system

Abstract

The system according to this embodiment aims to understand a child's behavioral patterns, detect abnormal behavior, and notify the guardian. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a detection unit, and an alert unit. The collection unit collects the child's location information. The analysis unit analyzes the location information collected by the collection unit to understand the child's daily behavior patterns. The detection unit detects unusual behavior based on the behavior patterns understood by the analysis unit. The alert unit sends an alert to the guardian based on the abnormality detected by the detection unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that the behavior patterns of children were not sufficiently grasped, and abnormal behaviors were not quickly detected and notified to guardians.

[0005] The system according to the embodiment aims to grasp the behavior patterns of children, detect abnormal behaviors, and notify guardians.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a detection unit, and an alert unit. The data collection unit collects location information of the child. The analysis unit analyzes the location information collected by the data collection unit to understand the child's daily behavioral patterns. The detection unit detects unusual behavior based on the behavioral patterns understood by the analysis unit. The alert unit sends an alert to the guardian based on the anomaly detected by the detection unit. [Effects of the Invention]

[0007] The system according to this embodiment can understand a child's behavioral patterns, detect abnormal behavior, and notify the guardian. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The child accident prevention system according to an embodiment of the present invention is a system that collects location information of children, uses AI to understand their daily behavior patterns, detects unusual behavior such as getting lost or being left unattended in a car, and sends an alert to the guardian. The child accident prevention system collects location information of children, and the AI ​​analyzes the collected location information to understand the child's daily behavior patterns. For example, the AI ​​learns information such as the route the child takes to school every day and the places they play. This allows the normal behavior patterns to be understood. Next, the AI ​​detects unusual behavior. For example, if a child gets lost or is left unattended in a car, the AI ​​detects the abnormality. When an abnormality is detected, the AI ​​sends an alert to the guardian. This allows the guardian to respond quickly. This system makes it possible to prevent accidents involving children. For example, if a child gets lost, the guardian can immediately check the location information and find the child. Also, if a child is left unattended in a car, an alert is sent to the guardian, allowing for a quick response. This system is an effective means of ensuring the safety of children. Guardians can constantly monitor their children's behavior and watch over them with peace of mind. Furthermore, as the AI ​​learns behavioral patterns, the system's accuracy improves, enabling more precise anomaly detection. This allows the child accident prevention system to prevent accidents before they occur.

[0029] The child accident prevention system according to this embodiment comprises a data collection unit, an analysis unit, a detection unit, and an alert unit. The data collection unit collects location information of children. The data collection unit collects location information of children using technologies such as GPS, Wi-Fi, and Bluetooth®. The data collection unit can track the location of children in real time using GPS, for example. The data collection unit can also determine the location of children using Wi-Fi access points. Furthermore, the data collection unit can detect the location of children using Bluetooth beacons. For example, the data collection unit receives GPS signals and determines the current location of children. It estimates the location of children using location information from Wi-Fi access points. It detects the location of children based on the signal strength of Bluetooth beacons. The analysis unit analyzes the location information collected by the data collection unit to understand the daily behavior patterns of children. The analysis unit learns information such as the route children take to school and playgrounds they play in every day, for example. The analysis unit analyzes the route children take to school and understands their normal behavior patterns, for example. The analysis unit can also analyze location information of playgrounds to understand the patterns of children's playgrounds. Furthermore, the analysis unit can analyze a child's behavioral patterns during specific time periods. For example, the analysis unit learns a child's school route and determines their usual school commute time. It also understands a child's playground patterns based on playground location information. It analyzes behavioral patterns during specific time periods and determines their usual activity times. The detection unit detects unusual behavior based on the behavioral patterns identified by the analysis unit. For example, the detection unit detects an anomaly if a child gets lost or is left unattended in a car. For example, the detection unit detects an anomaly if a child deviates from their usual school route. The detection unit can also detect an anomaly if a child stays in a specific area for an extended period. Furthermore, the detection unit can detect an anomaly if a child deviates from their usual behavioral patterns during specific time periods. For example, the detection unit detects an anomaly if a child deviates from their usual school route. It detects an anomaly if a child stays in a specific area for an extended period. It detects an anomaly if a child deviates from their usual behavioral patterns during specific time periods. The alert unit sends an alert to parents based on the anomaly detected by the detection unit.The alert unit sends alerts to parents, for example, via SMS, email, or app notifications. The alert unit notifies parents of an anomaly using SMS, for example. The alert unit can also notify parents of an anomaly using email. Furthermore, the alert unit can also notify parents of an anomaly using a dedicated app. For example, the alert unit notifies parents of an anomaly using SMS, notifies them of an anomaly using email, or notifies them of an anomaly using a dedicated app. As a result, the child accident prevention system according to this embodiment can prevent accidents involving children.

[0030] The data collection unit collects location information of children. The unit uses technologies such as GPS, Wi-Fi, and Bluetooth to collect this information. Specifically, it can track children's locations in real time using GPS. GPS receives signals from satellites and accurately determines the child's current location. This allows parents to always know where their child is. The data collection unit can also determine the child's location using Wi-Fi access points. By using the location information of Wi-Fi access points, the child's location can be estimated even indoors or in areas where GPS signals are weak. Furthermore, the data collection unit can detect the child's location using Bluetooth beacons. Bluetooth beacons transmit signals to devices within a certain range and determine their location based on the signal strength. For example, Bluetooth beacons installed in schools or parks can be used to detect whether a child is within that range. This allows the data collection unit to receive GPS signals and determine the child's current location. It then estimates the child's location using Wi-Fi access point location information and detects the child's location based on the Bluetooth beacon signal strength. In this way, the data collection unit utilizes a variety of technologies to collect children's location information with high accuracy and in real time. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and accessed by the analysis and detection units. Adjusting the data collection frequency and accuracy also allows for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes location information collected by the data collection unit to understand children's daily behavior patterns. For example, the analysis unit learns information such as the routes children take to school and the places they play. Specifically, it analyzes children's school routes to understand their usual behavior patterns. For instance, it analyzes the time of day and route children take to school to identify their usual commuting time and route. The analysis unit can also analyze playground location information to understand children's playground patterns. For example, if a child frequently visits a particular park or playground, it identifies that location and understands their usual playground patterns. Furthermore, the analysis unit can analyze children's behavior patterns at specific times of day. For example, if a child is often in a specific location at a specific time, it understands that behavior pattern. This allows the analysis unit to learn children's school routes and understand their usual commuting times. Based on playground location information, it understands children's playground patterns. It analyzes behavior patterns at specific times of day to understand their usual activity times. Additionally, the analysis unit can utilize past data and statistical information to analyze long-term behavior patterns and trends. For example, based on past behavioral data, it can predict behavioral patterns during specific seasons or events and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time but also to analyze long-term behavioral patterns and detect anomalies, thereby improving the overall reliability and safety of the system.

[0032] The detection unit detects unusual behavior based on the behavioral patterns identified by the analysis unit. For example, the detection unit detects an anomaly if a child gets lost or is left unattended in a car. Specifically, it detects an anomaly if a child deviates from their usual route to school. For example, if a child deviates from their usual route to school, the detection unit immediately detects the anomaly and issues an alert. The detection unit can also detect an anomaly if a child stays in a specific area for an extended period. For example, if a child stays in a specific playground or park for an extended period, the detection unit determines that their behavior deviates from the normal pattern and detects an anomaly. Furthermore, the detection unit can also detect an anomaly if a child deviates from their normal behavioral pattern during a specific time period. For example, if a child does not return home after the usual school time or is not in a specific place during a specific time period, the detection unit detects an anomaly. Thus, the detection unit can detect an anomaly if a child deviates from their usual route to school, if they stay in a specific area for an extended period, and if they deviate from their normal behavioral pattern during a specific time period. Furthermore, the detection unit can integrate and analyze multiple data sources to improve the accuracy of anomaly detection. For example, it can utilize data from accelerometers and temperature sensors in addition to location information to perform more accurate anomaly detection. The detection unit can also update the anomaly detection results in real time, enabling it to respond to the latest situation. As a result, the detection unit can ensure the safety of children and support a quick and appropriate response.

[0033] The alert unit sends alerts to parents based on anomalies detected by the detection unit. The alert unit sends alerts to parents using methods such as SMS, email, and app notifications. Specifically, it notifies parents of anomalies using SMS. For example, if a child deviates from their usual route to school or stays in a specific area for an extended period, the alert unit immediately sends an SMS to inform parents of the anomaly. The alert unit can also notify parents of anomalies using email. For example, it can send an email containing detailed anomaly information and location information to inform parents of the situation in detail. Furthermore, the alert unit can also notify parents of anomalies using a dedicated app. For example, it can notify parents of anomaly information in real time through the app, enabling them to take immediate action. Thus, the alert unit notifies parents of anomalies using SMS, email, and a dedicated app. In addition, the alert unit can use multiple communication methods in combination to improve the accuracy and reliability of notifications. For example, by sending SMS, email, and app notifications simultaneously, it ensures that parents are reliably notified of the anomaly. The alert unit can also customize the content of notifications, providing information tailored to the needs of parents. For example, the content and method of notification can be changed depending on the type and urgency of the anomaly. This allows the alert unit to quickly and reliably notify parents of anomalies and support them in taking action to ensure the child's safety.

[0034] The data collection unit can collect location information of children using technologies such as GPS, Wi-Fi, and Bluetooth. For example, the data collection unit can track the child's location in real time using GPS. The data collection unit can also determine the child's location using Wi-Fi access points. The data collection unit can also detect the child's location using Bluetooth beacons. For example, the data collection unit can receive GPS signals and determine the child's current location. It can estimate the child's location using location information from Wi-Fi access points. It can detect the child's location based on the signal strength of Bluetooth beacons. By using multiple technologies, the data collection unit can collect location information more accurately. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect location information using an AI model that receives GPS signals and determines the child's current location.

[0035] The alert unit may include a suggestion unit that proposes specific response methods when sending an alert to a parent or guardian. For example, when sending an alert to a parent or guardian, the alert unit may propose specific response methods such as providing contact information, notifying emergency contacts, and providing information on evacuation locations. For example, the alert unit may provide contact information to the parent or guardian. The alert unit may also notify emergency contacts. Furthermore, the alert unit may provide information on evacuation locations. For example, the alert unit may provide contact information to the parent or guardian. It may also notify emergency contacts. It may provide information on evacuation locations. This allows the alert unit to propose specific response methods so that parents or guardians can respond quickly and appropriately. Some or all of the above processing in the alert unit may be performed using AI, for example, or not using AI. For example, when sending an alert to a parent or guardian, the alert unit may propose response methods using an AI model that proposes specific response methods.

[0036] The analysis unit can learn information such as the routes children take to school and the places they play. For example, the analysis unit can analyze a child's school route to understand their usual behavior patterns. The analysis unit can also analyze the location information of playgrounds to understand the patterns of playgrounds children use. The analysis unit can also analyze a child's behavior patterns during specific time periods. For example, the analysis unit can learn a child's school route to understand their usual school commute time. Based on the location information of playgrounds, it can understand the patterns of playgrounds children use. It can analyze behavior patterns during specific time periods to understand their usual activity times. This allows for an accurate understanding of a child's daily behavior patterns. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use an AI model that learns a child's school route to understand their behavior patterns.

[0037] The detection unit can detect anomalies, such as when a child gets lost or is left unattended in a vehicle. The detection unit can detect anomalies, for example, when a child deviates from their usual route to school. The detection unit can also detect anomalies, for example, when a child stays in a specific area for an extended period of time. The detection unit can also detect anomalies, for example, when a child deviates from their usual behavioral patterns during a specific time period. For example, the detection unit can detect anomalies when a child deviates from their usual route to school. It can detect anomalies when a child stays in a specific area for an extended period of time. It can detect anomalies when a child deviates from their usual behavioral patterns during a specific time period. This allows for the rapid detection of abnormal behavior in children and notification to parents. Some or all of the above-described processes in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can detect anomalies using an AI model that analyzes a child's behavioral patterns and detects anomalies.

[0038] The data collection unit can analyze a child's past behavioral history during data collection and select the optimal collection method. For example, the data collection unit can prioritize collecting data from places the child has frequently visited in the past. For example, the data collection unit can increase the collection frequency during specific time periods based on the child's past behavioral patterns. For example, the data collection unit can select an efficient collection method based on the child's past travel history. For example, the data collection unit can prioritize collecting data from places the child has frequently visited in the past. For example, it can increase the collection frequency during specific time periods based on the child's past behavioral patterns. For example, it can select an efficient collection method based on the child's past travel history. By analyzing past behavioral history, it becomes possible to collect location information more efficiently. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can select the optimal collection method using an AI model that analyzes the child's past behavioral history.

[0039] The data collection unit can filter location information based on the child's current activity status and environment. For example, the data collection unit can set a lower collection frequency when the child is at school. For example, the data collection unit can increase the collection frequency to ensure safety when the child is playing in a park. For example, the data collection unit can minimize the collection frequency when the child is at home. For example, the data collection unit can set a lower collection frequency when the child is at school. For example, the data collection unit can increase the collection frequency to ensure safety when the child is playing in a park. For example, the data collection unit can minimize the collection frequency when the child is at home. By filtering location information based on the child's current activity status and environment, more accurate information can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can filter location information using an AI model that analyzes the child's current activity status and environment.

[0040] The data collection unit can prioritize the collection of highly relevant information by considering the child's geographical location when collecting location information. For example, if the child is at school, the data collection unit will prioritize the collection of information around the school. For example, if the child is at a park, the data collection unit can prioritize the collection of information within the park. For example, if the child is at a commercial facility, the data collection unit can prioritize the collection of information within the facility. For example, if the child is at school, the data collection unit will prioritize the collection of information around the school. If the child is at a park, it will prioritize the collection of information within the park. If the child is at a commercial facility, it will prioritize the collection of information within the facility. This allows for the priority collection of highly relevant information by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can prioritize the collection of highly relevant information using an AI model that analyzes the child's geographical location information.

[0041] The data collection unit can analyze a child's social media activity and collect relevant information when collecting location information. For example, the data collection unit can prioritize collecting locations where the child has posted on social media. The data collection unit can also prioritize collecting locations where the child's social media friends are located. The data collection unit can also prioritize collecting locations where the child has checked in on social media. For example, the data collection unit can prioritize collecting locations where the child has posted on social media. For example, the data collection unit can prioritize collecting locations where the child's social media friends are located. For example, the data collection unit can prioritize collecting locations where the child has checked in on social media. This allows for the efficient collection of relevant information by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect relevant information using an AI model that analyzes a child's social media activity.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the behavioral patterns during the analysis. For example, the analysis unit may analyze highly important behavioral patterns in detail. For example, the analysis unit may analyze less important behavioral patterns simply. For example, the analysis unit may analyze moderately important behavioral patterns. For example, the analysis unit may analyze highly important behavioral patterns in detail. For less important behavioral patterns simply. For moderately important behavioral patterns. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the behavioral patterns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the level of detail of the analysis using an AI model that evaluates the importance of behavioral patterns.

[0043] The analysis unit can apply different analysis algorithms depending on the category of behavioral pattern during analysis. For example, the analysis unit can apply a specific algorithm to school commuting patterns. The analysis unit can also apply a different algorithm to playground patterns. The analysis unit can also apply yet another algorithm to domestic patterns. For example, the analysis unit can apply a specific algorithm to school commuting patterns. It can apply a different algorithm to playground patterns. It can also apply yet another algorithm to domestic patterns. This improves the accuracy of the analysis by applying the appropriate analysis algorithm according to the category of behavioral pattern. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform analysis using an AI model that applies different analysis algorithms depending on the category of behavioral pattern.

[0044] The analysis unit can determine the priority of analysis based on the timing of the occurrence of behavioral patterns. For example, the analysis unit may prioritize the analysis of recent behavioral patterns. The analysis unit may also postpone the analysis of past behavioral patterns. The analysis unit may also prioritize the analysis of behavioral patterns during a specific time period. For example, the analysis unit may prioritize the analysis of recent behavioral patterns, postpone the analysis of past behavioral patterns, and prioritize the analysis of behavioral patterns during a specific time period. This allows the analysis unit to prioritize the analysis of the latest behavioral patterns by determining the priority of analysis based on the timing of the occurrence of behavioral patterns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may determine the priority of analysis using an AI model that evaluates the timing of the occurrence of behavioral patterns.

[0045] The analysis unit can adjust the order of analysis based on the relevance of behavioral patterns during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant behavioral patterns. For example, the analysis unit may postpone the analysis of less relevant behavioral patterns. For example, the analysis unit may moderately analyze behavioral patterns with a moderate degree of relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of behavioral patterns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the order of analysis using an AI model that evaluates the relevance of behavioral patterns.

[0046] The detection unit can improve the accuracy of anomaly detection by considering the interrelationships of behavioral patterns when detecting anomalies. For example, the detection unit can detect anomalies by considering the interrelationships between commuting patterns and playground patterns. The detection unit can also detect anomalies by considering the interrelationships between in-home patterns and outing patterns. The detection unit can also detect anomalies by considering the interrelationships of behavioral patterns during a specific time period. For example, the detection unit can detect anomalies by considering the interrelationships between commuting patterns and playground patterns. It can detect anomalies by considering the interrelationships between in-home patterns and outing patterns. It can detect anomalies by considering the interrelationships of behavioral patterns during a specific time period. This improves the accuracy of anomaly detection by considering the interrelationships of behavioral patterns. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can detect anomalies using an AI model that evaluates the interrelationships of behavioral patterns.

[0047] The detection unit can perform anomaly detection by considering the frequency of occurrence of behavioral patterns. For example, the detection unit may prioritize detecting behavioral patterns that occur frequently. The detection unit may also postpone detecting behavioral patterns that occur infrequently. The detection unit may also moderately detect behavioral patterns that occur in a moderate frequency. For example, the detection unit may prioritize detecting behavioral patterns that occur frequently, postpone detecting behavioral patterns that occur infrequently, and moderately detect behavioral patterns that occur in a moderate frequency. This improves the accuracy of anomaly detection by considering the frequency of occurrence of behavioral patterns. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can detect anomalies using an AI model that evaluates the frequency of occurrence of behavioral patterns.

[0048] The detection unit can perform anomaly detection by considering the geographical distribution of behavioral patterns at the time of detection. For example, the detection unit may prioritize detecting behavioral patterns in a specific area. The detection unit may also postpone detecting behavioral patterns that are geographically widespread. The detection unit may also moderately detect behavioral patterns that are geographically moderate. For example, the detection unit may prioritize detecting behavioral patterns in a specific area, postpone detecting behavioral patterns that are geographically widespread, and moderately detect behavioral patterns that are geographically moderate. This improves the accuracy of anomaly detection by considering the geographical distribution of behavioral patterns. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can detect anomalies using an AI model that evaluates the geographical distribution of behavioral patterns.

[0049] The detection unit can improve the accuracy of anomaly detection by referring to relevant literature on behavioral patterns when detection occurs. For example, the detection unit adjusts the anomaly detection algorithm based on relevant literature. The detection unit can also set anomaly detection criteria by referring to data in relevant literature. The detection unit can also improve the accuracy of anomaly detection by utilizing insights from relevant literature. For example, the detection unit adjusts the anomaly detection algorithm based on relevant literature. It sets anomaly detection criteria by referring to data in relevant literature. It improves the accuracy of anomaly detection by utilizing insights from relevant literature. As a result, the accuracy of anomaly detection is improved by referring to relevant literature. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can detect anomalies using an AI model that refers to relevant literature.

[0050] The alert unit can select the optimal alert method by referring to past alert history when sending an alert. For example, the alert unit can prioritize alert methods that have been effective in the past. The alert unit can also select the optimal sending timing from past alert history. The alert unit can also adjust the content of the alert based on past alert history. For example, the alert unit can prioritize alert methods that have been effective in the past. It can select the optimal sending timing from past alert history. It can adjust the content of the alert based on past alert history. In this way, the optimal alert method can be selected by referring to past alert history. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can select the optimal alert method using an AI model that analyzes past alert history.

[0051] The alert unit can adjust the priority of alerts based on the parent's current situation when sending alerts. For example, if the parent is driving, the alert unit will prioritize sending an audio alert. For example, if the parent is in a meeting, the alert unit may also prioritize sending a vibration alert. For example, if the parent is at home, the alert unit may also send a regular alert. For example, if the parent is driving, the alert unit will prioritize sending an audio alert. If the parent is in a meeting, it will prioritize sending a vibration alert. If the parent is at home, it will send a regular alert. This allows important information to be notified preferentially by adjusting the priority of alerts according to the parent's current situation. Some or all of the above processing in the alert unit may be performed using AI, for example, or not using AI. For example, the alert unit may adjust the priority of alerts using an AI model that evaluates the parent's current situation.

[0052] The alert unit can select the optimal alert method when sending an alert, taking into account the parent's geographical location. For example, if the parent is nearby, the alert unit may prioritize sending an audio alert. If the parent is far away, the alert unit may also prioritize sending a text alert. If the parent is in a specific location, the alert unit may also select an alert method appropriate to that location. For example, if the parent is nearby, the alert unit may prioritize sending an audio alert. If the parent is far away, it may prioritize sending a text alert. If the parent is in a specific location, it may select an alert method appropriate to that location. This allows the system to select the optimal alert method by considering the parent's geographical location. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit may select the optimal alert method using an AI model that evaluates the parent's geographical location.

[0053] The alert unit can analyze the parent's social media activity and suggest an alert method when sending an alert. For example, if the parent frequently uses social media, the alert unit will send an alert via social media. If the parent does not use social media often, the alert unit can also send an alert through other means. The alert unit can also suggest the optimal alert method based on the parent's social media activity. For example, if the parent frequently uses social media, the alert unit will send an alert via social media. If the parent does not use social media often, the alert unit will send an alert through other means. The alert unit suggests the optimal alert method based on the parent's social media activity. In this way, by analyzing the parent's social media activity, the optimal alert method can be suggested. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can suggest an alert method using an AI model that analyzes the parent's social media activity.

[0054] The proposal department can select the optimal response method by referring to past response history when making a proposal. For example, the proposal department will prioritize proposing response methods that have been effective in the past. The proposal department can also select the optimal response timing from past response history. The proposal department can also adjust the response method based on past response history. For example, the proposal department will prioritize proposing response methods that have been effective in the past. It will select the optimal response timing from past response history. It will adjust the response method based on past response history. In this way, the optimal response method can be selected by referring to past response history. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can select the optimal response method using an AI model that analyzes past response history.

[0055] The suggestion function can customize the response method based on the parent's current situation when making a suggestion. For example, if the parent is driving, the suggestion function can suggest a simple response method. If the parent is in a meeting, the suggestion function can also suggest a method that can be addressed later. If the parent is at home, the suggestion function can also suggest a detailed response method. For example, if the parent is driving, the suggestion function can suggest a simple response method. If the parent is in a meeting, it can suggest a method that can be addressed later. If the parent is at home, it can suggest a detailed response method. This allows parents to respond quickly by customizing the response method according to their current situation. Some or all of the above processing in the suggestion function may be performed using AI, for example, or not using AI. For example, the suggestion function can customize the response method using an AI model that evaluates the parent's current situation.

[0056] The proposal unit can select the optimal response method by considering the geographical location information of the parent when making a proposal. For example, if the parent is nearby, the proposal unit will propose a quick response method. If the parent is far away, the proposal unit may also propose alternative response methods. If the parent is in a specific location, the proposal unit may also propose a response method appropriate to that location. For example, if the parent is nearby, the proposal unit will propose a quick response method. If the parent is far away, it will propose alternative response methods. If the parent is in a specific location, it will propose a response method appropriate to that location. This allows the optimal response method to be selected by considering the geographical location information of the parent. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can select the optimal response method using an AI model that evaluates the geographical location information of the parent.

[0057] The proposal department can analyze the parents' social media activity and propose appropriate responses when making a proposal. For example, if the parents frequently use social media, the proposal department can propose a response method through social media. For example, if the parents do not use social media often, the proposal department can propose a response method through other means. The proposal department can also propose the optimal response method based on the parents' social media activity. For example, if the parents frequently use social media, the proposal department can propose a response method through social media. If the parents do not use social media often, the proposal department can propose a response method through other means. The proposal department can propose the optimal response method based on the parents' social media activity. In this way, by analyzing the parents' social media activity, the optimal response method can be proposed. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can propose a response method using an AI model that analyzes the parents' social media activity.

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

[0059] The data collection unit can analyze ambient sounds when collecting a child's location information and assess the safety of the child's location. For example, the unit can measure the ambient noise level and send a warning to the guardian if the noise level is high. It can also analyze ambient sounds and send an alert if it detects sounds indicating an emergency (e.g., screaming or alarm sounds). Furthermore, the unit can identify a child's voice from ambient sounds and notify the guardian if the child is calling for help. This allows for a safer environment by analyzing not only the child's location information but also ambient sounds.

[0060] The analysis unit can consider the child's health data (e.g., heart rate and body temperature) when analyzing the child's behavioral patterns. For example, if the child's heart rate is higher than normal, the analysis unit can perform a more detailed analysis of the behavioral patterns, as this may indicate stress or excitement. Similarly, if the child's body temperature is elevated, the analysis unit can prioritize detecting abnormal behavior, as this may indicate poor health. Furthermore, based on the child's health data, the analysis unit can notify parents if the behavioral patterns deviate from normal. This enables the analysis of behavioral patterns while considering the child's health status.

[0061] The detection unit can consider the location information of the child's friends and family when detecting the child's behavior patterns. For example, if the detection unit is with friends, it can consider this to be normal behavior. Also, if the detection unit is with family, it can mitigate the detection of abnormal behavior. Furthermore, if the detection unit is separated from friends or family, it can detect this as abnormal behavior and notify the guardian. This allows for more accurate detection of the child's behavior patterns.

[0062] The alert unit can adjust the content of alerts sent to parents based on their current activity status. For example, if a parent is driving, the alert unit will prioritize sending an audio alert. It can also prioritize sending a vibration alert if a parent is in a meeting. Furthermore, if a parent is at home, the alert unit can send a regular alert. This allows for quick notification of important information by adjusting the alert content according to the parent's current activity status.

[0063] The proposal department can select the most suitable response method by referring to past response history when making a proposal. For example, it can prioritize proposing response methods that have been effective in the past. It can also select the optimal timing for response based on past response history. It can also adjust the response method based on past response history. In this way, the optimal response method can be selected by referring to past response history.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The data collection unit collects the child's location information. The data collection unit collects the child's location information using technologies such as GPS, Wi-Fi, and Bluetooth. The data collection unit can track the child's location in real time using GPS. It can also determine the child's location using Wi-Fi access points. Furthermore, it can detect the child's location using Bluetooth beacons. Step 2: The analysis unit analyzes the location information collected by the data collection unit to understand the child's daily behavior patterns. The analysis unit learns information such as the child's daily route to school and playgrounds to understand their usual behavior patterns. It also analyzes behavior patterns during specific time periods. Step 3: The detection unit detects unusual behavior based on the behavioral patterns identified by the analysis unit. For example, it detects anomalies if a child deviates from their usual school route, stays in a specific area for an extended period, or deviates from their normal behavioral patterns during a specific time period. Step 4: The alert unit sends an alert to the parent based on the anomaly detected by the detection unit. The alert unit notifies the parent of the anomaly via methods such as SMS, email, or app notification.

[0066] (Example of form 2) The child accident prevention system according to an embodiment of the present invention is a system that collects location information of children, uses AI to understand their daily behavior patterns, detects unusual behavior such as getting lost or being left unattended in a car, and sends an alert to the guardian. The child accident prevention system collects location information of children, and the AI ​​analyzes the collected location information to understand the child's daily behavior patterns. For example, the AI ​​learns information such as the route the child takes to school every day and the places they play. This allows the normal behavior patterns to be understood. Next, the AI ​​detects unusual behavior. For example, if a child gets lost or is left unattended in a car, the AI ​​detects the abnormality. When an abnormality is detected, the AI ​​sends an alert to the guardian. This allows the guardian to respond quickly. This system makes it possible to prevent accidents involving children. For example, if a child gets lost, the guardian can immediately check the location information and find the child. Also, if a child is left unattended in a car, an alert is sent to the guardian, allowing for a quick response. This system is an effective means of ensuring the safety of children. Guardians can constantly monitor their children's behavior and watch over them with peace of mind. Furthermore, as the AI ​​learns behavioral patterns, the system's accuracy improves, enabling more precise anomaly detection. This allows the child accident prevention system to prevent accidents before they occur.

[0067] The child accident prevention system according to this embodiment comprises a data collection unit, an analysis unit, a detection unit, and an alert unit. The data collection unit collects location information of children. The data collection unit collects location information of children using technologies such as GPS, Wi-Fi, and Bluetooth. The data collection unit can track the location of children in real time using GPS, for example. The data collection unit can also determine the location of children using Wi-Fi access points. Furthermore, the data collection unit can detect the location of children using Bluetooth beacons. For example, the data collection unit receives GPS signals and determines the current location of children. It estimates the location of children using location information from Wi-Fi access points. It detects the location of children based on the signal strength of Bluetooth beacons. The analysis unit analyzes the location information collected by the data collection unit to understand the daily behavior patterns of children. The analysis unit learns information such as the route children take to school and the playgrounds they visit every day. The analysis unit analyzes the route children take to school and understands their normal behavior patterns. The analysis unit can also analyze the location information of playgrounds to understand the patterns of children's playgrounds. Furthermore, the analysis unit can analyze a child's behavioral patterns during specific time periods. For example, the analysis unit learns a child's school route and determines their usual school commute time. It also understands a child's playground patterns based on playground location information. It analyzes behavioral patterns during specific time periods and determines their usual activity times. The detection unit detects unusual behavior based on the behavioral patterns identified by the analysis unit. For example, the detection unit detects an anomaly if a child gets lost or is left unattended in a car. For example, the detection unit detects an anomaly if a child deviates from their usual school route. The detection unit can also detect an anomaly if a child stays in a specific area for an extended period. Furthermore, the detection unit can detect an anomaly if a child deviates from their usual behavioral patterns during specific time periods. For example, the detection unit detects an anomaly if a child deviates from their usual school route. It detects an anomaly if a child stays in a specific area for an extended period. It detects an anomaly if a child deviates from their usual behavioral patterns during specific time periods. The alert unit sends an alert to parents based on the anomaly detected by the detection unit.The alert unit sends alerts to parents, for example, via SMS, email, or app notifications. The alert unit notifies parents of an anomaly using SMS, for example. The alert unit can also notify parents of an anomaly using email. Furthermore, the alert unit can also notify parents of an anomaly using a dedicated app. For example, the alert unit notifies parents of an anomaly using SMS, notifies them of an anomaly using email, or notifies them of an anomaly using a dedicated app. As a result, the child accident prevention system according to this embodiment can prevent accidents involving children.

[0068] The data collection unit collects location information of children. The unit uses technologies such as GPS, Wi-Fi, and Bluetooth to collect this information. Specifically, it can track children's locations in real time using GPS. GPS receives signals from satellites and accurately determines the child's current location. This allows parents to always know where their child is. The data collection unit can also determine the child's location using Wi-Fi access points. By using the location information of Wi-Fi access points, the child's location can be estimated even indoors or in areas where GPS signals are weak. Furthermore, the data collection unit can detect the child's location using Bluetooth beacons. Bluetooth beacons transmit signals to devices within a certain range and determine their location based on the signal strength. For example, Bluetooth beacons installed in schools or parks can be used to detect whether a child is within that range. This allows the data collection unit to receive GPS signals and determine the child's current location. It then estimates the child's location using Wi-Fi access point location information and detects the child's location based on the Bluetooth beacon signal strength. In this way, the data collection unit utilizes a variety of technologies to collect children's location information with high accuracy and in real time. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and accessed by the analysis and detection units. Adjusting the data collection frequency and accuracy also allows for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0069] The analysis unit analyzes location information collected by the data collection unit to understand children's daily behavior patterns. For example, the analysis unit learns information such as the routes children take to school and the places they play. Specifically, it analyzes children's school routes to understand their usual behavior patterns. For instance, it analyzes the time of day and route children take to school to identify their usual commuting time and route. The analysis unit can also analyze playground location information to understand children's playground patterns. For example, if a child frequently visits a particular park or playground, it identifies that location and understands their usual playground patterns. Furthermore, the analysis unit can analyze children's behavior patterns at specific times of day. For example, if a child is often in a specific location at a specific time, it understands that behavior pattern. This allows the analysis unit to learn children's school routes and understand their usual commuting times. Based on playground location information, it understands children's playground patterns. It analyzes behavior patterns at specific times of day to understand their usual activity times. Additionally, the analysis unit can utilize past data and statistical information to analyze long-term behavior patterns and trends. For example, based on past behavioral data, it can predict behavioral patterns during specific seasons or events and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time but also to analyze long-term behavioral patterns and detect anomalies, thereby improving the overall reliability and safety of the system.

[0070] The detection unit detects unusual behavior based on the behavioral patterns identified by the analysis unit. For example, the detection unit detects an anomaly if a child gets lost or is left unattended in a car. Specifically, it detects an anomaly if a child deviates from their usual route to school. For example, if a child deviates from their usual route to school, the detection unit immediately detects the anomaly and issues an alert. The detection unit can also detect an anomaly if a child stays in a specific area for an extended period. For example, if a child stays in a specific playground or park for an extended period, the detection unit determines that their behavior deviates from the normal pattern and detects an anomaly. Furthermore, the detection unit can also detect an anomaly if a child deviates from their normal behavioral pattern during a specific time period. For example, if a child does not return home after the usual school time or is not in a specific place during a specific time period, the detection unit detects an anomaly. Thus, the detection unit can detect an anomaly if a child deviates from their usual route to school, if they stay in a specific area for an extended period, and if they deviate from their normal behavioral pattern during a specific time period. Furthermore, the detection unit can integrate and analyze multiple data sources to improve the accuracy of anomaly detection. For example, it can utilize data from accelerometers and temperature sensors in addition to location information to perform more accurate anomaly detection. The detection unit can also update the anomaly detection results in real time, enabling it to respond to the latest situation. As a result, the detection unit can ensure the safety of children and support a quick and appropriate response.

[0071] The alert unit sends alerts to parents based on anomalies detected by the detection unit. The alert unit sends alerts to parents using methods such as SMS, email, and app notifications. Specifically, it notifies parents of anomalies using SMS. For example, if a child deviates from their usual route to school or stays in a specific area for an extended period, the alert unit immediately sends an SMS to inform parents of the anomaly. The alert unit can also notify parents of anomalies using email. For example, it can send an email containing detailed anomaly information and location information to inform parents of the situation in detail. Furthermore, the alert unit can also notify parents of anomalies using a dedicated app. For example, it can notify parents of anomaly information in real time through the app, enabling them to take immediate action. Thus, the alert unit notifies parents of anomalies using SMS, email, and a dedicated app. In addition, the alert unit can use multiple communication methods in combination to improve the accuracy and reliability of notifications. For example, by sending SMS, email, and app notifications simultaneously, it ensures that parents are reliably notified of the anomaly. The alert unit can also customize the content of notifications, providing information tailored to the needs of parents. For example, the content and method of notification can be changed depending on the type and urgency of the anomaly. This allows the alert unit to quickly and reliably notify parents of anomalies and support them in taking action to ensure the child's safety.

[0072] The data collection unit can collect location information of children using technologies such as GPS, Wi-Fi, and Bluetooth. For example, the data collection unit can track the child's location in real time using GPS. The data collection unit can also determine the child's location using Wi-Fi access points. The data collection unit can also detect the child's location using Bluetooth beacons. For example, the data collection unit can receive GPS signals and determine the child's current location. It can estimate the child's location using location information from Wi-Fi access points. It can detect the child's location based on the signal strength of Bluetooth beacons. By using multiple technologies, the data collection unit can collect location information more accurately. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect location information using an AI model that receives GPS signals and determines the child's current location.

[0073] The alert unit may include a suggestion unit that proposes specific response methods when sending an alert to a parent or guardian. For example, when sending an alert to a parent or guardian, the alert unit may propose specific response methods such as providing contact information, notifying emergency contacts, and providing information on evacuation locations. For example, the alert unit may provide contact information to the parent or guardian. The alert unit may also notify emergency contacts. Furthermore, the alert unit may provide information on evacuation locations. For example, the alert unit may provide contact information to the parent or guardian. It may also notify emergency contacts. It may provide information on evacuation locations. This allows the alert unit to propose specific response methods so that parents or guardians can respond quickly and appropriately. Some or all of the above processing in the alert unit may be performed using AI, for example, or not using AI. For example, when sending an alert to a parent or guardian, the alert unit may propose response methods using an AI model that proposes specific response methods.

[0074] The analysis unit can learn information such as the routes children take to school and the places they play. For example, the analysis unit can analyze a child's school route to understand their usual behavior patterns. The analysis unit can also analyze the location information of playgrounds to understand the patterns of playgrounds children use. The analysis unit can also analyze a child's behavior patterns during specific time periods. For example, the analysis unit can learn a child's school route to understand their usual school commute time. Based on the location information of playgrounds, it can understand the patterns of playgrounds children use. It can analyze behavior patterns during specific time periods to understand their usual activity times. This allows for an accurate understanding of a child's daily behavior patterns. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use an AI model that learns a child's school route to understand their behavior patterns.

[0075] The detection unit can detect anomalies, such as when a child gets lost or is left unattended in a vehicle. The detection unit can detect anomalies, for example, when a child deviates from their usual route to school. The detection unit can also detect anomalies, for example, when a child stays in a specific area for an extended period of time. The detection unit can also detect anomalies, for example, when a child deviates from their usual behavioral patterns during a specific time period. For example, the detection unit can detect anomalies when a child deviates from their usual route to school. It can detect anomalies when a child stays in a specific area for an extended period of time. It can detect anomalies when a child deviates from their usual behavioral patterns during a specific time period. This allows for the rapid detection of abnormal behavior in children and notification to parents. Some or all of the above-described processes in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can detect anomalies using an AI model that analyzes a child's behavioral patterns and detects anomalies.

[0076] The data collection unit can estimate the child's emotions and adjust the frequency of location data collection based on the estimated emotions. For example, if the child is feeling anxious, the data collection unit increases the collection frequency and updates the location data in real time. For example, if the child is relaxed, the data collection unit can decrease the collection frequency to conserve battery power. For example, if the child is excited, the data collection unit can set the collection frequency to a moderate level to quickly detect changes in behavior. For example, if the child is feeling anxious, the data collection unit increases the collection frequency and updates the location data in real time. If the child is relaxed, the collection frequency decreases to conserve battery power. If the child is excited, the collection frequency is set to a moderate level to quickly detect changes in behavior. This allows for the collection of location data at a more appropriate time by adjusting the frequency of location data collection according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can adjust the collection frequency using an AI model that estimates children's emotions.

[0077] The data collection unit can analyze a child's past behavioral history during data collection and select the optimal collection method. For example, the data collection unit can prioritize collecting data from places the child has frequently visited in the past. For example, the data collection unit can increase the collection frequency during specific time periods based on the child's past behavioral patterns. For example, the data collection unit can select an efficient collection method based on the child's past travel history. For example, the data collection unit can prioritize collecting data from places the child has frequently visited in the past. For example, it can increase the collection frequency during specific time periods based on the child's past behavioral patterns. For example, it can select an efficient collection method based on the child's past travel history. By analyzing past behavioral history, it becomes possible to collect location information more efficiently. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can select the optimal collection method using an AI model that analyzes the child's past behavioral history.

[0078] The data collection unit can filter location information based on the child's current activity status and environment. For example, the data collection unit can set a lower collection frequency when the child is at school. For example, the data collection unit can increase the collection frequency to ensure safety when the child is playing in a park. For example, the data collection unit can minimize the collection frequency when the child is at home. For example, the data collection unit can set a lower collection frequency when the child is at school. For example, the data collection unit can increase the collection frequency to ensure safety when the child is playing in a park. For example, the data collection unit can minimize the collection frequency when the child is at home. By filtering location information based on the child's current activity status and environment, more accurate information can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can filter location information using an AI model that analyzes the child's current activity status and environment.

[0079] The data collection unit can estimate a child's emotions and determine the priority of location information to collect based on the estimated emotions. For example, if a child is feeling anxious, the data collection unit will prioritize collecting the child's current location. If a child is relaxed, the data collection unit may also prioritize collecting past behavioral patterns. If a child is excited, the data collection unit may also prioritize collecting information about the surrounding environment. For example, if a child is feeling anxious, the data collection unit will prioritize collecting the child's current location. If a child is relaxed, it will prioritize collecting past behavioral patterns. If a child is excited, it will prioritize collecting information about the surrounding environment. This allows for the priority collection of important information by determining the priority of location information according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can determine the priority of location information to collect using an AI model that estimates a child's emotions.

[0080] The data collection unit can prioritize the collection of highly relevant information by considering the child's geographical location when collecting location information. For example, if the child is at school, the data collection unit will prioritize the collection of information around the school. For example, if the child is at a park, the data collection unit can prioritize the collection of information within the park. For example, if the child is at a commercial facility, the data collection unit can prioritize the collection of information within the facility. For example, if the child is at school, the data collection unit will prioritize the collection of information around the school. If the child is at a park, it will prioritize the collection of information within the park. If the child is at a commercial facility, it will prioritize the collection of information within the facility. This allows for the priority collection of highly relevant information by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can prioritize the collection of highly relevant information using an AI model that analyzes the child's geographical location information.

[0081] The data collection unit can analyze a child's social media activity and collect relevant information when collecting location information. For example, the data collection unit can prioritize collecting locations where the child has posted on social media. The data collection unit can also prioritize collecting locations where the child's social media friends are located. The data collection unit can also prioritize collecting locations where the child has checked in on social media. For example, the data collection unit can prioritize collecting locations where the child has posted on social media. For example, the data collection unit can prioritize collecting locations where the child's social media friends are located. For example, the data collection unit can prioritize collecting locations where the child has checked in on social media. This allows for the efficient collection of relevant information by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect relevant information using an AI model that analyzes a child's social media activity.

[0082] The analysis unit can estimate a child's emotions and adjust the behavioral pattern analysis method based on the estimated emotions. For example, if the child is feeling anxious, the analysis unit will analyze detailed behavioral patterns. For example, if the child is relaxed, the analysis unit can also analyze general behavioral patterns. For example, if the child is excited, the analysis unit can focus on analyzing specific behavioral patterns. For example, if the child is feeling anxious, the analysis unit will analyze detailed behavioral patterns. If the child is relaxed, it will analyze general behavioral patterns. If the child is excited, it will focus on analyzing specific behavioral patterns. This allows for a more accurate analysis of behavioral patterns by adjusting the analysis method according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the behavioral pattern analysis method using an AI model that estimates a child's emotions.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the behavioral patterns during the analysis. For example, the analysis unit may analyze highly important behavioral patterns in detail. For example, the analysis unit may analyze less important behavioral patterns simply. For example, the analysis unit may analyze moderately important behavioral patterns. For example, the analysis unit may analyze highly important behavioral patterns in detail. For less important behavioral patterns simply. For moderately important behavioral patterns. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the behavioral patterns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the level of detail of the analysis using an AI model that evaluates the importance of behavioral patterns.

[0084] The analysis unit can apply different analysis algorithms depending on the category of behavioral pattern during analysis. For example, the analysis unit can apply a specific algorithm to school commuting patterns. The analysis unit can also apply a different algorithm to playground patterns. The analysis unit can also apply yet another algorithm to domestic patterns. For example, the analysis unit can apply a specific algorithm to school commuting patterns. It can apply a different algorithm to playground patterns. It can also apply yet another algorithm to domestic patterns. This improves the accuracy of the analysis by applying the appropriate analysis algorithm according to the category of behavioral pattern. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform analysis using an AI model that applies different analysis algorithms depending on the category of behavioral pattern.

[0085] The analysis unit can estimate a child's emotions and determine the priority of analysis based on the estimated emotions. For example, if the child is feeling anxious, the analysis unit will prioritize the analysis. If the child is relaxed, the analysis unit may also prioritize the analysis of the child's emotions. If the child is excited, the analysis unit may also prioritize the analysis of specific behavioral patterns. For example, if the child is feeling anxious, the analysis unit will prioritize the analysis. If the child is relaxed, the analysis will prioritize the analysis of the child's emotions. If the analysis unit prioritizes the analysis of a child's emotions, important behavioral patterns can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can determine the priority of analysis using an AI model that estimates a child's emotions.

[0086] The analysis unit can determine the priority of analysis based on the timing of the occurrence of behavioral patterns. For example, the analysis unit may prioritize the analysis of recent behavioral patterns. The analysis unit may also postpone the analysis of past behavioral patterns. The analysis unit may also prioritize the analysis of behavioral patterns during a specific time period. For example, the analysis unit may prioritize the analysis of recent behavioral patterns, postpone the analysis of past behavioral patterns, and prioritize the analysis of behavioral patterns during a specific time period. This allows the analysis unit to prioritize the analysis of the latest behavioral patterns by determining the priority of analysis based on the timing of the occurrence of behavioral patterns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may determine the priority of analysis using an AI model that evaluates the timing of the occurrence of behavioral patterns.

[0087] The analysis unit can adjust the order of analysis based on the relevance of behavioral patterns during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant behavioral patterns. For example, the analysis unit may postpone the analysis of less relevant behavioral patterns. For example, the analysis unit may moderately analyze behavioral patterns with a moderate degree of relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of behavioral patterns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the order of analysis using an AI model that evaluates the relevance of behavioral patterns.

[0088] The detection unit can estimate the child's emotions and adjust the anomaly detection criteria based on the estimated emotions. For example, if the child is feeling anxious, the detection unit can tighten the anomaly detection criteria. For example, if the child is relaxed, the detection unit can loosen the anomaly detection criteria. For example, if the child is excited, the detection unit can set the anomaly detection criteria to a moderate level. For example, if the child is feeling anxious, the detection unit tightens the anomaly detection criteria. If the child is relaxed, the anomaly detection criteria are loosened. If the child is excited, the anomaly detection criteria are set to a moderate level. By adjusting the anomaly detection criteria according to the child's emotions, more accurate anomaly detection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can adjust the criteria for detecting anomalies using an AI model that estimates a child's emotions.

[0089] The detection unit can improve the accuracy of anomaly detection by considering the interrelationships of behavioral patterns when detecting anomalies. For example, the detection unit can detect anomalies by considering the interrelationships between commuting patterns and playground patterns. The detection unit can also detect anomalies by considering the interrelationships between in-home patterns and outing patterns. The detection unit can also detect anomalies by considering the interrelationships of behavioral patterns during a specific time period. For example, the detection unit can detect anomalies by considering the interrelationships between commuting patterns and playground patterns. It can detect anomalies by considering the interrelationships between in-home patterns and outing patterns. It can detect anomalies by considering the interrelationships of behavioral patterns during a specific time period. This improves the accuracy of anomaly detection by considering the interrelationships of behavioral patterns. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can detect anomalies using an AI model that evaluates the interrelationships of behavioral patterns.

[0090] The detection unit can perform anomaly detection by considering the frequency of occurrence of behavioral patterns. For example, the detection unit may prioritize detecting behavioral patterns that occur frequently. The detection unit may also postpone detecting behavioral patterns that occur infrequently. The detection unit may also moderately detect behavioral patterns that occur in a moderate frequency. For example, the detection unit may prioritize detecting behavioral patterns that occur frequently, postpone detecting behavioral patterns that occur infrequently, and moderately detect behavioral patterns that occur in a moderate frequency. This improves the accuracy of anomaly detection by considering the frequency of occurrence of behavioral patterns. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can detect anomalies using an AI model that evaluates the frequency of occurrence of behavioral patterns.

[0091] The detection unit can estimate the child's emotions and adjust the order in which it displays the anomaly detection results based on the estimated emotions. For example, if the child is feeling anxious, the detection unit will prioritize displaying the anomaly detection results. If the child is relaxed, the detection unit can also display them in the normal order. If the child is excited, the detection unit can also prioritize displaying specific anomaly detection results. For example, if the child is feeling anxious, the detection unit will prioritize displaying the anomaly detection results. If the child is relaxed, it will display them in the normal order. If the child is excited, it will prioritize displaying specific anomaly detection results. This allows important information to be displayed preferentially by adjusting the order in which anomaly detection results are displayed according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can adjust the order in which it displays the anomaly detection results using an AI model that estimates the child's emotions.

[0092] The detection unit can perform anomaly detection by considering the geographical distribution of behavioral patterns at the time of detection. For example, the detection unit may prioritize detecting behavioral patterns in a specific area. The detection unit may also postpone detecting behavioral patterns that are geographically widespread. The detection unit may also moderately detect behavioral patterns that are geographically moderate. For example, the detection unit may prioritize detecting behavioral patterns in a specific area, postpone detecting behavioral patterns that are geographically widespread, and moderately detect behavioral patterns that are geographically moderate. This improves the accuracy of anomaly detection by considering the geographical distribution of behavioral patterns. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can detect anomalies using an AI model that evaluates the geographical distribution of behavioral patterns.

[0093] The detection unit can improve the accuracy of anomaly detection by referring to relevant literature on behavioral patterns when detection occurs. For example, the detection unit adjusts the anomaly detection algorithm based on relevant literature. The detection unit can also set anomaly detection criteria by referring to data in relevant literature. The detection unit can also improve the accuracy of anomaly detection by utilizing insights from relevant literature. For example, the detection unit adjusts the anomaly detection algorithm based on relevant literature. It sets anomaly detection criteria by referring to data in relevant literature. It improves the accuracy of anomaly detection by utilizing insights from relevant literature. As a result, the accuracy of anomaly detection is improved by referring to relevant literature. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can detect anomalies using an AI model that refers to relevant literature.

[0094] The alert unit can estimate a child's emotions and adjust how alerts are displayed based on the estimated emotions. For example, if a child is feeling anxious, the alert unit will display a high-urgency alert. If a child is relaxed, the alert unit may also display a normal alert. If a child is excited, the alert unit may prioritize displaying a specific alert. For example, if a child is feeling anxious, the alert unit will display a high-urgency alert. If a child is relaxed, it will display a normal alert. If a child is excited, it will prioritize displaying a specific alert. This allows parents to respond quickly by adjusting how alerts are displayed according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can adjust how alerts are displayed using an AI model that estimates a child's emotions.

[0095] The alert unit can select the optimal alert method by referring to past alert history when sending an alert. For example, the alert unit can prioritize alert methods that have been effective in the past. The alert unit can also select the optimal sending timing from past alert history. The alert unit can also adjust the content of the alert based on past alert history. For example, the alert unit can prioritize alert methods that have been effective in the past. It can select the optimal sending timing from past alert history. It can adjust the content of the alert based on past alert history. In this way, the optimal alert method can be selected by referring to past alert history. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can select the optimal alert method using an AI model that analyzes past alert history.

[0096] The alert unit can adjust the priority of alerts based on the parent's current situation when sending alerts. For example, if the parent is driving, the alert unit will prioritize sending an audio alert. For example, if the parent is in a meeting, the alert unit may also prioritize sending a vibration alert. For example, if the parent is at home, the alert unit may also send a regular alert. For example, if the parent is driving, the alert unit will prioritize sending an audio alert. If the parent is in a meeting, it will prioritize sending a vibration alert. If the parent is at home, it will send a regular alert. This allows important information to be notified preferentially by adjusting the priority of alerts according to the parent's current situation. Some or all of the above processing in the alert unit may be performed using AI, for example, or not using AI. For example, the alert unit may adjust the priority of alerts using an AI model that evaluates the parent's current situation.

[0097] The alert unit can estimate a child's emotions and adjust the content of the alert based on the estimated emotions. For example, if the child is feeling anxious, the alert unit will include urgent content in the alert. For example, if the child is relaxed, the alert unit may include normal content in the alert. For example, if the child is excited, the alert unit may include specific content in the alert. For example, if the child is feeling anxious, the alert unit will include urgent content in the alert. If the child is relaxed, the alert unit will include normal content in the alert. If the child is excited, the alert unit will include specific content in the alert. This allows parents to respond quickly by adjusting the content of the alert according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the alert unit may be performed using AI, for example, or not using AI. For example, the alert unit can adjust the content of the alert using an AI model that estimates a child's emotions.

[0098] The alert unit can select the optimal alert method when sending an alert, taking into account the parent's geographical location. For example, if the parent is nearby, the alert unit may prioritize sending an audio alert. If the parent is far away, the alert unit may also prioritize sending a text alert. If the parent is in a specific location, the alert unit may also select an alert method appropriate to that location. For example, if the parent is nearby, the alert unit may prioritize sending an audio alert. If the parent is far away, it may prioritize sending a text alert. If the parent is in a specific location, it may select an alert method appropriate to that location. This allows the system to select the optimal alert method by considering the parent's geographical location. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit may select the optimal alert method using an AI model that evaluates the parent's geographical location.

[0099] The alert unit can analyze the parent's social media activity and suggest an alert method when sending an alert. For example, if the parent frequently uses social media, the alert unit will send an alert via social media. If the parent does not use social media often, the alert unit can also send an alert through other means. The alert unit can also suggest the optimal alert method based on the parent's social media activity. For example, if the parent frequently uses social media, the alert unit will send an alert via social media. If the parent does not use social media often, the alert unit will send an alert through other means. The alert unit suggests the optimal alert method based on the parent's social media activity. In this way, by analyzing the parent's social media activity, the optimal alert method can be suggested. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can suggest an alert method using an AI model that analyzes the parent's social media activity.

[0100] The suggestion unit can estimate a child's emotions and adjust suggested responses based on the estimated emotions. For example, if the child is feeling anxious, the suggestion unit can suggest a quick response. If the child is relaxed, the suggestion unit can also suggest a normal response. If the child is excited, the suggestion unit can also suggest a specific response. For example, if the child is feeling anxious, the suggestion unit can suggest a quick response. If the child is relaxed, it can suggest a normal response. If the child is excited, it can suggest a specific response. This allows parents to respond quickly by adjusting suggested responses according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can adjust suggested responses using an AI model that estimates a child's emotions.

[0101] The proposal department can select the optimal response method by referring to past response history when making a proposal. For example, the proposal department will prioritize proposing response methods that have been effective in the past. The proposal department can also select the optimal response timing from past response history. The proposal department can also adjust the response method based on past response history. For example, the proposal department will prioritize proposing response methods that have been effective in the past. It will select the optimal response timing from past response history. It will adjust the response method based on past response history. In this way, the optimal response method can be selected by referring to past response history. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can select the optimal response method using an AI model that analyzes past response history.

[0102] The suggestion function can customize the response method based on the parent's current situation when making a suggestion. For example, if the parent is driving, the suggestion function can suggest a simple response method. If the parent is in a meeting, the suggestion function can also suggest a method that can be addressed later. If the parent is at home, the suggestion function can also suggest a detailed response method. For example, if the parent is driving, the suggestion function can suggest a simple response method. If the parent is in a meeting, it can suggest a method that can be addressed later. If the parent is at home, it can suggest a detailed response method. This allows parents to respond quickly by customizing the response method according to their current situation. Some or all of the above processing in the suggestion function may be performed using AI, for example, or not using AI. For example, the suggestion function can customize the response method using an AI model that evaluates the parent's current situation.

[0103] The suggestion unit can estimate a child's emotions and prioritize response methods based on the estimated emotions. For example, if the child is feeling anxious, the suggestion unit will prioritize suggesting a rapid response. If the child is relaxed, the suggestion unit may also suggest a normal response. If the child is excited, the suggestion unit may also prioritize suggesting a specific response. For example, if the child is feeling anxious, the suggestion unit will prioritize suggesting a rapid response. If the child is relaxed, it will suggest a normal response. If the child is excited, it will prioritize suggesting a specific response. This allows the suggestion unit to prioritize important response methods by determining the priority of response methods according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can determine the priority of response methods using an AI model that estimates a child's emotions.

[0104] The proposal unit can select the optimal response method by considering the geographical location information of the parent when making a proposal. For example, if the parent is nearby, the proposal unit will propose a quick response method. If the parent is far away, the proposal unit may also propose alternative response methods. If the parent is in a specific location, the proposal unit may also propose a response method appropriate to that location. For example, if the parent is nearby, the proposal unit will propose a quick response method. If the parent is far away, it will propose alternative response methods. If the parent is in a specific location, it will propose a response method appropriate to that location. This allows the optimal response method to be selected by considering the geographical location information of the parent. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can select the optimal response method using an AI model that evaluates the geographical location information of the parent.

[0105] The proposal department can analyze the parents' social media activity and propose appropriate responses when making a proposal. For example, if the parents frequently use social media, the proposal department can propose a response method through social media. For example, if the parents do not use social media often, the proposal department can propose a response method through other means. The proposal department can also propose the optimal response method based on the parents' social media activity. For example, if the parents frequently use social media, the proposal department can propose a response method through social media. If the parents do not use social media often, the proposal department can propose a response method through other means. The proposal department can propose the optimal response method based on the parents' social media activity. In this way, by analyzing the parents' social media activity, the optimal response method can be proposed. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can propose a response method using an AI model that analyzes the parents' social media activity.

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

[0107] The data collection unit can analyze ambient sounds when collecting a child's location information and assess the safety of the child's location. For example, the unit can measure the ambient noise level and send a warning to the guardian if the noise level is high. It can also analyze ambient sounds and send an alert if it detects sounds indicating an emergency (e.g., screaming or alarm sounds). Furthermore, the unit can identify a child's voice from ambient sounds and notify the guardian if the child is calling for help. This allows for a safer environment by analyzing not only the child's location information but also ambient sounds.

[0108] The analysis unit can consider the child's health data (e.g., heart rate and body temperature) when analyzing the child's behavioral patterns. For example, if the child's heart rate is higher than normal, the analysis unit can perform a more detailed analysis of the behavioral patterns, as this may indicate stress or excitement. Similarly, if the child's body temperature is elevated, the analysis unit can prioritize detecting abnormal behavior, as this may indicate poor health. Furthermore, based on the child's health data, the analysis unit can notify parents if the behavioral patterns deviate from normal. This enables the analysis of behavioral patterns while considering the child's health status.

[0109] The detection unit can consider the location information of the child's friends and family when detecting the child's behavior patterns. For example, if the detection unit is with friends, it can consider this to be normal behavior. Also, if the detection unit is with family, it can mitigate the detection of abnormal behavior. Furthermore, if the detection unit is separated from friends or family, it can detect this as abnormal behavior and notify the guardian. This allows for more accurate detection of the child's behavior patterns.

[0110] The alert unit can adjust the content of alerts sent to parents based on their current activity status. For example, if a parent is driving, the alert unit will prioritize sending an audio alert. It can also prioritize sending a vibration alert if a parent is in a meeting. Furthermore, if a parent is at home, the alert unit can send a regular alert. This allows for quick notification of important information by adjusting the alert content according to the parent's current activity status.

[0111] The data collection unit can estimate the child's emotions and adjust the frequency of location data collection based on the estimated emotions. For example, if the child is feeling anxious, the collection frequency can be increased to update location data in real time. If the child is relaxed, the collection frequency can be lowered to conserve battery power. If the child is excited, the collection frequency can be set to a moderate level to quickly detect changes in behavior. By adjusting the frequency of location data collection according to the child's emotions, location data can be collected at a more appropriate time.

[0112] The analysis unit can estimate a child's emotions and adjust the behavioral pattern analysis method based on the estimated emotions. For example, if a child is feeling anxious, it can analyze detailed behavioral patterns. If a child is relaxed, it can analyze general behavioral patterns. If a child is excited, it can focus on analyzing specific behavioral patterns. By adjusting the analysis method according to the child's emotions, a more accurate analysis of behavioral patterns becomes possible.

[0113] The detection unit can estimate the child's emotions and adjust the anomaly detection criteria based on the estimated emotions. For example, if the child is feeling anxious, the anomaly detection criteria can be made stricter. If the child is relaxed, the criteria can be made looser. If the child is excited, the criteria can be set to a moderate level. By adjusting the anomaly detection criteria according to the child's emotions, more accurate anomaly detection becomes possible.

[0114] The alert system can estimate a child's emotions and adjust how alerts are displayed based on that estimation. For example, if a child is feeling anxious, it can display a high-priority alert. If a child is relaxed, it can display a normal alert. If a child is agitated, it can prioritize displaying specific alerts. This allows parents to respond quickly by adjusting how alerts are displayed according to the child's emotions.

[0115] The suggestion function can estimate a child's emotions and adjust suggested responses based on those emotions. For example, if a child is feeling anxious, it can suggest a quick response. If a child is relaxed, it can suggest a normal response. If a child is agitated, it can suggest a specific response. This allows parents to respond quickly by adjusting the suggested responses according to the child's emotions.

[0116] The proposal department can select the most suitable response method by referring to past response history when making a proposal. For example, it can prioritize proposing response methods that have been effective in the past. It can also select the optimal timing for response based on past response history. It can also adjust the response method based on past response history. In this way, the optimal response method can be selected by referring to past response history.

[0117] The following briefly describes the processing flow for example form 2.

[0118] Step 1: The data collection unit collects the child's location information. The data collection unit collects the child's location information using technologies such as GPS, Wi-Fi, and Bluetooth. The data collection unit can track the child's location in real time using GPS. It can also determine the child's location using Wi-Fi access points. Furthermore, it can detect the child's location using Bluetooth beacons. Step 2: The analysis unit analyzes the location information collected by the data collection unit to understand the child's daily behavior patterns. The analysis unit learns information such as the child's daily route to school and playgrounds to understand their usual behavior patterns. It also analyzes behavior patterns during specific time periods. Step 3: The detection unit detects unusual behavior based on the behavioral patterns identified by the analysis unit. For example, it detects anomalies if a child deviates from their usual school route, stays in a specific area for an extended period, or deviates from their normal behavioral patterns during a specific time period. Step 4: The alert unit sends an alert to the parent based on the anomaly detected by the detection unit. The alert unit notifies the parent of the anomaly via methods such as SMS, email, or app notification.

[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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0121] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, and alert unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the child's location information using the GPS, Wi-Fi, or Bluetooth of the smart device 14. The analysis unit analyzes the location information collected by the identification processing unit 290 of the data processing unit 12 to understand the child's daily behavior patterns. The detection unit detects unusual behavior using the identification processing unit 290 of the data processing unit 12. The alert unit sends an alert to the guardian using the control unit 46A of the smart device 14 or the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0136] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

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

[0138] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, and alert unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the child's location information using the GPS, Wi-Fi, and Bluetooth of the smart glasses 214. The analysis unit analyzes the location information collected by the identification processing unit 290 of the data processing unit 12 to understand the child's daily behavior patterns. The detection unit detects unusual behavior using the identification processing unit 290 of the data processing unit 12. The alert unit sends an alert to the guardian using the control unit 46A of the smart glasses 214 or the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0140] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0147] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

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

[0154] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, and alert unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the child's location information using the GPS, Wi-Fi, or Bluetooth of the headset terminal 314. The analysis unit analyzes the location information collected by the identification processing unit 290 of the data processing unit 12 to understand the child's daily behavior patterns. The detection unit detects unusual behavior using the identification processing unit 290 of the data processing unit 12. The alert unit sends an alert to the guardian using the control unit 46A of the headset terminal 314 or the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

[0157] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0159] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0161] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0162] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0163] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0164] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0165] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0166] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0167] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0169] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

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

[0171] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, and alert unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the child's location information using the robot 414's GPS, Wi-Fi, or Bluetooth. The analysis unit analyzes the location information collected by the specific processing unit 290 of the data processing unit 12 to understand the child's daily behavior patterns. The detection unit detects unusual behavior using the specific processing unit 290 of the data processing unit 12. The alert unit sends an alert to the guardian using the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0172] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0174] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0175] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0176] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0180] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[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] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0183] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0184] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0185] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0187] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0188] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0189] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0190] (Note 1) A collection unit that collects the location information of children, An analysis unit analyzes the location information collected by the aforementioned collection unit to understand the child's daily behavioral patterns, Based on the behavioral patterns identified by the analysis unit, a detection unit detects behavior that is different from the norm. The system includes an alert unit that sends an alert to a guardian based on an abnormality detected by the detection unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect children's location information using technologies such as GPS, Wi-Fi, and Bluetooth. The system described in Appendix 1, characterized by the features described herein. (Note 3) The alert unit is, When sending alerts to parents, it includes a suggestion section that proposes specific actions to take. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Learn about the routes children take to school every day, playgrounds, and other related information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The detection unit is It detects abnormalities such as a child getting lost or being left unattended in a vehicle. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the child's emotions and adjusts the frequency of location data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is During data collection, the child's past behavioral history is analyzed to select the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting location data, filtering is performed based on the child's current activity status and environment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system estimates the child's emotions and prioritizes the location data to collect based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting location information, the system prioritizes collecting highly relevant information by considering the child's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting location data, we analyze the child's social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, We estimate the child's emotions and adjust the analysis method of behavioral patterns based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the behavioral pattern. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the child's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of analysis is determined based on the timing of the occurrence of behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relationships between behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 18) The detection unit is The system estimates the child's emotions and adjusts the criteria for detecting anomalies based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The detection unit is When detecting anomalies, the accuracy of anomaly detection is improved by considering the interrelationships of behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 20) The detection unit is When detection occurs, anomaly detection is performed by considering the frequency of occurrence of the behavioral pattern. The system described in Appendix 1, characterized by the features described herein. (Note 21) The detection unit is The system estimates the child's emotions and adjusts the order in which anomaly detection results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The detection unit is When detecting anomalies, the geographical distribution of behavioral patterns is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The detection unit is When detecting anomalies, we improve the accuracy of anomaly detection by referring to relevant literature on the behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 24) The alert unit is, It estimates the child's emotions and adjusts how alerts are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The alert unit is, When sending an alert, the system will refer to past alert history to select the most suitable alerting method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The alert unit is, When sending alerts, the alert priority is adjusted based on the parent's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The alert unit is, It estimates the child's emotions and adjusts the content of the alert based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The alert unit is, When sending an alert, the system will select the most appropriate alert method, taking into account the parent's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The alert unit is, When sending alerts, we analyze the parents' social media activity and suggest appropriate alert methods. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, The system estimates the child's emotions and adjusts suggested responses based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, When making a proposal, we will refer to past response history to select the most appropriate response method. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned proposal section is, When making a proposal, customize the approach based on the parents' current situation. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned proposal section is, The system estimates the child's emotions and prioritizes appropriate responses based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned proposal section is, When making a proposal, the most appropriate response method will be selected, taking into account the geographical location information of the parents. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned proposal section is, When making a proposal, we analyze the parents' social media activity and suggest appropriate responses. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

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

Claims

1. A collection unit that collects the location information of children, An analysis unit analyzes the location information collected by the aforementioned collection unit to understand the child's daily behavioral patterns, Based on the behavioral patterns identified by the analysis unit, a detection unit detects behavior that is different from the norm. The system includes an alert unit that sends an alert to a guardian based on an abnormality detected by the detection unit. A system characterized by the following features.

2. The alert unit is, When sending alerts to parents, it includes a suggestion section that proposes specific actions to take. The system according to feature 1.

3. The aforementioned analysis unit, Learn about the routes children take to school every day, playgrounds, and other related information. The system according to feature 1.

4. The detection unit is It detects abnormalities such as a child getting lost or being left unattended in a vehicle. The system according to feature 1.

5. The aforementioned collection unit is The system estimates the child's emotions and adjusts the frequency of location data collection based on the estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is During data collection, the child's past behavioral history is analyzed to select the most suitable collection method. The system according to feature 1.

7. The aforementioned collection unit is When collecting location data, filtering is performed based on the child's current activity status and environment. The system according to feature 1.

8. The aforementioned collection unit is The system estimates the child's emotions and prioritizes the location data to collect based on the estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is When collecting location information, the system prioritizes collecting highly relevant information by considering the child's geographical location. The system according to feature 1.

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