system

Abstract

The system according to this embodiment aims to comprehensively protect the safety of children in parks. [Solution] The system according to the embodiment comprises a monitoring unit, a warning unit, a tracking unit, a status monitoring unit, and a control unit. The monitoring unit monitors video footage within the park. The warning unit issues warnings based on suspicious persons or dangerous behavior detected by the monitoring unit. The tracking unit tracks the location of children. The status monitoring unit monitors the condition of playground equipment and weather. The control unit controls the lost child detection robot.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003] <000001​​​​​​​​​​​​​​​​​​​​​​​​ [Effects of the Invention]

[0007] The system according to this embodiment can comprehensively protect the safety of children in parks. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​system according to an embodiment of the present invention is a system that comprehensively protects the safety of children playing in parks. This AI system uses AI surveillance cameras installed in the park to detect suspicious persons and dangerous behavior, and issues real-time warnings for early response. Furthermore, by connecting children and guardians to the AI ​​system, it becomes possible to prevent lost children and track their location. In addition, by monitoring playground equipment and weather, and introducing lost child detection robots, a safer park environment is provided, realizing a park space where children can enjoy themselves with peace of mind. For example, the AI ​​system uses AI surveillance cameras installed in the park to detect suspicious persons and dangerous behavior. When the camera detects abnormal movement in the park, the AI ​​analyzes the footage and identifies the suspicious person or dangerous behavior. This allows for real-time warnings and early response. Next, by connecting children and guardians to the AI ​​system, it becomes possible to prevent lost children and track their location. For example, if a child gets lost in the park, guardians can track the child's location through the AI ​​system. This allows for the child to be found quickly. Furthermore, by monitoring playground equipment and weather, and introducing lost child detection robots, a safer park environment is provided. For example, the system monitors the condition of playground equipment and issues warnings if any abnormalities occur. It also monitors weather changes and issues warnings if extreme weather conditions occur. A lost child detection robot patrols the park and plays a role in finding lost children. In this way, the AI ​​system comprehensively protects the safety of children playing in the park and creates a park space where children can enjoy themselves with peace of mind. For parents, the assurance of their children's safety allows them to let their children play in the park with confidence. In this way, the AI ​​system comprehensively protects the safety of children playing in the park and creates a park space where children can enjoy themselves with peace of mind.

[0029] The AI ​​system according to this embodiment comprises a monitoring unit, a warning unit, a tracking unit, a status monitoring unit, and a control unit. The monitoring unit monitors video footage of the park. The monitoring unit monitors video footage of the park in real time, for example, using an AI surveillance camera, and detects suspicious persons and dangerous behavior. The monitoring unit can, for example, use AI to analyze the video footage and identify suspicious persons and dangerous behavior. The monitoring unit can also issue a warning if it detects abnormal movement. The warning unit issues a warning based on the suspicious person or dangerous behavior detected by the monitoring unit. The warning unit can issue a warning, for example, an audible warning or a visual warning. The warning unit can, for example, issue an audible warning using a speaker. The warning unit can also issue a visual warning using a display. The tracking unit tracks the location of children. The tracking unit can, for example, track the location of children using GPS or RFID tags. The tracking unit can, for example, provide location information to parents. The tracking unit can also quickly locate a child if they get lost. The status monitoring unit monitors the condition of playground equipment and weather. The condition monitoring unit can, for example, monitor the condition of playground equipment using sensors. The condition monitoring unit can, for example, issue a warning if an abnormality occurs. The condition monitoring unit can also monitor changes in weather and issue a warning if abnormal weather occurs. The control unit controls the lost child detection robot. The control unit can, for example, have the lost child detection robot patrol the park. The control unit can, for example, have the lost child detection robot find a lost child. In this way, the AI ​​system according to the embodiment can comprehensively protect safety in the park and provide an environment where children can play with peace of mind.

[0030] The monitoring unit monitors video footage within the park. For example, it uses AI-powered surveillance cameras to monitor the park's video in real time and detect suspicious individuals or dangerous behavior. Specifically, the AI ​​cameras acquire high-resolution video and analyze the video data in real time. The AI ​​uses deep learning technology to recognize people and movements within the video and detects abnormal movements compared to normal behavior patterns. For example, the AI ​​can identify individuals who stay in a specific area for extended periods or those who make sudden movements as suspicious. The AI ​​can also detect dangerous behaviors such as leaving objects unattended or acts of vandalism. This allows the monitoring unit to monitor the park's safety in real time and respond immediately if an anomaly occurs. Furthermore, the monitoring unit can issue warnings when it detects abnormal behavior. For example, if the AI ​​detects a suspicious person, it sends that information to the warning unit, instructing the warning unit to issue an appropriate warning. This ensures the safety of the park and enables a rapid response.

[0031] The warning unit issues warnings based on suspicious individuals or dangerous behavior detected by the monitoring unit. The warning unit can issue, for example, audio and visual warnings. Specifically, the warning unit can issue audio warnings using speakers. For example, an audio warning might say, "A suspicious person has been detected. Please leave immediately," and is set to be audible throughout the park. The warning unit can also issue visual warnings using displays. For example, a visual warning might display a message such as, "A suspicious person has been detected. Please be careful," on displays installed in key areas of the park. This allows the warning unit to issue warnings to park users quickly and effectively, ensuring their safety. Furthermore, the warning unit can flexibly change the content and method of warnings depending on the situation. For example, stronger warnings can be issued at night or during times when there are fewer people to encourage a quicker response. In this way, the warning unit can comprehensively protect safety within the park and provide an environment where users can feel safe.

[0032] The tracking unit tracks the child's location. For example, it can track a child's location using GPS or RFID tags. Specifically, it uses small GPS devices or RFID tags that the child can wear, collecting location information transmitted from these devices in real time. The tracking unit analyzes this location information to accurately determine the child's current location. Furthermore, the tracking unit can provide location information to parents. For example, a dedicated application can be installed on a parent's smartphone to display the child's location in real time. This allows parents to always know where their child is in the park, giving them peace of mind while their child plays. The tracking unit can also quickly locate a child if they get lost. For example, if a child leaves a designated area, the tracking unit immediately issues a warning and notifies parents and park administrators. This enables a quick response and ensures the child's safety.

[0033] The condition monitoring unit monitors the condition of playground equipment and weather. For example, the condition monitoring unit can monitor the condition of playground equipment using sensors. Specifically, sensors attached to the equipment monitor the usage status and physical condition of the equipment in real time. For example, it can detect wear on swing chains or damage to the surface of slides. This allows for early detection of abnormalities in the playground equipment and appropriate maintenance. The condition monitoring unit can also monitor changes in weather and issue warnings if abnormal weather conditions occur. For example, it collects weather data such as temperature, humidity, wind speed, and rainfall, and notifies the warning unit if abnormal weather conditions are detected. This allows for prompt warnings to users and ensures safety. Furthermore, the condition monitoring unit can accumulate data on the condition of playground equipment and weather and perform long-term analysis. This allows for prediction of the lifespan of playground equipment and the optimal timing for maintenance, enabling efficient management.

[0034] The control unit controls the lost child detection robot. For example, the control unit can make the lost child detection robot patrol the park. Specifically, the control unit controls the robot's movement path and actions in real time to patrol the park efficiently. The lost child detection robot is equipped with cameras and sensors, allowing it to move while recognizing its surroundings. For example, the robot uses its cameras to acquire images of the park, and AI analyzes these images to identify lost children. The robot can also use voice and a display to call out to children and guide them back to their guardians. This allows the control unit to effectively operate the lost child detection robot and ensure safety within the park. Furthermore, the control unit monitors the operating status and battery level of the lost child detection robot and can perform charging and maintenance as needed. This increases the robot's operational efficiency and ensures it is always operating in optimal condition.

[0035] The monitoring unit can monitor video footage from the park in real time and detect suspicious individuals or dangerous behavior. For example, the monitoring unit can use AI surveillance cameras to monitor video footage from the park in real time. For example, the monitoring unit can use AI to analyze the video and identify suspicious individuals or dangerous behavior. The monitoring unit can also issue warnings if it detects abnormal activity. This enables a rapid response through real-time monitoring. Real-time monitoring is achieved, for example, by analyzing video from surveillance cameras in real time and detecting anomalies. Some or all of the above processing in the monitoring unit may be performed using, for example, generative AI, or without generative AI. For example, the monitoring unit can input video from surveillance cameras into a generative AI, which can then analyze the video and detect anomalies.

[0036] The warning unit can issue warnings based on suspicious persons or dangerous activities detected by the monitoring unit. The warning unit can issue, for example, audio warnings or visual warnings. The warning unit can issue audio warnings using a speaker, for example. The warning unit can also issue visual warnings using a display. This allows for rapid warnings against suspicious persons or dangerous activities. The specific method and content of the warnings may include, for example, an audio warning such as "A suspicious person has been detected. Please leave immediately." As a visual warning, the message "A suspicious person has been detected. Please leave immediately." can be displayed on the screen. Some or all of the above processing in the warning unit may be performed using, for example, a generation AI, or without a generation AI. For example, the warning unit can input information from the monitoring unit into a generation AI, and the generation AI can generate a warning message.

[0037] The tracking unit can track a child's location and provide the location information to the guardian. The tracking unit tracks the child's location using, for example, GPS or RFID tags. The tracking unit can provide the location information to the guardian. The tracking unit can also quickly locate a child if they get lost. This allows the child's location to be tracked and the guardian to be notified quickly. Specific methods for acquiring and providing location information include, for example, tracking the location in real time using GPS data and notifying the guardian's smartphone. Some or all of the above-described processes in the tracking unit may be performed using, for example, a generating AI, or without a generating AI. For example, the tracking unit can input GPS data into a generating AI, which can then analyze the location information and notify the guardian.

[0038] The condition monitoring unit can monitor the condition of playground equipment and weather, and issue warnings if abnormalities occur. The condition monitoring unit can, for example, use sensors to monitor the condition of playground equipment. The condition monitoring unit can, for example, issue warnings if abnormalities occur. The condition monitoring unit can also monitor changes in weather and issue warnings if abnormal weather occurs. This allows for early detection and warning of abnormalities in playground equipment and weather. The specific definition of an abnormality and detection criteria can be, for example, by setting sensor thresholds, and issuing warnings when abnormalities are detected. Some or all of the above processing in the condition monitoring unit may be performed using, for example, a generation AI, or without a generation AI. For example, the condition monitoring unit can input sensor data into a generation AI, which can detect abnormalities and issue warnings.

[0039] The control unit can control the lost child detection robot to patrol the park and find lost children. For example, the control unit can make the lost child detection robot patrol the park. The control unit can, for example, have the lost child detection robot find the lost child. In this way, by controlling the lost child detection robot, lost children can be found quickly. The specific method and route of patrolling can be, for example, the lost child detection robot can periodically patrol the park and follow a set specific route. Some or all of the above processing in the control unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the control unit can input the patrol route of the lost child detection robot into the generative AI, and the generative AI can set the optimal patrol route.

[0040] The monitoring unit can monitor the level of congestion in specific areas within the park and issue warnings if congestion occurs. For example, the monitoring unit can monitor the level of congestion in the playground area in real time and issue a warning if the number of people exceeds a certain limit. For example, the monitoring unit can monitor the level of congestion in the rest area and issue a warning if it becomes overcrowded. For example, the monitoring unit can monitor the level of congestion near the entrance and issue a warning if entry restrictions are necessary. This allows for monitoring of congestion and issuing warnings at the appropriate time. The specific definition and monitoring method of congestion can be set based on, for example, the number of people or the size of the area. Some or all of the above processing in the monitoring unit may be performed using, for example, a generating AI, or it may be performed without a generating AI. For example, the monitoring unit can input congestion data into a generating AI, which can analyze the congestion and issue a warning.

[0041] The monitoring unit can monitor the movements of animals in the park and issue warnings if dangerous animals approach. For example, the monitoring unit can monitor the movements of wild animals that frequent the park and issue warnings if danger is imminent. For example, the monitoring unit can monitor the movements of animals in the pet area and issue warnings if they enter other areas. For example, the monitoring unit can monitor the movements of flocks of birds and issue warnings if unusual behavior is observed. This allows for early detection and warning of the approach of dangerous animals. The specific definition and detection criteria for dangerous animals are set based on, for example, specific animal species or behavioral patterns. Some or all of the above processing in the monitoring unit may be performed using, for example, generative AI, or without generative AI. For example, the monitoring unit can input animal movement data into generative AI, which can analyze the animal movements and issue warnings.

[0042] The monitoring unit can monitor sounds within the park and issue warnings if abnormal sounds occur. For example, if the monitoring unit hears a loud shout in the park, it will detect the abnormality and issue a warning. For example, the monitoring unit can monitor abnormal sounds from playground equipment and issue a warning if there is a possibility of malfunction. For example, the monitoring unit can monitor collision sounds in the park and issue a warning if there is a possibility of an accident. This allows for early detection of abnormal sounds and the issuance of warnings. The specific definition and detection criteria for abnormal sounds are set based on factors such as sound volume and frequency. Some or all of the above processing in the monitoring unit may be performed using, for example, a generating AI, or without a generating AI. For example, the monitoring unit can input audio data into a generating AI, which can analyze the audio to detect abnormalities and issue a warning.

[0043] The monitoring unit can monitor the lighting conditions within the park and issue warnings if there is insufficient lighting. For example, the monitoring unit may issue a warning if the lighting in a specific area of ​​the park is malfunctioning. For example, the monitoring unit may monitor areas with insufficient lighting at night and issue warnings to provide adequate lighting. For example, the monitoring unit may issue a warning and prompt maintenance if the brightness of the lighting decreases. This allows for early detection of lighting shortages and prompts appropriate action. Specific monitoring methods and criteria for lighting conditions include, for example, measuring the brightness of the lighting using an illuminance sensor and issuing a warning if it falls below a standard value. Some or all of the above-described processes in the monitoring unit may be performed using, for example, a generation AI, or without a generation AI. For example, the monitoring unit can input illuminance data into a generation AI, which can then analyze the lighting conditions and issue a warning.

[0044] The warning unit can automatically obtain the contact information of guardians when a warning is issued and make an emergency contact. For example, if a child gets lost, the warning unit can automatically obtain the contact information of the guardians and make an emergency contact. For example, if a child is engaging in dangerous behavior, the warning unit will make an emergency contact with the guardians. For example, if an accident occurs in a park, the warning unit will make an emergency contact with the guardians. This allows for quick contact with guardians in emergencies. The specific method and criteria for obtaining guardians' contact information can be, for example, by using a registration database to obtain guardians' contact information and making contact through an emergency contact system. Some or all of the above-described processes in the warning unit may be performed using, for example, a generating AI, or without a generating AI. For example, the warning unit can input the guardians' contact information into a generating AI, which can then generate and send an emergency contact message.

[0045] The warning unit can set the priority of warnings when an alert is issued and apply different warning methods depending on the severity. For example, the warning unit will issue an emergency warning if a serious danger is imminent. For example, the warning unit will issue a cautionary warning if there is a minor danger. For example, the warning unit will issue a maintenance warning if regular maintenance is required. This allows for the selection of an appropriate warning method according to the severity of the warning. The specific method and criteria for setting the warning priority are set, for example, based on the hazard assessment criteria. Some or all of the above processing in the warning unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the warning unit can input hazard data into a generating AI, which can then set the warning priority and select an appropriate warning method.

[0046] The warning unit can change the type of warning sound depending on the situation when a warning is issued. For example, if there is a serious danger, the warning unit will emit an emergency warning sound. For example, if there is a minor danger, the warning unit will emit a cautionary warning sound. For example, if periodic maintenance is required, the warning unit will emit a maintenance warning sound. This allows the unit to emit an appropriate warning sound according to the situation. The specific method and criteria for changing the type of warning sound are set, for example, based on adjustments to the type and volume of the sound. Some or all of the above processing in the warning unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the warning unit can input situation data into a generating AI, and the generating AI can select and emit an appropriate warning sound.

[0047] The warning unit can display warning messages in multiple languages ​​when a warning is issued. For example, the warning unit can display warning messages in multiple languages, such as English and Chinese, to foreign users in a park. For example, the warning unit can provide audio warning messages to visually impaired users. For example, the warning unit can display visual warning messages to hearing impaired users. This allows the system to accommodate foreign users by displaying warning messages in multiple languages. The specific methods and criteria for displaying warning messages in multiple languages ​​are set based on, for example, the types of languages ​​supported and the accuracy of translation. Some or all of the above-described processes in the warning unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the warning unit can input a warning message into a generation AI, which can then generate and display messages in multiple languages.

[0048] The tracking unit can select the optimal tracking route by referring to the child's past movement history during tracking. For example, the tracking unit may select the optimal tracking route based on places the child has frequently visited in the past. For example, the tracking unit may analyze the child's past movement patterns to select an efficient tracking route. For example, the tracking unit may select a tracking route based on places where the child has gotten lost in the past. This enables efficient tracking by selecting the optimal tracking route based on past movement history. The specific method and criteria for selecting the optimal tracking route are set based on, for example, the method for analyzing past movement history and the route selection algorithm. Some or all of the above processing in the tracking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the tracking unit can input past movement history data into a generative AI, and the generative AI can select the optimal tracking route.

[0049] The tracking unit can monitor the battery level of the child's device during tracking and issue warnings as needed. For example, the tracking unit will issue a warning if the child's device battery level drops low. For example, the tracking unit will notify the guardian if the device's battery level falls below a certain level. For example, if the device's battery level drops low, the tracking unit will guide the guardian to the location of a charging station. This prevents the device from running out of power by monitoring the device's battery level and issuing warnings as needed. The specific method and criteria for monitoring the battery level are set based on, for example, the type of battery and the method for measuring the remaining charge. Some or all of the above-described processes in the tracking unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the tracking unit can input battery data into a generating AI, which can then analyze the battery level and issue a warning.

[0050] The tracking unit can perform tracking by referring to the location information of the child's friends and family during tracking. For example, the tracking unit can select the optimal tracking route based on the location information of the child's friends and family. For example, if the child is with friends, the tracking unit will refer to the friends' location information to perform tracking. For example, if the child is with family, the tracking unit will refer to the family's location information to perform tracking. This makes tracking more efficient by referring to the location information of friends and family. The specific method and criteria for obtaining the location information of friends and family are set based on, for example, the method of obtaining location information and the method of sharing data. Some or all of the above processing in the tracking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the tracking unit can input the location information of friends and family into a generative AI, and the generative AI can select the optimal tracking route.

[0051] The tracking unit can change its tracking method depending on the type of device the child is wearing. For example, if the child is wearing a smartwatch, the tracking unit will use GPS for tracking. If the child has a smartphone, the tracking unit will use location services for tracking. If the child has a dedicated tracking device, the tracking unit will make full use of the device's functions for tracking. This allows the optimal tracking method to be applied according to the type of device. The specific methods and criteria for changing the tracking method according to the type of device are set based on, for example, the characteristics of the device and the type of tracking technology. Some or all of the above processing in the tracking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the tracking unit can input device type data into a generative AI, and the generative AI can select the optimal tracking method.

[0052] The condition monitoring unit can monitor the deterioration status of playground equipment and issue warnings when periodic maintenance is required. For example, the condition monitoring unit can monitor the deterioration status of playground equipment in real time and issue warnings if abnormalities occur. For example, the condition monitoring unit can issue warnings and prompt maintenance when periodic maintenance is required. For example, the condition monitoring unit can monitor the deterioration status of playground equipment and issue warnings when use is prohibited. This allows for early detection of the deterioration status of playground equipment and prompts appropriate maintenance. Specific monitoring methods and criteria for the deterioration status are set based on deterioration indicators and types of monitoring sensors, for example. Some or all of the above-described processes in the condition monitoring unit may be performed using, for example, a generating AI, or without a generating AI. For example, the condition monitoring unit can input deterioration status data into a generating AI, which can analyze the deterioration status and issue warnings.

[0053] The condition monitoring unit can predict changes in weather and issue warnings before extreme weather events occur. For example, the condition monitoring unit can monitor changes in weather in real time and issue warnings before extreme weather events occur. For example, the condition monitoring unit can predict changes in weather and issue warnings before thunderstorms occur. For example, the condition monitoring unit can predict changes in weather and issue warnings before strong winds occur. This allows for early prediction of extreme weather events and prompts appropriate responses. The specific methods and criteria for predicting changes in weather are set based on, for example, methods for analyzing weather data and prediction algorithms. Some or all of the above-described processes in the condition monitoring unit may be performed using, for example, a generating AI, or without a generating AI. For example, the condition monitoring unit can input weather data into a generating AI, which can then predict changes in weather and issue warnings.

[0054] The status monitoring unit monitors the usage status of playground equipment and can issue a warning if a particular piece of equipment is being used excessively. For example, the status monitoring unit can issue a warning and restrict use if a particular piece of equipment is being used excessively. For example, the status monitoring unit monitors the usage status of playground equipment and issues a warning if excessive use is observed. For example, the status monitoring unit monitors the usage status of playground equipment and issues a warning if maintenance is required. This allows for early detection of excessive use and prompts appropriate action. The specific definition and criteria for excessive use are set based on, for example, usage frequency thresholds and monitoring methods. Some or all of the above-described processes in the status monitoring unit may be performed using, for example, a generating AI, or without a generating AI. For example, the status monitoring unit can input usage data into a generating AI, which can analyze excessive use and issue a warning.

[0055] The condition monitoring unit can monitor changes in weather and restrict the use of playground equipment under specific weather conditions. For example, the condition monitoring unit may restrict the use of slippery playground equipment during rainy weather. For example, the condition monitoring unit may restrict the use of wind-vulnerable playground equipment during strong winds. For example, the condition monitoring unit may restrict the use of all playground equipment in the event of extreme weather. This allows for the setting of appropriate restrictions on the use of playground equipment according to weather conditions. The specific definitions and criteria for specific weather conditions are set based on, for example, temperature, precipitation, and wind speed. Some or all of the above-described processes in the condition monitoring unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the condition monitoring unit can input weather data into a generating AI, which can analyze the weather conditions and set restrictions on the use of playground equipment.

[0056] The control unit can monitor the battery level of the lost child detection robot and guide it to a charging station as needed. For example, if the battery level of the lost child detection robot decreases, the control unit will guide it to a charging station. For example, if the battery level falls below a certain level, the control unit will guide it to the location of the charging station. For example, the control unit can monitor the battery level of the lost child detection robot in real time and prompt it to charge as needed. This ensures that the robot's operating time is maintained by monitoring the battery level and prompting it to charge as needed. The specific location and functions of the charging station are set based on, for example, the criteria for selecting the installation location and the charging method. Some or all of the above-described processes in the control unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the control unit can input battery data into a generating AI, which can analyze the battery level and guide the robot to a charging station.

[0057] The control unit can analyze the camera footage of the lost child detection robot to improve the accuracy of identifying the lost child. For example, the control unit can analyze the camera footage of the lost child detection robot in real time to identify the lost child. For example, the control unit can analyze the camera footage and identify the lost child based on their characteristics. For example, the control unit can analyze the camera footage of the lost child detection robot to quickly find the lost child. In this way, the lost child can be quickly identified by analyzing the camera footage. The specific methods and techniques for analyzing the camera footage are set based on, for example, image recognition algorithms and analysis software. Some or all of the above processing in the control unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the control unit can input camera footage data into a generative AI, and the generative AI can analyze the footage to identify the lost child.

[0058] The control unit can utilize the voice function of the lost child discovery robot to send messages that provide reassurance to the lost child. For example, the control unit can send a message to the child that says, "It's okay, we'll find you soon." The control unit can send a message to the child that says, "Stay here, help is coming soon." The control unit can send a message to the child that says, "Don't be scared, we'll find you soon." In this way, a sense of security can be provided to the lost child through voice messages. The specific content and method of the reassuring message are set based on, for example, the type of message and the tone of voice. Some or all of the above processing in the control unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the control unit can input message data into a generation AI, and the generation AI can generate and send an appropriate message.

[0059] The control unit can optimize the patrol route of the lost child detection robot in cooperation with other robots in the park. For example, the control unit can enable the lost child detection robot to coordinate with other robots to set an efficient patrol route. For example, the control unit can optimize a wide-area patrol route in cooperation with other robots. For example, the control unit can enable the lost child detection robot to share information with other robots to set an optimal patrol route. This allows for the setting of a more efficient patrol route through cooperation with other robots. The specific methods and criteria for coordinating with other robots are set based on, for example, communication protocols and cooperation algorithms. Some or all of the above-described processes in the control unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the control unit can input data from other robots into a generative AI, which can then set an optimal patrol route.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The monitoring unit can monitor the level of congestion in specific areas within the park and issue warnings if congestion occurs. For example, it can monitor the level of congestion in the playground area in real time and issue a warning if it exceeds a certain number of people. It can also monitor the level of congestion in rest areas and issue a warning if it becomes overcrowded. It can monitor the level of congestion near entrances and issue a warning if entry restrictions are necessary. This allows for monitoring of congestion and issuing warnings at the appropriate time. The specific definition of congestion and monitoring methods are set based on the number of people and the size of the area. Some or all of the above processing in the monitoring unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0062] The monitoring unit can monitor the movements of animals within the park and issue warnings if dangerous animals approach. For example, it can monitor the movements of wild animals that frequent the park and issue warnings if danger is imminent. It can also monitor the movements of animals in pet areas and issue warnings if they intrude into other areas. It can monitor the movements of flocks of birds and issue warnings if unusual behavior is observed. This allows for early detection and warning of the approach of dangerous animals. The specific definition of dangerous animals and detection criteria are set based on specific animal species and behavioral patterns. Some or all of the above processing in the monitoring unit may be performed using generative AI, or it may be performed without using generative AI.

[0063] The monitoring unit can monitor sounds within the park and issue warnings if unusual sounds occur. For example, if a loud shout is heard in the park, it will detect the anomaly and issue a warning. It will also monitor unusual sounds from playground equipment and issue warnings if there is a possibility of malfunction. It will monitor collision sounds in the park and issue warnings if there is a possibility of an accident. This allows for the early detection of unusual sounds and the issuance of warnings. The specific definition of an unusual sound and the detection criteria are set based on the loudness and frequency of the sound. Some or all of the above processing in the monitoring unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0064] The warning unit can automatically obtain the guardian's contact information when a warning is issued and make an emergency contact. For example, if a child gets lost, it can automatically obtain the guardian's contact information and make an emergency contact. If a child is engaging in dangerous behavior, it will make an emergency contact with the guardian. If an accident occurs in the park, it will make an emergency contact with the guardian. This allows for quick contact with guardians in emergencies. The specific method and criteria for obtaining the guardian's contact information can be determined by using a registration database to obtain the guardian's contact information and making the contact through the emergency contact system. Some or all of the above-described processes in the warning unit may be performed using a generation AI, or they may be performed without using a generation AI.

[0065] The tracking unit can select the optimal tracking route by referring to the child's past movement history during tracking. For example, it can select the optimal tracking route based on places the child has frequently visited in the past. It can also select an efficient tracking route by analyzing the child's past movement patterns. It can select a tracking route based on places where the child has gotten lost in the past. This enables efficient tracking by selecting the optimal tracking route based on past movement history. The specific method and criteria for selecting the optimal tracking route are set based on the analysis method of past movement history and the route selection algorithm. Some or all of the above processing in the tracking unit may be performed using generative AI, or it may be performed without using generative AI.

[0066] The tracking unit can monitor the battery level of the child's device during tracking and issue warnings as needed. For example, it can issue a warning if the child's device battery level drops low. It can also notify the guardian if the device's battery level falls below a certain level. If the device's battery level drops low, it can guide the guardian to the location of a charging station. This allows the device to run out of power by monitoring its battery level and issuing warnings as needed. The specific method and criteria for monitoring the battery level are set based on the type of battery and the method for measuring the remaining charge. Some or all of the above-described processes in the tracking unit may be performed using or without generational AI.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The monitoring unit monitors the park's video feed. The monitoring unit uses, for example, AI surveillance cameras to monitor the park's video feed in real time and detect suspicious individuals or dangerous behavior. The AI ​​in the monitoring unit can analyze the video feed and identify suspicious individuals or dangerous behavior. It can also issue warnings if it detects abnormal activity. Step 2: The warning unit issues a warning based on suspicious individuals or dangerous activities detected by the monitoring unit. The warning unit can issue, for example, an audible warning or a visual warning. It can also issue an audible warning using a speaker and a visual warning using a display. Step 3: The tracking unit tracks the child's location. The tracking unit can track the child's location using, for example, GPS or RFID tags. The tracking unit can also provide location information to the guardian and quickly locate the child if they get lost. Step 4: The condition monitoring unit monitors the condition of the playground equipment and the weather. The condition monitoring unit can, for example, use sensors to monitor the condition of the playground equipment. It can also monitor for abnormalities and changes in the weather, and issue warnings if extreme weather occurs. Step 5: The control unit controls the lost child detection robot. The control unit can have the lost child detection robot patrol the park and find lost children.

[0069] (Example of form 2) The AI ​​system according to an embodiment of the present invention is a system that comprehensively protects the safety of children playing in parks. This AI system uses AI surveillance cameras installed in the park to detect suspicious persons and dangerous behavior, and issues real-time warnings for early response. Furthermore, by connecting children and guardians to the AI ​​system, it becomes possible to prevent lost children and track their location. In addition, by monitoring playground equipment and weather, and introducing lost child detection robots, a safer park environment is provided, realizing a park space where children can enjoy themselves with peace of mind. For example, the AI ​​system uses AI surveillance cameras installed in the park to detect suspicious persons and dangerous behavior. When the camera detects abnormal movement in the park, the AI ​​analyzes the footage and identifies the suspicious person or dangerous behavior. This allows for real-time warnings and early response. Next, by connecting children and guardians to the AI ​​system, it becomes possible to prevent lost children and track their location. For example, if a child gets lost in the park, guardians can track the child's location through the AI ​​system. This allows for the child to be found quickly. Furthermore, by monitoring playground equipment and weather, and introducing lost child detection robots, a safer park environment is provided. For example, the system monitors the condition of playground equipment and issues warnings if any abnormalities occur. It also monitors weather changes and issues warnings if extreme weather conditions occur. A lost child detection robot patrols the park and plays a role in finding lost children. In this way, the AI ​​system comprehensively protects the safety of children playing in the park and creates a park space where children can enjoy themselves with peace of mind. For parents, the assurance of their children's safety allows them to let their children play in the park with confidence. In this way, the AI ​​system comprehensively protects the safety of children playing in the park and creates a park space where children can enjoy themselves with peace of mind.

[0070] The AI ​​system according to this embodiment comprises a monitoring unit, a warning unit, a tracking unit, a status monitoring unit, and a control unit. The monitoring unit monitors video footage of the park. The monitoring unit monitors video footage of the park in real time, for example, using an AI surveillance camera, and detects suspicious persons and dangerous behavior. The monitoring unit can, for example, use AI to analyze the video footage and identify suspicious persons and dangerous behavior. The monitoring unit can also issue a warning if it detects abnormal movement. The warning unit issues a warning based on the suspicious person or dangerous behavior detected by the monitoring unit. The warning unit can issue a warning, for example, an audible warning or a visual warning. The warning unit can, for example, issue an audible warning using a speaker. The warning unit can also issue a visual warning using a display. The tracking unit tracks the location of children. The tracking unit can, for example, track the location of children using GPS or RFID tags. The tracking unit can, for example, provide location information to parents. The tracking unit can also quickly locate a child if they get lost. The status monitoring unit monitors the condition of playground equipment and weather. The condition monitoring unit can, for example, monitor the condition of playground equipment using sensors. The condition monitoring unit can, for example, issue a warning if an abnormality occurs. The condition monitoring unit can also monitor changes in weather and issue a warning if abnormal weather occurs. The control unit controls the lost child detection robot. The control unit can, for example, have the lost child detection robot patrol the park. The control unit can, for example, have the lost child detection robot find a lost child. In this way, the AI ​​system according to the embodiment can comprehensively protect safety in the park and provide an environment where children can play with peace of mind.

[0071] The monitoring unit monitors video footage within the park. For example, it uses AI-powered surveillance cameras to monitor the park's video in real time and detect suspicious individuals or dangerous behavior. Specifically, the AI ​​cameras acquire high-resolution video and analyze the video data in real time. The AI ​​uses deep learning technology to recognize people and movements within the video and detects abnormal movements compared to normal behavior patterns. For example, the AI ​​can identify individuals who stay in a specific area for extended periods or those who make sudden movements as suspicious. The AI ​​can also detect dangerous behaviors such as leaving objects unattended or acts of vandalism. This allows the monitoring unit to monitor the park's safety in real time and respond immediately if an anomaly occurs. Furthermore, the monitoring unit can issue warnings when it detects abnormal behavior. For example, if the AI ​​detects a suspicious person, it sends that information to the warning unit, instructing the warning unit to issue an appropriate warning. This ensures the safety of the park and enables a rapid response.

[0072] The warning unit issues warnings based on suspicious individuals or dangerous behavior detected by the monitoring unit. The warning unit can issue, for example, audio and visual warnings. Specifically, the warning unit can issue audio warnings using speakers. For example, an audio warning might say, "A suspicious person has been detected. Please leave immediately," and is set to be audible throughout the park. The warning unit can also issue visual warnings using displays. For example, a visual warning might display a message such as, "A suspicious person has been detected. Please be careful," on displays installed in key areas of the park. This allows the warning unit to issue warnings to park users quickly and effectively, ensuring their safety. Furthermore, the warning unit can flexibly change the content and method of warnings depending on the situation. For example, stronger warnings can be issued at night or during times when there are fewer people to encourage a quicker response. In this way, the warning unit can comprehensively protect safety within the park and provide an environment where users can feel safe.

[0073] The tracking unit tracks the child's location. For example, it can track a child's location using GPS or RFID tags. Specifically, it uses small GPS devices or RFID tags that the child can wear, collecting location information transmitted from these devices in real time. The tracking unit analyzes this location information to accurately determine the child's current location. Furthermore, the tracking unit can provide location information to parents. For example, a dedicated application can be installed on a parent's smartphone to display the child's location in real time. This allows parents to always know where their child is in the park, giving them peace of mind while their child plays. The tracking unit can also quickly locate a child if they get lost. For example, if a child leaves a designated area, the tracking unit immediately issues a warning and notifies parents and park administrators. This enables a quick response and ensures the child's safety.

[0074] The condition monitoring unit monitors the condition of playground equipment and weather. For example, the condition monitoring unit can monitor the condition of playground equipment using sensors. Specifically, sensors attached to the equipment monitor the usage status and physical condition of the equipment in real time. For example, it can detect wear on swing chains or damage to the surface of slides. This allows for early detection of abnormalities in the playground equipment and appropriate maintenance. The condition monitoring unit can also monitor changes in weather and issue warnings if abnormal weather conditions occur. For example, it collects weather data such as temperature, humidity, wind speed, and rainfall, and notifies the warning unit if abnormal weather conditions are detected. This allows for prompt warnings to users and ensures safety. Furthermore, the condition monitoring unit can accumulate data on the condition of playground equipment and weather and perform long-term analysis. This allows for prediction of the lifespan of playground equipment and the optimal timing for maintenance, enabling efficient management.

[0075] The control unit controls the lost child detection robot. For example, the control unit can make the lost child detection robot patrol the park. Specifically, the control unit controls the robot's movement path and actions in real time to patrol the park efficiently. The lost child detection robot is equipped with cameras and sensors, allowing it to move while recognizing its surroundings. For example, the robot uses its cameras to acquire images of the park, and AI analyzes these images to identify lost children. The robot can also use voice and a display to call out to children and guide them back to their guardians. This allows the control unit to effectively operate the lost child detection robot and ensure safety within the park. Furthermore, the control unit monitors the operating status and battery level of the lost child detection robot and can perform charging and maintenance as needed. This increases the robot's operational efficiency and ensures it is always operating in optimal condition.

[0076] The monitoring unit can monitor video footage from the park in real time and detect suspicious individuals or dangerous behavior. For example, the monitoring unit can use AI surveillance cameras to monitor video footage from the park in real time. For example, the monitoring unit can use AI to analyze the video and identify suspicious individuals or dangerous behavior. The monitoring unit can also issue warnings if it detects abnormal activity. This enables a rapid response through real-time monitoring. Real-time monitoring is achieved, for example, by analyzing video from surveillance cameras in real time and detecting anomalies. Some or all of the above processing in the monitoring unit may be performed using, for example, generative AI, or without generative AI. For example, the monitoring unit can input video from surveillance cameras into a generative AI, which can then analyze the video and detect anomalies.

[0077] The warning unit can issue warnings based on suspicious persons or dangerous activities detected by the monitoring unit. The warning unit can issue, for example, audio warnings or visual warnings. The warning unit can issue audio warnings using a speaker, for example. The warning unit can also issue visual warnings using a display. This allows for rapid warnings against suspicious persons or dangerous activities. The specific method and content of the warnings may include, for example, an audio warning such as "A suspicious person has been detected. Please leave immediately." As a visual warning, the message "A suspicious person has been detected. Please leave immediately." can be displayed on the screen. Some or all of the above processing in the warning unit may be performed using, for example, a generation AI, or without a generation AI. For example, the warning unit can input information from the monitoring unit into a generation AI, and the generation AI can generate a warning message.

[0078] The tracking unit can track a child's location and provide the location information to the guardian. The tracking unit tracks the child's location using, for example, GPS or RFID tags. The tracking unit can provide the location information to the guardian. The tracking unit can also quickly locate a child if they get lost. This allows the child's location to be tracked and the guardian to be notified quickly. Specific methods for acquiring and providing location information include, for example, tracking the location in real time using GPS data and notifying the guardian's smartphone. Some or all of the above-described processes in the tracking unit may be performed using, for example, a generating AI, or without a generating AI. For example, the tracking unit can input GPS data into a generating AI, which can then analyze the location information and notify the guardian.

[0079] The condition monitoring unit can monitor the condition of playground equipment and weather, and issue warnings if abnormalities occur. The condition monitoring unit can, for example, use sensors to monitor the condition of playground equipment. The condition monitoring unit can, for example, issue warnings if abnormalities occur. The condition monitoring unit can also monitor changes in weather and issue warnings if abnormal weather occurs. This allows for early detection and warning of abnormalities in playground equipment and weather. The specific definition of an abnormality and detection criteria can be, for example, by setting sensor thresholds, and issuing warnings when abnormalities are detected. Some or all of the above processing in the condition monitoring unit may be performed using, for example, a generation AI, or without a generation AI. For example, the condition monitoring unit can input sensor data into a generation AI, which can detect abnormalities and issue warnings.

[0080] The control unit can control the lost child detection robot to patrol the park and find lost children. For example, the control unit can make the lost child detection robot patrol the park. The control unit can, for example, have the lost child detection robot find the lost child. In this way, by controlling the lost child detection robot, lost children can be found quickly. The specific method and route of patrolling can be, for example, the lost child detection robot can patrol the park periodically and follow a specific route. Some or all of the above processing in the control unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the control unit can input the patrol route of the lost child detection robot into the generative AI, and the generative AI can set the optimal patrol route.

[0081] The monitoring unit can estimate a child's emotions and adjust its monitoring focus based on the estimated emotions. For example, if a child is feeling frightened, the monitoring unit can intensify monitoring of their surroundings to detect abnormal behavior early. For example, if a child is excited, the monitoring unit can intensify monitoring of their use of play equipment to ensure their safety. For example, if a child is tired, the monitoring unit can intensify monitoring of rest areas to provide appropriate support. This allows for more effective monitoring by adjusting the monitoring focus 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, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using a generative AI, or not. For example, the monitoring unit can input a child's facial expression data into a generative AI, which can then estimate emotions and adjust the monitoring focus.

[0082] The monitoring unit can monitor the level of congestion in specific areas within the park and issue warnings if congestion occurs. For example, the monitoring unit can monitor the level of congestion in the playground area in real time and issue a warning if the number of people exceeds a certain limit. For example, the monitoring unit can monitor the level of congestion in the rest area and issue a warning if it becomes overcrowded. For example, the monitoring unit can monitor the level of congestion near the entrance and issue a warning if entry restrictions are necessary. This allows for monitoring of congestion and issuing warnings at the appropriate time. The specific definition and monitoring method of congestion can be set based on, for example, the number of people or the size of the area. Some or all of the above processing in the monitoring unit may be performed using, for example, a generating AI, or it may be performed without a generating AI. For example, the monitoring unit can input congestion data into a generating AI, which can analyze the congestion and issue a warning.

[0083] The monitoring unit can monitor the movements of animals in the park and issue warnings if dangerous animals approach. For example, the monitoring unit can monitor the movements of wild animals that frequent the park and issue warnings if danger is imminent. For example, the monitoring unit can monitor the movements of animals in the pet area and issue warnings if they enter other areas. For example, the monitoring unit can monitor the movements of flocks of birds and issue warnings if unusual behavior is observed. This allows for early detection and warning of the approach of dangerous animals. The specific definition and detection criteria for dangerous animals are set based on, for example, specific animal species or behavioral patterns. Some or all of the above processing in the monitoring unit may be performed using, for example, generative AI, or without generative AI. For example, the monitoring unit can input animal movement data into generative AI, which can analyze the animal movements and issue warnings.

[0084] The monitoring unit can estimate a child's emotions and adjust the viewpoint of the surveillance cameras based on the estimated emotions. For example, if a child is feeling frightened, the monitoring unit adjusts the viewpoint of the surveillance cameras around them to obtain detailed footage. For example, if a child is excited, the monitoring unit adjusts the camera viewpoint to monitor the use of play equipment. For example, if a child is tired, the monitoring unit adjusts the viewpoint of the surveillance cameras in the rest area to provide appropriate support. This allows for the acquisition of more detailed footage by adjusting the viewpoint of the surveillance cameras 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 monitoring unit may be performed using a generative AI, or not using a generative AI. For example, the monitoring unit can input child facial expression data into a generative AI, which can estimate emotions and adjust the viewpoint of the surveillance cameras.

[0085] The monitoring unit can monitor sounds within the park and issue warnings if abnormal sounds occur. For example, if the monitoring unit hears a loud shout in the park, it will detect the abnormality and issue a warning. For example, the monitoring unit can monitor abnormal sounds from playground equipment and issue a warning if there is a possibility of malfunction. For example, the monitoring unit can monitor collision sounds in the park and issue a warning if there is a possibility of an accident. This allows for early detection of abnormal sounds and the issuance of warnings. The specific definition and detection criteria for abnormal sounds are set based on factors such as sound volume and frequency. Some or all of the above processing in the monitoring unit may be performed using, for example, a generating AI, or without a generating AI. For example, the monitoring unit can input audio data into a generating AI, which can analyze the audio to detect abnormalities and issue a warning.

[0086] The monitoring unit can monitor the lighting conditions within the park and issue warnings if there is insufficient lighting. For example, the monitoring unit may issue a warning if the lighting in a specific area of ​​the park is malfunctioning. For example, the monitoring unit may monitor areas with insufficient lighting at night and issue warnings to provide adequate lighting. For example, the monitoring unit may issue a warning and prompt maintenance if the brightness of the lighting decreases. This allows for early detection of lighting shortages and prompts appropriate action. Specific monitoring methods and criteria for lighting conditions include, for example, measuring the brightness of the lighting using an illuminance sensor and issuing a warning if it falls below a standard value. Some or all of the above-described processes in the monitoring unit may be performed using, for example, a generation AI, or without a generation AI. For example, the monitoring unit can input illuminance data into a generation AI, which can then analyze the lighting conditions and issue a warning.

[0087] The warning unit can estimate the child's emotions and adjust the content of the warning based on the estimated emotions. For example, if the child is feeling frightened, the warning unit will issue a warning in gentle words. For example, if the child is excited, the warning unit will issue a warning encouraging the child to act calmly. For example, if the child is tired, the warning unit will issue a warning encouraging the child to take a break. By adjusting the content of the warning according to the child's emotions, more effective warnings can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the warning unit may be performed using a generative AI, or not using a generative AI. For example, the warning unit can input the child's facial expression data into a generative AI, which can estimate the emotions and adjust the content of the warning.

[0088] The warning unit can automatically obtain the contact information of guardians when a warning is issued and make an emergency contact. For example, if a child gets lost, the warning unit can automatically obtain the contact information of the guardians and make an emergency contact. For example, if a child is engaging in dangerous behavior, the warning unit will make an emergency contact with the guardians. For example, if an accident occurs in a park, the warning unit will make an emergency contact with the guardians. This allows for quick contact with guardians in emergencies. The specific method and criteria for obtaining guardians' contact information can be, for example, by using a registration database to obtain guardians' contact information and making contact through an emergency contact system. Some or all of the above-described processes in the warning unit may be performed using, for example, a generating AI, or without a generating AI. For example, the warning unit can input the guardians' contact information into a generating AI, which can then generate and send an emergency contact message.

[0089] The warning unit can set the priority of warnings when an alert is issued and apply different warning methods depending on the severity. For example, the warning unit will issue an emergency warning if a serious danger is imminent. For example, the warning unit will issue a cautionary warning if there is a minor danger. For example, the warning unit will issue a maintenance warning if regular maintenance is required. This allows for the selection of an appropriate warning method according to the severity of the warning. The specific method and criteria for setting the warning priority are set, for example, based on the hazard assessment criteria. Some or all of the above processing in the warning unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the warning unit can input hazard data into a generating AI, which can then set the warning priority and select an appropriate warning method.

[0090] The warning unit can estimate the child's emotions and adjust the timing of the warning based on the estimated emotions. For example, if the child is feeling frightened, the warning unit will issue a warning immediately. If the child is excited, the warning unit will wait until the child calms down before issuing a warning. If the child is tired, the warning unit will issue a warning at the appropriate time to encourage a break. By adjusting the timing of the warning according to the child's emotions, more effective warnings can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the warning unit may be performed using a generative AI, or not using a generative AI. For example, the warning unit can input the child's facial expression data into a generative AI, which can then estimate the emotions and adjust the timing of the warning.

[0091] The warning unit can change the type of warning sound depending on the situation when a warning is issued. For example, if there is a serious danger, the warning unit will emit an emergency warning sound. For example, if there is a minor danger, the warning unit will emit a cautionary warning sound. For example, if periodic maintenance is required, the warning unit will emit a maintenance warning sound. This allows the unit to emit an appropriate warning sound according to the situation. The specific method and criteria for changing the type of warning sound are set, for example, based on adjustments to the type and volume of the sound. Some or all of the above processing in the warning unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the warning unit can input situation data into a generating AI, and the generating AI can select and emit an appropriate warning sound.

[0092] The warning unit can display warning messages in multiple languages ​​when a warning is issued. For example, the warning unit can display warning messages in multiple languages, such as English and Chinese, to foreign users in a park. For example, the warning unit can provide audio warning messages to visually impaired users. For example, the warning unit can display visual warning messages to hearing impaired users. This allows the system to accommodate foreign users by displaying warning messages in multiple languages. The specific methods and criteria for displaying warning messages in multiple languages ​​are set based on, for example, the types of languages ​​supported and the accuracy of translation. Some or all of the above-described processes in the warning unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the warning unit can input a warning message into a generation AI, which can then generate and display messages in multiple languages.

[0093] The tracking unit can estimate the child's emotions and adjust the tracking accuracy based on the estimated emotions. For example, if the child is frightened, the tracking unit can increase tracking accuracy to quickly locate the child. If the child is excited, the tracking unit can track over a wide area. If the child is tired, the tracking unit can focus tracking on rest areas. This allows for more effective tracking by adjusting the tracking accuracy according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 tracking unit may be performed using a generative AI, or not. For example, the tracking unit can input the child's facial expression data into a generative AI, which can then estimate emotions and adjust the tracking accuracy.

[0094] The tracking unit can select the optimal tracking route by referring to the child's past movement history during tracking. For example, the tracking unit may select the optimal tracking route based on places the child has frequently visited in the past. For example, the tracking unit may analyze the child's past movement patterns to select an efficient tracking route. For example, the tracking unit may select a tracking route based on places where the child has gotten lost in the past. This enables efficient tracking by selecting the optimal tracking route based on past movement history. The specific method and criteria for selecting the optimal tracking route are set based on, for example, the method for analyzing past movement history and the route selection algorithm. Some or all of the above processing in the tracking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the tracking unit can input past movement history data into a generative AI, and the generative AI can select the optimal tracking route.

[0095] The tracking unit can monitor the battery level of the child's device during tracking and issue warnings as needed. For example, the tracking unit will issue a warning if the child's device battery level drops low. For example, the tracking unit will notify the guardian if the device's battery level falls below a certain level. For example, if the device's battery level drops low, the tracking unit will guide the guardian to the location of a charging station. This prevents the device from running out of power by monitoring the device's battery level and issuing warnings as needed. The specific method and criteria for monitoring the battery level are set based on, for example, the type of battery and the method for measuring the remaining charge. Some or all of the above-described processes in the tracking unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the tracking unit can input battery data into a generating AI, which can then analyze the battery level and issue a warning.

[0096] The tracking unit can estimate the child's emotions and adjust the tracking frequency based on the estimated emotions. For example, if the child is frightened, the tracking unit increases the tracking frequency to quickly locate the child. If the child is excited, the tracking unit tracks over a wide area. If the child is tired, the tracking unit focuses on tracking rest areas. By adjusting the tracking frequency according to the child's emotions, more effective tracking becomes possible. Emotion estimation is achieved using an emotion estimation function, such as 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 tracking unit may be performed using a generative AI, or not. For example, the tracking unit can input the child's facial expression data into a generative AI, which can estimate emotions and adjust the tracking frequency.

[0097] The tracking unit can perform tracking by referring to the location information of the child's friends and family during tracking. For example, the tracking unit can select the optimal tracking route based on the location information of the child's friends and family. For example, if the child is with friends, the tracking unit will refer to the friends' location information to perform tracking. For example, if the child is with family, the tracking unit will refer to the family's location information to perform tracking. This makes tracking more efficient by referring to the location information of friends and family. The specific method and criteria for obtaining the location information of friends and family are set based on, for example, the method of obtaining location information and the method of sharing data. Some or all of the above processing in the tracking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the tracking unit can input the location information of friends and family into a generative AI, and the generative AI can select the optimal tracking route.

[0098] The tracking unit can change its tracking method depending on the type of device the child is wearing. For example, if the child is wearing a smartwatch, the tracking unit will use GPS for tracking. If the child has a smartphone, the tracking unit will use location services for tracking. If the child has a dedicated tracking device, the tracking unit will make full use of the device's functions for tracking. This allows the optimal tracking method to be applied according to the type of device. The specific methods and criteria for changing the tracking method according to the type of device are set based on, for example, the characteristics of the device and the type of tracking technology. Some or all of the above processing in the tracking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the tracking unit can input device type data into a generative AI, and the generative AI can select the optimal tracking method.

[0099] The state monitoring unit can estimate a child's emotions and monitor the use of play equipment based on the estimated emotions. For example, if a child is feeling frightened, the state monitoring unit will monitor the use of play equipment and ensure safety. For example, if a child is excited, the state monitoring unit will monitor the use of play equipment and provide appropriate support. For example, if a child is tired, the state monitoring unit will monitor the use of play equipment and encourage them to rest. This allows for safer use of play equipment by monitoring its use 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, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the state monitoring unit may be performed using a generative AI, or not using a generative AI. For example, the state monitoring unit can input a child's facial expression data into a generative AI, which can then estimate emotions and monitor the use of play equipment.

[0100] The condition monitoring unit can monitor the deterioration status of playground equipment and issue warnings when periodic maintenance is required. For example, the condition monitoring unit can monitor the deterioration status of playground equipment in real time and issue warnings if abnormalities occur. For example, the condition monitoring unit can issue warnings and prompt maintenance when periodic maintenance is required. For example, the condition monitoring unit can monitor the deterioration status of playground equipment and issue warnings when use is prohibited. This allows for early detection of the deterioration status of playground equipment and prompts appropriate maintenance. Specific monitoring methods and criteria for the deterioration status are set based on deterioration indicators and types of monitoring sensors, for example. Some or all of the above-described processes in the condition monitoring unit may be performed using, for example, a generating AI, or without a generating AI. For example, the condition monitoring unit can input deterioration status data into a generating AI, which can analyze the deterioration status and issue warnings.

[0101] The condition monitoring unit can predict changes in weather and issue warnings before extreme weather events occur. For example, the condition monitoring unit can monitor changes in weather in real time and issue warnings before extreme weather events occur. For example, the condition monitoring unit can predict changes in weather and issue warnings before thunderstorms occur. For example, the condition monitoring unit can predict changes in weather and issue warnings before strong winds occur. This allows for early prediction of extreme weather events and prompts appropriate responses. The specific methods and criteria for predicting changes in weather are set based on, for example, methods for analyzing weather data and prediction algorithms. Some or all of the above-described processes in the condition monitoring unit may be performed using, for example, a generating AI, or without a generating AI. For example, the condition monitoring unit can input weather data into a generating AI, which can then predict changes in weather and issue warnings.

[0102] The state monitoring unit can estimate a child's emotions and set restrictions on the use of play equipment based on the estimated emotions. For example, if a child is feeling frightened, the state monitoring unit may restrict the use of a specific piece of play equipment. For example, if a child is excited, the state monitoring unit may relax the restrictions on the use of play equipment. For example, if a child is tired, the state monitoring unit may restrict the use of play equipment to encourage them to rest. This allows for safer use of play equipment by setting restrictions on its use according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the state monitoring unit may be performed using a generative AI, or not using a generative AI. For example, the state monitoring unit can input the child's facial expression data into a generative AI, which can then estimate the emotions and set restrictions on the use of play equipment.

[0103] The status monitoring unit monitors the usage status of playground equipment and can issue a warning if a particular piece of equipment is being used excessively. For example, the status monitoring unit can issue a warning and restrict use if a particular piece of equipment is being used excessively. For example, the status monitoring unit monitors the usage status of playground equipment and issues a warning if excessive use is observed. For example, the status monitoring unit monitors the usage status of playground equipment and issues a warning if maintenance is required. This allows for early detection of excessive use and prompts appropriate action. The specific definition and criteria for excessive use are set based on, for example, usage frequency thresholds and monitoring methods. Some or all of the above-described processes in the status monitoring unit may be performed using, for example, a generating AI, or without a generating AI. For example, the status monitoring unit can input usage data into a generating AI, which can analyze excessive use and issue a warning.

[0104] The condition monitoring unit can monitor changes in weather and restrict the use of playground equipment under specific weather conditions. For example, the condition monitoring unit may restrict the use of slippery playground equipment during rainy weather. For example, the condition monitoring unit may restrict the use of wind-vulnerable playground equipment during strong winds. For example, the condition monitoring unit may restrict the use of all playground equipment in the event of extreme weather. This allows for the setting of appropriate restrictions on the use of playground equipment according to weather conditions. The specific definitions and criteria for specific weather conditions are set based on, for example, temperature, precipitation, and wind speed. Some or all of the above-described processes in the condition monitoring unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the condition monitoring unit can input weather data into a generating AI, which can analyze the weather conditions and set restrictions on the use of playground equipment.

[0105] The control unit can estimate a child's emotions and adjust the patrol route of the lost child detection robot based on the estimated emotions. For example, if a child is frightened, the control unit adjusts the patrol route of the lost child detection robot to find the child quickly. For example, if a child is excited, the control unit adjusts the patrol route to cover a wide area. For example, if a child is tired, the control unit focuses its patrol on rest areas. By adjusting the patrol route according to the child's emotions, more effective lost child detection becomes possible. 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 control unit may be performed using a generative AI, or not using a generative AI. For example, the control unit can input a child's facial expression data into a generative AI, which can estimate emotions and adjust the patrol route.

[0106] The control unit can monitor the battery level of the lost child detection robot and guide it to a charging station as needed. For example, if the battery level of the lost child detection robot decreases, the control unit will guide it to a charging station. For example, if the battery level falls below a certain level, the control unit will guide it to the location of the charging station. For example, the control unit can monitor the battery level of the lost child detection robot in real time and prompt it to charge as needed. This ensures that the robot's operating time is maintained by monitoring the battery level and prompting it to charge as needed. The specific location and functions of the charging station are set based on, for example, the criteria for selecting the installation location and the charging method. Some or all of the above-described processes in the control unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the control unit can input battery data into a generating AI, which can analyze the battery level and guide the robot to a charging station.

[0107] The control unit can analyze the camera footage of the lost child detection robot to improve the accuracy of identifying the lost child. For example, the control unit can analyze the camera footage of the lost child detection robot in real time to identify the lost child. For example, the control unit can analyze the camera footage and identify the lost child based on their characteristics. For example, the control unit can analyze the camera footage of the lost child detection robot to quickly find the lost child. In this way, the lost child can be quickly identified by analyzing the camera footage. The specific methods and techniques for analyzing the camera footage are set based on, for example, image recognition algorithms and analysis software. Some or all of the above processing in the control unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the control unit can input camera footage data into a generative AI, and the generative AI can analyze the footage to identify the lost child.

[0108] The control unit can estimate the child's emotions and adjust the speed of the lost child detection robot based on the estimated emotions. For example, if the child is frightened, the control unit can increase the robot's speed to find the child quickly. For example, if the child is excited, the control unit can adjust the speed to patrol a wide area. For example, if the child is tired, the control unit can adjust the speed to focus on patrolling rest areas. By adjusting the robot's speed according to the child's emotions, more effective lost child detection becomes possible. 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 control unit may be performed using a generative AI, or not using a generative AI. For example, the control unit can input the child's facial expression data into a generative AI, which can estimate the emotions and adjust the robot's speed.

[0109] The control unit can utilize the voice function of the lost child discovery robot to send messages that provide reassurance to the lost child. For example, the control unit can send a message to the child that says, "It's okay, we'll find you soon." The control unit can send a message to the child that says, "Stay here, help is coming soon." The control unit can send a message to the child that says, "Don't be scared, we'll find you soon." In this way, a sense of security can be provided to the lost child through voice messages. The specific content and method of the reassuring message are set based on, for example, the type of message and the tone of voice. Some or all of the above processing in the control unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the control unit can input message data into a generation AI, and the generation AI can generate and send an appropriate message.

[0110] The control unit can optimize the patrol route of the lost child detection robot in cooperation with other robots in the park. For example, the control unit can enable the lost child detection robot to coordinate with other robots to set an efficient patrol route. For example, the control unit can optimize a wide-area patrol route in cooperation with other robots. For example, the control unit can enable the lost child detection robot to share information with other robots to set an optimal patrol route. This allows for the setting of a more efficient patrol route through cooperation with other robots. The specific methods and criteria for coordinating with other robots are set based on, for example, communication protocols and cooperation algorithms. Some or all of the above-described processes in the control unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the control unit can input data from other robots into a generative AI, which can then set an optimal patrol route.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The monitoring unit can estimate a child's emotions and adjust its monitoring focus based on the estimated emotions. For example, if a child is frightened, it can intensify monitoring of their surroundings to detect abnormal behavior early. If a child is excited, it can focus on monitoring their use of play equipment to ensure their safety. If a child is tired, it can intensify monitoring of rest areas to provide appropriate support. This allows for more effective monitoring by adjusting the monitoring focus according to the child's emotions. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Some or all of the above processing in the monitoring unit may be performed using generative AI or not.

[0113] The monitoring unit can monitor the level of congestion in specific areas within the park and issue warnings if congestion occurs. For example, it can monitor the level of congestion in the playground area in real time and issue a warning if it exceeds a certain number of people. It can also monitor the level of congestion in rest areas and issue a warning if it becomes overcrowded. It can monitor the level of congestion near entrances and issue a warning if entry restrictions are necessary. This allows for monitoring of congestion and issuing warnings at the appropriate time. The specific definition of congestion and monitoring methods are set based on the number of people and the size of the area. Some or all of the above processing in the monitoring unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0114] The monitoring unit can monitor the movements of animals within the park and issue warnings if dangerous animals approach. For example, it can monitor the movements of wild animals that frequent the park and issue warnings if danger is imminent. It can also monitor the movements of animals in pet areas and issue warnings if they intrude into other areas. It can monitor the movements of flocks of birds and issue warnings if unusual behavior is observed. This allows for early detection and warning of the approach of dangerous animals. The specific definition of dangerous animals and detection criteria are set based on specific animal species and behavioral patterns. Some or all of the above processing in the monitoring unit may be performed using generative AI, or it may be performed without using generative AI.

[0115] The monitoring unit can estimate a child's emotions and adjust the viewpoint of the surveillance cameras based on the estimated emotions. For example, if a child is frightened, the viewpoint of the surveillance cameras around them can be adjusted to obtain detailed footage. If a child is excited, the camera viewpoint can be adjusted to monitor the use of play equipment. If a child is tired, the viewpoint of the surveillance cameras in the rest area can be adjusted to provide appropriate support. This allows for the acquisition of more detailed footage by adjusting the viewpoint of the surveillance cameras according to the child's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above processing in the monitoring unit may be performed using generative AI or not.

[0116] The monitoring unit can monitor sounds within the park and issue warnings if unusual sounds occur. For example, if a loud shout is heard in the park, it will detect the anomaly and issue a warning. It will also monitor unusual sounds from playground equipment and issue warnings if there is a possibility of malfunction. It will monitor collision sounds in the park and issue warnings if there is a possibility of an accident. This allows for the early detection of unusual sounds and the issuance of warnings. The specific definition of an unusual sound and the detection criteria are set based on the loudness and frequency of the sound. Some or all of the above processing in the monitoring unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0117] The warning unit can estimate the child's emotions and adjust the content of the warning based on the estimated emotions. For example, if the child is frightened, it will issue a warning in gentle words. If the child is excited, it will issue a warning encouraging them to act calmly. If the child is tired, it will issue a warning encouraging them to take a break. By adjusting the content of the warning according to the child's emotions, more effective warnings can be made. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the processing described above in the warning unit may be performed using generative AI, or it may be performed without using generative AI.

[0118] The warning unit can automatically obtain the guardian's contact information when a warning is issued and make an emergency contact. For example, if a child gets lost, it can automatically obtain the guardian's contact information and make an emergency contact. If a child is engaging in dangerous behavior, it will make an emergency contact with the guardian. If an accident occurs in the park, it will make an emergency contact with the guardian. This allows for quick contact with guardians in emergencies. The specific method and criteria for obtaining the guardian's contact information can be determined by using a registration database to obtain the guardian's contact information and making the contact through the emergency contact system. Some or all of the above-described processes in the warning unit may be performed using a generation AI, or they may be performed without using a generation AI.

[0119] The tracking unit can estimate the child's emotions and adjust the tracking accuracy based on the estimated emotions. For example, if the child is frightened, the tracking accuracy is increased to quickly pinpoint their location. If the child is excited, tracking is performed over a wide area. If the child is tired, tracking is focused on rest areas. By adjusting the tracking accuracy according to the child's emotions, more effective tracking becomes possible. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above processing in the tracking unit may be performed using generative AI or not.

[0120] The tracking unit can select the optimal tracking route by referring to the child's past movement history during tracking. For example, it can select the optimal tracking route based on places the child has frequently visited in the past. It can also select an efficient tracking route by analyzing the child's past movement patterns. It can select a tracking route based on places where the child has gotten lost in the past. This enables efficient tracking by selecting the optimal tracking route based on past movement history. The specific method and criteria for selecting the optimal tracking route are set based on the analysis method of past movement history and the route selection algorithm. Some or all of the above processing in the tracking unit may be performed using generative AI, or it may be performed without using generative AI.

[0121] The tracking unit can monitor the battery level of the child's device during tracking and issue warnings as needed. For example, it can issue a warning if the child's device battery level drops low. It can also notify the guardian if the device's battery level falls below a certain level. If the device's battery level drops low, it can guide the guardian to the location of a charging station. This allows the device to run out of power by monitoring its battery level and issuing warnings as needed. The specific method and criteria for monitoring the battery level are set based on the type of battery and the method for measuring the remaining charge. Some or all of the above-described processes in the tracking unit may be performed using or without generational AI.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The monitoring unit monitors the park's video feed. The monitoring unit uses, for example, AI surveillance cameras to monitor the park's video feed in real time and detect suspicious individuals or dangerous behavior. The AI ​​in the monitoring unit can analyze the video feed and identify suspicious individuals or dangerous behavior. It can also issue warnings if it detects abnormal activity. Step 2: The warning unit issues a warning based on suspicious individuals or dangerous activities detected by the monitoring unit. The warning unit can issue, for example, an audible warning or a visual warning. It can also issue an audible warning using a speaker and a visual warning using a display. Step 3: The tracking unit tracks the child's location. The tracking unit can track the child's location using, for example, GPS or RFID tags. The tracking unit can also provide location information to the guardian and quickly locate the child if they get lost. Step 4: The condition monitoring unit monitors the condition of the playground equipment and the weather. The condition monitoring unit can, for example, use sensors to monitor the condition of the playground equipment. It can also monitor for abnormalities and changes in the weather, and issue warnings if extreme weather occurs. Step 5: The control unit controls the lost child detection robot. The control unit can have the lost child detection robot patrol the park and find lost children.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the monitoring unit, warning unit, tracking unit, status monitoring unit, and control unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit uses the camera 42 of the smart device 14 to monitor video footage of the park in real time, and the control unit 46A detects suspicious persons and dangerous behavior. The warning unit uses the speaker 40B of the smart device 14 to issue audio warnings and the display 40A to issue visual warnings. The tracking unit uses the identification processing unit 290 of the data processing unit 12 to track the child's location using GPS or RFID tags and provides location information to the guardian. The status monitoring unit uses the sensors of the smart device 14 to monitor the condition of playground equipment and weather, and issues a warning if an abnormality occurs. The control unit uses the identification processing unit 290 of the data processing unit 12 to have a lost child detection robot patrol the park and find lost children. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the monitoring unit, warning unit, tracking unit, status monitoring unit, and control unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit uses the camera 42 of the smart glasses 214 to monitor images of the park in real time, and the control unit 46A detects suspicious persons or dangerous behavior. The warning unit uses the speaker 240 of the smart glasses 214 to issue audio warnings and the display to issue visual warnings. The tracking unit uses the identification processing unit 290 of the data processing unit 12 to track the child's location using GPS or RFID tags and provides location information to the guardian. The status monitoring unit uses the sensors of the smart glasses 214 to monitor the condition of playground equipment and weather, and issues a warning if an abnormality occurs. The control unit uses the identification processing unit 290 of the data processing unit 12 to have a lost child detection robot patrol the park and find lost children. The correspondence between each unit and the devices and control unit is not limited to the examples described above, and various modifications are possible.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the monitoring unit, warning unit, tracking unit, status monitoring unit, and control unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit uses the camera 42 of the headset terminal 314 to monitor video footage of the park in real time, and the control unit 46A detects suspicious persons and dangerous behavior. The warning unit uses the speaker 240 of the headset terminal 314 to issue audio warnings and the display 343 to issue visual warnings. The tracking unit uses the identification processing unit 290 of the data processing unit 12 to track the child's location using GPS or RFID tags and provides location information to the guardian. The status monitoring unit uses the sensors of the headset terminal 314 to monitor the condition of playground equipment and weather, and issues a warning if an abnormality occurs. The control unit uses the identification processing unit 290 of the data processing unit 12 to have a lost child detection robot patrol the park and find lost children. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the monitoring unit, warning unit, tracking unit, status monitoring unit, and control unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit uses the camera 42 of the robot 414 to monitor images of the park in real time, and the control unit 46A detects suspicious persons or dangerous behavior. The warning unit uses the speaker 240 of the robot 414 to issue audio warnings and the display to issue visual warnings. The tracking unit uses the identification processing unit 290 of the data processing unit 12 to track the child's location using GPS or RFID tags and provides location information to the guardian. The status monitoring unit uses the sensors of the robot 414 to monitor the condition of playground equipment and weather, and issues a warning if an abnormality occurs. The control unit uses the identification processing unit 290 of the data processing unit 12 to have the lost child detection robot patrol the park and find lost children. The correspondence between each unit and the devices and control unit is not limited to the examples described above, and various modifications are possible.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (Note 1) A surveillance unit that monitors the footage within the park, A warning unit that issues a warning based on suspicious persons or dangerous behavior detected by the aforementioned monitoring unit, A tracking unit that tracks the child's location, A condition monitoring unit that monitors the condition of playground equipment and weather, It comprises a control unit that controls a lost child detection robot, A system characterized by the following features. (Note 2) The aforementioned monitoring unit, The system monitors video footage from within the park in real time to detect suspicious individuals and dangerous behavior. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned warning unit is The monitoring unit issues warnings based on the detection of suspicious individuals or dangerous behavior. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned tracking unit is Track the child's location and provide location information to parents. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned status monitoring unit, It monitors the condition of playground equipment and weather, and issues warnings if any abnormalities occur. The system described in Appendix 1, characterized by the features described herein. (Note 6) The control unit, Control a lost child detection robot and patrol the park to find lost children. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned monitoring unit, Estimate the child's emotions and adjust monitoring focus based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned monitoring unit, The system monitors the level of congestion in specific areas within the park and issues warnings if congestion occurs. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned monitoring unit, The system monitors the movements of animals within the park and issues warnings if dangerous animals approach. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned monitoring unit, The system estimates the child's emotions and adjusts the surveillance camera's viewpoint based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned monitoring unit, The system monitors sounds within the park and issues a warning if any unusual noises are detected. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned monitoring unit, The system monitors the lighting conditions within the park and issues warnings if there is insufficient lighting. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned warning unit is The system estimates the child's emotions and adjusts the content of the warning based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned warning unit is When a warning is issued, the system automatically retrieves parental contact information and makes emergency contact. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned warning unit is When a warning is issued, set the warning priority and apply different warning methods depending on the severity. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned warning unit is It estimates the child's emotions and adjusts the timing of warnings based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned warning unit is When a warning is issued, the type of warning sound will change depending on the situation. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned warning unit is When a warning is issued, the warning message will be displayed in multiple languages. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned tracking unit is The system estimates the child's emotions and adjusts the tracking accuracy based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned tracking unit is During tracking, the system selects the optimal tracking route by referring to the child's past movement history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned tracking unit is During tracking, the system monitors the battery level of the child's device and issues warnings as needed. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned tracking unit is The system estimates the child's emotions and adjusts the frequency of tracking based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned tracking unit is During tracking, the system uses the location information of the child's friends and family to track them. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned tracking unit is When tracking, the tracking method changes depending on the type of device the child is wearing. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned status monitoring unit, The system estimates children's emotions and monitors their use of playground equipment based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned status monitoring unit, The system monitors the deterioration of playground equipment and issues warnings when regular maintenance is needed. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned status monitoring unit, Predicting weather changes and issuing warnings before extreme weather events occur. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned status monitoring unit, The system estimates children's emotions and sets restrictions on the use of playground equipment based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned status monitoring unit, The system monitors the usage of playground equipment and issues warnings if certain equipment is being used excessively. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned status monitoring unit, Monitor weather changes and restrict the use of playground equipment under certain weather conditions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The control unit, The system estimates the child's emotions and adjusts the patrol route of the lost child detection robot based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The control unit, The system monitors the battery level of the lost child detection robot and guides it to a charging station if necessary. The system described in Appendix 1, characterized by the features described herein. (Note 33) The control unit, Analyzing camera footage from lost child detection robots improves the accuracy of identifying lost children. The system described in Appendix 1, characterized by the features described herein. (Note 34) The control unit, The robot estimates the child's emotions and adjusts the speed of the lost child detection robot based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The control unit, The voice function of the lost child locator robot is used to deliver reassuring messages to lost children. The system described in Appendix 1, characterized by the features described herein. (Note 36) The control unit, The patrol routes of the lost child detection robot are optimized in cooperation with other robots in the park. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0196] 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 surveillance unit that monitors the footage within the park, A warning unit that issues a warning based on suspicious persons or dangerous behavior detected by the aforementioned monitoring unit, A tracking unit that tracks the child's location, A condition monitoring unit that monitors the condition of playground equipment and weather, It comprises a control unit that controls a lost child detection robot, A system characterized by the following features.

2. The aforementioned monitoring unit, The system monitors video footage from within the park in real time to detect suspicious individuals and dangerous behavior. The system according to feature 1.

3. The aforementioned warning unit is The monitoring unit issues warnings based on the detection of suspicious individuals or dangerous behavior. The system according to feature 1.

4. The aforementioned tracking unit is Track the child's location and provide location information to parents. The system according to feature 1.

5. The aforementioned status monitoring unit, It monitors the condition of playground equipment and weather, and issues warnings if any abnormalities occur. The system according to feature 1.

6. The control unit, Control a lost child detection robot and patrol the park to find lost children. The system according to feature 1.

7. The aforementioned monitoring unit, Estimate the child's emotions and adjust monitoring focus based on the estimated emotions. The system according to feature 1.

8. The aforementioned monitoring unit, The system monitors the level of congestion in specific areas within the park and issues warnings if congestion occurs. The system according to feature 1.

9. The aforementioned monitoring unit, The system monitors the movements of animals within the park and issues warnings if dangerous animals approach. The system according to feature 1.

10. The aforementioned monitoring unit, The system estimates the child's emotions and adjusts the surveillance camera's viewpoint based on those estimated emotions. The system according to feature 1.

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