system

Abstract

The system according to the embodiment aims to detect suspicious individuals and dangerous behavior in parks at an early stage and prevent lost children. [Solution] A system according to an embodiment includes a monitoring unit, a warning unit, a tracking unit, and a prevention unit. The monitoring unit detects suspicious individuals or dangerous behavior in a park. The warning unit issues a warning based on the information detected by the monitoring unit. The tracking unit tracks the location information of a child or guardian. The prevention unit prevents lost children based on the location information tracked by the tracking unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately detect suspicious individuals or dangerous behavior in parks early or prevent lost children, so there is room for improvement.

[0005] The system according to the embodiment aims to detect suspicious individuals and dangerous behavior in parks at an early stage and prevent lost children. [Means for solving the problem]

[0006] The system according to the embodiment includes a monitoring unit, a warning unit, a tracking unit, and a prevention unit. The monitoring unit detects suspicious individuals or dangerous behavior in a park. The warning unit issues a warning based on the information detected by the monitoring unit. The tracking unit tracks location information of a child or a guardian. The prevention unit prevents a lost child from being found based on the location information tracked by the tracking unit. [Effects of the Invention]

[0007] The system according to the embodiment can detect suspicious individuals and dangerous behavior in parks at an early stage and prevent lost children. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A park safety system according to an embodiment of the present invention is a comprehensive system that utilizes AI to protect the safety of children playing in parks. This park safety system first uses AI surveillance cameras installed in parks to detect suspicious individuals or dangerous behavior using image recognition technology and issue warnings. Next, the AI ​​system connects devices carried by children and their guardians to track their location in real time using GPS and Bluetooth®. Furthermore, sensors are used to monitor the condition of playground equipment in real time and issue warnings if an abnormality is detected. Weather data is also collected in real time and warnings are issued if abnormal weather is detected. Finally, a lost child detection robot patrols the park and notifies parents if a lost child is found. This provides a safer park environment. For example, AI surveillance cameras installed in parks use image recognition technology to detect suspicious individuals or dangerous behavior. For example, algorithms are used to detect people staying in a specific area for a long time or making sudden movements. This allows for early detection of suspicious individuals or dangerous behavior and issues warnings. Next, the AI ​​system links devices held by children and parents to track their location in real time using GPS and Bluetooth. For example, if a child gets lost in a park, parents can check their child's location via their smartphone. This helps prevent lost children. Furthermore, sensors are used to monitor the condition of playground equipment in real time and issue warnings if an abnormality is detected. For example, if a part of the equipment is damaged or if abnormal movement is detected during use, an alert can be issued to prevent accidents. Weather data is also collected in real time and an alert is issued if abnormal weather is detected. For example, if a sudden thunderstorm or strong winds are predicted, an alert can be issued to alert park users. Finally, a lost child detection robot patrols the park and notifies parents if it finds a lost child. For example, if a robot patrols the park and finds a lost child, it can notify parents, allowing the child to be found early. This creates a safer park environment. This park safety system comprehensively protects the safety of children playing in the park.

[0029] A park safety system according to an embodiment includes a monitoring unit, a warning unit, a tracking unit, and a prevention unit. The monitoring unit detects suspicious individuals or dangerous behavior in the park. The monitoring unit detects suspicious individuals or dangerous behavior using, for example, image recognition technology. For example, the monitoring unit uses an algorithm to detect individuals staying in a specific area for a long time or individuals making sudden movements. The monitoring unit can also identify specific individuals using facial recognition technology. For example, the monitoring unit can identify users in the park and identify suspicious individuals using facial recognition technology. The warning unit issues a warning based on information detected by the monitoring unit. The warning unit issues a warning using, for example, an audio warning or a text message. For example, the warning unit can issue an audio warning to alert users in the park. The warning unit can also send a text message to a smartphone to warn parents. The tracking unit tracks location information of the child or parent. The tracking unit tracks location information using, for example, GPS or Bluetooth. For example, the tracking unit tracks the location information of the child in real time using GPS. The tracking unit can also track the location information of the guardian using Bluetooth. The prevention unit prevents the child from being lost based on the location information tracked by the tracking unit. The prevention unit notifies the guardian, for example, if the child gets lost in a park. For example, the prevention unit issues a warning if the child is separated from the guardian for more than a certain period of time. The prevention unit can also issue a notification to the guardian based on the child's location information. As a result, the park safety system according to the embodiment can protect the safety of children by detecting suspicious individuals or dangerous behavior in the park early and issuing a warning.

[0030] The monitoring unit can detect suspicious individuals or dangerous behavior using image recognition technology. Image recognition technology includes, for example, facial recognition, motion recognition, and object detection. For example, the monitoring unit can use facial recognition technology to identify users in a park and identify suspicious individuals. For example, the monitoring unit can use facial recognition technology to identify specific individuals and monitor their behavior. The monitoring unit can also detect specific behavioral patterns using motion recognition technology. For example, the monitoring unit uses an algorithm to detect individuals who stay in a specific area for a long time or individuals who make sudden movements. The monitoring unit can also detect specific objects using object detection technology. For example, the monitoring unit can detect suspicious objects placed in a specific area. This allows for the use of image recognition technology to detect suspicious individuals and dangerous behavior with high accuracy. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input image data into a generation AI and have the generation AI perform image recognition.

[0031] The tracking unit can track the location information of the child or guardian using GPS or Bluetooth. GPS and Bluetooth include, for example, an update frequency and an error range for the location information. The tracking unit can track the location information of the child in real time using GPS. For example, the tracking unit can update the location information of the child every few seconds using GPS. The tracking unit can also track the location information of the guardian using Bluetooth. For example, the tracking unit can track the location information of the guardian within an error range of several meters using Bluetooth. In this way, the location information of the child and guardian can be tracked in real time using GPS or Bluetooth. Some or all of the above-mentioned processing in the tracking unit can be performed using, for example, AI, or can be performed without using AI. For example, the tracking unit can input location information data to a generation AI and cause the generation AI to track the location information.

[0032] The prevention unit can notify a parent or guardian if a child gets lost in a park. Specific definitions and criteria for a lost child include, for example, being separated from a parent or guardian for a certain period of time. The prevention unit issues an alert if a child is separated from a parent or guardian for a certain period of time. For example, the prevention unit notifies a parent or guardian if a child gets lost in a park. The prevention unit can also notify a parent or guardian based on the child's location information. For example, the prevention unit tracks a child's location information in real time and notifies a parent or guardian if the child gets lost. This makes it possible to prevent lost children by quickly notifying a parent or guardian if a child gets lost. Some or all of the above-described processing in the prevention unit may be performed using AI, for example, or may be performed without AI. For example, the prevention unit can input location information data into a generation AI and cause the generation AI to detect and notify a lost child.

[0033] The park safety system includes a monitoring unit that uses sensors to monitor the condition of playground equipment and issues an alert if an abnormality is detected. The monitoring unit monitors the condition of playground equipment using, for example, a temperature sensor, a vibration sensor, a position sensor, etc. For example, the monitoring unit uses a temperature sensor to monitor the surface temperature of the playground equipment and detects abnormally high or low temperatures. The monitoring unit can also monitor the vibration of the playground equipment using a vibration sensor to detect abnormal movement. For example, the monitoring unit can detect abnormal movement of the playground equipment using a vibration sensor and issue an alert. The monitoring unit can also monitor the position of the playground equipment using a position sensor and detect abnormal positional fluctuations. For example, the monitoring unit uses a position sensor to monitor positional fluctuations of the playground equipment and detects abnormal positional fluctuations. This enables early detection of abnormalities in the playground equipment and prevents accidents. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input sensor data to a generation AI and have the generation AI detect abnormalities in the playground equipment.

[0034] The park safety system includes a monitoring unit that collects meteorological data in real time and issues an alert when abnormal weather is detected. The monitoring unit collects meteorological data such as temperature, humidity, and wind speed in real time. For example, the monitoring unit monitors air temperature using a temperature sensor and detects abnormally high or low temperatures. The monitoring unit can also monitor humidity using a humidity sensor and detect abnormal humidity fluctuations. For example, the monitoring unit monitors humidity fluctuations using a humidity sensor and detects abnormal humidity fluctuations. The monitoring unit can also monitor wind speed using a wind speed sensor and detect abnormal wind speed fluctuations. For example, the monitoring unit monitors wind speed fluctuations using a wind speed sensor and detects abnormal wind speed fluctuations. This enables early detection of abnormal weather and alerts users in the park. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit may input meteorological data to a generation AI and cause the generation AI to detect abnormal weather.

[0035] The park safety system includes a lost child detection robot that patrols the park and notifies parents if it finds a lost child. The lost child detection robot patrols the park using, for example, a moving speed, a patrol route, a detection method, etc. For example, the lost child detection robot patrols the park at a constant speed and detects lost children. The lost child detection robot can also set a specific patrol route and patrol along that route. For example, the lost child detection robot patrols key areas of the park and detects lost children. The lost child detection robot can also find lost children using a specific detection method. For example, the lost child detection robot detects lost children using facial recognition technology and notifies parents. In this way, the lost child detection robot patrols the park, finds lost children early, and notifies parents, thereby ensuring the safety of lost children. Some or all of the above-described processing in the lost child detection robot may be performed using, for example, AI, or may be performed without AI. For example, a lost child detection robot can input image data into a generation AI and have the generation AI detect the lost child.

[0036] The monitoring unit can dynamically change the target to be monitored depending on a specific time period or day of the week. The monitoring unit changes the target to be monitored depending on a specific time period or day of the week, such as weekdays and weekends, or daytime and nighttime. For example, the monitoring unit prioritizes children's safety during the daytime on weekdays and focuses on monitoring children's behavior. The monitoring unit can also expand its overall monitoring range on weekends and holidays, as the number of park users increases on weekends and holidays. For example, the monitoring unit monitors the entire park over a wide area on weekends and holidays. The monitoring unit can also enhance suspicious person detection at night and perform highly accurate monitoring even in dark places. For example, the monitoring unit improves the detection accuracy of suspicious persons at night and performs highly accurate monitoring even in dark places. This enables more effective monitoring by changing the target to be monitored depending on the time period or day of the week. Some or all of the above-described processing by the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input time period and day of the week data into the generation AI and cause the generation AI to dynamically change the target to be monitored.

[0037] The monitoring unit can add people wearing specific clothing or belongings to the targets to be detected. For example, the monitoring unit can add people wearing clothing of a specific color or design, or people carrying large bags or backpacks, to the targets to be monitored. For example, the monitoring unit can prioritize monitoring people wearing clothing of a specific color or design. The monitoring unit can also focus on monitoring people carrying large bags or backpacks. For example, the monitoring unit can add people carrying large bags or backpacks to the targets to be monitored and monitor their behavior. The monitoring unit can also add people wearing specific accessories or hats to the targets to be monitored. For example, the monitoring unit can add people wearing specific accessories or hats to the targets to be monitored and monitor their behavior. By adding people wearing specific clothing or belongings to the targets to be monitored, more detailed monitoring is possible. Some or all of the above-described processing by the monitoring unit may be performed using, or without, AI. For example, the monitoring unit can input image data to a generation AI and have the generation AI detect people wearing specific clothing or belongings.

[0038] The monitoring unit can add animal behavior to the detection targets and prevent dangers posed by animals. The monitoring unit, for example, monitors wild animals that appear in a park and the behavior of users with pets. For example, the monitoring unit monitors wild animals that appear in a park and detects dangerous behavior. The monitoring unit can also monitor the behavior of users with pets to prevent the pets from harming children. For example, the monitoring unit monitors the behavior of users with pets to prevent the pets from harming children. The monitoring unit can also monitor the movements of birds and small animals to prevent children from being startled. For example, the monitoring unit monitors the movements of birds and small animals to prevent children from being startled. In this way, dangers posed by animals can be prevented by monitoring animal behavior. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input animal behavior data to a generation AI and cause the generation AI to monitor the animal behavior.

[0039] The monitoring unit can add specific sounds or noises to the detection targets and use voice recognition technology. The monitoring unit can monitor, for example, children's screams or crying, loud noises or collision sounds, suspicious conversations or noises, etc. For example, the monitoring unit can detect children's screams or crying to quickly identify abnormal situations. The monitoring unit can also detect loud noises or collision sounds to monitor for the occurrence of accidents. For example, the monitoring unit can detect loud noises or collision sounds to monitor for the occurrence of accidents. The monitoring unit can also detect suspicious conversations or noises to quickly detect dangerous behavior. For example, the monitoring unit can detect suspicious conversations or noises to quickly detect dangerous behavior. In this way, by monitoring specific sounds or noises, abnormal situations can be quickly identified. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input voice data to a generation AI and have the generation AI perform voice recognition.

[0040] The warning unit can customize the content of the warning to be issued according to the type of danger detected. The warning unit customizes the content of the warning according to the type of danger detected, such as a suspicious person, an abnormality in playground equipment, or abnormal weather. For example, if a suspicious person is detected, the warning unit issues a warning including specific characteristics of the person. Furthermore, if an abnormality in playground equipment is detected, the warning unit can issue a warning including the specific location of the playground equipment and details of the abnormality. For example, if an abnormality in playground equipment is detected, the warning unit can issue a warning including the location of the playground equipment and details of the abnormality. Furthermore, if abnormal weather is detected, the warning unit can issue a warning including specific weather conditions and countermeasures. For example, if a sudden thunderstorm or strong winds is predicted, the warning unit issues a warning including the weather conditions and countermeasures. This allows for more specific warnings by customizing the content of the warning according to the type of danger detected. Some or all of the above-described processing in the warning unit may be performed using, or without, AI. For example, the warning unit can input danger data into a generation AI and have the generation AI customize the content of the warning.

[0041] The warning unit can issue warnings in multiple formats, such as audio, text, and visual. The warning unit provides warnings in multiple formats, such as audio warnings, text messages, and visual displays. For example, the warning unit issues an audio warning to alert people in the vicinity. The warning unit can also send a text warning to a smartphone to notify a parent or guardian. For example, the warning unit can send a text warning to a smartphone to notify a parent or guardian. The warning unit can also display a visual warning on a display in the park to alert all users. For example, the warning unit displays a visual warning on a display in the park to alert all users. In this way, by providing warnings in multiple formats, more users can be alerted. Some or all of the above-described processing in the warning unit may be performed using, or without, AI. For example, the warning unit can input warning data to a generation AI and cause the generation AI to provide a warning method.

[0042] The warning unit can provide the issued warning in multiple languages. The warning unit can provide the warning in major languages ​​such as English, Spanish, and Chinese. For example, the warning unit can provide the warning in major languages ​​such as English, Spanish, and Chinese. The warning unit can also issue a warning in an appropriate language based on a language setting of a park user. For example, the warning unit can issue a warning in an appropriate language based on a language setting of a park user. The warning unit can also provide a warning to a user through a multilingual app. For example, the warning unit can provide a warning to a user through a multilingual app. By providing a warning in multiple languages, it is possible to accommodate users who speak different languages. Some or all of the above-described processing in the warning unit can be performed using, or without, AI. For example, the warning unit can input language data to a generation AI and cause the generation AI to provide a warning in multiple languages.

[0043] The warning unit can display the issued warning on speakers and displays in the park in addition to the parent's device. For example, the warning unit can send a warning to the parent's smartphone and simultaneously issue an audio warning on speakers in the park. For example, the warning unit can send a warning to the parent's smartphone and simultaneously issue an audio warning on speakers in the park. The warning unit can also send a warning to the parent's smartphone and simultaneously display a visual warning on a display in the park. For example, the warning unit can send a warning to the parent's smartphone and simultaneously display a visual warning on a display in the park. The warning unit can also send a warning to the parent's smartphone and simultaneously issue a warning on an announcement system in the park. For example, the warning unit can send a warning to the parent's smartphone and simultaneously issue a warning on an announcement system in the park. This allows the warning to be displayed on speakers and displays in the park, thereby alerting more users. Some or all of the above-mentioned processing in the warning unit may be performed using, for example, AI, or may be performed without AI. For example, the warning unit can input warning data to a generation AI and cause the generation AI to display the warning.

[0044] The tracking unit can limit the tracking target to a specific area or zone. The tracking unit performs tracking by limiting the tracking to a specific area or zone, such as a playground area or a rest area. For example, the tracking unit focuses on tracking a specific playground area where children are playing. The tracking unit can also limit tracking to a specific zone within a park (e.g., a sandbox or slide). For example, the tracking unit limits tracking to a specific zone within a park and tracks the location information of children within that area. The tracking unit can also focus tracking on an area where children are likely to get lost. For example, the tracking unit focuses tracking on an area where children are likely to get lost and tracks the location information of children within that area. This allows for more effective tracking by limiting the tracking to a specific area or zone. Some or all of the above-described processing by the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input area data to the generation AI and cause the generation AI to limit the tracking target.

[0045] The tracking unit can add specific actions or behavior patterns to the target to be tracked. The tracking unit tracks specific actions or behavior patterns, such as running, sitting, or stopping. For example, the tracking unit tracks the actions of a child running around and detects abnormal behavior. The tracking unit can also track the behavior pattern of a child playing with specific playground equipment. For example, the tracking unit tracks the behavior pattern of a child playing with specific playground equipment and monitors that behavior. The tracking unit can also track the behavior pattern of a child staying in a specific area for a long time. For example, the tracking unit tracks the behavior pattern of a child staying in a specific area for a long time and monitors that behavior. By adding specific actions or behavior patterns, abnormal behavior can be detected early. Some or all of the above-mentioned processing in the tracking unit may be performed using, or without, AI. For example, the tracking unit can input behavior data to a generation AI and cause the generation AI to track the actions or behavior patterns.

[0046] The tracking unit can add not only the parent's device but also the devices of other family members and friends to the tracking targets. The tracking unit, for example, provides location information to devices of grandparents, siblings, and friends in addition to the parent's smartphone. For example, the tracking unit provides location information to devices of grandparents and siblings in addition to the parent's smartphone. The tracking unit can also provide location information to devices of friends in addition to the parent's smartphone. For example, the tracking unit provides location information to devices of friends in addition to the parent's smartphone. The tracking unit can also provide location information to devices of childcare workers or teachers in addition to the parent's smartphone. For example, the tracking unit provides location information to devices of childcare workers or teachers in addition to the parent's smartphone. This allows more people to share location information by adding devices of other family members and friends. Some or all of the above-described processing in the tracking unit may be performed using, or without, AI. For example, the tracking unit may input device data to a generation AI and cause the generation AI to add a device.

[0047] The tracking unit can add a specific event or activity to the tracking targets. The tracking unit, for example, tracks participants in an event or activity held in a park. For example, the tracking unit tracks participants in an event or activity held in a park. The tracking unit can also track participants in an activity held at a specific time period. For example, the tracking unit tracks participants in an activity held at a specific time period. The tracking unit can also focus on tracking an area related to a specific event in the park. For example, the tracking unit focuses on tracking an area related to a specific event in the park. In this way, by adding a specific event or activity, the location information of participants can be effectively tracked. Some or all of the above-described processing in the tracking unit may be performed using AI, or may be performed without using AI. For example, the tracking unit can input event data to a generation AI and cause the generation AI to track the event or activity.

[0048] The prevention unit can customize the content of the notification to be issued depending on the circumstances of the lost child. The prevention unit customizes the content of the notification depending on circumstances such as the location, time, and behavior of the lost child. For example, if a child gets lost in a specific area, the prevention unit issues a notification including detailed information about the area. Furthermore, if a child gets lost during a specific time period, the prevention unit can issue a notification taking into account the circumstances of that time period. For example, if a child gets lost during a specific time period, the prevention unit can issue a notification taking into account the circumstances of that time period. Furthermore, if a child gets lost due to a specific behavioral pattern, the prevention unit can issue a notification based on that behavioral pattern. For example, if a child gets lost due to a specific behavioral pattern, the prevention unit issues a notification based on that behavioral pattern. This allows for more specific notifications by customizing the content of the notification depending on the circumstances of the lost child. Some or all of the above-described processing in the prevention unit may be performed using, for example, AI, or may be performed without AI. For example, the prevention unit can input situation data into a generation AI and cause the generation AI to customize the content of the notification.

[0049] The prevention unit can provide notification methods in multiple formats, such as audio, text, and visual. The prevention unit provides notification in multiple formats, such as audio notification, text message, and visual display. For example, the prevention unit issues an audio notification to quickly inform the parent or guardian. The prevention unit can also send a text notification to a smartphone to provide detailed information. For example, the prevention unit can send a text notification to a smartphone to provide detailed information. The prevention unit can also display a visual notification on a display in the park to alert all users. For example, the prevention unit displays a visual notification on a display in the park to alert all users. In this way, by providing notifications in multiple formats, more users can be alerted. Some or all of the above-described processing in the prevention unit may be performed using AI, or may be performed without using AI. For example, the prevention unit can input notification data to a generation AI and cause the generation AI to provide a notification method.

[0050] The prevention unit can provide the issued notification in multiple languages. The prevention unit can provide the notification in a primary language, such as English, Spanish, or Chinese. For example, the prevention unit can provide the notification in a primary language, such as English, Spanish, or Chinese. The prevention unit can also issue the notification in an appropriate language based on a language setting of a park user. For example, the prevention unit can issue the notification in an appropriate language based on a language setting of a park user. The prevention unit can also provide the notification to the user through a multilingual app. For example, the prevention unit can provide the notification to the user through a multilingual app. By providing the notification in multiple languages, it is possible to accommodate users who speak different languages. Some or all of the above-described processing in the prevention unit can be performed using AI, for example, or without AI. For example, the prevention unit can input language data to a generation AI and cause the generation AI to provide the notification in multiple languages.

[0051] The prevention unit can display the issued notification on speakers or displays in the park in addition to the parent's device. For example, the prevention unit can send a notification to the parent's smartphone and simultaneously issue an audio notification on a speaker in the park. For example, the prevention unit can send a notification to the parent's smartphone and simultaneously issue an audio notification on a speaker in the park. The prevention unit can also send a notification to the parent's smartphone and simultaneously display a visual notification on a display in the park. For example, the prevention unit can send a notification to the parent's smartphone and simultaneously display a visual notification on a display in the park. The prevention unit can also send a notification to the parent's smartphone and simultaneously issue a notification on an announcement system in the park. For example, the prevention unit can send a notification to the parent's smartphone and simultaneously issue a notification on an announcement system in the park. This allows the notification to be displayed on speakers or displays in the park, thereby alerting more users. Some or all of the above-mentioned processing in the prevention unit may be performed using AI, for example, or may be performed without AI. For example, the prevention unit can input notification data to a generation AI and cause the generation AI to display the notification.

[0052] The playground equipment monitoring unit can dynamically change the target of monitoring depending on a specific time period or day of the week. The playground equipment monitoring unit changes the target of monitoring depending on a specific time period or day of the week, such as weekdays and weekends, or daytime and nighttime. For example, the playground equipment monitoring unit prioritizes children's safety during the daytime on weekdays and focuses on monitoring the condition of the playground equipment. The playground equipment monitoring unit can also expand its overall monitoring range on weekends and holidays, as the number of park users increases on weekends and holidays. For example, the playground equipment monitoring unit monitors the entire park over a wide area on weekends and holidays. The playground equipment monitoring unit can also intensify monitoring for damage or abnormalities in playground equipment at night. For example, the playground equipment monitoring unit intensifies monitoring for damage or abnormalities in playground equipment at night. This enables more effective monitoring by changing the target of monitoring depending on the time period or day of the week. Some or all of the above-described processing by the playground equipment monitoring unit may be performed using, or without, AI. For example, the playground equipment monitoring unit can input time period and day of the week data into the generation AI and cause the generation AI to dynamically change the target of monitoring.

[0053] The play equipment monitoring unit can add the frequency of use and status of specific play equipment to the targets of monitoring. For example, the play equipment monitoring unit may focus on monitoring frequently used play equipment to detect abnormalities early. For example, the play equipment monitoring unit may focus on monitoring frequently used play equipment to detect abnormalities early. The play equipment monitoring unit can also monitor the status (e.g., wear and tear) of specific play equipment and notify users of required maintenance. For example, the play equipment monitoring unit may monitor the status of specific play equipment and notify users of required maintenance. The play equipment monitoring unit can also periodically monitor less frequently used play equipment to detect abnormalities. For example, the play equipment monitoring unit may periodically monitor less frequently used play equipment to detect abnormalities. In this way, by monitoring the frequency of use and status of specific play equipment, abnormalities can be detected early. Some or all of the above-described processing by the play equipment monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the play equipment monitoring unit can input usage frequency and status data into the generation AI and have the generation AI monitor the play equipment.

[0054] The play equipment monitoring unit can add the temperature and humidity of the play equipment to the objects it monitors. The play equipment monitoring unit, for example, monitors the surface temperature of the play equipment and the surrounding humidity to detect abnormal conditions early. For example, the play equipment monitoring unit monitors the surface temperature of the play equipment and detects abnormally high or low temperatures. The play equipment monitoring unit can also monitor the humidity around the play equipment to detect slippery conditions. For example, the play equipment monitoring unit monitors the humidity around the play equipment to detect slippery conditions. The play equipment monitoring unit can also monitor the internal temperature of the play equipment and detect abnormal temperature changes. For example, the play equipment monitoring unit monitors the internal temperature of the play equipment and detects abnormal temperature changes. In this way, by monitoring the temperature and humidity of the play equipment, abnormal conditions can be detected early. Some or all of the above-mentioned processing in the play equipment monitoring unit may be performed using, or without, AI. For example, the play equipment monitoring unit can input temperature and humidity data to the generation AI and cause the generation AI to monitor the temperature and humidity.

[0055] The play equipment monitoring unit can add the age and weight of a user of the play equipment to the targets to be monitored. The play equipment monitoring unit, for example, monitors the age and weight of a user of the play equipment and sets appropriate usage restrictions. For example, the play equipment monitoring unit monitors the age and weight of a user of the play equipment and sets appropriate age restrictions. The play equipment monitoring unit can also monitor the weight of a user of the play equipment and set appropriate weight restrictions. For example, the play equipment monitoring unit monitors the weight of a user of the play equipment and sets appropriate weight restrictions. The play equipment monitoring unit can also combine the age and weight of a user of the play equipment to set appropriate usage restrictions. For example, the play equipment monitoring unit combines the age and weight of a user of the play equipment and sets appropriate usage restrictions. In this way, appropriate usage restrictions can be set by monitoring the age and weight of a user of the play equipment. Some or all of the above-described processing in the play equipment monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the play equipment monitoring unit can input age and weight data into a generation AI and have the generation AI set usage restrictions.

[0056] The weather monitoring unit can dynamically change the data it collects depending on a specific time period or season. The weather monitoring unit changes the collected data depending on a specific time period or season, such as summer, winter, daytime, or nighttime. For example, the weather monitoring unit may focus on collecting temperature and UV index in summer. The weather monitoring unit may also focus on collecting temperature and snowfall in winter. For example, the weather monitoring unit may focus on collecting temperature and snowfall in winter. The weather monitoring unit may also change the weather data collection items depending on a specific time period (e.g., daytime or nighttime). For example, the weather monitoring unit may focus on collecting temperature and humidity during the daytime and wind speed and temperature at night. This enables more effective weather monitoring by changing the collected data depending on the time period or season. Some or all of the above-described processing in the weather monitoring unit may be performed using, or without, AI. For example, the weather monitoring unit may input time period and seasonal data into the generation AI and cause the generation AI to dynamically change the collected data.

[0057] The weather monitoring unit can add specific weather conditions and forecasts to the collected data. The weather monitoring unit collects specific weather conditions and forecasts, such as typhoons, heavy rain, and extreme heat. For example, the weather monitoring unit collects forecasts of sudden thunderstorms and issues a warning. The weather monitoring unit can also collect forecasts of strong winds and issue a warning. For example, the weather monitoring unit collects forecasts of strong winds and issues a warning. The weather monitoring unit can also collect forecasts of abnormal temperature changes and issue a warning. For example, the weather monitoring unit collects forecasts of abnormal temperature changes and issues a warning. By adding specific weather conditions and forecasts, abnormal weather can be detected early. Some or all of the above-mentioned processing in the weather monitoring unit may be performed using, or without, AI. For example, the weather monitoring unit can input weather condition and forecast data to the generation AI and cause the generation AI to collect the weather conditions and forecasts.

[0058] The weather monitoring unit can add the UV index to the data it collects in addition to temperature and humidity. The weather monitoring unit collects weather data such as temperature, humidity, and UV index. For example, the weather monitoring unit collects the UV index in addition to temperature and humidity and issues a warning. The weather monitoring unit can also issue a warning recommending the use of sunscreen when the UV index is high. For example, the weather monitoring unit issues a warning recommending the use of sunscreen when the UV index is high. The weather monitoring unit can also provide normal weather data when the UV index is low. For example, the weather monitoring unit provides normal weather data when the UV index is low. In this way, collecting the UV index in addition to temperature and humidity can provide more detailed weather information. Some or all of the above-mentioned processing in the weather monitoring unit may be performed using AI, for example, or without AI. For example, the weather monitoring unit can input UV index data to the generation AI and cause the generation AI to collect the UV index.

[0059] The weather monitoring unit can add park use restrictions based on weather conditions to the collected data. The weather monitoring unit collects park use restrictions based on weather conditions, such as strong winds, heavy rain, and high temperatures. For example, the weather monitoring unit issues a warning restricting the use of specific playground equipment when strong winds occur. The weather monitoring unit can also issue a warning restricting the use of the entire park when heavy rain occurs. For example, the weather monitoring unit issues a warning restricting the use of the entire park when heavy rain occurs. The weather monitoring unit can also issue a warning restricting the use of specific areas when temperatures are high. For example, the weather monitoring unit issues a warning restricting the use of specific areas when temperatures are high. This allows users to ensure their safety by adding park use restrictions based on weather conditions. Some or all of the above-described processing by the weather monitoring unit may be performed using AI, for example, or without AI. For example, the weather monitoring unit can input weather condition data into a generation AI and have the generation AI execute park use restrictions.

[0060] The lost child detection robot can dynamically change the area it patrols depending on a specific time of day or day of the week. The lost child detection robot changes its patrol area depending on a specific time of day or day of the week, such as weekdays and weekends, or daytime and nighttime. For example, the lost child detection robot may focus on patrolling areas with many children during the daytime on weekdays. The lost child detection robot can also patrol a wider area of ​​the entire park on weekends and holidays. For example, the lost child detection robot may patrol a wider area of ​​the entire park on weekends and holidays. The lost child detection robot can also focus on patrolling a specific area at night. For example, the lost child detection robot may focus on patrolling a specific area at night. This allows for more effective patrols by changing the patrol area depending on the time of day or day of the week. Some or all of the above-described processing in the lost child detection robot may be performed using, or without, AI. For example, the lost child detection robot can input time of day and day of the week data into a generation AI and have the generation AI dynamically change its patrol area.

[0061] The lost child detection robot can add children wearing specific clothing or belongings to the detection targets. The lost child detection robot detects, for example, children wearing clothing of a specific color or design, or children carrying specific belongings (e.g., backpacks, hats). For example, the lost child detection robot prioritizes detecting children wearing clothing of a specific color or design. The lost child detection robot can also prioritize detecting children carrying specific belongings (e.g., backpacks, hats). For example, the lost child detection robot prioritizes detecting children carrying specific belongings. The lost child detection robot can also detect children wearing specific accessories or shoes. For example, the lost child detection robot detects children wearing specific accessories or shoes. This allows for the detection of children carrying specific clothing or belongings, enabling the lost child to be found more quickly. Some or all of the above-described processing in the lost child detection robot may be performed using, for example, AI, or may be performed without AI. For example, the lost child detection robot can input clothing and belonging data into the generation AI and have the generation AI detect children carrying specific clothing or belongings.

[0062] The lost child detection robot can add specific play equipment or facilities to the target area it patrols. For example, the lost child detection robot patrols around specific play equipment (e.g., slides, swings) or specific facilities in the park (e.g., restrooms, rest areas). For example, the lost child detection robot may focus its patrol around specific play equipment. The lost child detection robot can also patrol specific facilities in the park. For example, the lost child detection robot patrols specific facilities in the park. The lost child detection robot can also patrol areas where children tend to gather (e.g., sandboxes, fountains). For example, the lost child detection robot patrols areas where children tend to gather. In this way, by patrolling specific play equipment and facilities, more effective detection of lost children is possible. Some or all of the above-described processing in the lost child detection robot may be performed using, or without, AI. For example, the lost child detection robot can input play equipment and facility data into a generation AI and have the generation AI set the patrol area.

[0063] A lost child finding robot can add specific sounds or noises to the detection targets and use voice recognition technology. A lost child finding robot can detect, for example, a child's screams or cries, loud noises or collision sounds, suspicious conversations or noises, etc. For example, a lost child finding robot can detect a child's screams or cries and respond quickly. A lost child finding robot can also detect loud noises or collision sounds to detect abnormalities early. For example, a lost child finding robot can detect loud noises or collision sounds to detect abnormalities early. A lost child finding robot can also detect suspicious conversations or noises to identify dangerous situations early. For example, a lost child finding robot can detect suspicious conversations or noises to identify dangerous situations early. By detecting specific sounds or noises, an abnormal situation can be identified early. Some or all of the above-described processing in a lost child finding robot may be performed using, for example, AI, or may be performed without using AI. For example, a lost child finding robot can input voice data into a generation AI and have the generation AI perform voice recognition.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The monitoring unit can dynamically change the monitoring target depending on the time of day or day of the week. For example, during the daytime on weekdays, priority is placed on children's safety, and monitoring of children's behavior is prioritized. Also, since the number of park users increases on weekends and holidays, the overall monitoring range can be expanded. For example, the entire park can be monitored over a wide area on weekends and holidays. Also, suspicious person detection can be strengthened at night, allowing for highly accurate monitoring even in dark places. For example, the detection accuracy of suspicious people can be improved at night, allowing for highly accurate monitoring even in dark places. This allows for more effective monitoring by changing the monitoring target depending on the time of day or day of the week.

[0066] The monitoring unit can add people wearing specific clothing or carrying specific items to the targets to be detected. For example, people wearing clothing of a specific color or design, or people carrying large bags or backpacks, can be added to the targets of monitoring. For example, people wearing clothing of a specific color or design can be monitored as a priority. It is also possible to focus on monitoring people carrying large bags or backpacks. For example, people carrying large bags or backpacks can be added to the targets of monitoring and their behavior can be monitored. It is also possible to add people wearing specific accessories or hats to the targets of monitoring. For example, people wearing specific accessories or hats can be added to the targets of monitoring and their behavior can be monitored. In this way, by adding people wearing specific clothing or carrying specific items to the targets of monitoring, more detailed monitoring is possible.

[0067] The tracking unit can limit the target to be tracked to a specific area or zone. For example, tracking can be limited to a specific area or zone such as a playground area or rest area. For example, tracking can be focused on a specific playground area where children are playing. Tracking can also be limited to a specific zone within a park (e.g., a sandbox or slide). For example, tracking can be focused on a specific zone within a park and the location information of children within that area. Tracking can also be focused on areas where children are likely to get lost. For example, tracking can be focused on areas where children are likely to get lost and the location information of children within that area. In this way, more effective tracking is possible by limiting tracking to a specific area or zone.

[0068] The prevention unit can customize the content of the notification to be issued depending on the circumstances of the lost child. For example, the notification content can be customized depending on circumstances such as the location, time, and behavior of the lost child. For example, if a child gets lost in a specific area, a notification including detailed information about that area can be issued. Also, if a child gets lost during a specific time period, a notification can be issued taking into account the circumstances of that time period. For example, if a child gets lost during a specific time period, a notification can be issued taking into account the circumstances of that time period. Also, if a child gets lost due to a specific behavior pattern, a notification based on that behavior pattern can be issued. For example, if a child gets lost due to a specific behavior pattern, a notification based on that behavior pattern can be issued. In this way, by customizing the notification content depending on the circumstances of the lost child, more specific notifications can be provided.

[0069] The prevention unit can provide notification in multiple formats, such as audio, text, and visual. For example, notification can be provided in multiple formats, such as audio notification, text message, and visual display. For example, an audio notification can be issued to quickly inform parents or guardians. A text notification can also be sent to a smartphone to provide detailed information. For example, a text notification can be sent to a smartphone to provide detailed information. A visual notification can also be displayed on a display in the park to alert all users. For example, a visual notification can be displayed on a display in the park to alert all users. In this way, by providing notification in multiple formats, more users can be alerted.

[0070] The processing flow of the first embodiment will be briefly explained below.

[0071] Step 1: The monitoring unit detects suspicious or dangerous behavior within the park. The monitoring unit uses algorithms that use image recognition technology to detect people who spend a long time in a specific area or make sudden movements. It can also use facial recognition technology to identify specific people and identify suspicious behavior. Step 2: The warning unit issues a warning based on the information detected by the monitoring unit. The warning unit issues a warning to users and parents in the park using an audio warning or a text message. Step 3: The tracking unit tracks the location information of the child or guardian. The tracking unit tracks the location information in real time using GPS or Bluetooth. Step 4: The prevention unit prevents the child from being lost based on the location information tracked by the tracking unit. The prevention unit issues a warning if the child is separated from the parent for more than a certain period of time, and notifies the parent based on the child's location information.

[0072] (Example 2) A park safety system according to an embodiment of the present invention is a comprehensive system that utilizes AI to protect the safety of children playing in parks. In this park safety system, AI surveillance cameras installed in parks use image recognition technology to detect suspicious individuals and dangerous behavior and issue warnings. Next, devices carried by children and parents are linked to the AI ​​system, tracking their location in real time using GPS and Bluetooth. Furthermore, sensors are used to monitor the condition of playground equipment in real time and issue warnings if an abnormality is detected. Weather data is also collected in real time and warnings are issued if abnormal weather is detected. Finally, a lost child detection robot patrols the park and notifies parents if a lost child is found. This provides a safer park environment. For example, AI surveillance cameras installed in parks use image recognition technology to detect suspicious individuals and dangerous behavior. For example, algorithms are used to detect people staying in a specific area for a long time or making sudden movements. This allows for early detection of suspicious individuals and dangerous behavior and issues warnings. Next, devices carried by children and parents are linked to the AI ​​system, tracking their location in real time using GPS and Bluetooth. For example, if a child gets lost in a park, parents can check the child's location via their smartphone. This helps prevent lost children. Furthermore, sensors are used to monitor the condition of playground equipment in real time, and an alert is issued if an abnormality is detected. For example, if part of the equipment is damaged or if abnormal movement is detected during use, an alert can be issued to prevent accidents. Weather data is also collected in real time, and an alert is issued if abnormal weather is detected. For example, if a sudden thunderstorm or strong winds are predicted, an alert can be issued to alert park users. Finally, a lost child detection robot patrols the park and notifies parents if it finds a lost child. For example, if a robot patrols the park and finds a lost child, it can notify parents, allowing the child to be found early. This provides a safer park environment. The park safety system thus comprehensively protects the safety of children playing in the park.

[0073] A park safety system according to an embodiment includes a monitoring unit, a warning unit, a tracking unit, and a prevention unit. The monitoring unit detects suspicious individuals or dangerous behavior in the park. The monitoring unit detects suspicious individuals or dangerous behavior using, for example, image recognition technology. For example, the monitoring unit uses an algorithm to detect individuals staying in a specific area for a long time or individuals making sudden movements. The monitoring unit can also identify specific individuals using facial recognition technology. For example, the monitoring unit can identify users in the park and identify suspicious individuals using facial recognition technology. The warning unit issues a warning based on information detected by the monitoring unit. The warning unit issues a warning using, for example, an audio warning or a text message. For example, the warning unit can issue an audio warning to alert users in the park. The warning unit can also send a text message to a smartphone to warn parents. The tracking unit tracks location information of the child or parent. The tracking unit tracks location information using, for example, GPS or Bluetooth. For example, the tracking unit tracks the location information of the child in real time using GPS. The tracking unit can also track the location information of the guardian using Bluetooth. The prevention unit prevents the child from being lost based on the location information tracked by the tracking unit. The prevention unit notifies the guardian, for example, if the child gets lost in a park. For example, the prevention unit issues a warning if the child is separated from the guardian for more than a certain period of time. The prevention unit can also issue a notification to the guardian based on the child's location information. As a result, the park safety system according to the embodiment can protect the safety of children by detecting suspicious individuals or dangerous behavior in the park early and issuing a warning.

[0074] The monitoring unit can detect suspicious individuals or dangerous behavior using image recognition technology. Image recognition technology includes, for example, facial recognition, motion recognition, and object detection. For example, the monitoring unit can use facial recognition technology to identify users in a park and identify suspicious individuals. For example, the monitoring unit can use facial recognition technology to identify specific individuals and monitor their behavior. The monitoring unit can also detect specific behavioral patterns using motion recognition technology. For example, the monitoring unit uses an algorithm to detect individuals who stay in a specific area for a long time or individuals who make sudden movements. The monitoring unit can also detect specific objects using object detection technology. For example, the monitoring unit can detect suspicious objects placed in a specific area. This allows for the use of image recognition technology to detect suspicious individuals and dangerous behavior with high accuracy. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input image data into a generation AI and have the generation AI perform image recognition.

[0075] The tracking unit can track the location information of the child or guardian using GPS or Bluetooth. GPS and Bluetooth include, for example, an update frequency and an error range for the location information. The tracking unit can track the location information of the child in real time using GPS. For example, the tracking unit can update the location information of the child every few seconds using GPS. The tracking unit can also track the location information of the guardian using Bluetooth. For example, the tracking unit can track the location information of the guardian within an error range of several meters using Bluetooth. In this way, the location information of the child and guardian can be tracked in real time using GPS or Bluetooth. Some or all of the above-mentioned processing in the tracking unit can be performed using, for example, AI, or can be performed without using AI. For example, the tracking unit can input location information data to a generation AI and cause the generation AI to track the location information.

[0076] The prevention unit can notify a parent or guardian if a child gets lost in a park. Specific definitions and criteria for a lost child include, for example, being separated from a parent or guardian for a certain period of time. The prevention unit issues an alert if a child is separated from a parent or guardian for a certain period of time. For example, the prevention unit notifies a parent or guardian if a child gets lost in a park. The prevention unit can also notify a parent or guardian based on the child's location information. For example, the prevention unit tracks a child's location information in real time and notifies a parent or guardian if the child gets lost. This makes it possible to prevent lost children by quickly notifying a parent or guardian if a child gets lost. Some or all of the above-described processing in the prevention unit may be performed using AI, for example, or may be performed without AI. For example, the prevention unit can input location information data into a generation AI and cause the generation AI to detect and notify a lost child.

[0077] The park safety system includes a monitoring unit that uses sensors to monitor the condition of playground equipment and issues an alert if an abnormality is detected. The monitoring unit monitors the condition of playground equipment using, for example, a temperature sensor, a vibration sensor, a position sensor, etc. For example, the monitoring unit uses a temperature sensor to monitor the surface temperature of the playground equipment and detects abnormally high or low temperatures. The monitoring unit can also monitor the vibration of the playground equipment using a vibration sensor to detect abnormal movement. For example, the monitoring unit can detect abnormal movement of the playground equipment using a vibration sensor and issue an alert. The monitoring unit can also monitor the position of the playground equipment using a position sensor and detect abnormal positional fluctuations. For example, the monitoring unit uses a position sensor to monitor positional fluctuations of the playground equipment and detects abnormal positional fluctuations. This enables early detection of abnormalities in the playground equipment and prevents accidents. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input sensor data to a generation AI and have the generation AI detect abnormalities in the playground equipment.

[0078] The park safety system includes a monitoring unit that collects meteorological data in real time and issues an alert when abnormal weather is detected. The monitoring unit collects meteorological data such as temperature, humidity, and wind speed in real time. For example, the monitoring unit monitors air temperature using a temperature sensor and detects abnormally high or low temperatures. The monitoring unit can also monitor humidity using a humidity sensor and detect abnormal humidity fluctuations. For example, the monitoring unit monitors humidity fluctuations using a humidity sensor and detects abnormal humidity fluctuations. The monitoring unit can also monitor wind speed using a wind speed sensor and detect abnormal wind speed fluctuations. For example, the monitoring unit monitors wind speed fluctuations using a wind speed sensor and detects abnormal wind speed fluctuations. This enables early detection of abnormal weather and alerts users in the park. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit may input meteorological data to a generation AI and cause the generation AI to detect abnormal weather.

[0079] The park safety system includes a lost child detection robot that patrols the park and notifies parents if it finds a lost child. The lost child detection robot patrols the park using, for example, a moving speed, a patrol route, a detection method, etc. For example, the lost child detection robot patrols the park at a constant speed and detects lost children. The lost child detection robot can also set a specific patrol route and patrol along that route. For example, the lost child detection robot patrols key areas of the park and detects lost children. The lost child detection robot can also find lost children using a specific detection method. For example, the lost child detection robot detects lost children using facial recognition technology and notifies parents. In this way, the lost child detection robot patrols the park, finds lost children early, and notifies parents, thereby ensuring the safety of lost children. Some or all of the above-described processing in the lost child detection robot may be performed using, for example, AI, or may be performed without AI. For example, a lost child detection robot can input image data into a generation AI and have the generation AI detect the lost child.

[0080] The monitoring unit can estimate the child's emotions and adjust the detection accuracy of suspicious individuals or dangerous behavior based on the estimated child's emotions. The monitoring unit uses, for example, facial expression recognition, voice analysis, behavioral patterns, etc. to estimate the child's emotions. For example, the monitoring unit estimates the child's emotions using facial expression recognition technology. For example, the monitoring unit analyzes the child's facial expression data to estimate emotions such as fear, excitement, and relaxation. The monitoring unit can also estimate the child's emotions using voice analysis technology. For example, the monitoring unit analyzes the tone and speed of the child's voice to estimate the emotion. The monitoring unit can also analyze behavioral patterns to estimate the child's emotions. For example, the monitoring unit analyzes the child's movements and behavioral patterns to estimate the emotion. This enables more appropriate monitoring by adjusting the detection accuracy according to the child's emotions. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input emotion data to a generation AI and cause the generation AI to estimate emotions and adjust the detection accuracy.

[0081] The monitoring unit can dynamically change the target to be monitored depending on a specific time period or day of the week. The monitoring unit changes the target to be monitored depending on a specific time period or day of the week, such as weekdays and weekends, or daytime and nighttime. For example, the monitoring unit prioritizes children's safety during the daytime on weekdays and focuses on monitoring children's behavior. The monitoring unit can also expand its overall monitoring range on weekends and holidays, as the number of park users increases on weekends and holidays. For example, the monitoring unit monitors the entire park over a wide area on weekends and holidays. The monitoring unit can also enhance suspicious person detection at night and perform highly accurate monitoring even in dark places. For example, the monitoring unit improves the detection accuracy of suspicious persons at night and performs highly accurate monitoring even in dark places. This enables more effective monitoring by changing the target to be monitored depending on the time period or day of the week. Some or all of the above-described processing by the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input time period and day of the week data into the generation AI and cause the generation AI to dynamically change the target to be monitored.

[0082] The monitoring unit can add people wearing specific clothing or belongings to the targets to be detected. For example, the monitoring unit can add people wearing clothing of a specific color or design, or people carrying large bags or backpacks, to the targets to be monitored. For example, the monitoring unit can prioritize monitoring people wearing clothing of a specific color or design. The monitoring unit can also focus on monitoring people carrying large bags or backpacks. For example, the monitoring unit can add people carrying large bags or backpacks to the targets to be monitored and monitor their behavior. The monitoring unit can also add people wearing specific accessories or hats to the targets to be monitored. For example, the monitoring unit can add people wearing specific accessories or hats to the targets to be monitored and monitor their behavior. By adding people wearing specific clothing or belongings to the targets to be monitored, more detailed monitoring is possible. Some or all of the above-described processing by the monitoring unit may be performed using, or without, AI. For example, the monitoring unit can input image data to a generation AI and have the generation AI detect people wearing specific clothing or belongings.

[0083] The monitoring unit can estimate the child's emotions and adjust the viewing angle of the surveillance camera based on the estimated child's emotions. The monitoring unit can use, for example, facial expression recognition, voice analysis, behavioral patterns, etc. to estimate the child's emotions. For example, the monitoring unit can estimate the child's emotions using facial expression recognition technology. For example, the monitoring unit can analyze the child's facial expression data to estimate emotions such as fear, excitement, and relaxation. The monitoring unit can also estimate the child's emotions using voice analysis technology. For example, the monitoring unit can analyze the tone and speed of the child's voice to estimate the emotions. The monitoring unit can also analyze behavioral patterns to estimate the child's emotions. For example, the monitoring unit can analyze the child's movements and behavioral patterns to estimate the emotions. This enables more appropriate monitoring by adjusting the viewing angle of the surveillance camera according to the child's emotions. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input emotion data to a generation AI and have the generation AI perform emotion estimation and view angle adjustment.

[0084] The monitoring unit can add animal behavior to the detection targets and prevent dangers posed by animals. The monitoring unit, for example, monitors wild animals that appear in a park and the behavior of users with pets. For example, the monitoring unit monitors wild animals that appear in a park and detects dangerous behavior. The monitoring unit can also monitor the behavior of users with pets to prevent the pets from harming children. For example, the monitoring unit monitors the behavior of users with pets to prevent the pets from harming children. The monitoring unit can also monitor the movements of birds and small animals to prevent children from being startled. For example, the monitoring unit monitors the movements of birds and small animals to prevent children from being startled. In this way, dangers posed by animals can be prevented by monitoring animal behavior. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input animal behavior data to a generation AI and cause the generation AI to monitor the animal behavior.

[0085] The monitoring unit can add specific sounds or noises to the detection targets and use voice recognition technology. The monitoring unit can monitor, for example, children's screams or crying, loud noises or collision sounds, suspicious conversations or noises, etc. For example, the monitoring unit can detect children's screams or crying to quickly identify abnormal situations. The monitoring unit can also detect loud noises or collision sounds to monitor for the occurrence of accidents. For example, the monitoring unit can detect loud noises or collision sounds to monitor for the occurrence of accidents. The monitoring unit can also detect suspicious conversations or noises to quickly detect dangerous behavior. For example, the monitoring unit can detect suspicious conversations or noises to quickly detect dangerous behavior. In this way, by monitoring specific sounds or noises, abnormal situations can be quickly identified. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input voice data to a generation AI and have the generation AI perform voice recognition.

[0086] The warning unit can estimate the parent's emotions and adjust the intensity and frequency of the warning based on the estimated parent's emotions. The warning unit can use, for example, facial expression recognition, voice analysis, behavioral patterns, etc. to estimate the parent's emotions. For example, the warning unit can estimate the parent's emotions using facial expression recognition technology. For example, the warning unit can analyze the parent's facial expression data to estimate emotions such as anxiety, relaxation, or hurry. The warning unit can also estimate the parent's emotions using voice analysis technology. For example, the warning unit can analyze the parent's tone and speed of voice to estimate emotions. The warning unit can also analyze behavioral patterns to estimate the parent's emotions. For example, the warning unit can analyze the parent's movements and behavioral patterns to estimate emotions. This enables more appropriate warnings by adjusting the intensity and frequency of the warning according to the parent's emotions. Some or all of the above-described processing in the warning unit can be performed using, for example, AI, or without AI. For example, the warning unit can input emotion data to a generation AI and cause the generation AI to estimate emotions and adjust the intensity and frequency of the warning.

[0087] The warning unit can customize the content of the warning to be issued according to the type of danger detected. The warning unit customizes the content of the warning according to the type of danger detected, such as a suspicious person, an abnormality in playground equipment, or abnormal weather. For example, if a suspicious person is detected, the warning unit issues a warning including specific characteristics of the person. Furthermore, if an abnormality in playground equipment is detected, the warning unit can issue a warning including the specific location of the playground equipment and details of the abnormality. For example, if an abnormality in playground equipment is detected, the warning unit can issue a warning including the location of the playground equipment and details of the abnormality. Furthermore, if abnormal weather is detected, the warning unit can issue a warning including specific weather conditions and countermeasures. For example, if a sudden thunderstorm or strong winds is predicted, the warning unit issues a warning including the weather conditions and countermeasures. This allows for more specific warnings by customizing the content of the warning according to the type of danger detected. Some or all of the above-described processing in the warning unit may be performed using, or without, AI. For example, the warning unit can input danger data into a generation AI and have the generation AI customize the content of the warning.

[0088] The warning unit can issue warnings in multiple formats, such as audio, text, and visual. The warning unit provides warnings in multiple formats, such as audio warnings, text messages, and visual displays. For example, the warning unit issues an audio warning to alert people in the vicinity. The warning unit can also send a text warning to a smartphone to notify a parent or guardian. For example, the warning unit can send a text warning to a smartphone to notify a parent or guardian. The warning unit can also display a visual warning on a display in the park to alert all users. For example, the warning unit displays a visual warning on a display in the park to alert all users. In this way, by providing warnings in multiple formats, more users can be alerted. Some or all of the above-described processing in the warning unit may be performed using, or without, AI. For example, the warning unit can input warning data to a generation AI and cause the generation AI to provide a warning method.

[0089] The warning unit can estimate the parent's emotions and adjust the timing of the warning based on the estimated parent's emotions. The warning unit can use, for example, facial expression recognition, voice analysis, behavioral patterns, etc. to estimate the parent's emotions. For example, the warning unit can estimate the parent's emotions using facial expression recognition technology. For example, the warning unit can analyze the parent's facial expression data and estimate emotions such as anxiety, relaxation, or hurry. The warning unit can also estimate the parent's emotions using voice analysis technology. For example, the warning unit can analyze the parent's tone and speed of voice to estimate emotions. The warning unit can also analyze behavioral patterns to estimate the parent's emotions. For example, the warning unit can analyze the parent's movements and behavioral patterns to estimate emotions. This allows the warning to be issued at a more appropriate time by adjusting the timing of the warning according to the parent's emotions. Some or all of the above-described processing in the warning unit can be performed using, for example, AI, or without AI. For example, the warning unit can input emotion data to a generation AI and cause the generation AI to estimate emotions and adjust the timing of the warning.

[0090] The warning unit can provide the issued warning in multiple languages. The warning unit can provide the warning in major languages ​​such as English, Spanish, and Chinese. For example, the warning unit can provide the warning in major languages ​​such as English, Spanish, and Chinese. The warning unit can also issue a warning in an appropriate language based on a language setting of a park user. For example, the warning unit can issue a warning in an appropriate language based on a language setting of a park user. The warning unit can also provide a warning to a user through a multilingual app. For example, the warning unit can provide a warning to a user through a multilingual app. By providing a warning in multiple languages, it is possible to accommodate users who speak different languages. Some or all of the above-described processing in the warning unit can be performed using, or without, AI. For example, the warning unit can input language data to a generation AI and cause the generation AI to provide a warning in multiple languages.

[0091] The warning unit can display the issued warning on speakers and displays in the park in addition to the parent's device. For example, the warning unit can send a warning to the parent's smartphone and simultaneously issue an audio warning on speakers in the park. For example, the warning unit can send a warning to the parent's smartphone and simultaneously issue an audio warning on speakers in the park. The warning unit can also send a warning to the parent's smartphone and simultaneously display a visual warning on a display in the park. For example, the warning unit can send a warning to the parent's smartphone and simultaneously display a visual warning on a display in the park. The warning unit can also send a warning to the parent's smartphone and simultaneously issue a warning on an announcement system in the park. For example, the warning unit can send a warning to the parent's smartphone and simultaneously issue a warning on an announcement system in the park. This allows the warning to be displayed on speakers and displays in the park, thereby alerting more users. Some or all of the above-mentioned processing in the warning unit may be performed using, for example, AI, or may be performed without AI. For example, the warning unit can input warning data to a generation AI and cause the generation AI to display the warning.

[0092] The tracking unit can estimate the child's emotions and adjust the tracking accuracy of the location information based on the estimated child's emotions. The tracking unit uses, for example, facial expression recognition, voice analysis, behavioral patterns, etc. to estimate the child's emotions. For example, the tracking unit estimates the child's emotions using facial expression recognition technology. For example, the tracking unit analyzes the child's facial expression data to estimate emotions such as fear, excitement, and relaxation. The tracking unit can also estimate the child's emotions using voice analysis technology. For example, the tracking unit analyzes the tone and speed of the child's voice to estimate the emotions. The tracking unit can also analyze behavioral patterns to estimate the child's emotions. For example, the tracking unit analyzes the child's movements and behavioral patterns to estimate the emotions. This enables more appropriate tracking of location information by adjusting the tracking accuracy according to the child's emotions. Some or all of the above-described processing in the tracking unit may be performed using, for example, AI, or may be performed without AI. For example, the tracking unit can input emotion data to a generation AI and cause the generation AI to estimate emotions and adjust the tracking accuracy.

[0093] The tracking unit can limit the tracking target to a specific area or zone. The tracking unit performs tracking by limiting the tracking to a specific area or zone, such as a playground area or a rest area. For example, the tracking unit focuses on tracking a specific playground area where children are playing. The tracking unit can also limit tracking to a specific zone within a park (e.g., a sandbox or slide). For example, the tracking unit limits tracking to a specific zone within a park and tracks the location information of children within that area. The tracking unit can also focus tracking on an area where children are likely to get lost. For example, the tracking unit focuses tracking on an area where children are likely to get lost and tracks the location information of children within that area. This allows for more effective tracking by limiting the tracking to a specific area or zone. Some or all of the above-described processing by the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input area data to the generation AI and cause the generation AI to limit the tracking target.

[0094] The tracking unit can add specific actions or behavior patterns to the target to be tracked. The tracking unit tracks specific actions or behavior patterns, such as running, sitting, or stopping. For example, the tracking unit tracks the actions of a child running around and detects abnormal behavior. The tracking unit can also track the behavior pattern of a child playing with specific playground equipment. For example, the tracking unit tracks the behavior pattern of a child playing with specific playground equipment and monitors that behavior. The tracking unit can also track the behavior pattern of a child staying in a specific area for a long time. For example, the tracking unit tracks the behavior pattern of a child staying in a specific area for a long time and monitors that behavior. By adding specific actions or behavior patterns, abnormal behavior can be detected early. Some or all of the above-mentioned processing in the tracking unit may be performed using, or without, AI. For example, the tracking unit can input behavior data to a generation AI and cause the generation AI to track the actions or behavior patterns.

[0095] The tracking unit can estimate the child's emotions and adjust the update frequency of the tracking information based on the estimated child's emotions. The tracking unit uses, for example, facial expression recognition, voice analysis, behavioral patterns, etc. to estimate the child's emotions. For example, the tracking unit estimates the child's emotions using facial expression recognition technology. For example, the tracking unit analyzes the child's facial expression data to estimate emotions such as fear, excitement, and relaxation. The tracking unit can also estimate the child's emotions using voice analysis technology. For example, the tracking unit analyzes the tone and speed of the child's voice to estimate the emotions. The tracking unit can also analyze behavioral patterns to estimate the child's emotions. For example, the tracking unit analyzes the child's movements and behavioral patterns to estimate the emotions. This allows the tracking unit to adjust the update frequency of the tracking information according to the child's emotions, thereby providing more appropriate tracking information. Some or all of the above-described processing in the tracking unit may be performed using, for example, AI, or may be performed without AI. For example, the tracking unit can input emotion data to a generation AI and cause the generation AI to estimate emotions and adjust the update frequency of the tracking information.

[0096] The tracking unit can add not only the parent's device but also the devices of other family members and friends to the tracking targets. The tracking unit, for example, provides location information to devices of grandparents, siblings, and friends in addition to the parent's smartphone. For example, the tracking unit provides location information to devices of grandparents and siblings in addition to the parent's smartphone. The tracking unit can also provide location information to devices of friends in addition to the parent's smartphone. For example, the tracking unit provides location information to devices of friends in addition to the parent's smartphone. The tracking unit can also provide location information to devices of childcare workers or teachers in addition to the parent's smartphone. For example, the tracking unit provides location information to devices of childcare workers or teachers in addition to the parent's smartphone. This allows more people to share location information by adding devices of other family members and friends. Some or all of the above-described processing in the tracking unit may be performed using, or without, AI. For example, the tracking unit may input device data to a generation AI and cause the generation AI to add a device.

[0097] The tracking unit can add a specific event or activity to the tracking targets. The tracking unit, for example, tracks participants in an event or activity held in a park. For example, the tracking unit tracks participants in an event or activity held in a park. The tracking unit can also track participants in an activity held at a specific time period. For example, the tracking unit tracks participants in an activity held at a specific time period. The tracking unit can also focus on tracking an area related to a specific event in the park. For example, the tracking unit focuses on tracking an area related to a specific event in the park. In this way, by adding a specific event or activity, the location information of participants can be effectively tracked. Some or all of the above-described processing in the tracking unit may be performed using AI, or may be performed without using AI. For example, the tracking unit can input event data to a generation AI and cause the generation AI to track the event or activity.

[0098] The prevention unit can estimate the guardian's emotions and adjust the method of preventing the child from getting lost based on the estimated emotions of the guardian. The prevention unit can use, for example, facial expression recognition, voice analysis, behavioral patterns, etc. to estimate the guardian's emotions. For example, the prevention unit can estimate the guardian's emotions using facial expression recognition technology. For example, the prevention unit can analyze the guardian's facial expression data to estimate emotions such as anxiety, relaxation, or hurry. The prevention unit can also estimate the guardian's emotions using voice analysis technology. For example, the prevention unit can analyze the tone and speed of the guardian's voice to estimate emotions. The prevention unit can also analyze behavioral patterns to estimate the guardian's emotions. For example, the prevention unit can analyze the guardian's movements and behavioral patterns to estimate emotions. This allows the method of preventing the child from getting lost to be adjusted according to the guardian's emotions, thereby enabling more appropriate prevention of the child from getting lost. Some or all of the above-described processing in the prevention unit can be performed using, for example, AI, or without AI. For example, the prevention unit can input emotion data into the generation AI and have the generation AI estimate emotions and adjust methods for preventing the child from getting lost.

[0099] The prevention unit can customize the content of the notification to be issued depending on the circumstances of the lost child. The prevention unit customizes the content of the notification depending on circumstances such as the location, time, and behavior of the lost child. For example, if a child gets lost in a specific area, the prevention unit issues a notification including detailed information about the area. Furthermore, if a child gets lost during a specific time period, the prevention unit can issue a notification taking into account the circumstances of that time period. For example, if a child gets lost during a specific time period, the prevention unit can issue a notification taking into account the circumstances of that time period. Furthermore, if a child gets lost due to a specific behavioral pattern, the prevention unit can issue a notification based on that behavioral pattern. For example, if a child gets lost due to a specific behavioral pattern, the prevention unit issues a notification based on that behavioral pattern. This allows for more specific notifications by customizing the content of the notification depending on the circumstances of the lost child. Some or all of the above-described processing in the prevention unit may be performed using, for example, AI, or may be performed without AI. For example, the prevention unit can input situation data into a generation AI and cause the generation AI to customize the content of the notification.

[0100] The prevention unit can provide notification methods in multiple formats, such as audio, text, and visual. The prevention unit provides notification in multiple formats, such as audio notification, text message, and visual display. For example, the prevention unit issues an audio notification to quickly inform the parent or guardian. The prevention unit can also send a text notification to a smartphone to provide detailed information. For example, the prevention unit can send a text notification to a smartphone to provide detailed information. The prevention unit can also display a visual notification on a display in the park to alert all users. For example, the prevention unit displays a visual notification on a display in the park to alert all users. In this way, by providing notifications in multiple formats, more users can be alerted. Some or all of the above-described processing in the prevention unit may be performed using AI, or may be performed without using AI. For example, the prevention unit can input notification data to a generation AI and cause the generation AI to provide a notification method.

[0101] The prevention unit can estimate the guardian's emotions and adjust the timing of notifications based on the estimated guardian's emotions. The prevention unit can use, for example, facial expression recognition, voice analysis, behavioral patterns, etc. to estimate the guardian's emotions. For example, the prevention unit can estimate the guardian's emotions using facial expression recognition technology. For example, the prevention unit can analyze the guardian's facial expression data to estimate emotions such as anxiety, relaxation, or hurry. The prevention unit can also estimate the guardian's emotions using voice analysis technology. For example, the prevention unit can analyze the guardian's tone and speed of voice to estimate emotions. The prevention unit can also analyze behavioral patterns to estimate the guardian's emotions. For example, the prevention unit can analyze the guardian's movements and behavioral patterns to estimate emotions. This allows the timing of notifications to be adjusted according to the guardian's emotions, thereby enabling notifications to be issued at more appropriate times. Some or all of the above-described processing in the prevention unit can be performed using, for example, AI, or without AI. For example, the prevention unit can input emotion data to a generation AI and cause the generation AI to estimate emotions and adjust the timing of notifications.

[0102] The prevention unit can provide the issued notification in multiple languages. The prevention unit can provide the notification in a primary language, such as English, Spanish, or Chinese. For example, the prevention unit can provide the notification in a primary language, such as English, Spanish, or Chinese. The prevention unit can also issue the notification in an appropriate language based on a language setting of a park user. For example, the prevention unit can issue the notification in an appropriate language based on a language setting of a park user. The prevention unit can also provide the notification to the user through a multilingual app. For example, the prevention unit can provide the notification to the user through a multilingual app. By providing the notification in multiple languages, it is possible to accommodate users who speak different languages. Some or all of the above-described processing in the prevention unit can be performed using AI, for example, or without AI. For example, the prevention unit can input language data to a generation AI and cause the generation AI to provide the notification in multiple languages.

[0103] The prevention unit can display the issued notification on speakers or displays in the park in addition to the parent's device. For example, the prevention unit can send a notification to the parent's smartphone and simultaneously issue an audio notification on a speaker in the park. For example, the prevention unit can send a notification to the parent's smartphone and simultaneously issue an audio notification on a speaker in the park. The prevention unit can also send a notification to the parent's smartphone and simultaneously display a visual notification on a display in the park. For example, the prevention unit can send a notification to the parent's smartphone and simultaneously display a visual notification on a display in the park. The prevention unit can also send a notification to the parent's smartphone and simultaneously issue a notification on an announcement system in the park. For example, the prevention unit can send a notification to the parent's smartphone and simultaneously issue a notification on an announcement system in the park. This allows the notification to be displayed on speakers or displays in the park, thereby alerting more users. Some or all of the above-mentioned processing in the prevention unit may be performed using AI, for example, or may be performed without AI. For example, the prevention unit can input notification data to a generation AI and cause the generation AI to display the notification.

[0104] The playground equipment monitoring unit can estimate a child's emotions and adjust the monitoring accuracy of the playground equipment based on the estimated child's emotions. The playground equipment monitoring unit uses, for example, facial expression recognition, voice analysis, behavioral patterns, etc. to estimate a child's emotions. For example, the playground equipment monitoring unit estimates a child's emotions using facial expression recognition technology. For example, the playground equipment monitoring unit analyzes the child's facial expression data to estimate emotions such as fear, excitement, and relaxation. The playground equipment monitoring unit can also estimate a child's emotions using voice analysis technology. For example, the playground equipment monitoring unit analyzes the tone and speed of the child's voice to estimate emotions. The playground equipment monitoring unit can also analyze behavioral patterns to estimate a child's emotions. For example, the playground equipment monitoring unit analyzes the child's movements and behavioral patterns to estimate emotions. This allows for more appropriate monitoring by adjusting the monitoring accuracy of the playground equipment according to the child's emotions. Some or all of the above-described processing in the playground equipment monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the playground equipment monitoring unit can input emotion data into the generation AI and have the generation AI estimate emotions and adjust monitoring accuracy.

[0105] The playground equipment monitoring unit can dynamically change the target of monitoring depending on a specific time period or day of the week. The playground equipment monitoring unit changes the target of monitoring depending on a specific time period or day of the week, such as weekdays and weekends, or daytime and nighttime. For example, the playground equipment monitoring unit prioritizes children's safety during the daytime on weekdays and focuses on monitoring the condition of the playground equipment. The playground equipment monitoring unit can also expand its overall monitoring range on weekends and holidays, as the number of park users increases on weekends and holidays. For example, the playground equipment monitoring unit monitors the entire park over a wide area on weekends and holidays. The playground equipment monitoring unit can also intensify monitoring for damage or abnormalities in playground equipment at night. For example, the playground equipment monitoring unit intensifies monitoring for damage or abnormalities in playground equipment at night. This enables more effective monitoring by changing the target of monitoring depending on the time period or day of the week. Some or all of the above-described processing by the playground equipment monitoring unit may be performed using, or without, AI. For example, the playground equipment monitoring unit can input time period and day of the week data into the generation AI and cause the generation AI to dynamically change the target of monitoring.

[0106] The play equipment monitoring unit can add the frequency of use and status of specific play equipment to the targets of monitoring. For example, the play equipment monitoring unit may focus on monitoring frequently used play equipment to detect abnormalities early. For example, the play equipment monitoring unit may focus on monitoring frequently used play equipment to detect abnormalities early. The play equipment monitoring unit can also monitor the status (e.g., wear and tear) of specific play equipment and notify users of required maintenance. For example, the play equipment monitoring unit may monitor the status of specific play equipment and notify users of required maintenance. The play equipment monitoring unit can also periodically monitor less frequently used play equipment to detect abnormalities. For example, the play equipment monitoring unit may periodically monitor less frequently used play equipment to detect abnormalities. In this way, by monitoring the frequency of use and status of specific play equipment, abnormalities can be detected early. Some or all of the above-described processing by the play equipment monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the play equipment monitoring unit can input usage frequency and status data into the generation AI and have the generation AI monitor the play equipment.

[0107] The playground equipment monitoring unit can estimate a child's emotions and adjust the use restrictions of the playground equipment based on the estimated emotions of the child. The playground equipment monitoring unit uses, for example, facial expression recognition, voice analysis, behavioral patterns, etc. to estimate a child's emotions. For example, the playground equipment monitoring unit estimates a child's emotions using facial expression recognition technology. For example, the playground equipment monitoring unit analyzes the child's facial expression data to estimate emotions such as fear, excitement, and relaxation. The playground equipment monitoring unit can also estimate a child's emotions using voice analysis technology. For example, the playground equipment monitoring unit analyzes the tone and speed of the child's voice to estimate emotions. The playground equipment monitoring unit can also analyze behavioral patterns to estimate a child's emotions. For example, the playground equipment monitoring unit analyzes the child's movements and behavioral patterns to estimate emotions. This allows for more appropriate use restrictions by adjusting the use restrictions of the playground equipment according to the child's emotions. Some or all of the above-described processing in the playground equipment monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the play equipment monitoring unit can input emotion data into the generation AI and have the generation AI estimate emotions and adjust usage restrictions.

[0108] The play equipment monitoring unit can add the temperature and humidity of the play equipment to the objects it monitors. The play equipment monitoring unit, for example, monitors the surface temperature of the play equipment and the surrounding humidity to detect abnormal conditions early. For example, the play equipment monitoring unit monitors the surface temperature of the play equipment and detects abnormally high or low temperatures. The play equipment monitoring unit can also monitor the humidity around the play equipment to detect slippery conditions. For example, the play equipment monitoring unit monitors the humidity around the play equipment to detect slippery conditions. The play equipment monitoring unit can also monitor the internal temperature of the play equipment and detect abnormal temperature changes. For example, the play equipment monitoring unit monitors the internal temperature of the play equipment and detects abnormal temperature changes. In this way, by monitoring the temperature and humidity of the play equipment, abnormal conditions can be detected early. Some or all of the above-mentioned processing in the play equipment monitoring unit may be performed using, or without, AI. For example, the play equipment monitoring unit can input temperature and humidity data to the generation AI and cause the generation AI to monitor the temperature and humidity.

[0109] The play equipment monitoring unit can add the age and weight of a user of the play equipment to the targets to be monitored. The play equipment monitoring unit, for example, monitors the age and weight of a user of the play equipment and sets appropriate usage restrictions. For example, the play equipment monitoring unit monitors the age and weight of a user of the play equipment and sets appropriate age restrictions. The play equipment monitoring unit can also monitor the weight of a user of the play equipment and set appropriate weight restrictions. For example, the play equipment monitoring unit monitors the weight of a user of the play equipment and sets appropriate weight restrictions. The play equipment monitoring unit can also combine the age and weight of a user of the play equipment to set appropriate usage restrictions. For example, the play equipment monitoring unit combines the age and weight of a user of the play equipment and sets appropriate usage restrictions. In this way, appropriate usage restrictions can be set by monitoring the age and weight of a user of the play equipment. Some or all of the above-described processing in the play equipment monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the play equipment monitoring unit can input age and weight data into a generation AI and have the generation AI set usage restrictions.

[0110] The weather monitoring unit can estimate the parent's emotions and adjust the frequency of weather data collection based on the estimated parent's emotions. The weather monitoring unit uses, for example, facial expression recognition, voice analysis, behavioral patterns, etc. to estimate the parent's emotions. For example, the weather monitoring unit estimates the parent's emotions using facial expression recognition technology. For example, the weather monitoring unit analyzes the parent's facial expression data to estimate emotions such as anxiety, relaxation, or hurry. The weather monitoring unit can also estimate the parent's emotions using voice analysis technology. For example, the weather monitoring unit analyzes the parent's tone and speed of voice to estimate emotions. The weather monitoring unit can also analyze behavioral patterns to estimate the parent's emotions. For example, the weather monitoring unit analyzes the parent's movements and behavioral patterns to estimate emotions. This allows the weather monitoring unit to adjust the frequency of weather data collection according to the parent's emotions, thereby providing more appropriate weather information. Some or all of the above-described processing in the weather monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the weather monitoring unit can input emotion data into the generation AI and have the generation AI estimate emotions and adjust the frequency of weather data collection.

[0111] The weather monitoring unit can dynamically change the data it collects depending on a specific time period or season. The weather monitoring unit changes the collected data depending on a specific time period or season, such as summer, winter, daytime, or nighttime. For example, the weather monitoring unit may focus on collecting temperature and UV index in summer. The weather monitoring unit may also focus on collecting temperature and snowfall in winter. For example, the weather monitoring unit may focus on collecting temperature and snowfall in winter. The weather monitoring unit may also change the weather data collection items depending on a specific time period (e.g., daytime or nighttime). For example, the weather monitoring unit may focus on collecting temperature and humidity during the daytime and wind speed and temperature at night. This enables more effective weather monitoring by changing the collected data depending on the time period or season. Some or all of the above-described processing in the weather monitoring unit may be performed using, or without, AI. For example, the weather monitoring unit may input time period and seasonal data into the generation AI and cause the generation AI to dynamically change the collected data.

[0112] The weather monitoring unit can add specific weather conditions and forecasts to the collected data. The weather monitoring unit collects specific weather conditions and forecasts, such as typhoons, heavy rain, and extreme heat. For example, the weather monitoring unit collects forecasts of sudden thunderstorms and issues a warning. The weather monitoring unit can also collect forecasts of strong winds and issue a warning. For example, the weather monitoring unit collects forecasts of strong winds and issues a warning. The weather monitoring unit can also collect forecasts of abnormal temperature changes and issue a warning. For example, the weather monitoring unit collects forecasts of abnormal temperature changes and issues a warning. By adding specific weather conditions and forecasts, abnormal weather can be detected early. Some or all of the above-mentioned processing in the weather monitoring unit may be performed using, or without, AI. For example, the weather monitoring unit can input weather condition and forecast data to the generation AI and cause the generation AI to collect the weather conditions and forecasts.

[0113] The weather monitoring unit can estimate the parent's emotions and adjust the timing of weather warnings based on the estimated parent's emotions. The weather monitoring unit uses, for example, facial expression recognition, voice analysis, behavioral patterns, etc. to estimate the parent's emotions. For example, the weather monitoring unit estimates the parent's emotions using facial expression recognition technology. For example, the weather monitoring unit analyzes the parent's facial expression data to estimate emotions such as anxiety, relaxation, or hurry. The weather monitoring unit can also estimate the parent's emotions using voice analysis technology. For example, the weather monitoring unit analyzes the parent's tone and speed of voice to estimate emotions. The weather monitoring unit can also analyze behavioral patterns to estimate the parent's emotions. For example, the weather monitoring unit analyzes the parent's movements and behavioral patterns to estimate emotions. This allows the timing of weather warnings to be adjusted according to the parent's emotions, thereby issuing warnings at more appropriate times. Some or all of the above-described processing in the weather monitoring unit may be performed using, for example, AI, or without AI. For example, the weather monitoring unit can input emotion data into the generation AI and have the generation AI estimate emotions and adjust the timing of weather warnings.

[0114] The weather monitoring unit can add the UV index to the data it collects in addition to temperature and humidity. The weather monitoring unit collects weather data such as temperature, humidity, and UV index. For example, the weather monitoring unit collects the UV index in addition to temperature and humidity and issues a warning. The weather monitoring unit can also issue a warning recommending the use of sunscreen when the UV index is high. For example, the weather monitoring unit issues a warning recommending the use of sunscreen when the UV index is high. The weather monitoring unit can also provide normal weather data when the UV index is low. For example, the weather monitoring unit provides normal weather data when the UV index is low. In this way, collecting the UV index in addition to temperature and humidity can provide more detailed weather information. Some or all of the above-mentioned processing in the weather monitoring unit may be performed using AI, for example, or without AI. For example, the weather monitoring unit can input UV index data to the generation AI and cause the generation AI to collect the UV index.

[0115] The weather monitoring unit can add park use restrictions based on weather conditions to the collected data. The weather monitoring unit collects park use restrictions based on weather conditions, such as strong winds, heavy rain, and high temperatures. For example, the weather monitoring unit issues a warning restricting the use of specific playground equipment when strong winds occur. The weather monitoring unit can also issue a warning restricting the use of the entire park when heavy rain occurs. For example, the weather monitoring unit issues a warning restricting the use of the entire park when heavy rain occurs. The weather monitoring unit can also issue a warning restricting the use of specific areas when temperatures are high. For example, the weather monitoring unit issues a warning restricting the use of specific areas when temperatures are high. This allows users to ensure their safety by adding park use restrictions based on weather conditions. Some or all of the above-described processing by the weather monitoring unit may be performed using AI, for example, or without AI. For example, the weather monitoring unit can input weather condition data into a generation AI and have the generation AI execute park use restrictions.

[0116] The lost child detection robot can estimate a child's emotions and adjust its patrol route based on the estimated child's emotions. The lost child detection robot uses, for example, facial expression recognition, voice analysis, behavioral patterns, etc. to estimate a child's emotions. For example, the lost child detection robot estimates a child's emotions using facial expression recognition technology. For example, the lost child detection robot analyzes a child's facial expression data to estimate emotions such as fear, excitement, and relaxation. The lost child detection robot can also estimate a child's emotions using voice analysis technology. For example, the lost child detection robot analyzes the tone and speed of the child's voice to estimate emotions. The lost child detection robot can also estimate a child's emotions by analyzing behavioral patterns. For example, the lost child detection robot analyzes the child's movements and behavioral patterns to estimate emotions. This allows the lost child to be found more effectively by adjusting its patrol route according to the child's emotions. Some or all of the above-described processing in the lost child detection robot may be performed using, for example, AI, or may be performed without using AI. For example, a lost child detection robot can input emotional data into a generating AI, which can then estimate the emotion and adjust the patrol route.

[0117] The lost child detection robot can dynamically change the area it patrols depending on a specific time of day or day of the week. The lost child detection robot changes its patrol area depending on a specific time of day or day of the week, such as weekdays and weekends, or daytime and nighttime. For example, the lost child detection robot may focus on patrolling areas with many children during the daytime on weekdays. The lost child detection robot can also patrol a wider area of ​​the entire park on weekends and holidays. For example, the lost child detection robot may patrol a wider area of ​​the entire park on weekends and holidays. The lost child detection robot can also focus on patrolling a specific area at night. For example, the lost child detection robot may focus on patrolling a specific area at night. This allows for more effective patrols by changing the patrol area depending on the time of day or day of the week. Some or all of the above-described processing in the lost child detection robot may be performed using, or without, AI. For example, the lost child detection robot can input time of day and day of the week data into a generation AI and have the generation AI dynamically change its patrol area.

[0118] The lost child detection robot can add children wearing specific clothing or belongings to the detection targets. The lost child detection robot detects, for example, children wearing clothing of a specific color or design, or children carrying specific belongings (e.g., backpacks, hats). For example, the lost child detection robot prioritizes detecting children wearing clothing of a specific color or design. The lost child detection robot can also prioritize detecting children carrying specific belongings (e.g., backpacks, hats). For example, the lost child detection robot prioritizes detecting children carrying specific belongings. The lost child detection robot can also detect children wearing specific accessories or shoes. For example, the lost child detection robot detects children wearing specific accessories or shoes. This allows for the detection of children carrying specific clothing or belongings, enabling the lost child to be found more quickly. Some or all of the above-described processing in the lost child detection robot may be performed using, for example, AI, or may be performed without AI. For example, the lost child detection robot can input clothing and belonging data into the generation AI and have the generation AI detect children carrying specific clothing or belongings.

[0119] The lost child finding robot can estimate a child's emotions and adjust its speed based on the estimated emotions. The lost child finding robot uses, for example, facial expression recognition, voice analysis, behavioral patterns, etc. to estimate a child's emotions. For example, the lost child finding robot estimates a child's emotions using facial expression recognition technology. For example, the lost child finding robot analyzes a child's facial expression data to estimate emotions such as fear, excitement, and relaxation. The lost child finding robot can also estimate a child's emotions using voice analysis technology. For example, the lost child finding robot analyzes the tone and speed of the child's voice to estimate emotions. The lost child finding robot can also estimate a child's emotions by analyzing behavioral patterns. For example, the lost child finding robot analyzes the child's movements and behavioral patterns to estimate emotions. This allows for more effective detection of lost children by adjusting its speed according to the child's emotions. Some or all of the above-described processing in the lost child finding robot may be performed using, for example, AI, or may be performed without using AI. For example, a lost child detection robot can input emotional data into a generation AI, which can then estimate the emotion and adjust the speed.

[0120] The lost child detection robot can add specific play equipment or facilities to the target area it patrols. For example, the lost child detection robot patrols around specific play equipment (e.g., slides, swings) or specific facilities in the park (e.g., restrooms, rest areas). For example, the lost child detection robot may focus its patrol around specific play equipment. The lost child detection robot can also patrol specific facilities in the park. For example, the lost child detection robot patrols specific facilities in the park. The lost child detection robot can also patrol areas where children tend to gather (e.g., sandboxes, fountains). For example, the lost child detection robot patrols areas where children tend to gather. In this way, by patrolling specific play equipment and facilities, more effective detection of lost children is possible. Some or all of the above-described processing in the lost child detection robot may be performed using, or without, AI. For example, the lost child detection robot can input play equipment and facility data into a generation AI and have the generation AI set the patrol area.

[0121] A lost child detection robot can add specific sounds or noises to the detection targets and use voice recognition technology. A lost child detection robot can detect, for example, a child's screams or cries, loud noises or collision sounds, suspicious conversations or noises, etc. For example, a lost child detection robot can detect a child's screams or cries and respond quickly. A lost child detection robot can also detect loud noises or collision sounds to detect abnormalities early. For example, a lost child detection robot can detect loud noises or collision sounds to detect abnormalities early. A lost child detection robot can also detect suspicious conversations or noises to identify dangerous situations early. For example, a lost child detection robot can detect suspicious conversations or noises to identify dangerous situations early. By detecting specific sounds or noises, an abnormal situation can be identified early. Some or all of the above-described processing in a lost child detection robot may be performed using, for example, AI, or may be performed without using AI. For example, a lost child finding robot can input voice data into a generation AI and have the generation AI perform voice recognition. === Hard Collateral 1-1 === Each of the above-described elements, including the monitoring unit, warning unit, tracking unit, prevention unit, sensor monitoring unit, weather monitoring unit, and lost child detection robot, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the monitoring unit detects suspicious individuals or dangerous behavior using the camera 42 and microphone 38B of the smart device 14 and is implemented by the control unit 46A. The warning unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and issues a warning based on the detected information. The tracking unit tracks location information using, for example, GPS or Bluetooth of the smart device 14 and is implemented by the control unit 46A. The prevention unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and prevents lost children based on the tracked location information. The sensor monitoring unit monitors the status of playground equipment using, for example, sensors of the smart device 14 and issues a warning if an abnormality is detected. The weather monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12, collects weather data in real time, and issues a warning when abnormal weather is detected. The lost child finding robot is realized, for example, by the control unit 46A of the smart device 14, patrols the park, and notifies the parents when it finds a lost child. === Hard Collateral 1-2 === Each of the above-described multiple elements, including the monitoring unit, warning unit, tracking unit, prevention unit, sensor monitoring unit, weather monitoring unit, and lost child detection robot, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the monitoring unit detects suspicious individuals or dangerous behavior using the camera 42 and microphone 238 of the smart glasses 214 and is realized by the control unit 46A. The warning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and issues a warning based on the detected information. The tracking unit tracks location information using, for example, GPS or Bluetooth of the smart glasses 214 and is realized by the control unit 46A. The prevention unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and prevents lost children based on the tracked location information. The sensor monitoring unit monitors the status of playground equipment using, for example, sensors in the smart glasses 214 and issues a warning if an abnormality is detected. The weather monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12, collects weather data in real time, and issues a warning when abnormal weather is detected. The lost child finding robot is realized, for example, by the control unit 46A of the smart glasses 214, patrols the park, and notifies the parents when it finds a lost child. === Hard Collateral 1-3 === Each of the above-described elements, including the monitoring unit, warning unit, tracking unit, prevention unit, sensor monitoring unit, weather monitoring unit, and lost child detection robot, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the monitoring unit detects suspicious individuals or dangerous behavior using the camera 42 or microphone 238 of the headset terminal 314 and is implemented by the control unit 46A. The warning unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and issues a warning based on the detected information. The tracking unit tracks location information using, for example, GPS or Bluetooth of the headset terminal 314 and is implemented by the control unit 46A. The prevention unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and prevents lost children based on the tracked location information. The sensor monitoring unit monitors the status of playground equipment using, for example, sensors in the headset terminal 314 and issues a warning if an abnormality is detected. The weather monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12, collects weather data in real time, and issues a warning if abnormal weather is detected. The lost child finding robot is realized, for example, by the control unit 46A of the headset terminal 314, patrols the park, and notifies the guardian if it finds a lost child. === Hard Collateral 1-4 === Each of the above-described multiple elements, including the monitoring unit, warning unit, tracking unit, prevention unit, sensor monitoring unit, weather monitoring unit, and lost child detection robot, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the monitoring unit detects suspicious individuals or dangerous behavior using the camera 42 or microphone 238 of the robot 414 and is realized by the control unit 46A. The warning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and issues a warning based on the detected information. The tracking unit tracks location information using, for example, GPS or Bluetooth of the robot 414 and is realized by the control unit 46A. The prevention unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and prevents lost children based on the tracked location information. The sensor monitoring unit monitors the status of play equipment using, for example, sensors of the robot 414 and issues a warning if an abnormality is detected. The weather monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12, collects weather data in real time, and issues a warning if abnormal weather is detected. The lost child finding robot is realized, for example, by the control unit 46A of the robot 414, patrols the park, and notifies the parents if it finds a lost child.

[0122] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0123] The monitoring unit can dynamically change the monitoring target depending on the time of day or day of the week. For example, during the daytime on weekdays, priority is placed on children's safety, and monitoring of children's behavior is prioritized. Also, since the number of park users increases on weekends and holidays, the overall monitoring range can be expanded. For example, the entire park can be monitored over a wide area on weekends and holidays. Also, suspicious person detection can be strengthened at night, allowing for highly accurate monitoring even in dark places. For example, the detection accuracy of suspicious people can be improved at night, allowing for highly accurate monitoring even in dark places. This allows for more effective monitoring by changing the monitoring target depending on the time of day or day of the week.

[0124] The monitoring unit can add people wearing specific clothing or carrying specific items to the targets to be detected. For example, people wearing clothing of a specific color or design, or people carrying large bags or backpacks, can be added to the targets of monitoring. For example, people wearing clothing of a specific color or design can be monitored as a priority. It is also possible to focus on monitoring people carrying large bags or backpacks. For example, people carrying large bags or backpacks can be added to the targets of monitoring and their behavior can be monitored. It is also possible to add people wearing specific accessories or hats to the targets of monitoring. For example, people wearing specific accessories or hats can be added to the targets of monitoring and their behavior can be monitored. In this way, by adding people wearing specific clothing or carrying specific items to the targets of monitoring, more detailed monitoring is possible.

[0125] The tracking unit can limit the target to be tracked to a specific area or zone. For example, tracking can be limited to a specific area or zone such as a playground area or rest area. For example, tracking can be focused on a specific playground area where children are playing. Tracking can also be limited to a specific zone within a park (e.g., a sandbox or slide). For example, tracking can be focused on a specific zone within a park and the location information of children within that area. Tracking can also be focused on areas where children are likely to get lost. For example, tracking can be focused on areas where children are likely to get lost and the location information of children within that area. In this way, more effective tracking is possible by limiting tracking to a specific area or zone.

[0126] The prevention unit can customize the content of the notification to be issued depending on the circumstances of the lost child. For example, the notification content can be customized depending on circumstances such as the location, time, and behavior of the lost child. For example, if a child gets lost in a specific area, a notification including detailed information about that area can be issued. Also, if a child gets lost during a specific time period, a notification can be issued taking into account the circumstances of that time period. For example, if a child gets lost during a specific time period, a notification can be issued taking into account the circumstances of that time period. Also, if a child gets lost due to a specific behavior pattern, a notification based on that behavior pattern can be issued. For example, if a child gets lost due to a specific behavior pattern, a notification based on that behavior pattern can be issued. In this way, by customizing the notification content depending on the circumstances of the lost child, more specific notifications can be provided.

[0127] The prevention unit can provide notification in multiple formats, such as audio, text, and visual. For example, notification can be provided in multiple formats, such as audio notification, text message, and visual display. For example, an audio notification can be issued to quickly inform parents or guardians. A text notification can also be sent to a smartphone to provide detailed information. For example, a text notification can be sent to a smartphone to provide detailed information. A visual notification can also be displayed on a display in the park to alert all users. For example, a visual notification can be displayed on a display in the park to alert all users. In this way, by providing notification in multiple formats, more users can be alerted.

[0128] The monitoring unit can estimate the child's emotions and adjust the detection accuracy of suspicious individuals or dangerous behavior based on the estimated child's emotions. For example, facial expression recognition, voice analysis, behavioral patterns, etc. are used to estimate the child's emotions. For example, facial expression recognition technology can be used to estimate the child's emotions. For example, the child's facial expression data can be analyzed to estimate emotions such as fear, excitement, and relaxation. Voice analysis technology can also be used to estimate the child's emotions. For example, the tone and speed of the child's voice can be analyzed to estimate the emotions. The child's emotions can also be estimated by analyzing behavioral patterns. For example, the child's movements and behavioral patterns can be analyzed to estimate the emotions. This allows more appropriate monitoring by adjusting the detection accuracy according to the child's emotions.

[0129] The warning unit can estimate the parent's emotions and adjust the strength and frequency of the warning based on the estimated parent's emotions. For example, facial expression recognition, voice analysis, behavioral patterns, etc. are used to estimate the parent's emotions. For example, facial expression recognition technology can be used to estimate the parent's emotions. For example, facial expression data of the parent can be analyzed to estimate emotions such as anxiety, relaxation, or hurry. Voice analysis technology can also be used to estimate the parent's emotions. For example, the tone and speed of the parent's voice can be analyzed to estimate the emotions. The parent's emotions can also be estimated by analyzing behavioral patterns. For example, the parent's movements and behavioral patterns can be analyzed to estimate the emotions. This allows the strength and frequency of the warning to be adjusted according to the parent's emotions, enabling more appropriate warnings.

[0130] The tracking unit can estimate the child's emotions and adjust the tracking accuracy of the location information based on the estimated child's emotions. For example, facial expression recognition, voice analysis, behavioral patterns, etc. are used to estimate the child's emotions. For example, facial expression recognition technology is used to estimate the child's emotions. For example, the child's facial expression data is analyzed to estimate emotions such as fear, excitement, and relaxation. Voice analysis technology can also be used to estimate the child's emotions. For example, the tone and speed of the child's voice are analyzed to estimate the emotions. The child's emotions can also be estimated by analyzing behavioral patterns. For example, the child's movements and behavioral patterns are analyzed to estimate the emotions. This allows the tracking accuracy to be adjusted according to the child's emotions, enabling more appropriate tracking of location information.

[0131] The prevention unit can estimate the guardian's emotions and adjust the method of preventing the child from getting lost based on the estimated guardian's emotions. For example, facial expression recognition, voice analysis, behavioral patterns, etc. are used to estimate the guardian's emotions. For example, facial expression recognition technology can be used to estimate the guardian's emotions. For example, the guardian's facial expression data can be analyzed to estimate emotions such as anxiety, relaxation, or hurry. Voice analysis technology can also be used to estimate the guardian's emotions. For example, the tone and speed of the guardian's voice can be analyzed to estimate the emotions. The behavioral patterns can also be analyzed to estimate the guardian's emotions. For example, the movements and behavioral patterns of the guardian can be analyzed to estimate the emotions. This allows the method of preventing the child from getting lost to be adjusted according to the guardian's emotions, enabling more appropriate prevention of the child from getting lost.

[0132] A lost child detection robot can estimate a child's emotions and adjust its patrol route based on the estimated child's emotions. For example, facial expression recognition, voice analysis, behavioral patterns, etc. are used to estimate a child's emotions. For example, a child's emotions can be estimated using facial expression recognition technology. For example, a child's facial expression data can be analyzed to estimate emotions such as fear, excitement, and relaxation. A child's emotions can also be estimated using voice analysis technology. For example, a child's tone and speed of voice can be analyzed to estimate emotions. A child's emotions can also be estimated by analyzing behavioral patterns. For example, a child's movements and behavioral patterns can be analyzed to estimate emotions. This allows for more effective detection of lost children by adjusting its patrol route according to the child's emotions.

[0133] The processing flow of the second embodiment will be briefly explained below.

[0134] Step 1: The monitoring unit detects suspicious or dangerous behavior within the park. The monitoring unit uses algorithms that use image recognition technology to detect people who spend a long time in a specific area or make sudden movements. It can also use facial recognition technology to identify specific people and identify suspicious behavior. Step 2: The warning unit issues a warning based on the information detected by the monitoring unit. The warning unit issues a warning to users and parents in the park using an audio warning or a text message. Step 3: The tracking unit tracks the location information of the child or guardian. The tracking unit tracks the location information in real time using GPS or Bluetooth. Step 4: The prevention unit prevents the child from being lost based on the location information tracked by the tracking unit. The prevention unit issues a warning if the child is separated from the parent for more than a certain period of time, and notifies the parent based on the child's location information.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0137] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0139] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0140] 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.

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

[0142] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

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

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0149] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0153] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0155] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0156] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

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

[0158] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0163] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0167] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0169] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0172] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0173] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0174] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0175] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0176] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0177] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0178] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0179] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0180] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0181] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0182] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0183] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0184] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0185] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0186] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0187] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0188] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0189] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0190] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0191] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0192] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0193] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0194] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0195] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0196] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0197] 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.

[0198] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0199] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0200] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0201] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0202] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0203] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0204] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0205] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0206] [Explanation of symbols]

[0207] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a monitoring unit that detects suspicious individuals or dangerous behavior within the park; a warning unit that issues a warning based on the information detected by the monitoring unit; a tracking unit for tracking location information of a child or a parent; a prevention unit that prevents a lost child based on the location information tracked by the tracking unit. A system characterized by:

2. The monitoring unit Uses image recognition technology to detect suspicious or dangerous behavior 2. The system of claim 1.

3. The tracking unit Tracking a child's or parent's location using GPS or Bluetooth 2. The system of claim 1.

4. The prevention unit is Notify parents if their child gets lost in the park 2. The system of claim 1.

5. The equipment is equipped with a monitoring unit that uses sensors to monitor the condition of the play equipment and issues an alarm if an abnormality is detected.

2. The system of claim 1.

6. Equipped with a monitoring unit that collects weather data in real time and issues a warning if abnormal weather is detected.

2. The system of claim 1.

7. The park is equipped with a robot that will patrol the park and notify parents if it finds a lost child.

2. The system of claim 1.

8. The monitoring unit Estimates a child's emotions and adjusts the accuracy of detecting suspicious people and dangerous behavior based on the estimated emotions.

2. The system of claim 1.

9. The monitoring unit Dynamically change the detection target depending on the time of day or day of the week 2. The system of claim 1.

10. The monitoring unit Add people with specific clothing or belongings to the detection targets 2. The system of claim 1.

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