system

Abstract

The system according to the embodiment aims to grasp the location information of a child in real time, detect abnormal behavior, and respond promptly. [Solution] The system according to the embodiment includes a collection unit, an analysis unit, an alert unit, and a notification unit. The collection unit acquires the child's location information in real time. The analysis unit stores the child's range of movement based on the location information acquired by the collection unit and compares it with the child's normal range of movement. The alert unit detects abnormal behavior and issues an alert based on the results of the comparison by the analysis unit. The notification unit sends a notification to the local police if the parent or guardian determines that the child is lost.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to grasp a child's location information in real time, detect abnormal behavior, and respond quickly.

[0005] The system according to the embodiment aims to grasp the location information of a child in real time, detect abnormal behavior, and respond promptly. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an alert unit, and a notification unit. The collection unit acquires the child's location information in real time. The analysis unit stores the child's range of movement based on the location information acquired by the collection unit and compares it with the child's normal range of movement. The alert unit detects abnormal behavior and issues an alert based on the comparison results by the analysis unit. The notification unit notifies the local police if the parent or guardian determines that the child is lost. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the location information of a child in real time, detect abnormal behavior, and respond quickly. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention uses a dedicated wearable device to monitor a child's range of movement and issues an alert when abnormal movement is detected. This system acquires the child's location information in real time, compares it with the child's normal range of movement, and issues an alert. Furthermore, if a parent or guardian determines that the child is lost, the system can send a notification to the local police station. For example, a dedicated wearable device is attached to a child and uses its GPS function to acquire the child's location information in real time. A generation AI stores the child's range of movement and compares it with the child's normal range of movement. If abnormal movement is detected, the generation AI issues an alert and notifies the parent or guardian's smartphone. If a parent or guardian determines that the child is lost, a notification is sent to the local police station, which then takes action to protect the child. This system ensures the child's safety and increases the parent's sense of security. This system monitors the child's range of movement, detects abnormal movement, and issues an alert. Furthermore, if a parent or guardian determines that the child is lost, a notification is sent to the local police station, allowing for prompt action to protect the child. This ensures the child's safety and increases the parent's sense of security.

[0029] A monitoring system according to an embodiment includes a collection unit, an analysis unit, an alert unit, and a notification unit. The collection unit acquires the child's location information in real time. For example, the collection unit acquires the child's location information using a GPS function. The collection unit can also supplement the location information using Wi-Fi or Bluetooth (registered trademark). The collection unit periodically updates the child's location information and monitors the child in real time. The analysis unit stores the child's range of activity based on the location information acquired by the collection unit and compares it with its normal range of activity. For example, the analysis unit stores the child's route from school to home as the normal range of activity. The analysis unit can also use a generation AI to learn the child's range of activity and detect abnormal behavior. The analysis unit can analyze the child's behavioral patterns and dynamically update the normal range of activity. The alert unit detects abnormal behavior and issues an alert based on the results of the comparison by the analysis unit. For example, the alert unit issues an alert if the child moves beyond the normal range of activity. The alert unit can also issue an alert if the child enters a dangerous area. Furthermore, the alert unit can send an alert to the parent's smartphone. The notification unit sends a notification to the local police when the parent or guardian certifies that the child is lost. For example, when the parent or guardian certifies that the child is lost through a smartphone app, the notification unit sends a notification to the local police. The notification unit can also provide the police with the child's location information to support prompt protection activities. As a result, the monitoring system according to the embodiment can ensure the safety of the child by obtaining the child's location information in real time and detecting abnormal movements and issuing an alert.

[0030] The collection unit can acquire the child's location information in real time using a GPS function. The collection unit, for example, acquires the child's location information in real time using a GPS function. For example, the collection unit acquires the child's location information using a dedicated wearable device with a built-in GPS module. The collection unit can also supplement the location information using Wi-Fi or Bluetooth. For example, the collection unit identifies the child's location using location information from a Wi-Fi access point. The collection unit can also identify the child's location using a Bluetooth beacon. Furthermore, the collection unit can periodically update the child's location information and monitor it in real time. This allows the GPS function to accurately acquire the child's location information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input GPS data to a generation AI and have the generation AI analyze the location information.

[0031] The analysis unit can store the child's range of activity and compare it with its normal range of activity. The analysis unit, for example, stores the child's range of activity and compares it with its normal range of activity. For example, the analysis unit stores the child's route from school to home as its normal range of activity. The analysis unit can also use the generation AI to learn the child's range of activity and detect abnormal behavior. For example, the generation AI analyzes the child's behavioral patterns based on the child's location information and dynamically updates the normal range of activity. The analysis unit can also analyze the child's behavioral patterns and dynamically update the normal range of activity. For example, the analysis unit dynamically updates the range of activity based on the child's past behavioral data. In this way, abnormal behavior can be detected by storing the child's range of activity and comparing it with its normal range of activity. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the child's behavioral data into the generation AI and have the generation AI analyze the range of activity.

[0032] The alert unit can issue an alert when it detects abnormal behavior. For example, the alert unit issues an alert when it detects abnormal behavior. For example, the alert unit issues an alert when a child moves beyond their normal range of movement. The alert unit can also issue an alert when a child enters a dangerous area. For example, the alert unit references a database of dangerous areas and issues an alert when a child enters that area. Furthermore, the alert unit can notify the guardian of the alert via a smartphone app. In this way, issuing an alert when abnormal behavior is detected allows for a prompt response. Some or all of the above-described processing in the alert unit may be performed using, for example, AI, or may be performed without using AI. For example, the alert unit can input data of abnormal behavior to a generation AI and cause the generation AI to issue an alert.

[0033] The notification unit can send a notification to a nearby police station when a guardian certifies a child as lost. For example, the notification unit sends a notification to a nearby police station when a guardian certifies a child as lost through a smartphone app. The notification unit can also provide the police with the child's location information to support prompt protection activities. For example, the notification unit provides the police with the child's current location and past movement history. This allows prompt protection activities to be carried out by sending a notification to a nearby police station when a guardian certifies a child as lost. Some or all of the above-mentioned 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 input the data on the child's certification as lost into the generation AI and cause the generation AI to notify the police.

[0034] The alert unit can notify the guardian's smartphone of the alert. For example, the alert unit notifies the guardian's smartphone of the alert. For example, the alert unit notifies the guardian's smartphone of the alert through a smartphone app. The alert unit can also notify the alert via email or SMS. For example, the alert unit sends the alert to the guardian's email address or phone number. Furthermore, the alert unit can customize the content of the alert. For example, the alert unit changes the content of the alert depending on the type of abnormal behavior. In this way, by notifying the guardian's smartphone of the alert, the guardian can immediately recognize the abnormality. Some or all of the above-mentioned processing in the alert unit may be performed using, for example, AI, or may be performed without using AI. For example, the alert unit can input alert data to a generation AI and cause the generation AI to notify the alert.

[0035] The collection unit can analyze the child's past movement history and select an appropriate location information acquisition method. The collection unit, for example, analyzes the child's past movement history and selects an appropriate location information acquisition method. For example, the collection unit adjusts the frequency of location information acquisition based on places the child has frequently visited in the past. The collection unit can also analyze the child's past movement patterns and select an efficient location information acquisition method. Furthermore, the collection unit can optimize the location information acquisition method for a specific time period based on the child's past movement history. This enables efficient monitoring by selecting the optimal location information acquisition method based on the past movement history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past movement data into a generation AI and cause the generation AI to select a location information acquisition method.

[0036] The collection unit can filter the location information based on the child's current activity status and environment when acquiring the location information. For example, the collection unit can filter the location information based on the child's current activity status and environment when acquiring the location information. For example, the collection unit can set the frequency of acquiring location information low when the child is at school. The collection unit can also increase the frequency of acquiring location information when the child is playing in a park. Furthermore, the collection unit can temporarily stop acquiring location information when the child is at home. This enables appropriate monitoring by filtering the location information based on the child's current activity status and environment. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input activity status data to a generation AI and cause the generation AI to filter the location information.

[0037] The collection unit can select an appropriate acquisition means depending on the child's mode of transportation when acquiring location information. For example, the collection unit can select an appropriate acquisition means depending on the child's mode of transportation when acquiring location information. For example, if the child is traveling on foot, the collection unit can acquire detailed location information using GPS. Furthermore, if the child is traveling by bicycle, the collection unit can increase the frequency of acquiring location information. Furthermore, if the child is traveling by car, the collection unit can change the means of acquiring location information to efficiently monitor. This enables efficient monitoring by selecting the optimal acquisition means depending on the mode of transportation. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input transportation data into the generation AI and cause the generation AI to select a location information acquisition means.

[0038] When acquiring location information, the collection unit can prioritize acquiring highly relevant information by taking into account the child's geographical location information. For example, when acquiring location information, the collection unit prioritizes acquiring highly relevant information by taking into account the child's geographical location information. For example, when acquiring location information, the collection unit prioritizes acquiring highly relevant information by taking into account the child's geographical location information. For example, if the child is at school, the collection unit prioritizes acquiring location information around the school. Furthermore, if the child is in a park, the collection unit can also prioritize acquiring location information around the park. Furthermore, if the child is in a commercial facility, the collection unit can also prioritize acquiring location information within the commercial facility. This enables efficient monitoring by prioritizing acquisition of highly relevant information by taking into account the geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input geographical location data to a generation AI and cause the generation AI to acquire highly relevant information.

[0039] The collection unit can analyze the child's social media activities and acquire related information when acquiring the location information. For example, the collection unit can analyze the child's social media activities and acquire related information when acquiring the location information. For example, the collection unit can acquire location information of locations where the child has checked in on social media. The collection unit can also analyze the content of the child's social media posts and acquire related location information. Furthermore, the collection unit can acquire related location information by referring to the activities of the child's friends on social media. This enables more detailed monitoring by analyzing social media activities and acquiring related information. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input social media data to a generation AI and cause the generation AI to acquire related information.

[0040] The collection unit can customize the acquisition method by reflecting the child's past feedback when acquiring location information. For example, the collection unit customizes the acquisition method by reflecting the child's past feedback when acquiring location information. For example, the collection unit adjusts the frequency of acquiring location information based on feedback provided by the child in the past. The collection unit can also optimize the method of acquiring location information in a specific location based on the child's past feedback. Furthermore, the collection unit can improve the accuracy of acquiring location information by referring to the child's past feedback. In this way, customizing the acquisition method by reflecting past feedback improves the accuracy of monitoring. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input feedback data to a generation AI and cause the generation AI to customize the acquisition method.

[0041] The analysis unit can identify important locations of the child when analyzing the range of activity, thereby improving the accuracy of the analysis. For example, the analysis unit can identify important locations of the child when analyzing the range of activity, thereby improving the accuracy of the analysis. For example, the analysis unit can identify places that the child frequently visits and reflect these in the analysis of the range of activity. The analysis unit can also improve the analysis accuracy of the range of activity based on the important locations of the child. Furthermore, the analysis unit can analyze the child's behavioral patterns, identify important locations, and reflect these in the analysis. In this way, identifying important locations improves the analysis accuracy of the range of activity. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input behavioral data into the generation AI and cause the generation AI to identify important locations.

[0042] The analysis unit can analyze the child's movement patterns and dynamically update the normal movement range when analyzing the movement range. For example, the analysis unit can analyze the child's movement patterns and dynamically update the normal movement range when analyzing the movement range. For example, the analysis unit can analyze the child's movement patterns in real time and dynamically update the movement range. The analysis unit can also dynamically update the movement range based on the child's past movement patterns. Furthermore, the analysis unit can detect changes in the child's movement patterns and dynamically update the movement range. This enables more accurate monitoring by analyzing the movement patterns and dynamically updating the movement range. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input movement pattern data to the generation AI and cause the generation AI to dynamically update the movement range.

[0043] The analysis unit can adjust the analysis algorithm by referring to the child's past behavioral data when analyzing the range of activity. For example, the analysis unit can adjust the analysis algorithm by referring to the child's past behavioral data when analyzing the range of activity. For example, the analysis unit optimizes the analysis algorithm based on the child's past behavioral data. The analysis unit can also analyze the child's behavior patterns and optimize the analysis algorithm. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to the child's past behavioral data. In this way, optimizing the analysis algorithm by referring to the past behavioral data improves the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input past behavioral data into the generation AI and cause the generation AI to adjust the analysis algorithm.

[0044] The analysis unit can improve the accuracy of the analysis by referring to related literature and data when analyzing the movement range. For example, the analysis unit improves the accuracy of the analysis by referring to related literature and data when analyzing the movement range. For example, the analysis unit improves the analysis accuracy of the movement range by referring to related literature. The analysis unit can also analyze the movement range based on related data. Furthermore, the analysis unit can also optimize the analysis algorithm of the movement range by referring to related literature and data. In this way, the accuracy of the analysis is improved by referring to related literature and data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input literature and data into the generation AI and cause the generation AI to optimize the analysis algorithm.

[0045] The analysis unit can perform the analysis by taking into consideration the type of activity of the child when analyzing the range of activity. For example, the analysis unit performs the analysis by taking into consideration the type of activity of the child when analyzing the range of activity. For example, the analysis unit analyzes the range of activity based on the type of activity of the child. The analysis unit can also improve the analysis accuracy of the range of activity by taking into consideration the type of activity of the child. Furthermore, the analysis unit can optimize the analysis algorithm of the range of activity by referring to the type of activity of the child. In this way, the analysis accuracy is improved by performing the analysis by taking into consideration the type of activity. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input activity data to the generation AI and cause the generation AI to optimize the analysis algorithm.

[0046] The alert unit can customize the content of the alert according to the type of abnormal movement when issuing an alert. For example, the alert unit customizes the content of the alert according to the type of abnormal movement when issuing an alert. For example, the alert unit issues a high-urgency alert if a child suddenly moves. The alert unit can also issue an alert calling for attention if a child remains stationary for a long period of time. Furthermore, the alert unit can also issue an alert issuing a warning if the child exceeds a normal range of movement. This allows for customizing the content of the alert according to the type of abnormal movement, enabling a more appropriate response. Some or all of the above-described processing in the alert unit may be performed using, or without, AI. For example, the alert unit can input data of abnormal movement to a generation AI and cause the generation AI to customize the content of the alert.

[0047] The alert unit can issue an alert taking into account the child's current environment when issuing an alert. For example, the alert unit issues an alert taking into account the child's current environment when issuing an alert. For example, the alert unit issues a normal alert when the child is indoors. The alert unit can also issue a high-urgency alert when the child is outdoors. Furthermore, the alert unit can also issue an alert to call attention when the child is on public transportation. This enables a more appropriate response by issuing an alert taking into account the current environment. Some or all of the above-described processing in the alert unit may be performed using, for example, AI, or may be performed without using AI. For example, the alert unit can input environmental data into a generation AI and cause the generation AI to issue an alert.

[0048] The alert unit can improve the accuracy of the alert by referring to the child's past alert history when issuing an alert. For example, the alert unit improves the accuracy of the alert by referring to the child's past alert history when issuing an alert. For example, the alert unit improves the accuracy of the alert based on the child's past alert history. The alert unit can also analyze the child's past alert history and issue an optimal alert. Furthermore, the alert unit can customize the content of the alert by referring to the child's past alert history. In this way, the accuracy of the alert is improved by referring to the past alert history. Some or all of the above-mentioned processing in the alert unit may be performed using, for example, AI, or may be performed without using AI. For example, the alert unit can input alert history data to a generation AI and cause the generation AI to improve the accuracy of the alert.

[0049] The alert unit can customize the content of the alert by taking into account the child's geographical location information when issuing an alert. For example, the alert unit customizes the content of the alert by taking into account the child's geographical location information when issuing an alert. For example, if the child is at school, the alert unit can issue an alert including information about the area around the school. Furthermore, if the child is in a park, the alert unit can issue an alert including information about the area around the park. Furthermore, if the child is in a commercial facility, the alert unit can issue an alert including information about the inside of the commercial facility. This enables a more appropriate response by customizing the content of the alert by taking into account the geographical location information. Some or all of the above-described processing in the alert unit may be performed using, for example, AI, or may be performed without using AI. For example, the alert unit can input geographical location data to a generation AI and cause the generation AI to customize the content of the alert.

[0050] The alert unit can analyze the child's social media activity and issue a related alert when issuing an alert. For example, the alert unit can analyze the child's social media activity and issue a related alert when issuing an alert. For example, the alert unit can issue an alert regarding places the child has checked in on social media. The alert unit can also analyze the content of the child's social media posts and issue a related alert. Furthermore, the alert unit can also issue a related alert based on the activity of the child's friends on social media. This enables more detailed monitoring by analyzing social media activity and issuing a related alert. Some or all of the above-described processing in the alert unit can be performed using, for example, AI, or can be performed without using AI. For example, the alert unit can input social media data into a generation AI and cause the generation AI to issue a related alert.

[0051] The alert unit can customize the content of the alert by reflecting the child's past feedback when issuing an alert. For example, the alert unit customizes the content of the alert by reflecting the child's past feedback when issuing an alert. For example, the alert unit adjusts the content of the alert based on feedback provided by the child in the past. The alert unit can also optimize the content of the alert for a specific situation based on the child's past feedback. Furthermore, the alert unit can improve the accuracy of the alert by referring to the child's past feedback. In this way, customizing the content of the alert by reflecting past feedback improves the accuracy of the alert. Some or all of the above-described processing in the alert unit may be performed using, for example, AI, or may be performed without using AI. For example, the alert unit can input feedback data to a generation AI and cause the generation AI to customize the content of the alert.

[0052] The notification unit can select the notification method taking into consideration the guardian's current situation when sending a notification. For example, the notification unit selects the notification method taking into consideration the guardian's current situation when sending a notification. For example, when the guardian is at work, the notification unit prioritizes sending notifications with a high level of urgency. The notification unit can also send a brief notification when the guardian is traveling. Furthermore, the notification unit can also send a detailed notification when the guardian is at home. In this way, by selecting the notification method taking into consideration the guardian's current situation, more appropriate notifications are sent. Some or all of the above-mentioned processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input the guardian's situation data into the generation AI and have the generation AI select the notification method.

[0053] The notification unit can improve the accuracy of notifications by referring to the guardian's past notification history when sending a notification. For example, the notification unit improves the accuracy of notifications by referring to the guardian's past notification history when sending a notification. For example, the notification unit improves the accuracy of notifications based on the guardian's past notification history. The notification unit can also analyze the guardian's past notification history and send an optimal notification. Furthermore, the notification unit can customize the content of the notification by referring to the guardian's past notification history. In this way, the accuracy of notifications is improved by referring to the past notification history. Some or all of the above-mentioned 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 input notification history data to a generation AI and cause the generation AI to improve the accuracy of notifications.

[0054] The notification unit can customize the content of the notification by reflecting the parent's feedback when sending the notification. For example, the notification unit customizes the content of the notification by reflecting the parent's feedback when sending the notification. For example, the notification unit adjusts the content of the notification based on feedback previously provided by the parent. The notification unit can also optimize the content of the notification for a specific situation based on the parent's past feedback. Furthermore, the notification unit can improve the accuracy of the notification by referring to the parent's past feedback. In this way, customizing the content of the notification by reflecting the feedback improves the accuracy of the 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 input feedback data to a generation AI and cause the generation AI to customize the content of the notification.

[0055] The notification unit can customize the content of the notification by taking into account the geographical location information of the guardian when sending a notification. For example, the notification unit customizes the content of the notification by taking into account the geographical location information of the guardian when sending a notification. For example, the notification unit sends a notification with a high level of urgency when the guardian is at work. The notification unit can also send a detailed notification when the guardian is at home. Furthermore, the notification unit can also send a concise notification when the guardian is out. This enables a more appropriate response by customizing the content of the notification by taking into account the geographical location information. 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 input geographical location data to a generation AI and cause the generation AI to customize the content of the notification.

[0056] The notification unit can analyze the parent's social media activity and send relevant notifications when sending a notification. For example, the notification unit can analyze the parent's social media activity and send relevant notifications when sending a notification. For example, the notification unit can send notifications about places the parent has checked in on social media. The notification unit can also analyze the content of the parent's social media posts and send relevant notifications. Furthermore, the notification unit can also send relevant notifications based on the activities of the parent's friends on social media. This enables more detailed monitoring by analyzing social media activity and sending relevant notifications. Some or all of the above-mentioned processing in the notification unit can be performed using AI, for example, or without AI. For example, the notification unit can input social media data into a generation AI and cause the generation AI to send relevant notifications.

[0057] The notification unit can customize the content of the notification by reflecting the parent's past feedback when sending a notification. For example, the notification unit customizes the content of the notification by reflecting the parent's past feedback when sending a notification. For example, the notification unit adjusts the content of the notification based on feedback provided by the parent in the past. The notification unit can also optimize the content of the notification for a specific situation based on the parent's past feedback. Furthermore, the notification unit can improve the accuracy of the notification by referring to the parent's past feedback. In this way, the accuracy of the notification is improved by customizing the content of the notification by reflecting the past feedback. Some or all of the above-mentioned 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 input feedback data to a generation AI and cause the generation AI to customize the content of the notification.

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

[0059] The analysis unit can store the child's range of activity and compare it with the child's normal range of activity. For example, the analysis unit can store the child's route from school to home as the child's normal range of activity. The analysis unit can also use the generation AI to learn the child's range of activity and detect abnormal behavior. Furthermore, the analysis unit can analyze the child's behavioral patterns and dynamically update the normal range of activity. This allows the child's range of activity to be stored and compared with the normal range of activity to detect abnormal behavior. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the child's behavioral data into the generation AI and have the generation AI analyze the range of activity.

[0060] The collection unit can acquire the child's location information in real time. For example, the collection unit acquires the child's location information using a GPS function. The collection unit can also supplement the location information using Wi-Fi or Bluetooth. Furthermore, the collection unit can periodically update the child's location information and monitor it in real time. This allows the child's location information to be accurately acquired using the GPS function. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input GPS data to a generation AI and have the generation AI analyze the location information.

[0061] The alert unit can issue an alert when it detects abnormal behavior. For example, the alert unit issues an alert when a child moves beyond their normal range of movement. The alert unit can also issue an alert when a child enters a dangerous area. Furthermore, the alert unit can notify the guardian of the alert via their smartphone. This allows for a prompt response by issuing an alert when abnormal behavior is detected. Some or all of the above-described processing in the alert unit may be performed using, for example, AI, or may be performed without using AI. For example, the alert unit can input data on abnormal behavior into a generation AI and cause the generation AI to issue an alert.

[0062] The notification unit can send a notification to a nearby police station when a parent or guardian certifies that a child is lost. For example, the notification unit can send a notification to a nearby police station when a parent or guardian certifies that a child is lost through a smartphone app. The notification unit can also provide the police with the child's location information to support prompt protection activities. This allows prompt protection activities to be carried out by sending a notification to a nearby police station when a parent or guardian certifies that a child is lost. Some or all of the above-mentioned 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 input the child's certification data as lost into the generation AI and cause the generation AI to notify the police.

[0063] The collection unit can analyze the child's past movement history and select an appropriate location information acquisition method. For example, the collection unit can adjust the frequency of location information acquisition based on places the child has frequently visited in the past. The collection unit can also analyze the child's past movement patterns and select an efficient location information acquisition method. Furthermore, the collection unit can optimize the location information acquisition method for a specific time period based on the child's past movement history. This enables efficient monitoring by selecting the optimal location information acquisition method based on the past movement history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past movement data into the generation AI and have the generation AI select the location information acquisition method.

[0064] When acquiring location information, the collection unit can filter the location information based on the child's current activity status and environment. For example, the collection unit can set the frequency of acquiring location information low when the child is at school. The collection unit can also increase the frequency of acquiring location information when the child is playing in a park. Furthermore, the collection unit can temporarily stop acquiring location information when the child is at home. This enables appropriate monitoring by filtering the location information based on the child's current activity status and environment. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input activity status data to a generation AI and cause the generation AI to filter the location information.

[0065] When acquiring location information, the collection unit can select an appropriate acquisition means depending on the child's means of transportation. For example, if the child is traveling on foot, the collection unit can acquire detailed location information using GPS. Furthermore, if the child is traveling by bicycle, the collection unit can increase the frequency of acquiring location information. Furthermore, if the child is traveling by car, the collection unit can change the means of acquiring location information to efficiently monitor. This enables efficient monitoring by selecting the optimal acquisition means depending on the means of transportation. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input transportation data into the generation AI and have the generation AI select the location information acquisition means.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The collection unit acquires the child's location information in real time. For example, the collection unit may acquire the child's location information using a GPS function, and may supplement the location information using Wi-Fi or Bluetooth. Furthermore, the collection unit periodically updates the child's location information and monitors it in real time. Step 2: The analysis unit memorizes the child's range of movement based on the location information acquired by the collection unit and compares it with the child's normal range of movement. For example, the analysis unit memorizes the child's route from school to home as the normal range of movement, and can use the generation AI to learn the child's range of movement and detect abnormal movements. Furthermore, the analysis unit can analyze the child's behavioral patterns and dynamically update the normal range of movement. Step 3: The alert unit detects abnormal movements based on the results collated by the analysis unit and issues an alert. For example, the alert unit can issue an alert if a child moves beyond their normal range of movement or enters a dangerous area. Furthermore, the alert unit can send the alert to the parent's smartphone. Step 4: The notification unit sends a notification to the local police if the parent or guardian reports the child as lost. For example, if the parent or guardian reports the child as lost through a smartphone app, the notification unit sends a notification to the local police. The notification unit can also provide the police with the child's location information to support prompt protection efforts.

[0068] (Example 2) A system according to an embodiment of the present invention uses a dedicated wearable device to monitor a child's range of movement and issues an alert when abnormal movement is detected. This system acquires the child's location information in real time, compares it with the child's normal range of movement, and issues an alert. Furthermore, if a parent or guardian determines that the child is lost, the system can send a notification to the local police station. For example, a dedicated wearable device is attached to a child and uses its GPS function to acquire the child's location information in real time. A generation AI stores the child's range of movement and compares it with the child's normal range of movement. If abnormal movement is detected, the generation AI issues an alert and notifies the parent or guardian's smartphone. If a parent or guardian determines that the child is lost, a notification is sent to the local police station, which then takes action to protect the child. This system ensures the child's safety and increases the parent's sense of security. This system monitors the child's range of movement, detects abnormal movement, and issues an alert. Furthermore, if a parent or guardian determines that the child is lost, a notification is sent to the local police station, allowing for prompt action to protect the child. This ensures the child's safety and increases the parent's sense of security.

[0069] The monitoring system according to the embodiment includes a collection unit, an analysis unit, an alert unit, and a notification unit. The collection unit acquires the child's location information in real time. For example, the collection unit acquires the child's location information using a GPS function. The collection unit can also supplement the location information using Wi-Fi or Bluetooth. The collection unit periodically updates the child's location information and monitors the child in real time. The analysis unit stores the child's range of activity based on the location information acquired by the collection unit and compares it with a normal range of activity. For example, the analysis unit stores the child's route from school to home as a normal range of activity. The analysis unit can also use a generation AI to learn the child's range of activity and detect abnormal behavior. The analysis unit can analyze the child's behavioral patterns and dynamically update the normal range of activity. The alert unit detects abnormal behavior and issues an alert based on the comparison results by the analysis unit. For example, the alert unit issues an alert if the child moves beyond the normal range of activity. The alert unit can also issue an alert if the child enters a dangerous area. Furthermore, the alert unit can send an alert to the parent's smartphone. The notification unit sends a notification to the local police when the parent or guardian certifies that the child is lost. For example, when the parent or guardian certifies that the child is lost through a smartphone app, the notification unit sends a notification to the local police. The notification unit can also provide the police with the child's location information to support prompt protection activities. As a result, the monitoring system according to the embodiment can ensure the safety of the child by obtaining the child's location information in real time and detecting abnormal movements and issuing an alert.

[0070] The collection unit can acquire the child's location information in real time using a GPS function. The collection unit, for example, acquires the child's location information in real time using a GPS function. For example, the collection unit acquires the child's location information using a dedicated wearable device with a built-in GPS module. The collection unit can also supplement the location information using Wi-Fi or Bluetooth. For example, the collection unit identifies the child's location using location information from a Wi-Fi access point. The collection unit can also identify the child's location using a Bluetooth beacon. Furthermore, the collection unit can periodically update the child's location information and monitor it in real time. This allows the GPS function to accurately acquire the child's location information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input GPS data to a generation AI and have the generation AI analyze the location information.

[0071] The analysis unit can store the child's range of activity and compare it with its normal range of activity. The analysis unit, for example, stores the child's range of activity and compares it with its normal range of activity. For example, the analysis unit stores the child's route from school to home as its normal range of activity. The analysis unit can also use the generation AI to learn the child's range of activity and detect abnormal behavior. For example, the generation AI analyzes the child's behavioral patterns based on the child's location information and dynamically updates the normal range of activity. The analysis unit can also analyze the child's behavioral patterns and dynamically update the normal range of activity. For example, the analysis unit dynamically updates the range of activity based on the child's past behavioral data. In this way, abnormal behavior can be detected by storing the child's range of activity and comparing it with its normal range of activity. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the child's behavioral data into the generation AI and have the generation AI analyze the range of activity.

[0072] The alert unit can issue an alert when it detects abnormal behavior. For example, the alert unit issues an alert when it detects abnormal behavior. For example, the alert unit issues an alert when a child moves beyond their normal range of movement. The alert unit can also issue an alert when a child enters a dangerous area. For example, the alert unit references a database of dangerous areas and issues an alert when a child enters that area. Furthermore, the alert unit can notify the guardian of the alert via a smartphone app. In this way, issuing an alert when abnormal behavior is detected allows for a prompt response. Some or all of the above-described processing in the alert unit may be performed using, for example, AI, or may be performed without using AI. For example, the alert unit can input data of abnormal behavior to a generation AI and cause the generation AI to issue an alert.

[0073] The notification unit can send a notification to a nearby police station when a guardian certifies a child as lost. For example, the notification unit sends a notification to a nearby police station when a guardian certifies a child as lost through a smartphone app. The notification unit can also provide the police with the child's location information to support prompt protection activities. For example, the notification unit provides the police with the child's current location and past movement history. This allows prompt protection activities to be carried out by sending a notification to a nearby police station when a guardian certifies a child as lost. Some or all of the above-mentioned 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 input the data on the child's certification as lost into the generation AI and cause the generation AI to notify the police.

[0074] The alert unit can notify the guardian's smartphone of the alert. For example, the alert unit notifies the guardian's smartphone of the alert. For example, the alert unit notifies the guardian's smartphone of the alert through a smartphone app. The alert unit can also notify the alert via email or SMS. For example, the alert unit sends the alert to the guardian's email address or phone number. Furthermore, the alert unit can customize the content of the alert. For example, the alert unit changes the content of the alert depending on the type of abnormal behavior. In this way, by notifying the guardian's smartphone of the alert, the guardian can immediately recognize the abnormality. Some or all of the above-mentioned processing in the alert unit may be performed using, for example, AI, or may be performed without using AI. For example, the alert unit can input alert data to a generation AI and cause the generation AI to notify the alert.

[0075] The collection unit can estimate the child's emotions and adjust the frequency of location information acquisition based on the estimated child's emotions. The collection unit, for example, estimates the child's emotions and adjusts the frequency of location information acquisition based on the estimated child's emotions. For example, if the child is feeling anxious, the collection unit increases the frequency of location information acquisition and monitors in real time. Also, if the child is relaxed, the collection unit can reduce the frequency of location information acquisition to reduce battery consumption. Furthermore, if the child is excited, the collection unit can set the frequency of location information acquisition to a medium level and perform appropriate monitoring. This allows for appropriate monitoring while reducing battery consumption by adjusting the frequency of location information acquisition according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using an AI, for example, or without an AI. For example, the collection unit can input emotion data to the generation AI and cause the generation AI to adjust the frequency of location information acquisition.

[0076] The collection unit can analyze the child's past movement history and select an appropriate location information acquisition method. The collection unit, for example, analyzes the child's past movement history and selects an appropriate location information acquisition method. For example, the collection unit adjusts the frequency of location information acquisition based on places the child has frequently visited in the past. The collection unit can also analyze the child's past movement patterns and select an efficient location information acquisition method. Furthermore, the collection unit can optimize the location information acquisition method for a specific time period based on the child's past movement history. This enables efficient monitoring by selecting the optimal location information acquisition method based on the past movement history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past movement data into a generation AI and cause the generation AI to select a location information acquisition method.

[0077] The collection unit can filter the location information based on the child's current activity status and environment when acquiring the location information. For example, the collection unit can filter the location information based on the child's current activity status and environment when acquiring the location information. For example, the collection unit can set the frequency of acquiring location information low when the child is at school. The collection unit can also increase the frequency of acquiring location information when the child is playing in a park. Furthermore, the collection unit can temporarily stop acquiring location information when the child is at home. This enables appropriate monitoring by filtering the location information based on the child's current activity status and environment. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input activity status data to a generation AI and cause the generation AI to filter the location information.

[0078] The collection unit can select an appropriate acquisition means depending on the child's mode of transportation when acquiring location information. For example, the collection unit can select an appropriate acquisition means depending on the child's mode of transportation when acquiring location information. For example, if the child is traveling on foot, the collection unit can acquire detailed location information using GPS. Furthermore, if the child is traveling by bicycle, the collection unit can increase the frequency of acquiring location information. Furthermore, if the child is traveling by car, the collection unit can change the means of acquiring location information to efficiently monitor. This enables efficient monitoring by selecting the optimal acquisition means depending on the mode of transportation. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input transportation data into the generation AI and cause the generation AI to select a location information acquisition means.

[0079] The collection unit can estimate the child's emotions and determine the priority of location information to be acquired based on the estimated child's emotions. The collection unit, for example, estimates the child's emotions and determines the priority of location information to be acquired based on the estimated child's emotions. For example, if the child is feeling anxious, the collection unit prioritizes acquiring location information of important places (such as school and home). Furthermore, if the child is relaxed, the collection unit can prioritize acquiring location information within the child's normal range of movement. Furthermore, if the child is excited, the collection unit can prioritize acquiring location information of destinations. In this way, by determining the priority of location information according to the child's emotions, important information can be acquired preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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-mentioned processing in the collection unit may be performed using, for example, an AI. For example, the collection unit can input emotion data to the generation AI and cause the generation AI to determine the priority of location information.

[0080] When acquiring location information, the collection unit can prioritize acquiring highly relevant information by taking into account the child's geographical location information. For example, when acquiring location information, the collection unit prioritizes acquiring highly relevant information by taking into account the child's geographical location information. For example, when acquiring location information, the collection unit prioritizes acquiring highly relevant information by taking into account the child's geographical location information. For example, if the child is at school, the collection unit prioritizes acquiring location information around the school. Furthermore, if the child is in a park, the collection unit can also prioritize acquiring location information around the park. Furthermore, if the child is in a commercial facility, the collection unit can also prioritize acquiring location information within the commercial facility. This enables efficient monitoring by prioritizing acquisition of highly relevant information by taking into account the geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input geographical location data to a generation AI and cause the generation AI to acquire highly relevant information.

[0081] The collection unit can analyze the child's social media activities and acquire related information when acquiring the location information. For example, the collection unit can analyze the child's social media activities and acquire related information when acquiring the location information. For example, the collection unit can acquire location information of locations where the child has checked in on social media. The collection unit can also analyze the content of the child's social media posts and acquire related location information. Furthermore, the collection unit can acquire related location information by referring to the activities of the child's friends on social media. This enables more detailed monitoring by analyzing social media activities and acquiring related information. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input social media data to a generation AI and cause the generation AI to acquire related information.

[0082] The collection unit can customize the acquisition method by reflecting the child's past feedback when acquiring location information. For example, the collection unit customizes the acquisition method by reflecting the child's past feedback when acquiring location information. For example, the collection unit adjusts the frequency of acquiring location information based on feedback provided by the child in the past. The collection unit can also optimize the method of acquiring location information in a specific location based on the child's past feedback. Furthermore, the collection unit can improve the accuracy of acquiring location information by referring to the child's past feedback. In this way, customizing the acquisition method by reflecting past feedback improves the accuracy of monitoring. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input feedback data to a generation AI and cause the generation AI to customize the acquisition method.

[0083] The analysis unit can estimate the child's emotions and adjust the analysis method of the activity range based on the estimated child's emotions. The analysis unit, for example, estimates the child's emotions and adjusts the analysis method of the activity range based on the estimated child's emotions. For example, if the child is feeling anxious, the analysis unit performs a detailed analysis of the activity range. Furthermore, if the child is relaxed, the analysis unit can also analyze a normal activity range. Furthermore, if the child is excited, the analysis unit can analyze a wider activity range. This enables more accurate analysis by adjusting the analysis method of the activity range according to the child's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation 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-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input emotion data to the generation AI and cause the generation AI to adjust the analysis method of the activity range.

[0084] The analysis unit can identify important locations of the child when analyzing the range of activity, thereby improving the accuracy of the analysis. For example, the analysis unit can identify important locations of the child when analyzing the range of activity, thereby improving the accuracy of the analysis. For example, the analysis unit can identify places that the child frequently visits and reflect these in the analysis of the range of activity. The analysis unit can also improve the analysis accuracy of the range of activity based on the important locations of the child. Furthermore, the analysis unit can analyze the child's behavioral patterns, identify important locations, and reflect these in the analysis. In this way, identifying important locations improves the analysis accuracy of the range of activity. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input behavioral data into the generation AI and cause the generation AI to identify important locations.

[0085] The analysis unit can analyze the child's movement patterns and dynamically update the normal movement range when analyzing the movement range. For example, the analysis unit can analyze the child's movement patterns and dynamically update the normal movement range when analyzing the movement range. For example, the analysis unit can analyze the child's movement patterns in real time and dynamically update the movement range. The analysis unit can also dynamically update the movement range based on the child's past movement patterns. Furthermore, the analysis unit can detect changes in the child's movement patterns and dynamically update the movement range. This enables more accurate monitoring by analyzing the movement patterns and dynamically updating the movement range. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input movement pattern data to the generation AI and cause the generation AI to dynamically update the movement range.

[0086] The analysis unit can adjust the analysis algorithm by referring to the child's past behavioral data when analyzing the range of activity. For example, the analysis unit can adjust the analysis algorithm by referring to the child's past behavioral data when analyzing the range of activity. For example, the analysis unit optimizes the analysis algorithm based on the child's past behavioral data. The analysis unit can also analyze the child's behavior patterns and optimize the analysis algorithm. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to the child's past behavioral data. In this way, optimizing the analysis algorithm by referring to the past behavioral data improves the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input past behavioral data into the generation AI and cause the generation AI to adjust the analysis algorithm.

[0087] The analysis unit can estimate the child's emotions and adjust the display method of the analysis results of the activity range based on the estimated child's emotions. The analysis unit, for example, estimates the child's emotions and adjusts the display method of the analysis results of the activity range based on the estimated child's emotions. For example, the analysis unit displays detailed analysis results when the child is feeling anxious. The analysis unit can also display normal analysis results when the child is relaxed. Furthermore, the analysis unit can display a wide range of analysis results when the child is excited. This enables more appropriate information to be provided by adjusting the display method of the analysis results according to the child's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input emotion data to the generation AI and cause the generation AI to adjust the display method of the analysis results.

[0088] The analysis unit can improve the accuracy of the analysis by referring to related literature and data when analyzing the movement range. For example, the analysis unit improves the accuracy of the analysis by referring to related literature and data when analyzing the movement range. For example, the analysis unit improves the analysis accuracy of the movement range by referring to related literature. The analysis unit can also analyze the movement range based on related data. Furthermore, the analysis unit can also optimize the analysis algorithm of the movement range by referring to related literature and data. In this way, the accuracy of the analysis is improved by referring to related literature and data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input literature and data into the generation AI and cause the generation AI to optimize the analysis algorithm.

[0089] The analysis unit can perform the analysis by taking into consideration the type of activity of the child when analyzing the range of activity. For example, the analysis unit performs the analysis by taking into consideration the type of activity of the child when analyzing the range of activity. For example, the analysis unit analyzes the range of activity based on the type of activity of the child. The analysis unit can also improve the analysis accuracy of the range of activity by taking into consideration the type of activity of the child. Furthermore, the analysis unit can optimize the analysis algorithm of the range of activity by referring to the type of activity of the child. In this way, the analysis accuracy is improved by performing the analysis by taking into consideration the type of activity. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input activity data to the generation AI and cause the generation AI to optimize the analysis algorithm.

[0090] The alert unit can estimate the child's emotions and adjust the alert transmission method based on the estimated child's emotions. The alert unit, for example, estimates the child's emotions and adjusts the alert transmission method based on the estimated child's emotions. For example, if the child is feeling anxious, the alert unit issues a high-urgency alert. If the child is relaxed, the alert unit can also issue a normal alert. Furthermore, if the child is excited, the alert unit can also issue an alert to call attention. In this way, by adjusting the alert transmission method according to the child's emotions, a more appropriate alert is transmitted. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation 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 alert unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the alert unit can input emotion data to the generation AI and cause the generation AI to adjust the alert transmission method.

[0091] The alert unit can customize the content of the alert according to the type of abnormal movement when issuing an alert. For example, the alert unit customizes the content of the alert according to the type of abnormal movement when issuing an alert. For example, the alert unit issues a high-urgency alert if a child suddenly moves. The alert unit can also issue an alert calling for attention if a child remains stationary for a long period of time. Furthermore, the alert unit can also issue an alert issuing a warning if the child exceeds a normal range of movement. This allows for customizing the content of the alert according to the type of abnormal movement, enabling a more appropriate response. Some or all of the above-described processing in the alert unit may be performed using, or without, AI. For example, the alert unit can input data of abnormal movement to a generation AI and cause the generation AI to customize the content of the alert.

[0092] The alert unit can issue an alert taking into account the child's current environment when issuing an alert. For example, the alert unit issues an alert taking into account the child's current environment when issuing an alert. For example, the alert unit issues a normal alert when the child is indoors. The alert unit can also issue a high-urgency alert when the child is outdoors. Furthermore, the alert unit can also issue an alert to call attention when the child is on public transportation. This enables a more appropriate response by issuing an alert taking into account the current environment. Some or all of the above-described processing in the alert unit may be performed using, for example, AI, or may be performed without using AI. For example, the alert unit can input environmental data into a generation AI and cause the generation AI to issue an alert.

[0093] The alert unit can improve the accuracy of the alert by referring to the child's past alert history when issuing an alert. For example, the alert unit improves the accuracy of the alert by referring to the child's past alert history when issuing an alert. For example, the alert unit improves the accuracy of the alert based on the child's past alert history. The alert unit can also analyze the child's past alert history and issue an optimal alert. Furthermore, the alert unit can customize the content of the alert by referring to the child's past alert history. In this way, the accuracy of the alert is improved by referring to the past alert history. Some or all of the above-mentioned processing in the alert unit may be performed using, for example, AI, or may be performed without using AI. For example, the alert unit can input alert history data to a generation AI and cause the generation AI to improve the accuracy of the alert.

[0094] The alert unit can estimate the child's emotions and determine the priority of alerts based on the estimated emotions. The alert unit, for example, estimates the child's emotions and determines the priority of alerts based on the estimated emotions. For example, if the child is feeling anxious, the alert unit can prioritize issuing an alert with a high level of urgency. Furthermore, if the child is relaxed, the alert unit can prioritize issuing a normal alert. Furthermore, if the child is excited, the alert unit can prioritize issuing an alert that calls for attention. In this way, by determining the priority of alerts according to the child's emotions, important alerts can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the alert unit may be performed using an AI, for example, or without an AI. For example, the alert unit can input emotion data to the generation AI and have the generation AI determine the priority of alerts.

[0095] The alert unit can customize the content of the alert by taking into account the child's geographical location information when issuing an alert. For example, the alert unit customizes the content of the alert by taking into account the child's geographical location information when issuing an alert. For example, if the child is at school, the alert unit can issue an alert including information about the area around the school. Furthermore, if the child is in a park, the alert unit can issue an alert including information about the area around the park. Furthermore, if the child is in a commercial facility, the alert unit can issue an alert including information about the inside of the commercial facility. This enables a more appropriate response by customizing the content of the alert by taking into account the geographical location information. Some or all of the above-described processing in the alert unit may be performed using, for example, AI, or may be performed without using AI. For example, the alert unit can input geographical location data to a generation AI and cause the generation AI to customize the content of the alert.

[0096] The alert unit can analyze the child's social media activity and issue a related alert when issuing an alert. For example, the alert unit can analyze the child's social media activity and issue a related alert when issuing an alert. For example, the alert unit can issue an alert regarding places the child has checked in on social media. The alert unit can also analyze the content of the child's social media posts and issue a related alert. Furthermore, the alert unit can also issue a related alert based on the activity of the child's friends on social media. This enables more detailed monitoring by analyzing social media activity and issuing a related alert. Some or all of the above-described processing in the alert unit can be performed using, for example, AI, or can be performed without using AI. For example, the alert unit can input social media data into a generation AI and cause the generation AI to issue a related alert.

[0097] The alert unit can customize the content of the alert by reflecting the child's past feedback when issuing an alert. For example, the alert unit customizes the content of the alert by reflecting the child's past feedback when issuing an alert. For example, the alert unit adjusts the content of the alert based on feedback provided by the child in the past. The alert unit can also optimize the content of the alert for a specific situation based on the child's past feedback. Furthermore, the alert unit can improve the accuracy of the alert by referring to the child's past feedback. In this way, customizing the content of the alert by reflecting past feedback improves the accuracy of the alert. Some or all of the above-described processing in the alert unit may be performed using, for example, AI, or may be performed without using AI. For example, the alert unit can input feedback data to a generation AI and cause the generation AI to customize the content of the alert.

[0098] The notification unit can estimate the child's emotion and adjust the content of the notification based on the estimated emotion. The notification unit, for example, estimates the child's emotion and adjusts the content of the notification based on the estimated emotion. For example, if the child is feeling anxious, the notification unit issues a high-urgency notification. If the child is relaxed, the notification unit can also issue a normal notification. Furthermore, if the child is excited, the notification unit can also issue a notification to warn the child. In this way, by adjusting the content of the notification according to the child's emotion, a more appropriate notification is issued. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the notification unit may be performed using an AI, for example, or without an AI. For example, the notification unit can input emotion data to the generation AI and have the generation AI adjust the content of the notification.

[0099] The notification unit can select the notification method taking into consideration the guardian's current situation when sending a notification. For example, the notification unit selects the notification method taking into consideration the guardian's current situation when sending a notification. For example, when the guardian is at work, the notification unit prioritizes sending notifications with a high level of urgency. The notification unit can also send a brief notification when the guardian is traveling. Furthermore, the notification unit can also send a detailed notification when the guardian is at home. In this way, by selecting the notification method taking into consideration the guardian's current situation, more appropriate notifications are sent. Some or all of the above-mentioned processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input the guardian's situation data into the generation AI and have the generation AI select the notification method.

[0100] The notification unit can improve the accuracy of notifications by referring to the guardian's past notification history when sending a notification. For example, the notification unit improves the accuracy of notifications by referring to the guardian's past notification history when sending a notification. For example, the notification unit improves the accuracy of notifications based on the guardian's past notification history. The notification unit can also analyze the guardian's past notification history and send an optimal notification. Furthermore, the notification unit can customize the content of the notification by referring to the guardian's past notification history. In this way, the accuracy of notifications is improved by referring to the past notification history. Some or all of the above-mentioned 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 input notification history data to a generation AI and cause the generation AI to improve the accuracy of notifications.

[0101] The notification unit can customize the content of the notification by reflecting the parent's feedback when sending the notification. For example, the notification unit customizes the content of the notification by reflecting the parent's feedback when sending the notification. For example, the notification unit adjusts the content of the notification based on feedback previously provided by the parent. The notification unit can also optimize the content of the notification for a specific situation based on the parent's past feedback. Furthermore, the notification unit can improve the accuracy of the notification by referring to the parent's past feedback. In this way, customizing the content of the notification by reflecting the feedback improves the accuracy of the 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 input feedback data to a generation AI and cause the generation AI to customize the content of the notification.

[0102] The notification unit can estimate the child's emotions and determine the priority of notifications based on the estimated child's emotions. The notification unit, for example, estimates the child's emotions and determines the priority of notifications based on the estimated child's emotions. For example, if the child is feeling anxious, the notification unit can prioritize sending notifications with a high level of urgency. Also, if the child is relaxed, the notification unit can prioritize sending normal notifications. Furthermore, if the child is excited, the notification unit can prioritize sending notifications that call for attention. In this way, by determining the priority of notifications according to the child's emotions, important notifications can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the notification unit may be performed using an AI, for example, or without an AI. For example, the notification unit can input emotion data to the generation AI and have the generation AI determine the priority of notifications.

[0103] The notification unit can customize the content of the notification by taking into account the geographical location information of the guardian when sending a notification. For example, the notification unit customizes the content of the notification by taking into account the geographical location information of the guardian when sending a notification. For example, the notification unit sends a notification with a high level of urgency when the guardian is at work. The notification unit can also send a detailed notification when the guardian is at home. Furthermore, the notification unit can also send a concise notification when the guardian is out. This enables a more appropriate response by customizing the content of the notification by taking into account the geographical location information. 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 input geographical location data to a generation AI and cause the generation AI to customize the content of the notification.

[0104] The notification unit can analyze the parent's social media activity and send relevant notifications when sending a notification. For example, the notification unit can analyze the parent's social media activity and send relevant notifications when sending a notification. For example, the notification unit can send notifications about places the parent has checked in on social media. The notification unit can also analyze the content of the parent's social media posts and send relevant notifications. Furthermore, the notification unit can also send relevant notifications based on the activities of the parent's friends on social media. This enables more detailed monitoring by analyzing social media activity and sending relevant notifications. Some or all of the above-mentioned processing in the notification unit can be performed using AI, for example, or without AI. For example, the notification unit can input social media data into a generation AI and cause the generation AI to send relevant notifications.

[0105] The notification unit can customize the content of the notification by reflecting the parent's past feedback when sending a notification. For example, the notification unit customizes the content of the notification by reflecting the parent's past feedback when sending a notification. For example, the notification unit adjusts the content of the notification based on feedback provided by the parent in the past. The notification unit can also optimize the content of the notification for a specific situation based on the parent's past feedback. Furthermore, the notification unit can improve the accuracy of the notification by referring to the parent's past feedback. In this way, the accuracy of the notification is improved by customizing the content of the notification by reflecting the past feedback. Some or all of the above-mentioned 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 input feedback data to a generation AI and cause the generation AI to customize the content of the notification. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, alert unit, and notification unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit acquires the child's location information in real time using the GPS function, Wi-Fi, or Bluetooth of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, stores the child's movement range, and compares it with the child's normal movement range. The alert unit is realized by the specific processing unit 290 of the data processing device 12, detects abnormal movement, and issues an alert. The notification unit is realized by the control unit 46A of the smart device 14, and sends a notification to the local police when the parent or guardian determines that the child is lost. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, alert unit, and notification unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit acquires the child's location information in real time using the GPS function, Wi-Fi, or Bluetooth of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, stores the child's range of movement, and compares it with the child's normal range of movement. The alert unit is realized by the specific processing unit 290 of the data processing device 12, detects abnormal movement, and issues an alert. The notification unit is realized by the control unit 46A of the smart glasses 214, and sends a notification to the local police when the parent or guardian determines that the child is lost. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, alert unit, and notification unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit acquires the child's location information in real time using the GPS function, Wi-Fi, or Bluetooth of the headset-type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, stores the child's range of movement, and compares it with the child's normal range of movement. The alert unit is realized by the specific processing unit 290 of the data processing device 12, detects abnormal movement, and issues an alert. The notification unit is realized by the control unit 46A of the headset-type terminal 314, and sends a notification to the local police when the parent or guardian determines that the child is lost. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, alert unit, and notification unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit acquires the child's location information in real time using the GPS function, Wi-Fi, or Bluetooth of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, stores the child's movement range, and compares it with the child's normal movement range. The alert unit is realized by the specific processing unit 290 of the data processing device 12, detects abnormal movement, and issues an alert. The notification unit is realized by the control unit 46A of the robot 414, and sends a notification to the local police when the parent or guardian determines that the child is lost.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The analysis unit can store the child's range of activity and compare it with the child's normal range of activity. For example, the analysis unit can store the child's route from school to home as the child's normal range of activity. The analysis unit can also use the generation AI to learn the child's range of activity and detect abnormal behavior. Furthermore, the analysis unit can analyze the child's behavioral patterns and dynamically update the normal range of activity. This allows the child's range of activity to be stored and compared with the normal range of activity to detect abnormal behavior. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the child's behavioral data into the generation AI and have the generation AI analyze the range of activity.

[0108] The collection unit can acquire the child's location information in real time. For example, the collection unit acquires the child's location information using a GPS function. The collection unit can also supplement the location information using Wi-Fi or Bluetooth. Furthermore, the collection unit can periodically update the child's location information and monitor it in real time. This allows the child's location information to be accurately acquired using the GPS function. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input GPS data to a generation AI and have the generation AI analyze the location information.

[0109] The alert unit can issue an alert when it detects abnormal behavior. For example, the alert unit issues an alert when a child moves beyond their normal range of movement. The alert unit can also issue an alert when a child enters a dangerous area. Furthermore, the alert unit can notify the guardian of the alert via their smartphone. This allows for a prompt response by issuing an alert when abnormal behavior is detected. Some or all of the above-described processing in the alert unit may be performed using, for example, AI, or may be performed without using AI. For example, the alert unit can input data on abnormal behavior into a generation AI and cause the generation AI to issue an alert.

[0110] The notification unit can send a notification to a nearby police station when a parent or guardian certifies that a child is lost. For example, the notification unit can send a notification to a nearby police station when a parent or guardian certifies that a child is lost through a smartphone app. The notification unit can also provide the police with the child's location information to support prompt protection activities. This allows prompt protection activities to be carried out by sending a notification to a nearby police station when a parent or guardian certifies that a child is lost. Some or all of the above-mentioned 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 input the child's certification data as lost into the generation AI and cause the generation AI to notify the police.

[0111] The collection unit can estimate the child's emotions and adjust the frequency of location information acquisition based on the estimated child's emotions. For example, if the child is feeling anxious, the collection unit can increase the frequency of location information acquisition and monitor in real time. If the child is relaxed, the collection unit can also reduce the frequency of location information acquisition to reduce battery consumption. If the child is excited, the collection unit can set the frequency of location information acquisition to a medium level and perform appropriate monitoring. This allows for appropriate monitoring while reducing battery consumption by adjusting the frequency of location information acquisition according to the child's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input emotion data to the generation AI and cause the generation AI to adjust the frequency of location information acquisition.

[0112] The collection unit can analyze the child's past movement history and select an appropriate location information acquisition method. For example, the collection unit can adjust the frequency of location information acquisition based on places the child has frequently visited in the past. The collection unit can also analyze the child's past movement patterns and select an efficient location information acquisition method. Furthermore, the collection unit can optimize the location information acquisition method for a specific time period based on the child's past movement history. This enables efficient monitoring by selecting the optimal location information acquisition method based on the past movement history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past movement data into the generation AI and have the generation AI select the location information acquisition method.

[0113] When acquiring location information, the collection unit can filter the location information based on the child's current activity status and environment. For example, the collection unit can set the frequency of acquiring location information low when the child is at school. The collection unit can also increase the frequency of acquiring location information when the child is playing in a park. Furthermore, the collection unit can temporarily stop acquiring location information when the child is at home. This enables appropriate monitoring by filtering the location information based on the child's current activity status and environment. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input activity status data to a generation AI and cause the generation AI to filter the location information.

[0114] When acquiring location information, the collection unit can select an appropriate acquisition means depending on the child's means of transportation. For example, if the child is traveling on foot, the collection unit can acquire detailed location information using GPS. Furthermore, if the child is traveling by bicycle, the collection unit can increase the frequency of acquiring location information. Furthermore, if the child is traveling by car, the collection unit can change the means of acquiring location information to efficiently monitor. This enables efficient monitoring by selecting the optimal acquisition means depending on the means of transportation. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input transportation data into the generation AI and have the generation AI select the location information acquisition means.

[0115] The collection unit can estimate the child's emotions and determine the priority of location information to be acquired based on the estimated child's emotions. For example, if the child is feeling anxious, the collection unit can prioritize acquiring location information of important places (such as school and home). Furthermore, if the child is relaxed, the collection unit can prioritize acquiring location information within the child's normal range of movement. Furthermore, if the child is excited, the collection unit can prioritize acquiring location information of destinations. Thus, by prioritizing location information according to the child's emotions, important information can be acquired preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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 collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input emotion data to the generation AI and have the generation AI determine the priority of location information.

[0116] The analysis unit can estimate the child's emotions and adjust the analysis method of the activity range based on the estimated child's emotions. For example, if the child is feeling anxious, the analysis unit can analyze the activity range in detail. Furthermore, if the child is relaxed, the analysis unit can analyze the child's normal activity range. Furthermore, if the child is excited, the analysis unit can analyze a wider activity range. This allows for more accurate analysis by adjusting the analysis method of the activity range according to the child's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation 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 analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input emotion data into the generation AI and have the generation AI adjust the analysis method of the activity range.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The collection unit acquires the child's location information in real time. For example, the collection unit may acquire the child's location information using a GPS function, and may supplement the location information using Wi-Fi or Bluetooth. Furthermore, the collection unit periodically updates the child's location information and monitors it in real time. Step 2: The analysis unit memorizes the child's range of movement based on the location information acquired by the collection unit and compares it with the child's normal range of movement. For example, the analysis unit memorizes the child's route from school to home as the normal range of movement, and can use the generation AI to learn the child's range of movement and detect abnormal movements. Furthermore, the analysis unit can analyze the child's behavioral patterns and dynamically update the normal range of movement. Step 3: The alert unit detects abnormal movements based on the results collated by the analysis unit and issues an alert. For example, the alert unit can issue an alert if a child moves beyond their normal range of movement or enters a dangerous area. Furthermore, the alert unit can send the alert to the parent's smartphone. Step 4: The notification unit sends a notification to the local police if the parent or guardian reports the child as lost. For example, if the parent or guardian reports the child as lost through a smartphone app, the notification unit sends a notification to the local police. The notification unit can also provide the police with the child's location information to support prompt protection efforts.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that acquires the location information of the child in real time; an analysis unit that stores the child's range of movement based on the location information acquired by the collection unit and compares the range of movement with a normal range of movement; an alert unit that detects abnormal behavior and issues an alert based on the result of the comparison by the analysis unit; A notification unit that sends a notification to the local police when the guardian certifies the child as lost. A system characterized by:

2. The collecting unit Use the GPS function to obtain your child's location information in real time 2. The system of claim 1.

3. The analysis unit Remember your child's range of movement and compare it with their normal range of movement 2. The system of claim 1.

4. The alert unit Issues an alert when abnormal behavior is detected 2. The system of claim 1.

5. The notification unit If a parent or guardian certifies a child as lost, a notification will be sent to the local police.

2. The system of claim 1.

6. The alert unit Send alerts to parents' smartphones 2. The system of claim 1.

7. The collecting unit Estimate the child's emotions and adjust the frequency of location information acquisition based on the estimated emotions of the child.

2. The system of claim 1.

8. The collecting unit Analyze the child's past movement history and select the appropriate method for obtaining location information 2. The system of claim 1.

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