for example, the success rate of instructions and the content of instructions, but are not limited to such examples. The instruction unit improves the accuracy of instructions, for example, based on instructions provided by the parent or guardian in the past. The instruction unit can also provide optimal instructions by referring to past instructions from the parent or guardian. Furthermore, the instruction unit can adjust the instruction algorithm by referring to past instructions from the parent or guardian. For example, the accuracy of instructions can be improved based on instructions provided by the parent or guardian in the past. The instruction unit can also provide optimal instructions by referring to past instructions from the parent or guardian. In this way, the accuracy of instructions can be improved by referring to past instructions from the parent or guardian. Some or all of the above-described processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can input past instructions from the parent or guardian and refer to an AI model that improves the accuracy of instructions.

[0069] The instruction unit, when issuing a crossing instruction, can determine the priority of the instruction based on the timing of the traffic signal change. The instruction unit, when issuing a crossing instruction, can determine the priority of the instruction based on the timing of the traffic signal change. The timing of the traffic signal change is considered based on criteria such as, but not limited to, the change in traffic signal color or the signal cycle. The instruction unit can determine the priority of the crossing instruction based on, for example, the time of day when the traffic signal changes. The instruction unit can also determine the priority of the crossing instruction based on the day of the week when the traffic signal changes. Furthermore, the instruction unit can determine the priority of the crossing instruction based on the season when the traffic signal changes. For example, the instruction unit determines the priority of the crossing instruction based on the time of day when the traffic signal changes. The instruction unit can also determine the priority of the crossing instruction based on the day of the week when the traffic signal changes. Thus, determining the priority of the instruction based on the time of day when the traffic signal changes enables more appropriate crossing instructions. Some or all of the above-described processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can make its determination using an AI model that inputs the time of day when the traffic signal changes and outputs the priority of the instruction.

[0070] The instruction unit can adjust the order of instructions based on the relevance of traffic signals when issuing a crossing instruction. The instruction unit can adjust the order of instructions based on the relevance of traffic signals when issuing a crossing instruction. The relevance of traffic signals is adjusted based on criteria such as, but not limited to, a signal change pattern or a signal period. For example, the instruction unit prioritizes issuing the most relevant crossing instruction based on the relevance of traffic signals. The instruction unit can also adjust the order of crossing instructions based on the relevance of traffic signals. Furthermore, the instruction unit can optimize the order of crossing instructions based on the relevance of traffic signals. For example, the instruction unit prioritizes issuing the most relevant crossing instruction based on the relevance of traffic signals. The instruction unit can also adjust the order of crossing instructions based on the relevance of traffic signals. In this way, adjusting the order of instructions based on the relevance of traffic signals enables more appropriate crossing instructions. Some or all of the above-described processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can make adjustments using an AI model that inputs the relevance of traffic signals and outputs the order of instructions.

[0071] The instruction unit can adjust the use of technical terms in the instructions depending on the parent's or guardian's level of expertise when instructing the child to cross the street. The instruction unit can adjust the use of technical terms in the instructions depending on the parent's or guardian's level of expertise when instructing the child to cross the street. The level of expertise can be adjusted based on criteria such as, for example, the frequency of use of technical terms and the level of detail in the explanation, but is not limited to such examples. For example, if the parent or guardian has technical expertise, the instruction unit can provide detailed instructions to cross the street. Alternatively, if the parent or guardian does not have technical expertise, the instruction unit can provide concise instructions to cross the street. Furthermore, the instruction unit can adjust the manner in which the crossing instructions are expressed depending on the parent's or guardian's level of expertise. For example, if the parent or guardian has technical expertise, the instruction unit can provide detailed instructions to cross the street. Alternatively, if the parent or guardian does not have technical expertise, the instruction unit can provide concise instructions to cross the street. In this way, adjusting the technical terms in the instructions depending on the parent's or guardian's level of expertise enables the instructions to be more easily understood. Some or all of the above-described processing by the instruction unit may be performed using, for example, AI, or may be performed without AI. For example, the instruction unit can make adjustments using an AI model that inputs the parent's or guardian's level of expertise and outputs the manner in which the instructions are expressed.

[0072] The warning unit can adjust the level of detail of the warning based on the importance of the red light running when issuing a warning. The warning unit adjusts the level of detail of the warning based on the importance of the red light running when issuing a warning. The importance of the red light running is evaluated based on criteria such as, but not limited to, the frequency of the red light running and the impact of the red light running. For example, the warning unit issues a detailed warning in the case of a serious red light running. The warning unit can also issue a brief warning in the case of a less serious red light running. Furthermore, the warning unit can adjust the level of detail of the warning based on the importance of the red light running. For example, a detailed warning is issued in the case of a serious red light running. The warning unit can also issue a brief warning in the case of a less serious red light running. In this way, adjusting the level of detail of the warning based on the importance of the red light running enables a more appropriate warning. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can make the adjustment using an AI model that inputs the importance of the red light running and outputs the level of detail of the warning.

[0073] The warning unit can apply different warning algorithms depending on the category of red light violation when issuing a warning. The warning unit can apply different warning algorithms depending on the category of red light violation when issuing a warning. The categories of red light violations are classified based on criteria such as, but not limited to, red light violations and yellow light violations. For example, the warning unit can apply a specific warning algorithm to red light violations related to traffic signals. The warning unit can also apply a different warning algorithm to red light violations related to vehicle movement. Furthermore, the warning unit can apply yet another warning algorithm to red light violations related to pedestrian movement. For example, the warning unit can apply a specific warning algorithm to red light violations related to traffic signals. The warning unit can also apply a different warning algorithm to red light violations related to vehicle movement. This enables more appropriate warnings by applying a warning algorithm depending on the category of red light violation. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can apply the processing using an AI model that inputs the category of red light violation and outputs a warning algorithm.

[0074] The warning unit can improve the accuracy of the warning by referring to past warning results from the parent or guardian when issuing a warning. The warning unit can improve the accuracy of the warning by referring to past warning results from the parent or guardian when issuing a warning. Past warning results are referenced based on criteria such as, but not limited to, the success rate of the warning and the content of the warning. The warning unit can improve the accuracy of the warning, for example, based on warning results provided by the parent or guardian in the past. The warning unit can also provide an optimal warning by referring to past warning results from the parent or guardian. Furthermore, the warning unit can adjust the warning algorithm by referring to past warning results from the parent or guardian. For example, the accuracy of the warning can be improved based on past warning results provided by the parent or guardian. The optimal warning can also be provided by referring to past warning results from the parent or guardian. In this way, the accuracy of the warning can be improved by referring to the past warning results from the parent or guardian. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can use past warning results from the parent or guardian as input and refer to an AI model that improves the accuracy of the warning.

[0075] The warning unit, when issuing a warning, can determine the priority of the warning based on the time when the red light ignition occurred. The warning unit, when issuing a warning, can determine the priority of the warning based on the time when the red light ignition occurred. The time when the red light ignition occurred is considered based on criteria such as, for example, the time of day when the red light ignition occurred, the day of the week when the red light ignition occurred, etc., but is not limited to these examples. The warning unit, for example, can determine the priority of the warning based on the time of day when the red light ignition occurred. The warning unit can also determine the priority of the warning based on the day of the week when the red light ignition occurred. Furthermore, the warning unit can determine the priority of the warning based on the season when the red light ignition occurred. For example, the warning unit can determine the priority of the warning based on the time of day when the red light ignition occurred. The warning unit can also determine the priority of the warning based on the day of the week when the red light ignition occurred. This enables more appropriate warnings by determining the priority of the warning based on the time when the red light ignition occurred. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can make its determination using an AI model that inputs the time when the red light ignition occurred and outputs the priority of the warning.

[0076] The warning unit can adjust the order of warnings based on the relevance of red light violations when issuing a warning. The warning unit adjusts the order of warnings based on the relevance of red light violations when issuing a warning. The relevance of red light violations is adjusted based on criteria such as, but not limited to, the degree of overlap of red light violations and the type of red light violation. For example, the warning unit prioritizes issuing the most relevant warning based on the relevance of red light violations. The warning unit can also adjust the order of warnings based on the relevance of red light violations. Furthermore, the warning unit can optimize the order of warnings based on the relevance of red light violations. For example, the warning unit prioritizes issuing the most relevant warning based on the relevance of red light violations. The warning unit can also adjust the order of warnings based on the relevance of red light violations. As a result, adjusting the order of warnings based on the relevance of red light violations enables more appropriate warnings. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can make the adjustment using an AI model that inputs the relevance of red light violations and outputs the order of warnings.

[0077] The warning unit can adjust the use of technical terms in the warning depending on the parent's or guardian's level of expertise. The warning unit can adjust the use of technical terms in the warning depending on the parent's or guardian's level of expertise. The level of expertise can be adjusted based on criteria such as, for example, the frequency of use of technical terms and the level of detail in the explanation, but is not limited to these examples. For example, the warning unit can issue a detailed warning if the parent or guardian has technical expertise. Alternatively, the warning unit can issue a concise warning if the parent or guardian does not have technical expertise. Furthermore, the warning unit can adjust the way the warning is expressed depending on the parent's or guardian's level of expertise. For example, the warning unit can issue a detailed warning if the parent or guardian has technical expertise. Alternatively, the warning unit can issue a concise warning if the parent or guardian does not have technical expertise. In this way, adjusting the technical terms in the warning depending on the parent's or guardian's level of expertise enables the warning to be more easily understood. Some or all of the above-described processing in the warning unit can be performed using, for example, AI, or without AI. For example, the warning unit can make adjustments using an AI model that inputs the parent's or guardian's level of expertise and outputs the way the warning is expressed.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The school advocacy system can also suggest routes taking into account the living conditions of parents and guardians. For example, if a parent or guardian is busy, the suggestion unit can provide route options that can be easily input. Also, if a parent or guardian is interested in a specific area, routes related to that area can be displayed preferentially. Furthermore, the system can suggest the optimal route input method depending on the living conditions of the parent or guardian. This allows for flexible route suggestions that suit the living conditions of parents and guardians.

[0080] The school-age child advocacy system can also analyze the parent's or guardian's past route input history and suggest appropriate input methods. For example, it can automatically display routes that the parent or guardian has frequently input in the past as candidates. It can also prioritize input methods (voice, text, etc.) that the parent or guardian has used in the past. It can also predict and suggest routes to be used during specific times based on the parent's or guardian's past input history. This allows it to suggest the optimal input method for the parent or guardian by analyzing their past route input history.

[0081] The school advocacy system can also prioritize the input of highly relevant routes by taking into account the geographic location information of the parent or guardian. For example, it can prioritize the suggested routes that are close to the parent or guardian's current location. It can also suggest routes based on places that the parent or guardian frequently visits. It can also suggest the optimal route based on the geographic location information of the parent or guardian. This allows it to prioritize the input of highly relevant routes by taking into account the geographic location information of the parent or guardian.

[0082] The school advocacy system can also analyze the social media activities of parents and guardians to suggest relevant routes. For example, it can suggest routes based on the places where parents and guardians have checked in on social media. It can also analyze the content of social media posts by parents and guardians to suggest relevant routes. It can also suggest relevant routes based on the activities of parents and guardians' friends on social media. In this way, it can suggest relevant routes by analyzing the social media activities of parents and guardians.

[0083] The school advocacy system can further customize the input method by reflecting the parents' or guardians' past feedback. For example, it can suggest the optimal input method based on the feedback provided by the parents or guardians in the past. It can also customize the input interface by reflecting the parents' or guardians' past feedback. It can also optimize the input procedure based on the parents' or guardians' past feedback. This allows the input method to be customized by reflecting the parents' or guardians' past feedback.

[0084] The processing flow of the first embodiment will be briefly explained below.

[0085] Step 1: The reception unit accepts input of the route to and from school. The route to and from school may include, for example, the route from school to home or a route during a specific time period. Parents or guardians can input the route using a smartphone or computer, and voice or image input is also possible. Step 2: The analysis unit analyzes the route received by the reception unit and identifies dangerous points. Dangerous points are identified based on criteria such as the frequency of traffic accidents, traffic volume, and the presence or absence of traffic lights. The analysis unit can analyze past traffic accident data, real-time changes in traffic lights, and the movement of surrounding vehicles. Step 3: The suggestion unit suggests safe routes based on the danger points identified by the analysis unit. Safe routes are suggested based on criteria such as roads with low traffic volume and roads with many traffic lights. The suggestion unit can also notify parents or guardians of safe routes and provide real-time navigation for children. Step 4: The monitoring unit monitors the children's location information. Location information is obtained using technologies such as GPS and beacons. The monitoring unit monitors the children's location information in real time and detects abnormal movements or unplanned route changes. Step 5: If an abnormality is detected, the notification unit immediately notifies the parent or guardian via a smartphone app, email, SMS, or other means.

[0086] (Example 2) An embodiment of the present invention is an after-school care system that reduces the risk of traffic accidents for first- and second-grade elementary school children on their way to and from school. The system requires parents or guardians to input their children's routes to and from school in advance, and AI analyzes the routes to identify risk points, such as dangerous intersections and busy roads. Based on this information, the AI ​​suggests safe routes for children and provides real-time navigation. Furthermore, the system constantly monitors children's location information and immediately notifies parents or guardians if any abnormal movements or unplanned route changes are detected. The AI ​​also analyzes traffic signal changes and the movement of surrounding vehicles in real time to instruct children to cross the street at the appropriate time. For example, if a child is about to run a red light, the AI ​​will immediately issue a warning to warn the child. Furthermore, the AI ​​is configured to pay special attention in areas with high traffic accident rates, helping children to safely go to and from school. This allows the after-school care system to ensure children's safety even in situations where it is difficult for parents or guardians to directly supervise them. This will enable the school protection system to keep children safe on their way to and from school and eliminate the term "the terrible sevens" altogether.

[0087] The school child protection system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, a monitoring unit, and a notification unit. The reception unit accepts input of routes to and from school. Examples of routes to and from school include, but are not limited to, routes from school to home and routes during specific time periods. The reception unit allows, for example, a parent or guardian to input the route using a smartphone or a computer. The reception unit can also accept routes using voice input or image input. The analysis unit analyzes the route accepted by the reception unit and identifies danger points. The danger points are identified based on, for example, criteria such as the frequency of traffic accidents, traffic volume, and the presence or absence of traffic lights, but are not limited to, examples. The analysis unit identifies danger points based on, for example, past traffic accident data. The analysis unit can also analyze changes in traffic lights and the movement of surrounding vehicles in real time. The proposal unit proposes a safe route based on the danger points identified by the analysis unit. The safe route is proposed based on, for example, criteria such as roads with low traffic volume and roads with many traffic lights, but are not limited to, examples. The proposal unit notifies, for example, parents or guardians of the safe route. The suggestion unit can also provide real-time navigation for the children. The monitoring unit monitors the children's location information. The location information is obtained using, for example, but not limited to, technologies such as GPS or beacons. The monitoring unit, for example, monitors the children's location information in real time and detects abnormal movements or unplanned route changes. The notification unit immediately notifies parents or guardians when an abnormality is detected. The notification is performed using, for example, but not limited to, a smartphone app, email, SMS, or other means. As a result, the school child protection system according to the embodiment can ensure the safety of children on their way to and from school and reduce the risk of traffic accidents.

[0088] The school child protection system includes an analysis unit that analyzes traffic signal changes and the movement of surrounding vehicles. The analysis unit analyzes traffic signal changes and the movement of surrounding vehicles. Traffic signal changes are analyzed based on, for example, but not limited to, criteria such as changes in signal color and signal cycle. For example, the analysis unit detects signal color changes in real time and analyzes the signal cycle. The analysis unit also analyzes the movement of surrounding vehicles. The movement of surrounding vehicles is analyzed based on, for example, but not limited to, criteria such as vehicle speed, direction of travel, and following distance. For example, the analysis unit detects vehicle speed in real time and analyzes the direction of travel and following distance. This allows for analysis of traffic signals and vehicle movement to provide safer routes. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit may perform analysis using an AI model that inputs traffic signal changes and the movement of surrounding vehicles and outputs analysis results.

[0089] The school child protection system includes an instruction unit that instructs children when to cross the street. The instruction unit instructs children when to cross the street. The timing to cross is instructed based on, for example, a timing based on a traffic light change or a timing based on vehicle movement, but is not limited to these examples. The instruction unit instructs children when to cross the street based on, for example, a traffic light change. The instruction unit can also instruct children when to cross the street based on vehicle movement. This reduces the risk of traffic accidents by instructing children to cross the street at the appropriate time. Some or all of the above-described processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can give instructions using an AI model that inputs traffic light changes and vehicle movement and outputs the timing to cross the street.

[0090] The school child protection system includes a warning unit that issues a warning when there is a possibility of red light ignition. The warning unit issues a warning when there is a possibility of red light ignition. The possibility of red light ignition is detected based on criteria such as, but not limited to, the relationship between the color of the traffic light and the movement of vehicles, the movement of pedestrians, etc. The warning unit, for example, analyzes the relationship between the color of the traffic light and the movement of vehicles to detect the possibility of red light ignition. The warning unit can also analyze the movement of pedestrians to detect the possibility of red light ignition. This prevents red light ignition and reduces the risk of traffic accidents. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can issue a warning using an AI model that inputs the color of the traffic light, the movement of vehicles, and the movement of pedestrians, and outputs the possibility of red light ignition.

[0091] The suggestion unit is configured to pay attention to areas where traffic accidents frequently occur. The suggestion unit is configured to pay attention to areas where traffic accidents frequently occur. Areas where traffic accidents frequently occur are identified based on criteria such as, for example, past accident data, traffic volume, and road structure, but are not limited to these examples. The suggestion unit identifies areas where traffic accidents frequently occur based on, for example, past accident data. The suggestion unit can also analyze traffic volume and road structure to identify areas where traffic accidents frequently occur. This improves safety in areas where traffic accidents frequently occur. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can perform analysis using an AI model that inputs past accident data, traffic volume, and road structure, and outputs areas where traffic accidents frequently occur.

[0092] The monitoring unit monitors the children's location information in real time and can detect abnormal movements or unplanned route changes. The monitoring unit monitors the children's location information in real time and can detect abnormal movements or unplanned route changes. Abnormal movements are detected based on criteria such as, but not limited to, unplanned route changes or stoppages for a certain period of time. For example, the monitoring unit compares the location information with a pre-set route to detect unplanned route changes. The monitoring unit can also monitor changes in location information to detect stoppages for a certain period of time. This allows for immediate detection of abnormal movements or unplanned route changes and rapid response. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can perform monitoring using an AI model that inputs location information and outputs abnormal movements or unplanned route changes.

[0093] The notification unit can immediately notify a parent or guardian when an abnormality is detected. The notification unit immediately notifies a parent or guardian when an abnormality is detected. An abnormality is detected based on criteria such as, for example, an unplanned route change or a stop for a certain period of time or longer, but is not limited to such examples. For example, the notification unit notifies a parent or guardian when an unplanned route change is detected. The notification unit can also notify a parent or guardian when a stop for a certain period of time or longer is detected. This makes it possible to quickly notify a parent or guardian when an abnormality occurs and prompt them to take action. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can perform notification using an AI model that inputs an abnormality and outputs a notification.

[0094] The reception unit can estimate the child's emotions and adjust the timing of route input based on the estimated child's emotions. The reception unit can estimate the child's emotions and adjust the timing of route input based on the estimated child's emotions. The child's emotions are estimated based on criteria such as, but not limited to, facial expression recognition, voice analysis, and behavioral patterns. For example, the reception unit can analyze the child's facial expressions to estimate the emotion. The reception unit can also analyze the child's voice to estimate the emotion. The reception unit can also analyze the child's behavioral patterns to estimate the emotion. For example, if the child is excited, the reception unit can temporarily delay the route input to calm the child. If the child is tired, the reception unit can quickly input the route using a simple interface. If the child is anxious, the reception unit can display a message encouraging support from a parent or guardian before inputting the route. This allows for more appropriate route input by adjusting the timing of route input according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation 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 processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may estimate emotions using an AI model that receives a child's facial expressions, voice, and behavioral patterns as input and outputs emotions.

[0095] The reception unit can analyze the parent's or guardian's past route input history and suggest an appropriate input method. The reception unit can analyze the parent's or guardian's past route input history and suggest an appropriate input method. The past route input history is analyzed based on criteria such as, for example, the frequency of route input, the time period, and the type of route, but these examples are not limited to these. The reception unit can, for example, automatically display routes that the parent or guardian has frequently input in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the parent or guardian has used in the past. Furthermore, the reception unit can predict and suggest routes to be used during specific time periods based on the parent's or guardian's past input history. For example, the reception unit can suggest an optimal route based on routes that the parent or guardian has frequently input in the past. The reception unit can also suggest an optimal input method based on the input method the parent or guardian has used in the past. In this way, the analysis of the past route input history can suggest an optimal input method for the parent or guardian. Some or all of the above-described processing by the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can perform analysis using an AI model that takes past route input history as input and outputs an appropriate input method.

[0096] The reception unit can filter route input based on the parent's or guardian's current living situation and areas of interest. The reception unit can filter route input based on the parent's or guardian's current living situation and areas of interest. The current living situation can be filtered based on criteria such as, but not limited to, work schedule and family situation. The areas of interest can be filtered based on criteria such as, but not limited to, hobbies and places of interest. For example, if the parent or guardian is busy, the reception unit can provide a simple route input option. Furthermore, if the parent or guardian is interested in a specific area, the reception unit can prioritize displaying routes related to that area. Furthermore, the reception unit can suggest an optimal route input method depending on the parent's or guardian's living situation. For example, if the parent or guardian is busy, the reception unit can quickly input a route using a simple interface. Furthermore, if the parent or guardian is interested in a specific area, the reception unit can prioritize displaying routes related to that area. This allows filtering based on the parent's or guardian's living situation and areas of interest, enabling more appropriate route input. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can perform filtering using an AI model that takes the living conditions and areas of interest of parents or guardians as input and outputs the filtering results.

[0097] The reception unit can select an appropriate input means depending on the parent or guardian's input method when inputting a route. The reception unit can select an appropriate input means depending on the parent or guardian's input method when inputting a route. The input method can be selected based on criteria such as, but not limited to, voice input, text input, or image input. For example, if the parent or guardian prefers voice input, the reception unit can preferentially provide a voice input option. Also, if the parent or guardian prefers text input, the reception unit can preferentially provide a text input option. Furthermore, if the parent or guardian prefers image input, the reception unit can preferentially provide an image input option. For example, if the parent or guardian prefers voice input, the reception unit can provide a voice input option and perform route input using voice recognition technology. Also, if the parent or guardian prefers text input, the reception unit can provide a text input option and perform route input using a text input interface. This facilitates route input by selecting the optimal input means depending on the parent or guardian's input method. Some or all of the above-described processing in the reception unit can be performed using, for example, AI or without AI. For example, the reception unit can make a selection using an AI model that takes the input method of a parent or guardian as input and outputs an appropriate input means.

[0098] The reception unit can estimate the child's emotions and prioritize the routes to be input based on the estimated emotions. The reception unit can estimate the child's emotions and prioritize the routes to be input based on the estimated emotions. The child's emotions are estimated based on criteria such as, but not limited to, facial expression recognition, voice analysis, and behavioral patterns. For example, the reception unit can analyze the child's facial expressions to estimate the emotions. The reception unit can also analyze the child's voice to estimate the emotions. The reception unit can also analyze the child's behavioral patterns to estimate the emotions. For example, if the child is excited, the reception unit can prioritize the safest route. If the child is tired, the reception unit can prioritize the shortest route. If the child is anxious, the reception unit can prioritize the route that will reassure the parent or guardian. This allows for a safer route to be provided by prioritizing routes according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation 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 processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may estimate emotions using an AI model that receives a child's facial expressions, voice, and behavioral patterns as input and outputs emotions.

[0099] When inputting a route, the reception unit can prioritize inputting a highly relevant route by taking into account the geographical location information of the parent or guardian. When inputting a route, the reception unit prioritizes inputting a highly relevant route by taking into account the geographical location information of the parent or guardian. The geographical location information is considered based on criteria such as, for example, the current location and past movement history, but is not limited to such examples. For example, the reception unit prioritizes suggesting a route close to the current location of the parent or guardian. The reception unit can also suggest a route based on places frequently visited by the parent or guardian. Furthermore, the reception unit can also suggest an optimal route based on the geographical location information of the parent or guardian. For example, the reception unit prioritizes suggesting a route close to the current location of the parent or guardian. The reception unit can also suggest a route based on places frequently visited by the parent or guardian. In this way, by taking the geographical location information of the parent or guardian into account, a highly relevant route can be preferentially input. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can consider the geographical location information of the parent or guardian using an AI model that inputs the geographical location information of the parent or guardian and outputs a highly relevant route.

[0100] The reception unit can analyze the social media activity of the parent or guardian when the route is input and suggest a related route. The reception unit can analyze the social media activity of the parent or guardian when the route is input and suggest a related route. Social media activity is analyzed based on criteria such as, but not limited to, the content of posts, location information, and friendships. The reception unit can, for example, suggest a route based on the locations where the parent or guardian has checked in on social media. The reception unit can also analyze the content of the parent's or guardian's social media posts and suggest a related route. The reception unit can also suggest a related route by referring to the activity of the parent's or guardian's friends on social media. For example, the reception unit can suggest an optimal route based on the locations where the parent or guardian has checked in on social media. The reception unit can also suggest an optimal route based on the content of the parent's or guardian's social media posts. In this way, a related route can be suggested by analyzing the social media activity of the parent or guardian. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can perform analysis using an AI model that inputs the social media activity of the parent or guardian and outputs a related route.

[0101] The reception unit can customize the input method by reflecting past feedback from the parent or guardian when inputting a route. The reception unit customizes the input method by reflecting past feedback from the parent or guardian when inputting a route. The past feedback is reflected based on criteria such as, for example, the content of the evaluation and areas for improvement, but is not limited to such examples. The reception unit, for example, suggests an optimal input method based on feedback previously provided by the parent or guardian. The reception unit can also customize the input interface by reflecting past feedback from the parent or guardian. Furthermore, the reception unit can optimize the input procedure based on past feedback from the parent or guardian. For example, the reception unit suggests an optimal input method based on feedback previously provided by the parent or guardian. The reception unit can also customize the input interface by reflecting past feedback from the parent or guardian. In this way, the input method can be customized by reflecting past feedback from the parent or guardian. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can use an AI model that uses past feedback from the parent or guardian as input and customizes the input method.

[0102] The analysis unit can estimate the child's emotions and adjust the analysis method for dangerous points based on the estimated child's emotions. The analysis unit can estimate the child's emotions and adjust the analysis method for dangerous points based on the estimated child's emotions. The child's emotions can be estimated based on criteria such as, but not limited to, facial expression recognition, voice analysis, and behavioral patterns. For example, the analysis unit can analyze the child's facial expressions to estimate the emotions. The analysis unit can also analyze the child's voice to estimate the emotions. The analysis unit can also analyze the child's behavioral patterns to estimate the emotions. For example, if the child is excited, the analysis unit can perform a more detailed analysis to identify dangerous points. If the child is tired, the analysis unit can perform a simplified analysis to quickly identify dangerous points. If the child is anxious, the analysis unit can also perform an analysis based on the opinions of the parent or guardian. This allows for more accurate analysis by adjusting the analysis method for dangerous points according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may estimate emotions using an AI model that takes a child's facial expressions, voice, and behavioral patterns as input and outputs emotions.

[0103] The analysis unit can improve the accuracy of identifying dangerous points by referring to past accident data during analysis. The analysis unit can improve the accuracy of identifying dangerous points by referring to past accident data during analysis. The past accident data is referenced based on criteria such as, for example, accident frequency, accident type, and accident cause, but is not limited to these examples. The analysis unit can identify dangerous points based on, for example, past traffic accident data. The analysis unit can also analyze past accident data to identify dangerous points in specific time periods or locations. The analysis unit can also improve the accuracy of identifying dangerous points by referring to past accident data. For example, dangerous points are identified based on past traffic accident data. The analysis unit can also analyze past accident data to identify dangerous points in specific time periods or locations. Thus, by referring to past accident data, the accuracy of identifying dangerous points is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform reference using an AI model that inputs past accident data and outputs dangerous points.

[0104] The analysis unit can apply different analysis algorithms depending on the route category during analysis. The analysis unit can apply different analysis algorithms depending on the route category during analysis. Route categories are classified based on criteria such as, for example, main roads, residential areas, and areas around schools, but are not limited to these examples. For example, the analysis unit can apply a specific analysis algorithm to routes around schools. The analysis unit can also apply a different analysis algorithm to routes in residential areas. Furthermore, the analysis unit can apply yet another analysis algorithm to routes in commercial areas. For example, a specific analysis algorithm can be applied to routes around schools. A different analysis algorithm can also be applied to routes in residential areas. This enables more accurate analysis by applying an analysis algorithm depending on the route category. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can apply an AI model that inputs the route category and outputs an analysis algorithm.

[0105] The analysis unit can improve the accuracy of the analysis by referring to the parent's or guardian's past route analysis results during analysis. The analysis unit improves the accuracy of the analysis by referring to the parent's or guardian's past route analysis results during analysis. The past route analysis results are referenced based on criteria such as, for example, the accuracy of the analysis results and the type of analysis results, but are not limited to these examples. The analysis unit improves the accuracy of the analysis, for example, based on route analysis results previously provided by the parent or guardian. The analysis unit can also identify dangerous points by referring to the parent's or guardian's past route analysis results. Furthermore, the analysis unit can adjust the analysis algorithm by referring to the parent's or guardian's past route analysis results. For example, the analysis accuracy is improved based on the parent's or guardian's past route analysis results. The analysis unit can also identify dangerous points by referring to the parent's or guardian's past route analysis results. In this way, the accuracy of the analysis is improved by referring to the parent's or guardian's past route analysis results. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI or without AI. For example, the analysis unit can input the results of a parent's or guardian's past route analysis and use an AI model to improve the accuracy of the analysis.

[0106] The analysis unit can estimate the child's emotions and prioritize dangerous areas based on the estimated emotions. The analysis unit can estimate the child's emotions and prioritize dangerous areas based on the estimated emotions. The child's emotions can be estimated based on criteria such as, but not limited to, facial expression recognition, voice analysis, and behavioral patterns. For example, the analysis unit can analyze the child's facial expressions to estimate the emotions. The analysis unit can also analyze the child's voice to estimate the emotions. The analysis unit can also analyze the child's behavioral patterns to estimate the emotions. For example, if the child is excited, the analysis unit can prioritize analyzing the most dangerous areas. If the child is tired, the analysis unit can prioritize analyzing the nearest dangerous areas. If the child is anxious, the analysis unit can prioritize analyzing areas that parents or guardians are concerned about. This allows for the provision of a safer route by prioritizing dangerous areas based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may estimate emotions using an AI model that takes a child's facial expressions, voice, and behavioral patterns as input and outputs emotions.

[0107] The analysis unit can determine the priority of dangerous points based on the time of route submission during analysis. The analysis unit can determine the priority of dangerous points based on the time of route submission during analysis. The time of route submission is considered based on criteria such as, but not limited to, the time of day when the route was submitted or the day of the week when the route was submitted. The analysis unit can also determine the priority of dangerous points based on, for example, the time of day when the route was submitted. The analysis unit can also determine the priority of dangerous points based on the day of the week when the route was submitted. The analysis unit can also determine the priority of dangerous points based on the season when the route was submitted. For example, the analysis unit can determine the priority of dangerous points based on the time of day when the route was submitted. The analysis unit can also determine the priority of dangerous points based on the day of the week when the route was submitted. This enables more appropriate analysis by determining the priority of dangerous points based on the time of route submission. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can make its determination using an AI model that inputs the time of route submission and outputs the priority of dangerous points.

[0108] The analysis unit can adjust the order of danger points based on the route relevance during analysis. The analysis unit can adjust the order of danger points based on the route relevance during analysis. The route relevance is adjusted based on criteria such as, but not limited to, the degree of route overlap and the route distance. For example, the analysis unit prioritizes the analysis of the most relevant danger point based on the route relevance. The analysis unit can also adjust the order of danger points based on the route relevance. Furthermore, the analysis unit can optimize the analysis order of danger points based on the route relevance. For example, the analysis unit prioritizes the analysis of the most relevant danger point based on the route relevance. The analysis unit can also adjust the order of danger points based on the route relevance. This enables more accurate analysis by adjusting the order of danger points based on the route relevance. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can make the adjustment using an AI model that inputs the route relevance and outputs the order of danger points.

[0109] The analysis unit can adjust the use of technical terms in the analysis according to the parent's or guardian's level of expertise during analysis. The analysis unit can adjust the use of technical terms in the analysis according to the parent's or guardian's level of expertise during analysis. The level of expertise is adjusted based on criteria such as, but not limited to, the frequency of use of technical terms and the level of detail in the explanation. For example, the analysis unit can provide detailed analysis results if the parent or guardian has technical expertise. Alternatively, the analysis unit can provide concise analysis results if the parent or guardian does not have technical expertise. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the parent's or guardian's level of expertise. For example, detailed analysis results are provided if the parent or guardian has technical expertise. Alternatively, concise analysis results are provided if the parent or guardian does not have technical expertise. In this way, by adjusting the technical terms in the analysis according to the parent's or guardian's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can make adjustments using an AI model that inputs the parent's or guardian's level of expertise and outputs the way the analysis results are presented.

[0110] The suggestion unit can estimate the child's emotions and adjust the way the suggestions are expressed based on the estimated child's emotions. The suggestion unit can estimate the child's emotions and adjust the way the suggestions are expressed based on the estimated child's emotions. The child's emotions are estimated based on criteria such as, but not limited to, facial expression recognition, voice analysis, and behavioral patterns. For example, the suggestion unit can analyze the child's facial expressions to estimate the emotions. The suggestion unit can also analyze the child's voice to estimate the emotions. The suggestion unit can also analyze the child's behavioral patterns to estimate the emotions. For example, if the child is excited, the suggestion unit can make a visually calming suggestion. If the child is tired, the suggestion unit can make a concise and easy-to-understand suggestion. If the child is anxious, the suggestion unit can make a suggestion encouraging support from a parent or guardian. This allows for more appropriate suggestions to be made by adjusting the way the suggestions are expressed based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation 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 processing in the proposing unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposing unit may estimate emotions using an AI model that receives a child's facial expressions, voice, and behavioral patterns as input and outputs emotions.

[0111] The suggestion unit can adjust the level of detail of the proposal based on the importance of the route when making the proposal. The suggestion unit adjusts the level of detail of the proposal based on the importance of the route when making the proposal. The importance of the route is evaluated based on criteria such as, but not limited to, the number of dangerous points and a safety rating. For example, the suggestion unit makes a detailed proposal for an important route. The suggestion unit can also make a concise proposal for a low-importance route. Furthermore, the suggestion unit can adjust the level of detail of the proposal based on the importance of the route. For example, the suggestion unit makes a detailed proposal for an important route. The suggestion unit can also make a concise proposal for a low-importance route. In this way, adjusting the level of detail of the proposal based on the importance of the route enables more appropriate proposals. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can make the adjustment using an AI model that inputs the importance of the route and outputs the level of detail of the proposal.

[0112] The suggestion unit can apply different suggestion algorithms depending on the route category when proposing a route. The suggestion unit can apply different suggestion algorithms depending on the route category when proposing a route. Route categories are classified based on criteria such as, for example, main roads, residential areas, and areas around schools, but are not limited to these examples. The suggestion unit can apply a specific suggestion algorithm to routes around schools. The suggestion unit can also apply a different suggestion algorithm to routes in residential areas. The suggestion unit can also apply a different suggestion algorithm to routes in commercial areas. For example, the suggestion unit can apply a specific suggestion algorithm to routes around schools. The suggestion unit can also apply a different suggestion algorithm to routes in residential areas. This allows for more appropriate suggestions by applying a suggestion algorithm depending on the route category. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can perform the application using an AI model that inputs the route category and outputs a suggestion algorithm.

[0113] The suggestion unit can improve the accuracy of the suggestion by referring to the parent's or guardian's past suggestion results when making a suggestion. The suggestion unit improves the accuracy of the suggestion by referring to the parent's or guardian's past suggestion results when making a suggestion. The past suggestion results are referenced based on criteria such as, for example, the success rate of the suggestion, the content of the suggestion, etc., but are not limited to these examples. The suggestion unit improves the accuracy of the suggestion, for example, based on the suggestion results provided in the past by the parent or guardian. The suggestion unit can also make optimal suggestions by referring to the parent's or guardian's past suggestion results. Furthermore, the suggestion unit can adjust the suggestion algorithm by referring to the parent's or guardian's past suggestion results. For example, the suggestion unit improves the accuracy of the suggestion based on the parent's or guardian's past suggestion results. The suggestion unit can also make optimal suggestions by referring to the parent's or guardian's past suggestion results. In this way, the accuracy of the suggestion is improved by referring to the parent's or guardian's past suggestion results. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can use the parent's or guardian's past suggestion results as input and perform reference using an AI model that improves the accuracy of the suggestion.

[0114] The suggestion unit can estimate the child's emotions and adjust the length of the suggestions based on the estimated emotions. The suggestion unit can estimate the child's emotions and adjust the length of the suggestions based on the estimated emotions. The child's emotions are estimated based on criteria such as, but not limited to, facial expression recognition, voice analysis, and behavioral patterns. For example, the suggestion unit can analyze the child's facial expressions to estimate the emotions. The suggestion unit can also analyze the child's voice to estimate the emotions. The suggestion unit can also analyze the child's behavioral patterns to estimate the emotions. For example, if the child is excited, the suggestion unit can make a short, concise suggestion. If the child is tired, the suggestion unit can make a longer suggestion with detailed explanations. If the child is anxious, the suggestion unit can make a suggestion encouraging support from a parent or guardian. This allows for more appropriate suggestions by adjusting the length of the suggestions based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation 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 processing in the proposing unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposing unit may estimate emotions using an AI model that receives a child's facial expressions, voice, and behavioral patterns as input and outputs emotions.

[0115] The suggestion unit may determine the priority of the proposal based on the time of submission of the route when the proposal is made. The suggestion unit may determine the priority of the proposal based on the time of submission of the route when the proposal is made. The time of submission of the route is considered based on criteria such as, but not limited to, the time of submission or the day of the week when the route was submitted. The suggestion unit may determine the priority of the proposal based on, for example, the time of submission of the route. The suggestion unit may also determine the priority of the proposal based on the day of the week when the route was submitted. The suggestion unit may also determine the priority of the proposal based on the season when the route was submitted. For example, the suggestion unit may determine the priority of the proposal based on the time of submission of the route. The suggestion unit may also determine the priority of the proposal based on the day of the week when the route was submitted. This enables more appropriate suggestions by determining the priority of the proposal based on the time of submission of the route. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may make a decision using an AI model that inputs the time of submission of the route and outputs the priority of the proposal.

[0116] The suggestion unit can adjust the order of proposals based on the relevance of the routes when making a proposal. The suggestion unit can adjust the order of proposals based on the relevance of the routes when making a proposal. The route relevance is adjusted based on criteria such as, but not limited to, the degree of overlap of the routes and the distance of the routes. For example, the suggestion unit prioritizes the most relevant proposal based on the relevance of the routes. The suggestion unit can also adjust the order of proposals based on the relevance of the routes. Furthermore, the suggestion unit can optimize the order of proposals based on the relevance of the routes. For example, the most relevant proposal is prioritized based on the relevance of the routes. The suggestion unit can also adjust the order of proposals based on the relevance of the routes. As a result, adjusting the order of proposals based on the relevance of the routes enables more appropriate proposals. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can make the adjustment using an AI model that inputs the relevance of the routes and outputs the order of proposals.

[0117] The suggestion unit can adjust the use of technical terms in the proposal according to the parent's or guardian's level of expertise. The suggestion unit can adjust the use of technical terms in the proposal according to the parent's or guardian's level of expertise. The level of expertise can be adjusted based on criteria such as, for example, the frequency of use of technical terms and the level of detail in the explanation, but is not limited to these examples. For example, if the parent or guardian has technical expertise, the suggestion unit can make a detailed proposal. Alternatively, if the parent or guardian does not have technical expertise, the suggestion unit can make a concise proposal. Furthermore, the suggestion unit can adjust the way the proposal is expressed according to the parent's or guardian's level of expertise. For example, if the parent or guardian has technical expertise, the suggestion unit can make a detailed proposal. Alternatively, if the parent or guardian does not have technical expertise, the suggestion unit can make a concise proposal. In this way, adjusting the technical terms in the proposal according to the parent's or guardian's level of expertise enables the proposal to be more easily understood. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can make adjustments using an AI model that inputs the parent's or guardian's level of expertise and outputs the way the proposal is expressed.

[0118] The monitoring unit can estimate the child's emotions and adjust the monitoring frequency based on the estimated child's emotions. The monitoring unit can estimate the child's emotions and adjust the monitoring frequency based on the estimated child's emotions. The child's emotions are estimated based on criteria such as, but not limited to, facial expression recognition, voice analysis, and behavioral patterns. For example, the monitoring unit can analyze the child's facial expressions to estimate the emotions. The monitoring unit can also analyze the child's voice to estimate the emotions. The monitoring unit can also analyze the child's behavioral patterns to estimate the emotions. For example, if the child is excited, the monitoring unit can increase the monitoring frequency. If the child is tired, the monitoring unit can decrease the monitoring frequency. If the child is anxious, the monitoring unit can notify the parent or guardian and adjust the monitoring frequency. This allows for more appropriate monitoring by adjusting the monitoring frequency according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation 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 processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit may estimate emotions using an AI model that receives a child's facial expressions, voice, and behavioral patterns as input and outputs emotions.

[0119] The monitoring unit can improve the accuracy of monitoring by referring to past location information data during monitoring. The monitoring unit can improve the accuracy of monitoring by referring to past location information data during monitoring. The past location information data is referenced based on criteria such as, for example, the accuracy of the location information and the frequency of location information updates, but examples are not limited to these. The monitoring unit can improve the accuracy of monitoring based on, for example, past location information data. The monitoring unit can also analyze past location information data to improve the accuracy of monitoring at specific time periods or locations. The monitoring unit can also improve the accuracy of monitoring by referring to past location information data. For example, the monitoring unit can improve the accuracy of monitoring based on past location information data. The monitoring unit can also analyze past location information data to improve the accuracy of monitoring at specific time periods or locations. In this way, the accuracy of monitoring is improved by referring to past location information data. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input past location information data and perform reference using an AI model that improves the accuracy of monitoring.

[0120] The monitoring unit can apply different monitoring algorithms depending on the route category during monitoring. The monitoring unit applies different monitoring algorithms depending on the route category during monitoring. Route categories are classified based on criteria such as, for example, main roads, residential areas, and areas around schools, but are not limited to these examples. For example, the monitoring unit can apply a specific monitoring algorithm to routes around schools. The monitoring unit can also apply a different monitoring algorithm to routes in residential areas. Furthermore, the monitoring unit can apply yet another monitoring algorithm to routes in commercial areas. For example, a specific monitoring algorithm can be applied to routes around schools. A different monitoring algorithm can also be applied to routes in residential areas. This enables more appropriate monitoring by applying a monitoring algorithm depending on the route category. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can apply the monitoring algorithm using an AI model that inputs the route category and outputs the monitoring algorithm.

[0121] The monitoring unit can improve the accuracy of monitoring by referring to the parent's or guardian's past monitoring results during monitoring. The monitoring unit can improve the accuracy of monitoring by referring to the parent's or guardian's past monitoring results during monitoring. The past monitoring results are referenced based on criteria such as, for example, monitoring accuracy and monitoring content, but are not limited to these examples. The monitoring unit can improve the accuracy of monitoring, for example, based on monitoring results previously provided by the parent or guardian. The monitoring unit can also improve the accuracy of monitoring by referring to the parent's or guardian's past monitoring results. Furthermore, the monitoring unit can adjust the monitoring algorithm by referring to the parent's or guardian's past monitoring results. For example, the monitoring unit can improve the accuracy of monitoring based on the parent's or guardian's past monitoring results. The monitoring unit can also improve the accuracy of monitoring by referring to the parent's or guardian's past monitoring results. In this way, the accuracy of monitoring is improved by referring to the parent's or guardian's past monitoring results. Some or all of the above-described processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the parent's or guardian's past monitoring results and perform reference using an AI model that improves the accuracy of monitoring.

[0122] The monitoring unit can estimate the child's emotions and determine monitoring priorities based on the estimated emotions. The monitoring unit can estimate the child's emotions and determine monitoring priorities based on the estimated emotions. The child's emotions are estimated based on criteria such as, but not limited to, facial expression recognition, voice analysis, and behavioral patterns. For example, the monitoring unit can analyze the child's facial expressions to estimate the emotions. The monitoring unit can also analyze the child's voice to estimate the emotions. The monitoring unit can also analyze the child's behavioral patterns to estimate the emotions. For example, if the child is excited, the monitoring unit can prioritize monitoring the most dangerous points. If the child is tired, the monitoring unit can prioritize monitoring the nearest dangerous points. If the child is anxious, the monitoring unit can prioritize monitoring points that parents or guardians are concerned about. This enables more appropriate monitoring by determining monitoring priorities based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation 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 processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit may estimate emotions using an AI model that receives a child's facial expressions, voice, and behavioral patterns as input and outputs emotions.

[0123] The monitoring unit can determine the monitoring priority based on the time when the location information was submitted during monitoring. The monitoring unit can determine the monitoring priority based on the time when the location information was submitted during monitoring. The time when the location information was submitted is considered based on criteria such as, but not limited to, the time of day when the location information was submitted or the day of the week when the location information was submitted. The monitoring unit can also determine the monitoring priority based on, for example, the time of day when the location information was submitted. The monitoring unit can also determine the monitoring priority based on the day of the week when the location information was submitted. Furthermore, the monitoring unit can determine the monitoring priority based on the season when the location information was submitted. For example, the monitoring unit can determine the monitoring priority based on the time of day when the location information was submitted. The monitoring unit can also determine the monitoring priority based on the day of the week when the location information was submitted. This enables more appropriate monitoring by determining the monitoring priority based on the time when the location information was submitted. Some or all of the above-described processing by the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can make its determination using an AI model that inputs the time when the location information was submitted and outputs the monitoring priority.

[0124] The monitoring unit can adjust the monitoring order based on the relevance of the location information during monitoring. The monitoring unit can adjust the monitoring order based on the relevance of the location information during monitoring. The relevance of the location information is adjusted based on criteria such as, for example, the degree of overlap of the location information and the distance between the location information, but is not limited to these examples. For example, the monitoring unit prioritizes monitoring the most relevant point based on the relevance of the location information. The monitoring unit can also adjust the monitoring order based on the relevance of the location information. Furthermore, the monitoring unit can optimize the monitoring order based on the relevance of the location information. For example, the monitoring unit prioritizes monitoring the most relevant point based on the relevance of the location information. The monitoring unit can also adjust the monitoring order based on the relevance of the location information. As a result, adjusting the monitoring order based on the relevance of the location information enables more appropriate monitoring. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can make the adjustment using an AI model that inputs the relevance of the location information and outputs the monitoring order.

[0125] The monitoring unit can adjust the use of monitoring terminology according to the parent's or guardian's level of expertise during monitoring. The monitoring unit can adjust the use of monitoring terminology according to the parent's or guardian's level of expertise during monitoring. The level of expertise is adjusted based on criteria such as, but not limited to, the frequency of use of terminology and the level of detail in the explanation. For example, the monitoring unit can provide detailed monitoring results if the parent or guardian has specialized knowledge. The monitoring unit can also provide concise monitoring results if the parent or guardian does not have specialized knowledge. Furthermore, the monitoring unit can adjust the way the monitoring results are presented according to the parent's or guardian's level of expertise. For example, the monitoring unit can provide detailed monitoring results if the parent or guardian has specialized knowledge. The monitoring unit can also provide concise monitoring results if the parent or guardian does not have specialized knowledge. In this way, by adjusting the monitoring terminology according to the parent's or guardian's level of expertise, it is possible to provide monitoring results that are easier to understand. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can make adjustments using an AI model that inputs the parent's or guardian's level of expertise and outputs the way the monitoring results are presented.

[0126] The notification unit can estimate the child's emotions and adjust the notification expression method based on the estimated child's emotions. The notification unit can estimate the child's emotions and adjust the notification expression method based on the estimated child's emotions. The child's emotions are estimated based on criteria such as, but not limited to, facial expression recognition, voice analysis, and behavioral patterns. For example, the notification unit can analyze the child's facial expressions to estimate the emotion. The notification unit can also analyze the child's voice to estimate the emotion. The notification unit can also analyze the child's behavioral patterns to estimate the emotion. For example, if the child is excited, the notification unit can issue a notification using calm expression. If the child is tired, the notification unit can issue a simple and easy-to-understand notification. If the child is anxious, the notification unit can issue a notification encouraging support from a parent or guardian. This allows for more appropriate notifications by adjusting the notification expression method according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation 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 processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit may estimate emotions using an AI model that receives a child's facial expressions, voice, and behavioral patterns as input and outputs emotions.

[0127] The notification unit can adjust the level of detail of the notification based on the importance of the anomaly at the time of notification. The notification unit adjusts the level of detail of the notification based on the importance of the anomaly at the time of notification. The importance of the anomaly is evaluated based on criteria such as, for example, the type of anomaly and the frequency of occurrence of the anomaly, but is not limited to such examples. For example, the notification unit provides a detailed notification in the case of a significant anomaly. The notification unit can also provide a brief notification in the case of a low-importance anomaly. Furthermore, the notification unit can adjust the level of detail of the notification based on the importance of the anomaly. For example, a detailed notification in the case of a significant anomaly. The notification unit can also provide a brief notification in the case of a low-importance anomaly. In this way, adjusting the level of detail of the notification based on the importance of the anomaly enables more appropriate notification. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can make the adjustment using an AI model that inputs the importance of the anomaly and outputs the level of detail of the notification.

[0128] The notification unit can apply different notification algorithms depending on the category of the abnormality when issuing a notification. The notification unit can apply different notification algorithms depending on the category of the abnormality when issuing a notification. The categories of abnormalities are classified based on criteria such as, for example, a traffic accident, a lost child, an unplanned route change, etc., but are not limited to these examples. For example, the notification unit can apply a specific notification algorithm to an abnormality related to a traffic signal. The notification unit can also apply a different notification algorithm to an abnormality related to vehicle movement. Furthermore, the notification unit can apply yet another notification algorithm to an abnormality related to pedestrian movement. For example, a specific notification algorithm can be applied to an abnormality related to a traffic signal. A different notification algorithm can also be applied to an abnormality related to vehicle movement. This enables more appropriate notification by applying a notification algorithm depending on the category of the abnormality. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can apply the notification algorithm using an AI model that inputs the category of the abnormality and outputs the notification algorithm.

[0129] The notification unit can improve the accuracy of notifications by referring to the parent's or guardian's past notification results when providing notifications. The notification unit can improve the accuracy of notifications by referring to the parent's or guardian's past notification results when providing notifications. Past notification results are referenced based on criteria such as, but not limited to, the success rate of notifications and the content of notifications. The notification unit can improve the accuracy of notifications, for example, based on notification results provided by the parent or guardian in the past. The notification unit can also provide optimal notifications by referring to the parent's or guardian's past notification results. The notification unit can also adjust the notification algorithm by referring to the parent's or guardian's past notification results. For example, the notification unit can improve the accuracy of notifications based on the parent's or guardian's past notification results. The notification unit can also provide optimal notifications by referring to the parent's or guardian's past notification results. In this way, the accuracy of notifications is improved by referring to the parent's or guardian's past notification results. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without AI. For example, the notification unit can input the parent's or guardian's past notification results and perform reference using an AI model that improves the accuracy of notifications.

[0130] The notification unit can estimate the child's emotions and adjust the length of the notification based on the estimated child's emotions. The notification unit can estimate the child's emotions and adjust the length of the notification based on the estimated child's emotions. The child's emotions are estimated based on criteria such as, but not limited to, facial expression recognition, voice analysis, and behavioral patterns. For example, the notification unit can analyze the child's facial expressions to estimate the emotion. The notification unit can also analyze the child's voice to estimate the emotion. The notification unit can also analyze the child's behavioral patterns to estimate the emotion. For example, if the child is excited, the notification unit can send a short, to-the-point notification. If the child is tired, the notification unit can send a longer notification with a detailed explanation. If the child is anxious, the notification unit can send a notification encouraging support from a parent or guardian. This allows for more appropriate notifications by adjusting the length of the notification according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation 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 processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit may estimate emotions using an AI model that receives a child's facial expressions, voice, and behavioral patterns as input and outputs emotions.

[0131] The notification unit can determine the priority of notifications based on the time of occurrence of the abnormality at the time of notification. The notification unit determines the priority of notifications based on the time of occurrence of the abnormality at the time of notification. The time of occurrence of the abnormality is considered based on criteria such as, for example, the time period in which the abnormality occurred and the day of the week in which the abnormality occurred, but is not limited to these examples. The notification unit can determine the priority of notifications based on, for example, the time period in which the abnormality occurred. The notification unit can also determine the priority of notifications based on the day of the week in which the abnormality occurred. Furthermore, the notification unit can determine the priority of notifications based on the season in which the abnormality occurred. For example, the notification unit can determine the priority of notifications based on the time period in which the abnormality occurred. The notification unit can also determine the priority of notifications based on the day of the week in which the abnormality occurred. This enables more appropriate notifications by determining the priority of notifications based on the time of occurrence of the abnormality. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can make its determination using an AI model that inputs the time of occurrence of the abnormality and outputs the priority of notifications.

[0132] The notification unit can adjust the order of notifications based on the relevance of the anomalies when issuing notifications. The notification unit adjusts the order of notifications based on the relevance of the anomalies when issuing notifications. The relevance of the anomalies is adjusted based on criteria such as, for example, the degree of overlap of the anomalies and the type of anomaly, but is not limited to these examples. For example, the notification unit prioritizes the most relevant notification based on the relevance of the anomalies. The notification unit can also adjust the order of notifications based on the relevance of the anomalies. Furthermore, the notification unit can optimize the order of notifications based on the relevance of the anomalies. For example, the most relevant notification is prioritized based on the relevance of the anomalies. The notification unit can also adjust the order of notifications based on the relevance of the anomalies. As a result, adjusting the order of notifications based on the relevance of the anomalies enables more appropriate notifications. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can make the adjustment using an AI model that inputs the relevance of the anomalies and outputs the order of notifications.

[0133] The notification unit can adjust the use of technical terms in the notification according to the parent's or guardian's level of expertise. The notification unit can adjust the use of technical terms in the notification according to the parent's or guardian's level of expertise. The level of expertise can be adjusted based on criteria such as, for example, the frequency of use of technical terms and the level of detail in the explanation, but is not limited to these examples. For example, the notification unit can provide a detailed notification if the parent or guardian has technical expertise. The notification unit can also provide a concise notification if the parent or guardian does not have technical expertise. Furthermore, the notification unit can adjust the way the notification is expressed according to the parent's or guardian's level of expertise. For example, the notification unit can provide a detailed notification if the parent or guardian has technical expertise. The notification unit can also provide a concise notification if the parent or guardian does not have technical expertise. In this way, adjusting the technical terms in the notification according to the parent's or guardian's level of expertise enables the notification to be more easily understood. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or without AI. For example, the notification unit can make the adjustment using an AI model that inputs the parent's or guardian's level of expertise and outputs the way the notification is expressed.

[0134] The instruction unit can estimate the child's emotions and adjust the crossing instruction method based on the estimated child's emotions. The instruction unit can estimate the child's emotions and adjust the crossing instruction method based on the estimated child's emotions. The child's emotions are estimated based on criteria such as, but not limited to, facial expression recognition, voice analysis, and behavioral patterns. For example, the instruction unit can analyze the child's facial expressions to estimate the emotion. The instruction unit can also analyze the child's voice to estimate the emotion. The instruction unit can also analyze the child's behavioral patterns to estimate the emotion. For example, if the child is excited, the instruction unit can give crossing instructions in a calm voice. If the child is tired, the instruction unit can give crossing instructions that are simple and easy to understand. If the child is anxious, the instruction unit can give crossing instructions that encourage the support of a parent or guardian. This allows for more appropriate crossing instructions to be provided by adjusting the crossing instruction method according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation 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 processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit may estimate emotions using an AI model that receives a child's facial expressions, voice, and behavioral patterns as input and outputs emotions.

[0135] The instruction unit can improve the accuracy of the crossing instruction by referring to past traffic signal data when issuing a crossing instruction. The instruction unit can improve the accuracy of the crossing instruction by referring to past traffic signal data when issuing a crossing instruction. The past traffic signal data is referenced based on, for example, criteria such as signal change patterns and signal cycles, but is not limited to such examples. The instruction unit can improve the accuracy of the crossing instruction based on, for example, past traffic signal data. The instruction unit can also analyze past traffic signal data to improve the accuracy of the crossing instruction at a specific time period or location. The instruction unit can also improve the accuracy of the crossing instruction by referring to past traffic signal data. For example, the accuracy of the crossing instruction can be improved based on past traffic signal data. The accuracy of the crossing instruction can also be improved at a specific time period or location by analyzing past traffic signal data. In this way, the accuracy of the crossing instruction is improved by referring to past traffic signal data. Some or all of the above-described processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can input past traffic signal data and perform reference using an AI model that improves the accuracy of the crossing instruction.

[0136] The instruction unit can apply different instruction algorithms depending on traffic conditions when issuing a crossing instruction. The instruction unit applies different instruction algorithms depending on traffic conditions when issuing a crossing instruction. Traffic conditions are analyzed based on criteria such as, but not limited to, vehicle speed, vehicle density, and the number of pedestrians. For example, the instruction unit applies a specific instruction algorithm when traffic volume is heavy. The instruction unit can also apply a different instruction algorithm when traffic volume is light. Furthermore, the instruction unit can apply an optimal instruction algorithm depending on traffic conditions. For example, a specific instruction algorithm is applied when traffic volume is heavy. A different instruction algorithm can also be applied when traffic volume is light. This enables more appropriate crossing instructions to be issued by applying an instruction algorithm depending on the traffic conditions. Some or all of the above-described processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can apply an AI model that inputs traffic conditions and outputs an instruction algorithm.

[0137] The instruction unit can improve the accuracy of instructions when instructing the child to cross the street by referring to past instructions from the parent or guardian. The instruction unit improves the accuracy of instructions when instructing the child to cross the street by referring to past instructions from the parent or guardian. Past instruction results are referenced based on criteria such as, for example, the success rate of instructions and the content of instructions, but are not limited to such examples. The instruction unit improves the accuracy of instructions, for example, based on instructions provided by the parent or guardian in the past. The instruction unit can also provide optimal instructions by referring to past instructions from the parent or guardian. Furthermore, the instruction unit can adjust the instruction algorithm by referring to past instructions from the parent or guardian. For example, the accuracy of instructions can be improved based on instructions provided by the parent or guardian in the past. The instruction unit can also provide optimal instructions by referring to past instructions from the parent or guardian. In this way, the accuracy of instructions can be improved by referring to past instructions from the parent or guardian. Some or all of the above-described processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can input past instructions from the parent or guardian and refer to an AI model that improves the accuracy of instructions.

[0138] The instruction unit can estimate the child's emotions and prioritize crossing instructions based on the estimated child's emotions. The instruction unit can estimate the child's emotions and prioritize crossing instructions based on the estimated child's emotions. The child's emotions can be estimated based on criteria such as, but not limited to, facial expression recognition, voice analysis, and behavioral patterns. For example, the instruction unit can analyze the child's facial expressions to estimate the emotions. The instruction unit can also analyze the child's voice to estimate the emotions. The instruction unit can also analyze the child's behavioral patterns to estimate the emotions. For example, if the child is excited, the instruction unit can prioritize the safest crossing instructions. If the child is tired, the instruction unit can prioritize the simplest crossing instructions. If the child is anxious, the instruction unit can prioritize crossing instructions that will reassure the parent or guardian. This allows for more appropriate crossing instructions by prioritizing crossing instructions based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation 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 processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit may estimate emotions using an AI model that receives a child's facial expressions, voice, and behavioral patterns as input and outputs emotions.

[0139] The instruction unit, when issuing a crossing instruction, can determine the priority of the instruction based on the timing of the traffic signal change. The instruction unit, when issuing a crossing instruction, can determine the priority of the instruction based on the timing of the traffic signal change. The timing of the traffic signal change is considered based on criteria such as, but not limited to, the change in traffic signal color or the signal cycle. The instruction unit can determine the priority of the crossing instruction based on, for example, the time of day when the traffic signal changes. The instruction unit can also determine the priority of the crossing instruction based on the day of the week when the traffic signal changes. Furthermore, the instruction unit can determine the priority of the crossing instruction based on the season when the traffic signal changes. For example, the instruction unit determines the priority of the crossing instruction based on the time of day when the traffic signal changes. The instruction unit can also determine the priority of the crossing instruction based on the day of the week when the traffic signal changes. Thus, determining the priority of the instruction based on the time of day when the traffic signal changes enables more appropriate crossing instructions. Some or all of the above-described processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can make its determination using an AI model that inputs the time of day when the traffic signal changes and outputs the priority of the instruction.

[0140] The instruction unit can adjust the order of instructions based on the relevance of traffic signals when issuing a crossing instruction. The instruction unit can adjust the order of instructions based on the relevance of traffic signals when issuing a crossing instruction. The relevance of traffic signals is adjusted based on criteria such as, but not limited to, a signal change pattern or a signal period. For example, the instruction unit prioritizes issuing the most relevant crossing instruction based on the relevance of traffic signals. The instruction unit can also adjust the order of crossing instructions based on the relevance of traffic signals. Furthermore, the instruction unit can optimize the order of crossing instructions based on the relevance of traffic signals. For example, the instruction unit prioritizes issuing the most relevant crossing instruction based on the relevance of traffic signals. The instruction unit can also adjust the order of crossing instructions based on the relevance of traffic signals. In this way, adjusting the order of instructions based on the relevance of traffic signals enables more appropriate crossing instructions. Some or all of the above-described processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can make adjustments using an AI model that inputs the relevance of traffic signals and outputs the order of instructions.

[0141] The instruction unit can adjust the use of technical terms in the instructions depending on the parent's or guardian's level of expertise when instructing the child to cross the street. The instruction unit can adjust the use of technical terms in the instructions depending on the parent's or guardian's level of expertise when instructing the child to cross the street. The level of expertise can be adjusted based on criteria such as, for example, the frequency of use of technical terms and the level of detail in the explanation, but is not limited to such examples. For example, if the parent or guardian has technical expertise, the instruction unit can provide detailed instructions to cross the street. Alternatively, if the parent or guardian does not have technical expertise, the instruction unit can provide concise instructions to cross the street. Furthermore, the instruction unit can adjust the manner in which the crossing instructions are expressed depending on the parent's or guardian's level of expertise. For example, if the parent or guardian has technical expertise, the instruction unit can provide detailed instructions to cross the street. Alternatively, if the parent or guardian does not have technical expertise, the instruction unit can provide concise instructions to cross the street. In this way, adjusting the technical terms in the instructions depending on the parent's or guardian's level of expertise enables the instructions to be more easily understood. Some or all of the above-described processing by the instruction unit may be performed using, for example, AI, or may be performed without AI. For example, the instruction unit can make adjustments using an AI model that inputs the parent's or guardian's level of expertise and outputs the manner in which the instructions are expressed.

[0142] The warning unit can estimate the child's emotions and adjust the way the warning is expressed based on the estimated child's emotions. The warning unit can estimate the child's emotions and adjust the way the warning is expressed based on the estimated child's emotions. The child's emotions are estimated based on criteria such as, but not limited to, facial expression recognition, voice analysis, and behavioral patterns. For example, the warning unit can analyze the child's facial expressions to estimate the emotions. The warning unit can also analyze the child's voice to estimate the emotions. The warning unit can also analyze the child's behavioral patterns to estimate the emotions. For example, if the child is excited, the warning unit can issue a warning in a calm voice. If the child is tired, the warning unit can issue a simple and easy-to-understand warning. If the child is anxious, the warning unit can issue a warning urging support from a parent or guardian. This allows for more appropriate warnings by adjusting the way the warning is expressed based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation 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 processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit may estimate emotions using an AI model that receives a child's facial expressions, voice, and behavioral patterns as input and outputs emotions.

[0143] The warning unit can adjust the level of detail of the warning based on the importance of the red light running when issuing a warning. The warning unit adjusts the level of detail of the warning based on the importance of the red light running when issuing a warning. The importance of the red light running is evaluated based on criteria such as, but not limited to, the frequency of the red light running and the impact of the red light running. For example, the warning unit issues a detailed warning in the case of a serious red light running. The warning unit can also issue a brief warning in the case of a less serious red light running. Furthermore, the warning unit can adjust the level of detail of the warning based on the importance of the red light running. For example, a detailed warning is issued in the case of a serious red light running. The warning unit can also issue a brief warning in the case of a less serious red light running. In this way, adjusting the level of detail of the warning based on the importance of the red light running enables a more appropriate warning. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can make the adjustment using an AI model that inputs the importance of the red light running and outputs the level of detail of the warning.

[0144] The warning unit can apply different warning algorithms depending on the category of red light violation when issuing a warning. The warning unit can apply different warning algorithms depending on the category of red light violation when issuing a warning. The categories of red light violations are classified based on criteria such as, but not limited to, red light violations and yellow light violations. For example, the warning unit can apply a specific warning algorithm to red light violations related to traffic signals. The warning unit can also apply a different warning algorithm to red light violations related to vehicle movement. Furthermore, the warning unit can apply yet another warning algorithm to red light violations related to pedestrian movement. For example, a specific warning algorithm can be applied to red light violations related to traffic signals. A different warning algorithm can also be applied to red light violations related to vehicle movement. This enables more appropriate warnings by applying a warning algorithm depending on the category of red light violation. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can apply the warning algorithm using an AI model that inputs the category of red light violation and outputs a warning algorithm.

[0145] The warning unit can improve the accuracy of the warning by referring to past warning results from the parent or guardian when issuing a warning. The warning unit can improve the accuracy of the warning by referring to past warning results from the parent or guardian when issuing a warning. Past warning results are referenced based on criteria such as, but not limited to, the success rate of the warning and the content of the warning. The warning unit can improve the accuracy of the warning, for example, based on warning results provided by the parent or guardian in the past. The warning unit can also provide an optimal warning by referring to past warning results from the parent or guardian. Furthermore, the warning unit can adjust the warning algorithm by referring to past warning results from the parent or guardian. For example, the accuracy of the warning can be improved based on past warning results provided by the parent or guardian. The optimal warning can also be provided by referring to past warning results from the parent or guardian. In this way, the accuracy of the warning can be improved by referring to the past warning results from the parent or guardian. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can use past warning results from the parent or guardian as input and refer to an AI model that improves the accuracy of the warning.

[0146] The warning unit can estimate the child's emotions and adjust the length of the warning based on the estimated child's emotions. The warning unit can estimate the child's emotions and adjust the length of the warning based on the estimated child's emotions. The child's emotions are estimated based on criteria such as, but not limited to, facial expression recognition, voice analysis, and behavioral patterns. For example, the warning unit can analyze the child's facial expressions to estimate the emotions. The warning unit can also analyze the child's voice to estimate the emotions. The warning unit can also analyze the child's behavioral patterns to estimate the emotions. For example, if the child is excited, the warning unit can issue a short, concise warning. If the child is tired, the warning unit can issue a longer warning with detailed explanations. If the child is anxious, the warning unit can issue a warning urging support from a parent or guardian. This allows for more appropriate warnings by adjusting the length of the warning based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation 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 processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit may estimate emotions using an AI model that receives a child's facial expressions, voice, and behavioral patterns as input and outputs emotions.

[0147] The warning unit, when issuing a warning, can determine the priority of the warning based on the time when the red light ignition occurred. The warning unit, when issuing a warning, can determine the priority of the warning based on the time when the red light ignition occurred. The time when the red light ignition occurred is considered based on criteria such as, for example, the time of day when the red light ignition occurred, the day of the week when the red light ignition occurred, etc., but is not limited to these examples. The warning unit, for example, can determine the priority of the warning based on the time of day when the red light ignition occurred. The warning unit can also determine the priority of the warning based on the day of the week when the red light ignition occurred. Furthermore, the warning unit can determine the priority of the warning based on the season when the red light ignition occurred. For example, the warning unit can determine the priority of the warning based on the time of day when the red light ignition occurred. The warning unit can also determine the priority of the warning based on the day of the week when the red light ignition occurred. This enables more appropriate warnings by determining the priority of the warning based on the time when the red light ignition occurred. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can make its determination using an AI model that inputs the time when the red light ignition occurred and outputs the priority of the warning.

[0148] The warning unit can adjust the order of warnings based on the relevance of red light violations when issuing a warning. The warning unit adjusts the order of warnings based on the relevance of red light violations when issuing a warning. The relevance of red light violations is adjusted based on criteria such as, but not limited to, the degree of overlap of red light violations and the type of red light violation. For example, the warning unit prioritizes issuing the most relevant warning based on the relevance of red light violations. The warning unit can also adjust the order of warnings based on the relevance of red light violations. Furthermore, the warning unit can optimize the order of warnings based on the relevance of red light violations. For example, the warning unit prioritizes issuing the most relevant warning based on the relevance of red light violations. The warning unit can also adjust the order of warnings based on the relevance of red light violations. As a result, adjusting the order of warnings based on the relevance of red light violations enables more appropriate warnings. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can make the adjustment using an AI model that inputs the relevance of red light violations and outputs the order of warnings.

[0149] The warning unit can adjust the use of technical terms in the warning depending on the parent's or guardian's level of expertise. The warning unit can adjust the use of technical terms in the warning depending on the parent's or guardian's level of expertise. The level of expertise can be adjusted based on criteria such as, for example, the frequency of use of technical terms and the level of detail in the explanation, but is not limited to these examples. For example, the warning unit can issue a detailed warning if the parent or guardian has technical expertise. Alternatively, the warning unit can issue a concise warning if the parent or guardian does not have technical expertise. Furthermore, the warning unit can adjust the way the warning is expressed depending on the parent's or guardian's level of expertise. For example, the warning unit can issue a detailed warning if the parent or guardian has technical expertise. Alternatively, the warning unit can issue a concise warning if the parent or guardian does not have technical expertise. In this way, adjusting the technical terms in the warning depending on the parent's or guardian's level of expertise enables the warning to be more easily understood. Some or all of the above-described processing in the warning unit can be performed using, for example, AI, or without AI. For example, the warning unit can make adjustments using an AI model that inputs the parent's or guardian's level of expertise and outputs the way the warning is expressed. === Hard Collateral 1-1 === For example, each of the multiple elements including the reception unit, analysis unit, suggestion unit, monitoring unit, and notification unit is realized by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and a parent or guardian can input a route using a smartphone or a PC. The analysis unit is realized by the identification processing unit 290 of the data processing device 12, and identifies dangerous points based on past traffic accident data. The suggestion unit is realized by the identification processing unit 290 of the data processing device 12, and suggests a safe route. The monitoring unit is realized by the control unit 46A of the smart device 14, and monitors the location information of children in real time. The notification unit is realized by the control unit 46A of the smart device 14, and notifies a parent or guardian if an abnormality is detected. === Hard Collateral 1-2 === For example, each of the multiple elements including the reception unit, analysis unit, suggestion unit, monitoring unit, and notification unit is realized by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and a parent or guardian can input a route using a smartphone or a personal computer. The analysis unit is realized by the identification processing unit 290 of the data processing device 12, and identifies dangerous points based on past traffic accident data. The suggestion unit is realized by the identification processing unit 290 of the data processing device 12, and suggests a safe route. The monitoring unit is realized by the control unit 46A of the smart glasses 214, and monitors the location information of children in real time. The notification unit is realized by the control unit 46A of the smart glasses 214, and notifies a parent or guardian if an abnormality is detected. === Hard Collateral 1-3 === For example, each of the multiple elements including the reception unit, analysis unit, suggestion unit, monitoring unit, and notification unit is realized by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314, and a parent or guardian can input a route using a smartphone or a personal computer. The analysis unit is realized by the identification processing unit 290 of the data processing device 12, and identifies dangerous points based on past traffic accident data. The suggestion unit is realized by the identification processing unit 290 of the data processing device 12, and suggests safe routes. The monitoring unit is realized by the control unit 46A of the headset-type terminal 314, and monitors the location information of children in real time. The notification unit is realized by the control unit 46A of the headset-type terminal 314, and notifies parents or guardians when an abnormality is detected. === Hard Collateral 1-4 === For example, each of the multiple elements including the reception unit, analysis unit, suggestion unit, monitoring unit, and notification unit is realized by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and a parent or guardian can input a route using a smartphone or a personal computer. The analysis unit is realized by the identification processing unit 290 of the data processing device 12, and identifies dangerous points based on past traffic accident data. The suggestion unit is realized by the identification processing unit 290 of the data processing device 12, and suggests a safe route. The monitoring unit is realized by the control unit 46A of the robot 414, and monitors the location information of the children in real time. The notification unit is realized by the control unit 46A of the robot 414, and notifies the parent or guardian if an abnormality is detected.

[0150] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0151] The school child advocacy system can also estimate children's emotions and adjust the suggested routes to and from school based on the estimated emotions. For example, if a child is feeling anxious, the suggestion unit will prioritize suggesting safer routes to parents or guardians. Also, if a child is excited, the suggestion unit can suggest safer routes. Furthermore, if a child is tired, the suggestion unit can suggest the shortest route. This makes it possible to suggest routes that correspond to children's emotions, supporting a safer and more comfortable commute to and from school.

[0152] The school advocacy system can also suggest routes taking into account the living conditions of parents and guardians. For example, if a parent or guardian is busy, the suggestion unit can provide route options that can be easily input. Also, if a parent or guardian is interested in a specific area, routes related to that area can be displayed preferentially. Furthermore, the system can suggest the optimal route input method depending on the living conditions of the parent or guardian. This allows for flexible route suggestions that suit the living conditions of parents and guardians.

[0153] The school child protection system can also estimate children's emotions and adjust the timing of crossing the street based on the estimated emotions. For example, if a child is excited, the instruction unit will instruct the child when to cross the street in a calm voice. If a child is tired, the instruction unit can provide simple and easy-to-understand instructions. Furthermore, if a child is feeling anxious, the instruction unit can also provide instructions encouraging the support of parents or guardians. This makes it possible to provide appropriate crossing instructions according to the child's emotions.

[0154] The school-age child advocacy system can also analyze the parent's or guardian's past route input history and suggest appropriate input methods. For example, it can automatically display routes that the parent or guardian has frequently input in the past as candidates. It can also prioritize input methods (voice, text, etc.) that the parent or guardian has used in the past. It can also predict and suggest routes to be used during specific times based on the parent's or guardian's past input history. This allows it to suggest the optimal input method for the parent or guardian by analyzing their past route input history.

[0155] The school advocacy system can also estimate the emotions of children and adjust the way the warning is expressed based on the estimated emotions. For example, if a child is excited, the warning unit can issue a warning in a calm voice. If a child is tired, the warning unit can issue a concise and easy-to-understand warning. Furthermore, if a child is feeling anxious, the warning unit can issue a warning urging support from parents or guardians. This makes it possible to issue appropriate warnings according to the child's emotions.

[0156] The school advocacy system can also prioritize the input of highly relevant routes by taking into account the geographic location information of the parent or guardian. For example, it can prioritize the suggested routes that are close to the parent or guardian's current location. It can also suggest routes based on places that the parent or guardian frequently visits. It can also suggest the optimal route based on the geographic location information of the parent or guardian. This allows it to prioritize the input of highly relevant routes by taking into account the geographic location information of the parent or guardian.

[0157] The school advocacy system can also estimate children's emotions and adjust the monitoring frequency based on the estimated emotions. For example, if a child is excited, the monitoring unit can increase the monitoring frequency. Also, if a child is tired, the monitoring unit can decrease the monitoring frequency. Furthermore, if a child feels anxious, the monitoring unit can notify the parent or guardian and adjust the monitoring frequency. This enables appropriate monitoring according to the child's emotions.

[0158] The school advocacy system can also analyze the social media activities of parents and guardians to suggest relevant routes. For example, it can suggest routes based on the places where parents and guardians have checked in on social media. It can also analyze the content of social media posts by parents and guardians to suggest relevant routes. It can also suggest relevant routes based on the activities of parents and guardians' friends on social media. In this way, it can suggest relevant routes by analyzing the social media activities of parents and guardians.

[0159] The school advocacy system can also estimate the emotions of children and adjust the way notifications are expressed based on the estimated emotions. For example, if a child is excited, the notification unit can use calm language to notify them. If a child is tired, the notification unit can provide a simple and easy-to-understand notification. Furthermore, if a child is feeling anxious, the notification unit can also provide a notification encouraging support from parents or guardians. This makes it possible to provide appropriate notifications according to the child's emotions.

[0160] The school advocacy system can further customize the input method by reflecting the parents' or guardians' past feedback. For example, it can suggest the optimal input method based on the feedback provided by the parents or guardians in the past. It can also customize the input interface by reflecting the parents' or guardians' past feedback. It can also optimize the input procedure based on the parents' or guardians' past feedback. This allows the input method to be customized by reflecting the parents' or guardians' past feedback.

[0161] The processing flow of the second embodiment will be briefly explained below.

[0162] Step 1: The reception unit accepts input of the route to and from school. The route to and from school may include, for example, the route from school to home or a route during a specific time period. Parents or guardians can input the route using a smartphone or computer, and voice or image input is also possible. Step 2: The analysis unit analyzes the route received by the reception unit and identifies dangerous points. Dangerous points are identified based on criteria such as the frequency of traffic accidents, traffic volume, and the presence or absence of traffic lights. The analysis unit can analyze past traffic accident data, real-time changes in traffic lights, and the movement of surrounding vehicles. Step 3: The suggestion unit suggests safe routes based on the danger points identified by the analysis unit. Safe routes are suggested based on criteria such as roads with low traffic volume and roads with many traffic lights. The suggestion unit can also notify parents or guardians of safe routes and provide real-time navigation for children. Step 4: The monitoring unit monitors the children's location information. Location information is obtained using technologies such as GPS and beacons. The monitoring unit monitors the children's location information in real time and detects abnormal movements or unplanned route changes. Step 5: If an abnormality is detected, the notification unit immediately notifies the parent or guardian via a smartphone app, email, SMS, or other means.

[0163] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0165] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0168] 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.

[0169] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0170] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0171] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0172] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0173] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0174] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0175] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0176] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0177] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0178] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0179] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0180] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0181] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0182] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0183] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0184] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0185] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0186] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0187] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0188] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0189] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0190] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0191] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0192] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0193] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0194] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0195] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0196] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0197] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0198] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0199] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0200] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0201] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0202] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0203] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0204] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0205] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0206] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0207] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0208] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0209] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0210] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0211] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0212] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0213] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0214] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0215] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0216] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0217] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0218] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0219] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0220] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0221] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0222] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0223] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0224] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0225] 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.

[0226] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0227] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0228] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0229] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0230] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0231] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0232] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0233] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0234] [Explanation of symbols]

[0235] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input of routes to and from school; an analysis unit that analyzes the route accepted by the acceptance unit and identifies dangerous points; a suggestion unit that proposes a safe route based on the dangerous points identified by the analysis unit; A monitoring unit that monitors the location information of children, A notification unit that detects and notifies an abnormality. A system characterized by:

2. Equipped with an analysis unit that analyzes changes in traffic signals and the movement of surrounding vehicles 2. The system of claim 1.

3. Equipped with an indicator that tells children when to cross the street 2. The system of claim 1.

4. Equipped with a warning unit that issues a warning when there is a possibility of ignoring a traffic signal 2. The system of claim 1.

5. The proposal unit It will be set to pay attention to areas where traffic accidents are frequent.

2. The system of claim 1.

6. The monitoring unit Monitor children's location in real time to detect unusual movements and unplanned route changes 2. The system of claim 1.

7. The notification unit Immediately notify parents or guardians if an abnormality is detected 2. The system of claim 1.

8. The reception unit Estimate the child's emotions and adjust the timing of route inputs based on the estimated emotions.

2. The system of claim 1.

9. The reception unit Analyzes parent / guardian's route input history and suggests appropriate input methods 2. The system of claim 1.

Images