system

Abstract

We provide the system. [Solution] A means for acquiring the child's location information and transmitting said location information to a central processing unit, A means for memorizing past behavioral patterns and detecting deviations from a detailed range of behavior, A means of providing a notification to the parent's device when a deviation is detected, If a parent or guardian confirms a child is missing, what are the means to contact emergency services? A system that includes this.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In modern society, ensuring the safety of children is an important issue. However, there is a problem that it is difficult for guardians to know in real time when a child goes out of the daily activity range or approaches a specific dangerous area. This problem increases the anxiety of guardians and is a major obstacle to ensuring the safety of children. 【Means for Solving the Problems】 【0005】 This invention provides a system that uses a device to acquire a child's location information and transmit it to a central processing unit, thereby memorizing the child's daily behavior patterns and detecting deviations from their normal range of activity in real time. Furthermore, if a deviation is detected, a notification is quickly sent to the parent's device, and if the parent confirms the child is lost, the system contacts emergency services to ensure the child's safety. This system allows parents to immediately understand their child's unusual behavior, increasing their sense of security and enabling a quick response. 【0006】 "Location information" refers to data used to identify the geographical location of an object, and includes information such as GPS coordinates. 【0007】 A "central processing unit" is a computer system that receives, analyzes, and processes data transmitted from external sources. 【0008】 A "behavioral pattern" is a dataset that shows the history and trends of a subject's repetitive behaviors over a specific period. 【0009】 "Deviation" refers to a state of being that deviates from or deviates from normal behavioral patterns or defined ranges. 【0010】 A "parental device" is an electronic device, such as a smartphone or tablet, used by a parent or guardian to receive and display information. 【0011】 "Notifications" refer to alerts and informational messages sent from the system to parents. 【0012】 "Lost child confirmation" refers to the action taken by a parent or guardian to confirm with the system that their child is missing. 【0013】 "Emergency services" refers to public institutions that should be contacted in an emergency, such as the police and ambulance services. [Brief explanation of the drawing] 【0014】 [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined. 【Embodiments for Carrying Out the Invention】 【0015】 An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings. 【0016】 First, the terms used in the following description will be explained. 【0017】 In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like. 【0018】 In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0019】 In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like. 【0020】 In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0021】 In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0022】 [First Embodiment] 【0023】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0024】 As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server. 【0025】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0026】 The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0027】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0028】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor. 【0029】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54. 【0030】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0031】 As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30. 【0032】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0033】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0034】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0035】 The present invention is a system for ensuring the safety of children, and includes a wearable device that provides safety functions mainly using location information, and a central processing unit that processes the data. Examples are described below. 【0036】 The wearable device (terminal) acquires the child's location information in real time and transmits this information to a central processing unit (server) every 5 minutes. The GPS sensor in the terminal acquires the location information, and this data is sent to the server via a secure communication protocol. The server stores the received location information in a database and forms past behavioral patterns that are accumulated over time. 【0037】 The server uses machine learning algorithms to analyze past behavioral data and model the normal range of movement. Based on this profile, the server compares incoming new location information with normal behavioral patterns in real time and generates alerts when deviations are detected. 【0038】 When an anomaly is detected, the server sends a push notification to the parent's device. The user (parent) can check the child's current location and escape information via the app. If necessary, the parent can use the "Missing Child" function in the app to report the situation to the system. This allows the server to immediately contact emergency services and provide the child's last known location and activity history. 【0039】 For example, suppose a child who normally goes straight home from school is detected by the server to be heading in a completely different direction one day. In this case, the server determines that the current location transmitted by the device deviates from the usual route and notifies the parents. The parents can then make a quick decision based on this information and, if necessary, coordinate with emergency services. 【0040】 This embodiment of the present invention enables efficient monitoring of children's safety and immediate response to abnormal situations. This system will be a reliable safety tool for parents, especially in urban areas and neighborhoods where safety is a concern. 【0041】 The following describes the processing flow. 【0042】 Step 1: 【0043】 The device uses its built-in GPS sensor to obtain the child's current location information. This information is obtained at regular intervals, for example, every 5 minutes. The obtained data includes location information and a timestamp. 【0044】 Step 2: 【0045】 The device transmits the acquired location information to the server using a secure protocol. This transmission is encrypted to prevent data tampering by third parties. 【0046】 Step 3: 【0047】 The server receives location information transmitted from the device and stores it in a database. During this storage process, the data is organized based on date and time and recorded as a pattern of past activity. 【0048】 Step 4: 【0049】 The server uses machine learning algorithms based on accumulated behavioral data to analyze the child's typical range of movement. This analysis profiles the child's general activity patterns. 【0050】 Step 5: 【0051】 The server analyzes new location information in real time and compares it with existing behavioral profiles. This comparison helps determine whether the user is deviating from their normal range of activity. 【0052】 Step 6: 【0053】 If the server detects an deviation, it will send a notification to the parent's device. The notification will include the current location, details of the deviation, and potential dangers. 【0054】 Step 7: 【0055】 The user (parent / guardian) checks the received notification and opens the app to assess the situation. Within the app, they can view the child's current location and activity history on a map. 【0056】 Step 8: 【0057】 If the user deems it necessary, they can use the "Missing Person" feature within the app to report the situation to the server. This will prompt the server to take further action. 【0058】 Step 9: 【0059】 Upon receiving confirmation from a parent that their child is missing, the server provides emergency services with the child's last known location and activity history, requesting a swift response. 【0060】 In this way, a series of processing steps allows for efficient and safe monitoring of the child's location, and immediate action can be taken in the event of an anomaly. 【0061】 (Example 1) 【0062】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0063】 Ensuring child safety is a crucial issue in modern society. However, traditional methods make real-time monitoring of children's daily movements difficult, and there is a particular need to quickly detect and respond to abnormal behavior. Therefore, a system is needed that accurately monitors children's location and promptly notifies parents of any abnormalities, enabling them to respond appropriately. 【0064】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0065】 In this invention, the server includes means for acquiring the location information of an individual using a location information acquisition device and transmitting the location information to a central control unit, means for accumulating past behavioral history and detecting deviations from the normal range of activity, and means for sending a notification to a supervisor terminal when a deviation is detected. This makes it possible to monitor the safety of children in real time, quickly notify parents when an abnormality occurs, and take necessary action. 【0066】 A "location information acquisition device" is a device that measures an individual's current location in real time and records and transmits that information as digital data. 【0067】 A "central control unit" is a computer system used to process, store, and analyze collected location information. 【0068】 "Behavioral history" refers to a collection of location data collected in the past, which shows an individual's past movement patterns. 【0069】 "Deviation" refers to detecting unusual movement or behavior outside of the normal range of activity. 【0070】 A "supervisor terminal" is a communication terminal used by supervisors (parents / guardians) to receive notifications from a server and check information. 【0071】 A "rescue agency" is a public or private organization established to respond to emergencies and provide necessary support. 【0072】 A "learning algorithm" is an automated computational method for analyzing data, identifying patterns, and making predictions. 【0073】 This invention relates to a monitoring system for ensuring the safety of individuals, and includes a location information acquisition device, a central control unit, and a supervisor terminal. The terminal functions as a wearable device attached to the individual and acquires location information in real time using a GPS sensor. The acquired location information is transmitted to a server via a secure communication protocol. 【0074】 The server stores the received location information in a database, thereby accumulating a history of past movements. The server uses machine learning algorithms to analyze the accumulated data and model the user's normal range of movement. Based on this profile, it monitors for deviations each time new location information is sent and generates an alert if an anomaly is detected. 【0075】 As a concrete example, when monitoring an individual whose normal behavior pattern is to go directly home from school, if they move in a completely different direction, the server instantly detects this abnormal behavior. This allows the supervising parent to receive a quick alert and, if necessary, use the app's features to contact emergency services. 【0076】 An example of a prompt using a generative AI model is as follows: 【0077】 "Create an algorithm for a system that monitors an individual's location in real time and notifies the supervisor if there is a deviation from their normal behavioral pattern." 【0078】 In this way, the system can efficiently protect the safety of individuals and respond quickly to abnormal situations. Particularly in urban areas and regions where safety is a concern, this invention will function as a reliable safety tool. 【0079】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0080】 Step 1: 【0081】 The device obtains its current location using a built-in GPS sensor. This input is received from satellites as a GPS signal. After obtaining the location information, location data in digital format is generated. This data is then sent to the server via a secure communication protocol. HTTPS or TLS are commonly used as the transmission protocol. 【0082】 Step 2: 【0083】 The server receives location information transmitted from the terminal. This input is latitude and longitude data with a timestamp. The server stores this location data in a database, forming a history of past movements. The individual's movement history is updated when a new entry is added to the database. 【0084】 Step 3: 【0085】 The server retrieves behavioral history stored in the database and performs analysis using machine learning algorithms. The input is a dataset of past location data, and the output is a model representing typical behavioral patterns. Specifically, a clustering algorithm analyzes the location data and models the range of movement. This result is saved as a profile. 【0086】 Step 4: 【0087】 When new location information is received, the server compares this data to a normal behavioral pattern model. The input is real-time location data, and the output is a judgment result indicating whether or not an anomaly is present. Specifically, it determines whether the data deviation exceeds a set threshold, and if an anomaly is detected, it generates an alert. 【0088】 Step 5: 【0089】 When the server detects an anomaly, it sends a push notification to the supervisor's (user's) device. The input is the behavioral data that was determined to be an anomaly, and the output is a warning message to the user. Specifically, the notification is displayed via the app, allowing the user to quickly check the situation. 【0090】 Step 6: 【0091】 Users can check their current location and details of their departure within the app. They can also use the "Missing Person Certification" function to report an emergency, and the system will automatically initiate contact with rescue agencies. Input consists of the user's judgment and report, while output is the necessary information forwarded to rescue agencies. 【0092】 (Application Example 1) 【0093】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0094】 In modern society, ensuring the safety of children is a crucial issue, and there is a particular need for a swift response when children deviate from their normal range of activity. However, conventional systems only acquire and notify location information, making it difficult to encourage children themselves to take safety-conscious actions. 【0095】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0096】 In this invention, the server includes means for acquiring the child's location information and transmitting said location information to an information processing device, means for storing past behavioral patterns and detecting deviations from a detailed range of behavior, and means for alerting the monitored person using voice communication via a home automated device. This improves safety by providing prompt notification and allowing the child to receive the alert on the spot, thereby ensuring the child's safety. 【0097】 The term "child" usually refers to a minor who requires safety and protection from a parent or teacher. 【0098】 "Location information" refers to data used to identify the current location of a specific object through a geographic information system. 【0099】 An "information processing device" is an electronic device such as a computer or server that has the function of receiving and analyzing data. 【0100】 "Past behavioral patterns" refer to the subject's past movement history and behavioral tendencies, analyzed based on location information and time. 【0101】 "Deviation" refers to movement or behavior that deviates from the normal range or pattern of activity. 【0102】 "Notification" refers to a warning or message that is issued when a deviation or anomaly is detected, prompting prompt action. 【0103】 "Rescue agencies" refer to public or private organizations such as the police and fire departments that respond to emergencies. 【0104】 "Household automated appliances" refer to convenient electric devices used in everyday household environments, such as robots and smart devices. 【0105】 "Voice communication" is a technology that transmits messages to humans as voice through a machine. 【0106】 This system tracks children's movements in real time to ensure their safety. The server receives location information transmitted from home automated devices and mobile terminals and detects deviations based on past behavioral patterns. 【0107】 The device uses its built-in GPS sensor to determine the child's current location. The acquired location information is sent to the server via a secure protocol (HTTPS). The server uses machine learning libraries such as scikit-learn to analyze normal behavior patterns. Based on the analyzed information, if a deviation is detected, the server immediately sends a push notification to the parent's smartphone. 【0108】 Furthermore, if a home automated device detects an anomaly in location information, it can alert the child through voice communication. For example, it might say, "You've strayed too far from the road. Please come back." This encourages the child to become aware of the danger and take safe actions. 【0109】 An example of a prompt using a generative AI model is, "Please advise on how to monitor today's planned route within an appropriate distance." Based on this, the model can efficiently analyze behavioral patterns and detect anomalies. 【0110】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0111】 Step 1: 【0112】 The device uses its built-in GPS sensor to obtain the child's current location. The input is location data from the GPS sensor, and the output is the coordinate information of the detected current location. This data is obtained from a GPS device. 【0113】 Step 2: 【0114】 The device sends the acquired location information to the server using a secure protocol (HTTPS). The input is the acquired location information, and the output is the data sent to the server. The SSL / TLS protocol is used to ensure security. 【0115】 Step 3: 【0116】 The server stores the received location information in a database, accumulating past behavioral data. The input is the location information sent to the server, and the output is the stored database records. Information is stored and managed here. 【0117】 Step 4: 【0118】 The server uses the scikit-learn library to analyze and model typical behavioral patterns from accumulated data. The input is historical behavioral data in the database, and the output is a behavioral model. Machine learning is used to learn patterns of behavioral tendencies. 【0119】 Step 5: 【0120】 The server compares real-time location data with a modeled normal behavior pattern to determine if there is a deviation. The input is real-time location data and a behavioral model, and the output is whether or not there is a deviation. An anomaly detection algorithm is used for evaluation. 【0121】 Step 6: 【0122】 If a deviation is detected, the server sends a push notification to the user's smartphone. The input is the deviation information and the user's device information, and the output is the sent notification. This notification immediately draws the user's attention. 【0123】 Step 7: 【0124】 The home-use automated device transmits warning messages to children via voice communication. Inputs are deviation information and pre-set messages, and output is an audible warning. This device enables rapid response in the field. 【0125】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0126】 This invention provides a system that combines a location information management function with an emotion engine that recognizes the user's emotional state, in order to improve child safety management. This system includes means for a wearable device (terminal) to acquire the child's location information and transmit that data to a server. Furthermore, it is possible to analyze the child's daily behavior patterns and detect deviations. 【0127】 The device sends location information to the server at regular intervals. The server uses machine learning algorithms based on the received data to record and update normal behavior patterns. The server analyzes new location information in real time to determine if the user is deviating from their normal range of activity. Based on this information, it sends notifications to parents as needed. 【0128】 The emotion engine analyzes the user's emotions from voice, text, and facial recognition on the parent's device. This analysis is sent to a server, and the emotional data is considered when issuing deviation notifications. For example, if the user shows signs of tension or anxiety, the notification is prioritized, and an alert prompting immediate attention is provided. 【0129】 As a concrete example, suppose the server detects that a child has deviated from their usual route and is approaching a dangerous area. In this case, if the server determines, based on the user's sentiment data obtained from the device, that the user is already feeling anxious, it will immediately send an highlighted notification. This allows the user to quickly check the situation and take necessary measures. 【0130】 Thus, the embodiment of the present invention provides a system that integrates location monitoring and user emotion recognition, enabling parents to respond quickly and effectively to their child's safety. This system becomes an even more reliable safety tool for parents through flexible alert management that responds to emotions. 【0131】 The following describes the processing flow. 【0132】 Step 1: 【0133】 The device uses its built-in GPS sensor to obtain the child's current location. Location information is acquired at regular intervals and a timestamp is added. 【0134】 Step 2: 【0135】 The device transmits the acquired location information to the server. This transmission is encrypted to maintain the confidentiality and integrity of the location information. 【0136】 Step 3: 【0137】 The server stores the received location information in a database and builds a record of past behavioral patterns. This data is typically used to identify the range of the user's movements. 【0138】 Step 4: 【0139】 The server compares normal behavior patterns with real-time location information to detect deviations. When a deviation is detected, the server generates an alert. 【0140】 Step 5: 【0141】 An emotion engine runs on the user's device, analyzing the user's emotional data through voice, text, and facial recognition. This emotional data is used to adjust the importance of system notifications. 【0142】 Step 6: 【0143】 User emotion data is sent to the server. The server uses this data to determine notification priority. For example, if the user is feeling anxious, the notification priority is increased. 【0144】 Step 7: 【0145】 If a deviation is detected and the user's emotions indicate tension, the server will send a high-priority notification to the user. This notification will include details of the action and the need for immediate action. 【0146】 Step 8: 【0147】 Users receive high-priority notifications and can view detailed information through the app. They can then mark the pet as "lost" if necessary, enabling a quick response. 【0148】 Step 9: 【0149】 The server, upon receiving a user's lost child status, contacts pre-configured emergency services. The server supports a rapid response by providing the child's last known location and activity history. 【0150】 This series of steps enables highly accurate security management by combining location monitoring with emotional data. 【0151】 (Example 2) 【0152】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0153】 While systems exist to acquire children's location information and manage their safety, these systems primarily rely on notifications based on deviations from behavioral patterns and fail to consider the user's emotional state. This makes them ineffective in prompting responses in situations where users feel anxious. Furthermore, the limited information provided to emergency communication devices during emergencies means there is insufficient information to make quick situational judgments. 【0154】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0155】 In this invention, the server includes means for acquiring the child's location information and transmitting said location information to an information processing device, means for storing past behavioral patterns and detecting deviations from a modeled range of behavior, and means for acquiring the user's emotional data using an emotion analysis device and transmitting that data to an information processing device. This enables effective notification in more urgent situations by simultaneously considering deviations in location information and the user's emotional state. Furthermore, it facilitates a rapid response by providing more detailed and comprehensive information to emergency communication devices. 【0156】 "Location information" refers to data on the longitude and latitude that indicates a child's current location, and is obtained using GPS or other location measurement technologies. 【0157】 An "information processing device" refers to a series of computer systems that receive and analyze location information and emotional data, and then perform defined actions. 【0158】 "Modeled range of movement" is a concept that represents the typical range of movement generated by a machine learning algorithm based on the behavioral patterns a child has exhibited in the past. 【0159】 "Deviation" refers to a physical departure from a modeled range of behavior, and describes a situation that deviates from normal behavioral patterns. 【0160】 An "emotion analysis device" is a hardware and software system that estimates an emotional state using input such as voice, facial expressions, and text from the user. 【0161】 A "user" refers to a parent or supervisor who uses this system, and is the entity that receives the necessary information and makes decisions about actions. 【0162】 An "emergency communication device" refers to a communication infrastructure used to contact designated emergency services and has the function of quickly transmitting information in critical situations. 【0163】 This invention provides a system for effectively managing child safety, integrating location information acquisition and emotional state recognition. The system is specifically implemented as follows: 【0164】 Terminal hardware and software: 【0165】 The device operates as a wearable device worn by children and acquires location information using a built-in GPS module. GPS data consists of longitude and latitude, is temporarily stored within the device, and then transmitted to a server. Wi-Fi and cellular network technologies are used for data transmission, enabling real-time location tracking. 【0166】 Server hardware and software: 【0167】 The server functions as a cloud-based processing unit, enabling efficient processing of large amounts of location and sentiment data. Received location data is analyzed by machine learning algorithms implemented using programming languages ​​such as Python. This analysis models daily behavioral patterns, and any deviations detected immediately notify parents. User sentiment data is also analyzed using libraries such as OpenCV and TENSORFLOW®, taking emotional states into consideration. This allows for dynamic notification prioritization and highlighted notifications as needed. 【0168】 User actions: 【0169】 Users receive notifications through a dedicated application provided on their smartphones or tablets. The app provides detailed information about the child's current location and emotional state. If emotional data indicating anxiety is detected, a warning prompting immediate attention will be displayed on the user's device. 【0170】 Specific example: 【0171】 For example, suppose the server receives information that a device is taking a route the child doesn't normally take and is approaching a potentially dangerous area. Simultaneously, if the emotion analysis device suggests the child is experiencing anxiety, the server sends an highlighted notification to the parent's device. Through this, the user can quickly understand the child's dangerous situation and take prompt action. 【0172】 Example of a prompt: 【0173】 "Please explain how this system combines children's location data and emotional data to enhance safety management." 【0174】 This system, by using these means in combination with a dedicated application and cloud server, provides parents with the ability to properly monitor and manage their children's safety. 【0175】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0176】 Step 1: 【0177】 The device uses a built-in GPS module to obtain the child's location information. This location data (input) consists of longitude and latitude and is temporarily stored in the device. Storing the location information allows the device to prepare for subsequent data transmission. 【0178】 Step 2: 【0179】 The device sends the stored location data to the server. Specifically, it is sent (output) as encrypted data using the HTTPS protocol via Wi-Fi or a cellular network. This ensures secure communication, and the location information is received by the server. 【0180】 Step 3: 【0181】 The server receives location data as input and compares it with past behavioral patterns. A machine learning algorithm written in Python is used for this data analysis. As a result of the analysis, the modeled range of movement is updated, and it is determined whether the current location has deviated from the model (output). 【0182】 Step 4: 【0183】 The server receives emotion data sent from the terminal. This emotion data includes audio, facial expressions, and text input, captured using the smartphone's camera and microphone. OpenCV and TensorFlow are used to analyze the emotional state. The analysis results (output) are stored on the server as an indicator of the user's emotional state. 【0184】 Step 5: 【0185】 The server analyzes and integrates location deviations and emotional data. Based on this integration, notification priority is determined. For example, if a deviation is detected and the user indicates anxiety, the notification priority will be increased. The notification content (output) includes specific location information and emotional state. 【0186】 Step 6: 【0187】 Based on the analysis results described above, the server sends a notification to the parent's device. The notification is displayed on a dedicated app on the parent's smartphone, providing highlighted information. This allows the user to quickly understand the situation and take necessary actions. 【0188】 (Application Example 2) 【0189】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0190】 In modern society, ensuring children's safety requires not only monitoring their location but also sophisticated monitoring systems that take into account their emotional state. However, conventional systems only track location and lack sufficient notifications and emergency response that reflect the child's emotional state. 【0191】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0192】 In this invention, the server includes means for acquiring the child's location information and transmitting said location information to a central processing unit, means for storing past behavioral patterns and detecting deviations from the recorded range of behavior, and means for adjusting notification priority based on recognized emotional states. This enables flexible and rapid notification and response based on the child's location and emotional state. 【0193】 "Location information" refers to data that indicates the geographical coordinates of a specific object or individual. 【0194】 A "central processing unit" is a computer system that integrates and analyzes various types of data. 【0195】 "Behavioral patterns" refer to a collection of data that shows the characteristics of a user's movements and activities over a certain period of time. 【0196】 "Deviation" refers to a phenomenon that deviates from the usual range or pattern of behavior that has been recorded in advance. 【0197】 A "notification" is a message or alert used to inform a user of a specific event. 【0198】 "Emotions" refer to information that indicates an individual's affective state or psychological tendencies. 【0199】 "Priority" is an indicator that shows how important a particular task or notification is compared to other tasks or notifications. 【0200】 An "emergency organization" is a trained, specialized agency or group designed to respond to an emergency. 【0201】 A "detector" is a device used to detect specific signals from the environment. 【0202】 An "analysis engine" is a software or hardware system that organizes input data and produces a specific output. 【0203】 The system that realizes this invention aims to monitor a child's behavior and emotions using wearable devices and terminals, and to provide information to parents at the appropriate time. 【0204】 First, the device is equipped with a GPS module that periodically acquires the child's location information and sends it to the server. The server uses machine learning algorithms to analyze past behavioral patterns based on this data. Specifically, the server uses the PyTorch library to model these patterns and detect deviations from behavior. 【0205】 Next, regarding emotion recognition, the device is equipped with a camera and microphone, which are used to analyze the child's facial expressions and voice in real time. For the emotion analysis engine, YOLOv5 is used for image processing, and OpenAI's Whisper is used for voice analysis. As a result, the device understands the child's emotional state and sends that data to the server. 【0206】 Based on the emotional data it receives, the server adjusts the priority of notifications as needed. For example, if a child deviates from their normal range of activity and an anxious emotional state is detected, the server sends a higher-priority notification to the parent's device. This allows the parent to quickly check the situation and take appropriate action. 【0207】 For example, a child might move to a place they don't normally visit, and their expression might show signs of anxiety. In such a situation, the server would immediately send a warning to the parents, requiring a real-time response. 【0208】 An example of a prompt would be, "Create a notification message for when a child's location deviates from their usual behavioral pattern and they are showing signs of anxiety based on emotional data." This would allow the system to provide parents with specific and appropriate information. 【0209】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0210】 Step 1: 【0211】 The device uses a GPS module to obtain the child's location information. The input information is GPS data, which is sent to the device as location coordinates. The location coordinates are then ready to be sent directly to the server. 【0212】 Step 2: 【0213】 The device uses its built-in camera and microphone to capture and record the child's facial expressions and voice, collecting emotional data. Input information consists of image and audio data, which are analyzed using YOLOv5 and OpenAI's Whisper. Output consists of numerical data and tags representing emotions. 【0214】 Step 3: 【0215】 The device sends the collected location information and sentiment data to the server. The input is the location coordinates and sentiment data mentioned earlier, and the output is processed as a dataset for analysis on the server. 【0216】 Step 4: 【0217】 The server analyzes the received location information using a machine learning algorithm and retrieves past behavioral patterns from a database. The input is location coordinates, and the output is a judgment on whether those coordinates represent a deviation from the behavioral range. This allows for an assessment of the normality of the behavior. 【0218】 Step 5: 【0219】 The server analyzes the emotional state based on the received emotional data and a pre-configured emotional model. The input is emotional data, and the output is data indicating the intensity and type of emotion the user is experiencing. The analysis then sets the priority of the emotions. 【0220】 Step 6: 【0221】 Based on the analysis results, the server determines the necessity and priority of notifications to the parent's device. Inputs include data on behavioral deviations and emotional priority data. Outputs include information such as the content of the notification and the degree of warning. This prepares the notification to be sent to the parent. 【0222】 Step 7: 【0223】 Parents, as users, receive notifications, check their child's location as needed, and take appropriate action. Input is information in the form of notifications, and output is the next action based on that information. Parents can ensure their child's safety by reacting immediately. 【0224】 The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data. 【0225】 Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0226】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14. 【0227】 [Second Embodiment] 【0228】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0229】 As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server. 【0230】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0231】 The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52. 【0232】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0233】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0234】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0235】 Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0236】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30. 【0237】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0238】 In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0239】 Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0240】 The present invention is a system for ensuring the safety of children, and includes a wearable device that provides safety functions mainly using location information, and a central processing unit that processes the data. Examples are described below. 【0241】 The wearable device (terminal) acquires the child's location information in real time and transmits this information to a central processing unit (server) every 5 minutes. The GPS sensor in the terminal acquires the location information, and this data is sent to the server via a secure communication protocol. The server stores the received location information in a database and forms past behavioral patterns that are accumulated over time. 【0242】 The server uses machine learning algorithms to analyze past behavioral data and model the normal range of movement. Based on this profile, the server compares incoming new location information with normal behavioral patterns in real time and generates alerts when deviations are detected. 【0243】 When an anomaly is detected, the server sends a push notification to the parent's device. The user (parent) can check the child's current location and escape information via the app. If necessary, the parent can use the "Missing Child" function in the app to report the situation to the system. This allows the server to immediately contact emergency services and provide the child's last known location and activity history. 【0244】 For example, suppose a child who normally goes straight home from school is detected by the server to be heading in a completely different direction one day. In this case, the server determines that the current location transmitted by the device deviates from the usual route and notifies the parents. The parents can then make a quick decision based on this information and, if necessary, coordinate with emergency services. 【0245】 This embodiment of the present invention enables efficient monitoring of children's safety and immediate response to abnormal situations. This system will be a reliable safety tool for parents, especially in urban areas and neighborhoods where safety is a concern. 【0246】 The following describes the processing flow. 【0247】 Step 1: 【0248】 The device uses its built-in GPS sensor to obtain the child's current location information. This information is obtained at regular intervals, for example, every 5 minutes. The obtained data includes location information and a timestamp. 【0249】 Step 2: 【0250】 The device transmits the acquired location information to the server using a secure protocol. This transmission is encrypted to prevent data tampering by third parties. 【0251】 Step 3: 【0252】 The server receives location information transmitted from the device and stores it in a database. During this storage process, the data is organized based on date and time and recorded as a pattern of past activity. 【0253】 Step 4: 【0254】 The server uses machine learning algorithms based on accumulated behavioral data to analyze the child's typical range of movement. This analysis profiles the child's general activity patterns. 【0255】 Step 5: 【0256】 The server analyzes new location information in real time and compares it with existing behavioral profiles. This comparison helps determine whether the user is deviating from their normal range of activity. 【0257】 Step 6: 【0258】 If the server detects an deviation, it will send a notification to the parent's device. The notification will include the current location, details of the deviation, and potential dangers. 【0259】 Step 7: 【0260】 The user (parent / guardian) checks the received notification and opens the app to assess the situation. Within the app, they can view the child's current location and activity history on a map. 【0261】 Step 8: 【0262】 If the user deems it necessary, they can use the "Missing Person" feature within the app to report the situation to the server. This will prompt the server to take further action. 【0263】 Step 9: 【0264】 Upon receiving confirmation from a parent that their child is missing, the server provides emergency services with the child's last known location and activity history, requesting a swift response. 【0265】 In this way, a series of processing steps allows for efficient and safe monitoring of the child's location, and immediate action can be taken in the event of an anomaly. 【0266】 (Example 1) 【0267】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0268】 Ensuring child safety is a crucial issue in modern society. However, traditional methods make real-time monitoring of children's daily movements difficult, and there is a particular need to quickly detect and respond to abnormal behavior. Therefore, a system is needed that accurately monitors children's location and promptly notifies parents of any abnormalities, enabling them to respond appropriately. 【0269】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0270】 In this invention, the server includes means for acquiring the location information of an individual using a location information acquisition device and transmitting the location information to a central control unit, means for accumulating past behavioral history and detecting deviations from the normal range of activity, and means for sending a notification to a supervisor terminal when a deviation is detected. This makes it possible to monitor the safety of children in real time, quickly notify parents when an abnormality occurs, and take necessary action. 【0271】 A "location information acquisition device" is a device that measures an individual's current location in real time and records and transmits that information as digital data. 【0272】 A "central control unit" is a computer system used to process, store, and analyze collected location information. 【0273】 "Behavioral history" refers to a collection of location data collected in the past, which shows an individual's past movement patterns. 【0274】 "Deviation" refers to detecting unusual movement or behavior outside of the normal range of activity. 【0275】 A "supervisor terminal" is a communication terminal used by supervisors (parents / guardians) to receive notifications from a server and check information. 【0276】 A "rescue agency" is a public or private organization established to respond to emergencies and provide necessary support. 【0277】 A "learning algorithm" is an automated computational method for analyzing data, identifying patterns, and making predictions. 【0278】 This invention relates to a monitoring system for ensuring the safety of individuals, and includes a location information acquisition device, a central control unit, and a supervisor terminal. The terminal functions as a wearable device attached to the individual and acquires location information in real time using a GPS sensor. The acquired location information is transmitted to a server via a secure communication protocol. 【0279】 The server stores the received location information in a database, thereby accumulating a history of past movements. The server uses machine learning algorithms to analyze the accumulated data and model the user's normal range of movement. Based on this profile, it monitors for deviations each time new location information is sent and generates an alert if an anomaly is detected. 【0280】 As a specific example, when monitoring an individual whose normal behavior pattern is to go directly home from school, if the individual moves in a completely different direction, the server can instantly detect this abnormal behavior. As a result, the guardian, who is the supervisor, can receive a warning promptly and, if necessary, can use the functions within the app to cooperate with the emergency services. 【0281】 Examples of prompt texts using the generative AI model are as follows: 【0282】 "Create an algorithm for a system that monitors the location information of an individual in real time and notifies the supervisor if there is a deviation from the normal behavior pattern." 【0283】 In this way, the system can efficiently protect the safety of the individual and can respond promptly to abnormal situations. Especially in urban areas or regions with safety concerns, the present invention will function as a reliable safety tool. 【0284】 The flow of the specific process in Example 1 will be described using FIG. 11. 【0285】 Step 1: 【0286】 The terminal uses the built-in GPS sensor to obtain the current location information. This input is received from satellites as GPS signals. After obtaining the location information, digital format location data is generated. Next, this data is transmitted to the server via a secure communication protocol. As the transmission protocol, HTTPS or TLS is generally used. 【0287】 Step 2: 【0288】 The server receives the location information transmitted from the terminal. This input is latitude and longitude data with a timestamp. The server stores this location data in the database to form a past behavior history. By adding a new entry to the database, the movement history of the individual is updated. 【0289】 Step 3: 【0290】 The server retrieves behavioral history stored in the database and performs analysis using machine learning algorithms. The input is a dataset of past location data, and the output is a model representing typical behavioral patterns. Specifically, a clustering algorithm analyzes the location data and models the range of movement. This result is saved as a profile. 【0291】 Step 4: 【0292】 When new location information is received, the server compares this data to a normal behavioral pattern model. The input is real-time location data, and the output is a judgment result indicating whether or not an anomaly is present. Specifically, it determines whether the data deviation exceeds a set threshold, and if an anomaly is detected, it generates an alert. 【0293】 Step 5: 【0294】 When the server detects an anomaly, it sends a push notification to the supervisor's (user's) device. The input is the behavioral data that was determined to be an anomaly, and the output is a warning message to the user. Specifically, the notification is displayed via the app, allowing the user to quickly check the situation. 【0295】 Step 6: 【0296】 Users can check their current location and details of their departure within the app. They can also use the "Missing Person Certification" function to report an emergency, and the system will automatically initiate contact with rescue agencies. Input consists of the user's judgment and report, while output is the necessary information forwarded to rescue agencies. 【0297】 (Application Example 1) 【0298】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0299】 In modern society, ensuring the safety of children is a crucial issue, and there is a particular need for a swift response when children deviate from their normal range of activity. However, conventional systems only acquire and notify location information, making it difficult to encourage children themselves to take safety-conscious actions. 【0300】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0301】 In this invention, the server includes means for acquiring the child's location information and transmitting said location information to an information processing device, means for storing past behavioral patterns and detecting deviations from a detailed range of behavior, and means for alerting the monitored person using voice communication via a home automated device. This improves safety by providing prompt notification and allowing the child to receive the alert on the spot, thereby ensuring the child's safety. 【0302】 The term "child" usually refers to a minor who requires safety and protection from a parent or teacher. 【0303】 "Location information" refers to data used to identify the current location of a specific object through a geographic information system. 【0304】 An "information processing device" is an electronic device such as a computer or server that has the function of receiving and analyzing data. 【0305】 "Past behavioral patterns" refer to the subject's past movement history and behavioral tendencies, analyzed based on location information and time. 【0306】 "Deviation" refers to movement or behavior that deviates from the normal range or pattern of activity. 【0307】 "Notification" refers to warnings and messages that occur when deviations or abnormalities are detected, prompting prompt action. 【0308】 "Relief agency" refers to public or private organizations that respond in emergencies such as the police and fire departments. 【0309】 "Household automatic equipment" refers to convenient electric equipment used in daily household environments, such as robots and smart devices. 【0310】 "Voice communication" is a technology that transmits messages to humans as voice through machines. 【0311】 This system tracks the movements of children in real-time to ensure safety. The server receives location information sent from household automatic equipment and mobile terminals, and detects deviations based on past behavior patterns. 【0312】 The terminal uses the built-in GPS sensor to obtain the current location of the child. The acquired location information is sent to the server via a secure protocol (HTTPS). The server utilizes machine learning libraries such as scikit-learn to analyze normal behavior patterns. Based on the analyzed information, when a deviation is detected, the server immediately sends a push notification to the guardian's smartphone. 【0313】 In addition, when the household automatic equipment detects an abnormality in the location information, it can prompt the child's attention through voice communication. As a specific example, it conveys messages such as "You've strayed too far from the path. Come back." in voice. This prompts the child to be aware of the danger on the spot and encourages them to take safe actions. 【0314】 An example of a prompt sentence using the generative AI model is "Please advise on how to monitor today's planned route within an appropriate distance." Based on this, the model can efficiently analyze behavior patterns and detect abnormalities. 【0315】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0316】 Step 1: 【0317】 The device uses its built-in GPS sensor to obtain the child's current location. The input is location data from the GPS sensor, and the output is the coordinate information of the detected current location. This data is obtained from a GPS device. 【0318】 Step 2: 【0319】 The device sends the acquired location information to the server using a secure protocol (HTTPS). The input is the acquired location information, and the output is the data sent to the server. The SSL / TLS protocol is used to ensure security. 【0320】 Step 3: 【0321】 The server stores the received location information in a database, accumulating past behavioral data. The input is the location information sent to the server, and the output is the stored database records. Information is stored and managed here. 【0322】 Step 4: 【0323】 The server uses the scikit-learn library to analyze and model typical behavioral patterns from accumulated data. The input is historical behavioral data in the database, and the output is a behavioral model. Machine learning is used to learn patterns of behavioral tendencies. 【0324】 Step 5: 【0325】 The server compares real-time location data with a modeled normal behavior pattern to determine if there is a deviation. The input is real-time location data and a behavioral model, and the output is whether or not there is a deviation. An anomaly detection algorithm is used for evaluation. 【0326】 Step 6: 【0327】 If a deviation is detected, the server sends a push notification to the user's smartphone. The input is the deviation information and the user's device information, and the output is the sent notification. This notification immediately draws the user's attention. 【0328】 Step 7: 【0329】 The home-use automated device transmits warning messages to children via voice communication. Inputs are deviation information and pre-set messages, and output is an audible warning. This device enables rapid response in the field. 【0330】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0331】 This invention provides a system that combines a location information management function with an emotion engine that recognizes the user's emotional state, in order to improve child safety management. This system includes means for a wearable device (terminal) to acquire the child's location information and transmit that data to a server. Furthermore, it is possible to analyze the child's daily behavior patterns and detect deviations. 【0332】 The device sends location information to the server at regular intervals. The server uses machine learning algorithms based on the received data to record and update normal behavior patterns. The server analyzes new location information in real time to determine if the user is deviating from their normal range of activity. Based on this information, it sends notifications to parents as needed. 【0333】 The emotion engine analyzes the user's emotions from voice, text, and facial recognition on the parent's device. This analysis is sent to a server, and the emotional data is considered when issuing deviation notifications. For example, if the user shows signs of tension or anxiety, the notification is prioritized, and an alert prompting immediate attention is provided. 【0334】 As a concrete example, suppose the server detects that a child has deviated from their usual route and is approaching a dangerous area. In this case, if the server determines, based on the user's sentiment data obtained from the device, that the user is already feeling anxious, it will immediately send an highlighted notification. This allows the user to quickly check the situation and take necessary measures. 【0335】 Thus, the embodiment of the present invention provides a system that integrates location monitoring and user emotion recognition, enabling parents to respond quickly and effectively to their child's safety. This system becomes an even more reliable safety tool for parents through flexible alert management that responds to emotions. 【0336】 The following describes the processing flow. 【0337】 Step 1: 【0338】 The device uses its built-in GPS sensor to obtain the child's current location. Location information is acquired at regular intervals and a timestamp is added. 【0339】 Step 2: 【0340】 The device transmits the acquired location information to the server. This transmission is encrypted to maintain the confidentiality and integrity of the location information. 【0341】 Step 3: 【0342】 The server stores the received location information in a database and builds a record of past behavioral patterns. This data is typically used to identify the range of the user's movements. 【0343】 Step 4: 【0344】 The server compares normal behavior patterns with real-time location information to detect deviations. When a deviation is detected, the server generates an alert. 【0345】 Step 5: 【0346】 An emotion engine runs on the user's device, analyzing the user's emotional data through voice, text, and facial recognition. This emotional data is used to adjust the importance of system notifications. 【0347】 Step 6: 【0348】 User emotion data is sent to the server. The server uses this data to determine notification priority. For example, if the user is feeling anxious, the notification priority is increased. 【0349】 Step 7: 【0350】 If a deviation is detected and the user's emotions indicate tension, the server will send a high-priority notification to the user. This notification will include details of the action and the need for immediate action. 【0351】 Step 8: 【0352】 Users receive high-priority notifications and can view detailed information through the app. They can then mark the pet as "lost" if necessary, enabling a quick response. 【0353】 Step 9: 【0354】 The server, upon receiving a user's lost child status, contacts pre-configured emergency services. The server supports a rapid response by providing the child's last known location and activity history. 【0355】 This series of steps enables highly accurate security management by combining location monitoring with emotional data. 【0356】 (Example 2) 【0357】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0358】 While systems exist to acquire children's location information and manage their safety, these systems primarily rely on notifications based on deviations from behavioral patterns and fail to consider the user's emotional state. This makes them ineffective in prompting responses in situations where users feel anxious. Furthermore, the limited information provided to emergency communication devices during emergencies means there is insufficient information to make quick situational judgments. 【0359】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0360】 In this invention, the server includes means for acquiring the child's location information and transmitting said location information to an information processing device, means for storing past behavioral patterns and detecting deviations from a modeled range of behavior, and means for acquiring the user's emotional data using an emotion analysis device and transmitting that data to an information processing device. This enables effective notification in more urgent situations by simultaneously considering deviations in location information and the user's emotional state. Furthermore, it facilitates a rapid response by providing more detailed and comprehensive information to emergency communication devices. 【0361】 "Location information" refers to data on the longitude and latitude that indicates a child's current location, and is obtained using GPS or other location measurement technologies. 【0362】 An "information processing device" refers to a series of computer systems that receive and analyze location information and emotional data, and then perform defined actions. 【0363】 "Modeled range of movement" is a concept that represents the typical range of movement generated by a machine learning algorithm based on the behavioral patterns a child has exhibited in the past. 【0364】 "Deviation" refers to a physical departure from a modeled range of behavior, and describes a situation that deviates from normal behavioral patterns. 【0365】 An "emotion analysis device" is a hardware and software system that estimates an emotional state using input such as voice, facial expressions, and text from the user. 【0366】 A "user" refers to a parent or supervisor who uses this system, and is the entity that receives the necessary information and makes decisions about actions. 【0367】 An "emergency communication device" refers to a communication infrastructure used to contact designated emergency services and has the function of quickly transmitting information in critical situations. 【0368】 This invention provides a system for effectively managing child safety, integrating location information acquisition and emotional state recognition. The system is specifically implemented as follows: 【0369】 Terminal hardware and software: 【0370】 The device operates as a wearable device worn by children and acquires location information using a built-in GPS module. GPS data consists of longitude and latitude, is temporarily stored within the device, and then transmitted to a server. Wi-Fi and cellular network technologies are used for data transmission, enabling real-time location tracking. 【0371】 Server hardware and software: 【0372】 The server functions as a cloud-based processing unit, enabling efficient processing of large amounts of location and sentiment data. Received location data is analyzed by machine learning algorithms implemented using programming languages ​​such as Python. This analysis models daily behavioral patterns, and any deviations detected immediately notify parents. User sentiment data is also analyzed using libraries such as OpenCV and TensorFlow, taking emotional states into consideration. This allows for dynamic notification prioritization and highlighted notifications as needed. 【0373】 User actions: 【0374】 Users receive notifications through a dedicated application provided on their smartphones or tablets. The app provides detailed information about the child's current location and emotional state. If emotional data indicating anxiety is detected, a warning prompting immediate attention will be displayed on the user's device. 【0375】 Specific example: 【0376】 For example, suppose the server receives information that a device is taking a route the child doesn't normally take and is approaching a potentially dangerous area. Simultaneously, if the emotion analysis device suggests the child is experiencing anxiety, the server sends an highlighted notification to the parent's device. Through this, the user can quickly understand the child's dangerous situation and take prompt action. 【0377】 Example of a prompt: 【0378】 "Please explain how this system combines children's location data and emotional data to enhance safety management." 【0379】 This system, by using these means in combination with a dedicated application and cloud server, provides parents with the ability to properly monitor and manage their children's safety. 【0380】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0381】 Step 1: 【0382】 The device uses a built-in GPS module to obtain the child's location information. This location data (input) consists of longitude and latitude and is temporarily stored in the device. Storing the location information allows the device to prepare for subsequent data transmission. 【0383】 Step 2: 【0384】 The device sends the stored location data to the server. Specifically, it is sent (output) as encrypted data using the HTTPS protocol via Wi-Fi or a cellular network. This ensures secure communication, and the location information is received by the server. 【0385】 Step 3: 【0386】 The server receives location data as input and compares it with past behavioral patterns. A machine learning algorithm written in Python is used for this data analysis. As a result of the analysis, the modeled range of movement is updated, and it is determined whether the current location has deviated from the model (output). 【0387】 Step 4: 【0388】 The server receives emotion data sent from the terminal. This emotion data includes audio, facial expressions, and text input, captured using the smartphone's camera and microphone. OpenCV and TensorFlow are used to analyze the emotional state. The analysis results (output) are stored on the server as an indicator of the user's emotional state. 【0389】 Step 5: 【0390】 The server analyzes and integrates location deviations and emotional data. Based on this integration, notification priority is determined. For example, if a deviation is detected and the user indicates anxiety, the notification priority will be increased. The notification content (output) includes specific location information and emotional state. 【0391】 Step 6: 【0392】 Based on the analysis results described above, the server sends a notification to the parent's device. The notification is displayed on a dedicated app on the parent's smartphone, providing highlighted information. This allows the user to quickly understand the situation and take necessary actions. 【0393】 (Application Example 2) 【0394】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0395】 In modern society, ensuring children's safety requires not only monitoring their location but also sophisticated monitoring systems that take into account their emotional state. However, conventional systems only track location and lack sufficient notifications and emergency response that reflect the child's emotional state. 【0396】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0397】 In this invention, the server includes means for acquiring the child's location information and transmitting said location information to a central processing unit, means for storing past behavioral patterns and detecting deviations from the recorded range of behavior, and means for adjusting notification priority based on recognized emotional states. This enables flexible and rapid notification and response based on the child's location and emotional state. 【0398】 "Location information" refers to data that indicates the geographical coordinates of a specific object or individual. 【0399】 A "central processing unit" is a computer system that integrates and analyzes various types of data. 【0400】 "Behavioral patterns" refer to a collection of data that shows the characteristics of a user's movements and activities over a certain period of time. 【0401】 "Deviation" refers to a phenomenon that deviates from the usual range or pattern of behavior that has been recorded in advance. 【0402】 A "notification" is a message or alert used to inform a user of a specific event. 【0403】 "Emotions" refer to information that indicates an individual's affective state or psychological tendencies. 【0404】 "Priority" is an indicator that shows how important a particular task or notification is compared to other tasks or notifications. 【0405】 An "emergency organization" is a trained, specialized agency or group designed to respond to an emergency. 【0406】 A "detector" is a device used to detect specific signals from the environment. 【0407】 An "analysis engine" is a software or hardware system that organizes input data and produces a specific output. 【0408】 The system that realizes this invention aims to monitor a child's behavior and emotions using wearable devices and terminals, and to provide information to parents at the appropriate time. 【0409】 First, the device is equipped with a GPS module that periodically acquires the child's location information and sends it to the server. The server uses machine learning algorithms to analyze past behavioral patterns based on this data. Specifically, the server uses the PyTorch library to model these patterns and detect deviations from behavior. 【0410】 Next, regarding emotion recognition, the device is equipped with a camera and microphone, which are used to analyze the child's facial expressions and voice in real time. For the emotion analysis engine, YOLOv5 is used for image processing and OpenAI's Whisper is used for voice analysis. This allows the device to understand the child's emotional state and send that data to the server. 【0411】 Based on the emotional data it receives, the server adjusts the priority of notifications as needed. For example, if a child deviates from their normal range of activity and an anxious emotional state is detected, the server sends a higher-priority notification to the parent's device. This allows the parent to quickly check the situation and take appropriate action. 【0412】 For example, a child might move to a place they don't normally visit, and their expression might show signs of anxiety. In such a situation, the server would immediately send a warning to the parents, requiring a real-time response. 【0413】 An example of a prompt would be, "Create a notification message for when a child's location deviates from their usual behavioral pattern and they are showing signs of anxiety based on emotional data." This would allow the system to provide parents with specific and appropriate information. 【0414】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0415】 Step 1: 【0416】 The device uses a GPS module to obtain the child's location information. The input information is GPS data, which is sent to the device as location coordinates. The location coordinates are then ready to be sent directly to the server. 【0417】 Step 2: 【0418】 The device uses its built-in camera and microphone to capture and record the child's facial expressions and voice, collecting emotional data. Input information consists of image and audio data, which are analyzed using YOLOv5 and OpenAI's Whisper. Output consists of numerical data and tags representing emotions. 【0419】 Step 3: 【0420】 The device sends the collected location information and sentiment data to the server. The input is the location coordinates and sentiment data mentioned earlier, and the output is processed as a dataset for analysis on the server. 【0421】 Step 4: 【0422】 The server analyzes the received location information using a machine learning algorithm and retrieves past behavioral patterns from a database. The input is location coordinates, and the output is a judgment on whether those coordinates represent a deviation from the behavioral range. This allows for an assessment of the normality of the behavior. 【0423】 Step 5: 【0424】 The server analyzes the emotional state based on the received emotional data and a pre-configured emotional model. The input is emotional data, and the output is data indicating the intensity and type of emotion the user is experiencing. The analysis then sets the priority of the emotions. 【0425】 Step 6: 【0426】 Based on the analysis results, the server determines the necessity and priority of notifications to the parent's device. Inputs include data on behavioral deviations and emotional priority data. Outputs include information such as the content of the notification and the degree of warning. This prepares the notification to be sent to the parent. 【0427】 Step 7: 【0428】 Parents, as users, receive notifications, check their child's location as needed, and take appropriate action. Input is information in the form of notifications, and output is the next action based on that information. Parents can ensure their child's safety by reacting immediately. 【0429】 The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0430】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0431】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214. 【0432】 [Third Embodiment] 【0433】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0434】 As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server. 【0435】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0436】 The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52. 【0437】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0438】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0439】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0440】 Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0441】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30. 【0442】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0443】 In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0444】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal". 【0445】 The present invention is a system for ensuring the safety of children, and includes a wearable device that provides safety functions mainly using location information, and a central processing unit that processes the data. Examples are described below. 【0446】 The wearable device (terminal) acquires the child's location information in real time and transmits this information to a central processing unit (server) every 5 minutes. The GPS sensor in the terminal acquires the location information, and this data is sent to the server via a secure communication protocol. The server stores the received location information in a database and forms past behavioral patterns that are accumulated over time. 【0447】 The server uses machine learning algorithms to analyze past behavioral data and model the normal range of movement. Based on this profile, the server compares incoming new location information with normal behavioral patterns in real time and generates alerts when deviations are detected. 【0448】 When an anomaly is detected, the server sends a push notification to the parent's device. The user (parent) can check the child's current location and escape information via the app. If necessary, the parent can use the "Missing Child" function in the app to report the situation to the system. This allows the server to immediately contact emergency services and provide the child's last known location and activity history. 【0449】 For example, suppose a child who normally goes straight home from school is detected by the server to be heading in a completely different direction one day. In this case, the server determines that the current location transmitted by the device deviates from the usual route and notifies the parents. The parents can then make a quick decision based on this information and, if necessary, coordinate with emergency services. 【0450】 This embodiment of the present invention enables efficient monitoring of children's safety and immediate response to abnormal situations. This system will be a reliable safety tool for parents, especially in urban areas and neighborhoods where safety is a concern. 【0451】 The following describes the processing flow. 【0452】 Step 1: 【0453】 The device uses its built-in GPS sensor to obtain the child's current location information. This information is obtained at regular intervals, for example, every 5 minutes. The obtained data includes location information and a timestamp. 【0454】 Step 2: 【0455】 The device transmits the acquired location information to the server using a secure protocol. This transmission is encrypted to prevent data tampering by third parties. 【0456】 Step 3: 【0457】 The server receives location information transmitted from the device and stores it in a database. During this storage process, the data is organized based on date and time and recorded as a pattern of past activity. 【0458】 Step 4: 【0459】 The server uses machine learning algorithms based on accumulated behavioral data to analyze the child's typical range of movement. This analysis profiles the child's general activity patterns. 【0460】 Step 5: 【0461】 The server analyzes new location information in real time and compares it with existing behavioral profiles. This comparison helps determine whether the user is deviating from their normal range of activity. 【0462】 Step 6: 【0463】 If the server detects an deviation, it will send a notification to the parent's device. The notification will include the current location, details of the deviation, and potential dangers. 【0464】 Step 7: 【0465】 The user (parent / guardian) checks the received notification and opens the app to assess the situation. Within the app, they can view the child's current location and activity history on a map. 【0466】 Step 8: 【0467】 If the user deems it necessary, they can use the "Missing Person" feature within the app to report the situation to the server. This will prompt the server to take further action. 【0468】 Step 9: 【0469】 Upon receiving confirmation from a parent that their child is missing, the server provides emergency services with the child's last known location and activity history, requesting a swift response. 【0470】 In this way, a series of processing steps allows for efficient and safe monitoring of the child's location, and immediate action can be taken in the event of an anomaly. 【0471】 (Example 1) 【0472】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0473】 Ensuring child safety is a crucial issue in modern society. However, traditional methods make real-time monitoring of children's daily movements difficult, and there is a particular need to quickly detect and respond to abnormal behavior. Therefore, a system is needed that accurately monitors children's location and promptly notifies parents of any abnormalities, enabling them to respond appropriately. 【0474】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0475】 In this invention, the server includes means for acquiring the location information of an individual using a location information acquisition device and transmitting the location information to a central control unit, means for accumulating past behavioral history and detecting deviations from the normal range of activity, and means for sending a notification to a supervisor terminal when a deviation is detected. This makes it possible to monitor the safety of children in real time, quickly notify parents when an abnormality occurs, and take necessary action. 【0476】 A "location information acquisition device" is a device that measures an individual's current location in real time and records and transmits that information as digital data. 【0477】 A "central control unit" is a computer system used to process, store, and analyze collected location information. 【0478】 "Behavioral history" refers to a collection of location data collected in the past, which shows an individual's past movement patterns. 【0479】 "Deviation" refers to detecting unusual movement or behavior outside of the normal range of activity. 【0480】 A "supervisor terminal" is a communication terminal used by supervisors (parents / guardians) to receive notifications from a server and check information. 【0481】 A "rescue agency" is a public or private organization established to respond to emergencies and provide necessary support. 【0482】 A "learning algorithm" is an automated computational method for analyzing data, identifying patterns, and making predictions. 【0483】 This invention relates to a monitoring system for ensuring the safety of individuals, and includes a location information acquisition device, a central control unit, and a supervisor terminal. The terminal functions as a wearable device attached to the individual and acquires location information in real time using a GPS sensor. The acquired location information is transmitted to a server via a secure communication protocol. 【0484】 The server stores the received location information in a database, thereby accumulating a history of past movements. The server uses machine learning algorithms to analyze the accumulated data and model the user's normal range of movement. Based on this profile, it monitors for deviations each time new location information is sent and generates an alert if an anomaly is detected. 【0485】 As a concrete example, when monitoring an individual whose normal behavior pattern is to go directly home from school, if they move in a completely different direction, the server instantly detects this abnormal behavior. This allows the supervising parent to receive a quick alert and, if necessary, use the app's features to contact emergency services. 【0486】 An example of a prompt using a generative AI model is as follows: 【0487】 "Create an algorithm for a system that monitors an individual's location in real time and notifies the supervisor if there is a deviation from their normal behavioral pattern." 【0488】 In this way, the system can efficiently protect the safety of individuals and respond quickly to abnormal situations. Particularly in urban areas and regions where safety is a concern, this invention will function as a reliable safety tool. 【0489】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0490】 Step 1: 【0491】 The device obtains its current location using a built-in GPS sensor. This input is received from satellites as a GPS signal. After obtaining the location information, location data in digital format is generated. This data is then sent to the server via a secure communication protocol. HTTPS or TLS are commonly used as the transmission protocol. 【0492】 Step 2: 【0493】 The server receives location information transmitted from the terminal. This input is latitude and longitude data with a timestamp. The server stores this location data in a database, forming a history of past movements. The individual's movement history is updated when a new entry is added to the database. 【0494】 Step 3: 【0495】 The server retrieves behavioral history stored in the database and performs analysis using machine learning algorithms. The input is a dataset of past location data, and the output is a model representing typical behavioral patterns. Specifically, a clustering algorithm analyzes the location data and models the range of movement. This result is saved as a profile. 【0496】 Step 4: 【0497】 When new location information is received, the server compares this data to a normal behavioral pattern model. The input is real-time location data, and the output is a judgment result indicating whether or not an anomaly is present. Specifically, it determines whether the data deviation exceeds a set threshold, and if an anomaly is detected, it generates an alert. 【0498】 Step 5: 【0499】 When the server detects an anomaly, it sends a push notification to the supervisor's (user's) device. The input is the behavioral data that was determined to be an anomaly, and the output is a warning message to the user. Specifically, the notification is displayed via the app, allowing the user to quickly check the situation. 【0500】 Step 6: 【0501】 Users can check their current location and details of their departure within the app. They can also use the "Missing Person Certification" function to report an emergency, and the system will automatically initiate contact with rescue agencies. Input consists of the user's judgment and report, while output is the necessary information forwarded to rescue agencies. 【0502】 (Application Example 1) 【0503】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0504】 In modern society, ensuring the safety of children is a crucial issue, and there is a particular need for a swift response when children deviate from their normal range of activity. However, conventional systems only acquire and notify location information, making it difficult to encourage children themselves to take safety-conscious actions. 【0505】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0506】 In this invention, the server includes means for acquiring the child's location information and transmitting said location information to an information processing device, means for storing past behavioral patterns and detecting deviations from a detailed range of behavior, and means for alerting the monitored person using voice communication via a home automated device. This improves safety by providing prompt notification and allowing the child to receive the alert on the spot, thereby ensuring the child's safety. 【0507】 The term "child" usually refers to a minor who requires safety and protection from a parent or teacher. 【0508】 "Location information" refers to data used to identify the current location of a specific object through a geographic information system. 【0509】 An "information processing device" is an electronic device such as a computer or server that has the function of receiving and analyzing data. 【0510】 "Past behavioral patterns" refer to the subject's past movement history and behavioral tendencies, analyzed based on location information and time. 【0511】 "Deviation" refers to movement or behavior that deviates from the normal range or pattern of activity. 【0512】 "Notification" refers to a warning or message that is issued when a deviation or anomaly is detected, prompting prompt action. 【0513】 "Rescue agencies" refer to public or private organizations such as the police and fire departments that respond to emergencies. 【0514】 "Household automated appliances" refer to convenient electric devices used in everyday household environments, such as robots and smart devices. 【0515】 "Voice communication" is a technology that transmits messages to humans as voice through a machine. 【0516】 This system tracks children's movements in real time to ensure their safety. The server receives location information transmitted from home automated devices and mobile terminals and detects deviations based on past behavioral patterns. 【0517】 The device uses its built-in GPS sensor to determine the child's current location. The acquired location information is sent to the server via a secure protocol (HTTPS). The server uses machine learning libraries such as scikit-learn to analyze normal behavior patterns. Based on the analyzed information, if a deviation is detected, the server immediately sends a push notification to the parent's smartphone. 【0518】 Furthermore, if a home automated device detects an anomaly in location information, it can alert the child through voice communication. For example, it might say, "You've strayed too far from the road. Please come back." This encourages the child to become aware of the danger and take safe actions. 【0519】 An example of a prompt using a generative AI model is, "Please advise on how to monitor today's planned route within an appropriate distance." Based on this, the model can efficiently analyze behavioral patterns and detect anomalies. 【0520】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0521】 Step 1: 【0522】 The device uses its built-in GPS sensor to obtain the child's current location. The input is location data from the GPS sensor, and the output is the coordinate information of the detected current location. This data is obtained from a GPS device. 【0523】 Step 2: 【0524】 The device sends the acquired location information to the server using a secure protocol (HTTPS). The input is the acquired location information, and the output is the data sent to the server. The SSL / TLS protocol is used to ensure security. 【0525】 Step 3: 【0526】 The server stores the received location information in a database, accumulating past behavioral data. The input is the location information sent to the server, and the output is the stored database records. Information is stored and managed here. 【0527】 Step 4: 【0528】 The server uses the scikit-learn library to analyze and model typical behavioral patterns from accumulated data. The input is historical behavioral data in the database, and the output is a behavioral model. Machine learning is used to learn patterns of behavioral tendencies. 【0529】 Step 5: 【0530】 The server compares real-time location data with a modeled normal behavior pattern to determine if there is a deviation. The input is real-time location data and a behavioral model, and the output is whether or not there is a deviation. An anomaly detection algorithm is used for evaluation. 【0531】 Step 6: 【0532】 If a deviation is detected, the server sends a push notification to the user's smartphone. The input is the deviation information and the user's device information, and the output is the sent notification. This notification immediately draws the user's attention. 【0533】 Step 7: 【0534】 The home-use automated device transmits warning messages to children via voice communication. Inputs are deviation information and pre-set messages, and output is an audible warning. This device enables rapid response in the field. 【0535】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0536】 This invention provides a system that combines a location information management function with an emotion engine that recognizes the user's emotional state, in order to improve child safety management. This system includes means for a wearable device (terminal) to acquire the child's location information and transmit that data to a server. Furthermore, it is possible to analyze the child's daily behavior patterns and detect deviations. 【0537】 The device sends location information to the server at regular intervals. The server uses machine learning algorithms based on the received data to record and update normal behavior patterns. The server analyzes new location information in real time to determine if the user is deviating from their normal range of activity. Based on this information, it sends notifications to parents as needed. 【0538】 The emotion engine analyzes the user's emotions from voice, text, and facial recognition on the parent's device. This analysis is sent to a server, and the emotional data is considered when issuing deviation notifications. For example, if the user shows signs of tension or anxiety, the notification is prioritized, and an alert prompting immediate attention is provided. 【0539】 As a concrete example, suppose the server detects that a child has deviated from their usual route and is approaching a dangerous area. In this case, if the server determines, based on the user's sentiment data obtained from the device, that the user is already feeling anxious, it will immediately send an highlighted notification. This allows the user to quickly check the situation and take necessary measures. 【0540】 Thus, the embodiment of the present invention provides a system that integrates location monitoring and user emotion recognition, enabling parents to respond quickly and effectively to their child's safety. This system becomes an even more reliable safety tool for parents through flexible alert management that responds to emotions. 【0541】 The following describes the processing flow. 【0542】 Step 1: 【0543】 The device uses its built-in GPS sensor to obtain the child's current location. Location information is acquired at regular intervals and a timestamp is added. 【0544】 Step 2: 【0545】 The device transmits the acquired location information to the server. This transmission is encrypted to maintain the confidentiality and integrity of the location information. 【0546】 Step 3: 【0547】 The server stores the received location information in a database and builds a record of past behavioral patterns. This data is typically used to identify the range of the user's movements. 【0548】 Step 4: 【0549】 The server compares normal behavior patterns with real-time location information to detect deviations. When a deviation is detected, the server generates an alert. 【0550】 Step 5: 【0551】 An emotion engine runs on the user's device, analyzing the user's emotional data through voice, text, and facial recognition. This emotional data is used to adjust the importance of system notifications. 【0552】 Step 6: 【0553】 User emotion data is sent to the server. The server uses this data to determine notification priority. For example, if the user is feeling anxious, the notification priority is increased. 【0554】 Step 7: 【0555】 If a deviation is detected and the user's emotions indicate tension, the server will send a high-priority notification to the user. This notification will include details of the action and the need for immediate action. 【0556】 Step 8: 【0557】 Users receive high-priority notifications and can view detailed information through the app. They can then mark the pet as "lost" if necessary, enabling a quick response. 【0558】 Step 9: 【0559】 The server, upon receiving a user's lost child status, contacts pre-configured emergency services. The server supports a rapid response by providing the child's last known location and activity history. 【0560】 This series of steps enables highly accurate security management by combining location monitoring with emotional data. 【0561】 (Example 2) 【0562】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0563】 While systems exist to acquire children's location information and manage their safety, these systems primarily rely on notifications based on deviations from behavioral patterns and fail to consider the user's emotional state. This makes them ineffective in prompting responses in situations where users feel anxious. Furthermore, the limited information provided to emergency communication devices during emergencies means there is insufficient information to make quick situational judgments. 【0564】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0565】 In this invention, the server includes means for acquiring the child's location information and transmitting said location information to an information processing device, means for storing past behavioral patterns and detecting deviations from a modeled range of behavior, and means for acquiring the user's emotional data using an emotion analysis device and transmitting that data to an information processing device. This enables effective notification in more urgent situations by simultaneously considering deviations in location information and the user's emotional state. Furthermore, it facilitates a rapid response by providing more detailed and comprehensive information to emergency communication devices. 【0566】 "Location information" refers to data on the longitude and latitude that indicates a child's current location, and is obtained using GPS or other location measurement technologies. 【0567】 An "information processing device" refers to a series of computer systems that receive and analyze location information and emotional data, and then perform defined actions. 【0568】 "Modeled range of movement" is a concept that represents the typical range of movement generated by a machine learning algorithm based on the behavioral patterns a child has exhibited in the past. 【0569】 "Deviation" refers to a physical departure from a modeled range of behavior, and describes a situation that deviates from normal behavioral patterns. 【0570】 An "emotion analysis device" is a hardware and software system that estimates an emotional state using input such as voice, facial expressions, and text from the user. 【0571】 A "user" refers to a parent or supervisor who uses this system, and is the entity that receives the necessary information and makes decisions about actions. 【0572】 An "emergency communication device" refers to a communication infrastructure used to contact designated emergency services and has the function of quickly transmitting information in critical situations. 【0573】 This invention provides a system for effectively managing child safety, integrating location information acquisition and emotional state recognition. The system is specifically implemented as follows: 【0574】 Terminal hardware and software: 【0575】 The device operates as a wearable device worn by children and acquires location information using a built-in GPS module. GPS data consists of longitude and latitude, is temporarily stored within the device, and then transmitted to a server. Wi-Fi and cellular network technologies are used for data transmission, enabling real-time location tracking. 【0576】 Server hardware and software: 【0577】 The server functions as a cloud-based processing unit, enabling efficient processing of large amounts of location and sentiment data. Received location data is analyzed by machine learning algorithms implemented using programming languages ​​such as Python. This analysis models daily behavioral patterns, and any deviations detected immediately notify parents. User sentiment data is also analyzed using libraries such as OpenCV and TensorFlow, taking emotional states into consideration. This allows for dynamic notification prioritization and highlighted notifications as needed. 【0578】 User actions: 【0579】 Users receive notifications through a dedicated application provided on their smartphones or tablets. The app provides detailed information about the child's current location and emotional state. If emotional data indicating anxiety is detected, a warning prompting immediate attention will be displayed on the user's device. 【0580】 Specific example: 【0581】 For example, suppose the server receives information that a device is taking a route the child doesn't normally take and is approaching a potentially dangerous area. Simultaneously, if the emotion analysis device suggests the child is experiencing anxiety, the server sends an highlighted notification to the parent's device. Through this, the user can quickly understand the child's dangerous situation and take prompt action. 【0582】 Example of a prompt: 【0583】 "Please explain how this system combines children's location data and emotional data to enhance safety management." 【0584】 This system, by using these means in combination with a dedicated application and cloud server, provides parents with the ability to properly monitor and manage their children's safety. 【0585】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0586】 Step 1: 【0587】 The device uses a built-in GPS module to obtain the child's location information. This location data (input) consists of longitude and latitude and is temporarily stored in the device. Storing the location information allows the device to prepare for subsequent data transmission. 【0588】 Step 2: 【0589】 The device sends the stored location data to the server. Specifically, it is sent (output) as encrypted data using the HTTPS protocol via Wi-Fi or a cellular network. This ensures secure communication, and the location information is received by the server. 【0590】 Step 3: 【0591】 The server receives location data as input and compares it with past behavioral patterns. A machine learning algorithm written in Python is used for this data analysis. As a result of the analysis, the modeled range of movement is updated, and it is determined whether the current location has deviated from the model (output). 【0592】 Step 4: 【0593】 The server receives emotion data sent from the terminal. This emotion data includes audio, facial expressions, and text input, captured using the smartphone's camera and microphone. OpenCV and TensorFlow are used to analyze the emotional state. The analysis results (output) are stored on the server as an indicator of the user's emotional state. 【0594】 Step 5: 【0595】 The server analyzes and integrates location deviations and emotional data. Based on this integration, notification priority is determined. For example, if a deviation is detected and the user indicates anxiety, the notification priority will be increased. The notification content (output) includes specific location information and emotional state. 【0596】 Step 6: 【0597】 Based on the analysis results described above, the server sends a notification to the parent's device. The notification is displayed on a dedicated app on the parent's smartphone, providing highlighted information. This allows the user to quickly understand the situation and take necessary actions. 【0598】 (Application Example 2) 【0599】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0600】 In modern society, ensuring children's safety requires not only monitoring their location but also sophisticated monitoring systems that take into account their emotional state. However, conventional systems only track location and lack sufficient notifications and emergency response that reflect the child's emotional state. 【0601】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0602】 In this invention, the server includes means for acquiring the child's location information and transmitting said location information to a central processing unit, means for storing past behavioral patterns and detecting deviations from the recorded range of behavior, and means for adjusting notification priority based on recognized emotional states. This enables flexible and rapid notification and response based on the child's location and emotional state. 【0603】 "Location information" refers to data that indicates the geographical coordinates of a specific object or individual. 【0604】 A "central processing unit" is a computer system that integrates and analyzes various types of data. 【0605】 "Behavioral patterns" refer to a collection of data that shows the characteristics of a user's movements and activities over a certain period of time. 【0606】 "Deviation" refers to a phenomenon that deviates from the usual range or pattern of behavior that has been recorded in advance. 【0607】 A "notification" is a message or alert used to inform a user of a specific event. 【0608】 "Emotions" refer to information that indicates an individual's affective state or psychological tendencies. 【0609】 "Priority" is an indicator that shows how important a particular task or notification is compared to other tasks or notifications. 【0610】 An "emergency organization" is a trained, specialized agency or group designed to respond to an emergency. 【0611】 A "detector" is a device used to detect specific signals from the environment. 【0612】 An "analysis engine" is a software or hardware system that organizes input data and produces a specific output. 【0613】 The system that realizes this invention aims to monitor a child's behavior and emotions using wearable devices and terminals, and to provide information to parents at the appropriate time. 【0614】 First, the device is equipped with a GPS module that periodically acquires the child's location information and sends it to the server. The server uses machine learning algorithms to analyze past behavioral patterns based on this data. Specifically, the server uses the PyTorch library to model these patterns and detect deviations from behavior. 【0615】 Next, regarding emotion recognition, the device is equipped with a camera and microphone, which are used to analyze the child's facial expressions and voice in real time. For the emotion analysis engine, YOLOv5 is used for image processing and OpenAI's Whisper is used for voice analysis. This allows the device to understand the child's emotional state and send that data to the server. 【0616】 Based on the emotional data it receives, the server adjusts the priority of notifications as needed. For example, if a child deviates from their normal range of activity and an anxious emotional state is detected, the server sends a higher-priority notification to the parent's device. This allows the parent to quickly check the situation and take appropriate action. 【0617】 For example, a child might move to a place they don't normally visit, and their expression might show signs of anxiety. In such a situation, the server would immediately send a warning to the parents, requiring a real-time response. 【0618】 An example of a prompt would be, "Create a notification message for when a child's location deviates from their usual behavioral pattern and they are showing signs of anxiety based on emotional data." This would allow the system to provide parents with specific and appropriate information. 【0619】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0620】 Step 1: 【0621】 The device uses a GPS module to obtain the child's location information. The input information is GPS data, which is sent to the device as location coordinates. The location coordinates are then ready to be sent directly to the server. 【0622】 Step 2: 【0623】 The device uses its built-in camera and microphone to capture and record the child's facial expressions and voice, collecting emotional data. Input information consists of image and audio data, which are analyzed using YOLOv5 and OpenAI's Whisper. Output consists of numerical data and tags representing emotions. 【0624】 Step 3: 【0625】 The device sends the collected location information and sentiment data to the server. The input is the location coordinates and sentiment data mentioned earlier, and the output is processed as a dataset for analysis on the server. 【0626】 Step 4: 【0627】 The server analyzes the received location information using a machine learning algorithm and retrieves past behavioral patterns from a database. The input is location coordinates, and the output is a judgment on whether those coordinates represent a deviation from the behavioral range. This allows for an assessment of the normality of the behavior. 【0628】 Step 5: 【0629】 The server analyzes the emotional state based on the received emotional data and a pre-configured emotional model. The input is emotional data, and the output is data indicating the intensity and type of emotion the user is experiencing. The analysis then sets the priority of the emotions. 【0630】 Step 6: 【0631】 Based on the analysis results, the server determines the necessity and priority of notifications to the parent's device. Inputs include data on behavioral deviations and emotional priority data. Outputs include information such as the content of the notification and the degree of warning. This prepares the notification to be sent to the parent. 【0632】 Step 7: 【0633】 Parents, as users, receive notifications, check their child's location as needed, and take appropriate action. Input is information in the form of notifications, and output is the next action based on that information. Parents can ensure their child's safety by reacting immediately. 【0634】 The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0635】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0636】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314. 【0637】 [Fourth Embodiment] 【0638】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0639】 As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server. 【0640】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0641】 The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52. 【0642】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0643】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0644】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0645】 The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes. 【0646】 Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0647】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30. 【0648】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0649】 In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0650】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0651】 The present invention is a system for ensuring the safety of children, and includes a wearable device that provides safety functions mainly using location information, and a central processing unit that processes the data. Examples are described below. 【0652】 The wearable device (terminal) acquires the child's location information in real time and transmits this information to a central processing unit (server) every 5 minutes. The GPS sensor in the terminal acquires the location information, and this data is sent to the server via a secure communication protocol. The server stores the received location information in a database and forms past behavioral patterns that are accumulated over time. 【0653】 The server uses machine learning algorithms to analyze past behavioral data and model the normal range of movement. Based on this profile, the server compares incoming new location information with normal behavioral patterns in real time and generates alerts when deviations are detected. 【0654】 When an anomaly is detected, the server sends a push notification to the parent's device. The user (parent) can check the child's current location and escape information via the app. If necessary, the parent can use the "Missing Child" function in the app to report the situation to the system. This allows the server to immediately contact emergency services and provide the child's last known location and activity history. 【0655】 For example, suppose a child who normally goes straight home from school is detected by the server to be heading in a completely different direction one day. In this case, the server determines that the current location transmitted by the device deviates from the usual route and notifies the parents. The parents can then make a quick decision based on this information and, if necessary, coordinate with emergency services. 【0656】 This embodiment of the present invention enables efficient monitoring of children's safety and immediate response to abnormal situations. This system will be a reliable safety tool for parents, especially in urban areas and neighborhoods where safety is a concern. 【0657】 The following describes the processing flow. 【0658】 Step 1: 【0659】 The device uses its built-in GPS sensor to obtain the child's current location information. This information is obtained at regular intervals, for example, every 5 minutes. The obtained data includes location information and a timestamp. 【0660】 Step 2: 【0661】 The device transmits the acquired location information to the server using a secure protocol. This transmission is encrypted to prevent data tampering by third parties. 【0662】 Step 3: 【0663】 The server receives location information transmitted from the device and stores it in a database. During this storage process, the data is organized based on date and time and recorded as a pattern of past activity. 【0664】 Step 4: 【0665】 The server uses machine learning algorithms based on accumulated behavioral data to analyze the child's typical range of movement. This analysis profiles the child's general activity patterns. 【0666】 Step 5: 【0667】 The server analyzes new location information in real time and compares it with existing behavioral profiles. This comparison helps determine whether the user is deviating from their normal range of activity. 【0668】 Step 6: 【0669】 If the server detects an deviation, it will send a notification to the parent's device. The notification will include the current location, details of the deviation, and potential dangers. 【0670】 Step 7: 【0671】 The user (parent / guardian) checks the received notification and opens the app to assess the situation. Within the app, they can view the child's current location and activity history on a map. 【0672】 Step 8: 【0673】 If the user deems it necessary, they can use the "Missing Person" feature within the app to report the situation to the server. This will prompt the server to take further action. 【0674】 Step 9: 【0675】 Upon receiving confirmation from a parent that their child is missing, the server provides emergency services with the child's last known location and activity history, requesting a swift response. 【0676】 In this way, a series of processing steps allows for efficient and safe monitoring of the child's location, and immediate action can be taken in the event of an anomaly. 【0677】 (Example 1) 【0678】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0679】 Ensuring child safety is a crucial issue in modern society. However, traditional methods make real-time monitoring of children's daily movements difficult, and there is a particular need to quickly detect and respond to abnormal behavior. Therefore, a system is needed that accurately monitors children's location and promptly notifies parents of any abnormalities, enabling them to respond appropriately. 【0680】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0681】 In this invention, the server includes means for acquiring the location information of an individual using a location information acquisition device and transmitting the location information to a central control unit, means for accumulating past behavioral history and detecting deviations from the normal range of activity, and means for sending a notification to a supervisor terminal when a deviation is detected. This makes it possible to monitor the safety of children in real time, quickly notify parents when an abnormality occurs, and take necessary action. 【0682】 A "location information acquisition device" is a device that measures an individual's current location in real time and records and transmits that information as digital data. 【0683】 A "central control unit" is a computer system used to process, store, and analyze collected location information. 【0684】 "Behavioral history" refers to a collection of location data collected in the past, which shows an individual's past movement patterns. 【0685】 "Deviation" refers to detecting unusual movement or behavior outside of the normal range of activity. 【0686】 A "supervisor terminal" is a communication terminal used by supervisors (parents / guardians) to receive notifications from a server and check information. 【0687】 A "rescue agency" is a public or private organization established to respond to emergencies and provide necessary support. 【0688】 A "learning algorithm" is an automated computational method for analyzing data, identifying patterns, and making predictions. 【0689】 This invention relates to a monitoring system for ensuring the safety of individuals, and includes a location information acquisition device, a central control unit, and a supervisor terminal. The terminal functions as a wearable device attached to the individual and acquires location information in real time using a GPS sensor. The acquired location information is transmitted to a server via a secure communication protocol. 【0690】 The server stores the received location information in a database, thereby accumulating a history of past movements. The server uses machine learning algorithms to analyze the accumulated data and model the user's normal range of movement. Based on this profile, it monitors for deviations each time new location information is sent and generates an alert if an anomaly is detected. 【0691】 As a concrete example, when monitoring an individual whose normal behavior pattern is to go directly home from school, if they move in a completely different direction, the server instantly detects this abnormal behavior. This allows the supervising parent to receive a quick alert and, if necessary, use the app's features to contact emergency services. 【0692】 An example of a prompt using a generative AI model is as follows: 【0693】 "Create an algorithm for a system that monitors an individual's location in real time and notifies the supervisor if there is a deviation from their normal behavioral pattern." 【0694】 In this way, the system can efficiently protect the safety of individuals and respond quickly to abnormal situations. Particularly in urban areas and regions where safety is a concern, this invention will function as a reliable safety tool. 【0695】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0696】 Step 1: 【0697】 The device obtains its current location using a built-in GPS sensor. This input is received from satellites as a GPS signal. After obtaining the location information, location data in digital format is generated. This data is then sent to the server via a secure communication protocol. HTTPS or TLS are commonly used as the transmission protocol. 【0698】 Step 2: 【0699】 The server receives location information transmitted from the terminal. This input is latitude and longitude data with a timestamp. The server stores this location data in a database, forming a history of past movements. The individual's movement history is updated when a new entry is added to the database. 【0700】 Step 3: 【0701】 The server retrieves behavioral history stored in the database and performs analysis using machine learning algorithms. The input is a dataset of past location data, and the output is a model representing typical behavioral patterns. Specifically, a clustering algorithm analyzes the location data and models the range of movement. This result is saved as a profile. 【0702】 Step 4: 【0703】 When new location information is received, the server compares this data to a normal behavioral pattern model. The input is real-time location data, and the output is a judgment result indicating whether or not an anomaly is present. Specifically, it determines whether the data deviation exceeds a set threshold, and if an anomaly is detected, it generates an alert. 【0704】 Step 5: 【0705】 When the server detects an anomaly, it sends a push notification to the supervisor's (user's) device. The input is the behavioral data that was determined to be an anomaly, and the output is a warning message to the user. Specifically, the notification is displayed via the app, allowing the user to quickly check the situation. 【0706】 Step 6: 【0707】 Users can check their current location and details of their departure within the app. They can also use the "Missing Person Certification" function to report an emergency, and the system will automatically initiate contact with rescue agencies. Input consists of the user's judgment and report, while output is the necessary information forwarded to rescue agencies. 【0708】 (Application Example 1) 【0709】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0710】 In modern society, ensuring the safety of children is a crucial issue, and there is a particular need for a swift response when children deviate from their normal range of activity. However, conventional systems only acquire and notify location information, making it difficult to encourage children themselves to take safety-conscious actions. 【0711】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0712】 In this invention, the server includes means for acquiring the child's location information and transmitting said location information to an information processing device, means for storing past behavioral patterns and detecting deviations from a detailed range of behavior, and means for alerting the monitored person using voice communication via a home automated device. This improves safety by providing prompt notification and allowing the child to receive the alert on the spot, thereby ensuring the child's safety. 【0713】 The term "child" usually refers to a minor who requires safety and protection from a parent or teacher. 【0714】 "Location information" refers to data used to identify the current location of a specific object through a geographic information system. 【0715】 An "information processing device" is an electronic device such as a computer or server that has the function of receiving and analyzing data. 【0716】 "Past behavioral patterns" refer to the subject's past movement history and behavioral tendencies, analyzed based on location information and time. 【0717】 "Deviation" refers to movement or behavior that deviates from the normal range or pattern of activity. 【0718】 "Notification" refers to a warning or message that is issued when a deviation or anomaly is detected, prompting prompt action. 【0719】 "Rescue agencies" refer to public or private organizations such as the police and fire departments that respond to emergencies. 【0720】 "Household automated appliances" refer to convenient electric devices used in everyday household environments, such as robots and smart devices. 【0721】 "Voice communication" is a technology that transmits messages to humans as voice through a machine. 【0722】 This system tracks children's movements in real time to ensure their safety. The server receives location information transmitted from home automated devices and mobile terminals and detects deviations based on past behavioral patterns. 【0723】 The device uses its built-in GPS sensor to determine the child's current location. The acquired location information is sent to the server via a secure protocol (HTTPS). The server uses machine learning libraries such as scikit-learn to analyze normal behavior patterns. Based on the analyzed information, if a deviation is detected, the server immediately sends a push notification to the parent's smartphone. 【0724】 Furthermore, if a home automated device detects an anomaly in location information, it can alert the child through voice communication. For example, it might say, "You've strayed too far from the road. Please come back." This encourages the child to become aware of the danger and take safe actions. 【0725】 An example of a prompt using a generative AI model is, "Please advise on how to monitor today's planned route within an appropriate distance." Based on this, the model can efficiently analyze behavioral patterns and detect anomalies. 【0726】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0727】 Step 1: 【0728】 The device uses its built-in GPS sensor to obtain the child's current location. The input is location data from the GPS sensor, and the output is the coordinate information of the detected current location. This data is obtained from a GPS device. 【0729】 Step 2: 【0730】 The device sends the acquired location information to the server using a secure protocol (HTTPS). The input is the acquired location information, and the output is the data sent to the server. The SSL / TLS protocol is used to ensure security. 【0731】 Step 3: 【0732】 The server stores the received location information in a database, accumulating past behavioral data. The input is the location information sent to the server, and the output is the stored database records. Information is stored and managed here. 【0733】 Step 4: 【0734】 The server uses the scikit-learn library to analyze and model typical behavioral patterns from accumulated data. The input is historical behavioral data in the database, and the output is a behavioral model. Machine learning is used to learn patterns of behavioral tendencies. 【0735】 Step 5: 【0736】 The server compares real-time location data with a modeled normal behavior pattern to determine if there is a deviation. The input is real-time location data and a behavioral model, and the output is whether or not there is a deviation. An anomaly detection algorithm is used for evaluation. 【0737】 Step 6: 【0738】 If a deviation is detected, the server sends a push notification to the user's smartphone. The input is the deviation information and the user's device information, and the output is the sent notification. This notification immediately draws the user's attention. 【0739】 Step 7: 【0740】 The home-use automated device transmits warning messages to children via voice communication. Inputs are deviation information and pre-set messages, and output is an audible warning. This device enables rapid response in the field. 【0741】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0742】 This invention provides a system that combines a location information management function with an emotion engine that recognizes the user's emotional state, in order to improve child safety management. This system includes means for a wearable device (terminal) to acquire the child's location information and transmit that data to a server. Furthermore, it is possible to analyze the child's daily behavior patterns and detect deviations. 【0743】 The device sends location information to the server at regular intervals. The server uses machine learning algorithms based on the received data to record and update normal behavior patterns. The server analyzes new location information in real time to determine if the user is deviating from their normal range of activity. Based on this information, it sends notifications to parents as needed. 【0744】 The emotion engine analyzes the user's emotions from voice, text, and facial recognition on the parent's device. This analysis is sent to a server, and the emotional data is considered when issuing deviation notifications. For example, if the user shows signs of tension or anxiety, the notification is prioritized, and an alert prompting immediate attention is provided. 【0745】 As a concrete example, suppose the server detects that a child has deviated from their usual route and is approaching a dangerous area. In this case, if the server determines, based on the user's sentiment data obtained from the device, that the user is already feeling anxious, it will immediately send an highlighted notification. This allows the user to quickly check the situation and take necessary measures. 【0746】 Thus, the embodiment of the present invention provides a system that integrates location monitoring and user emotion recognition, enabling parents to respond quickly and effectively to their child's safety. This system becomes an even more reliable safety tool for parents through flexible alert management that responds to emotions. 【0747】 The following describes the processing flow. 【0748】 Step 1: 【0749】 The device uses its built-in GPS sensor to obtain the child's current location. Location information is acquired at regular intervals and a timestamp is added. 【0750】 Step 2: 【0751】 The device transmits the acquired location information to the server. This transmission is encrypted to maintain the confidentiality and integrity of the location information. 【0752】 Step 3: 【0753】 The server stores the received location information in a database and builds a record of past behavioral patterns. This data is typically used to identify the range of the user's movements. 【0754】 Step 4: 【0755】 The server compares normal behavior patterns with real-time location information to detect deviations. When a deviation is detected, the server generates an alert. 【0756】 Step 5: 【0757】 An emotion engine runs on the user's device, analyzing the user's emotional data through voice, text, and facial recognition. This emotional data is used to adjust the importance of system notifications. 【0758】 Step 6: 【0759】 User emotion data is sent to the server. The server uses this data to determine notification priority. For example, if the user is feeling anxious, the notification priority is increased. 【0760】 Step 7: 【0761】 If a deviation is detected and the user's emotions indicate tension, the server will send a high-priority notification to the user. This notification will include details of the action and the need for immediate action. 【0762】 Step 8: 【0763】 Users receive high-priority notifications and can view detailed information through the app. They can then mark the pet as "lost" if necessary, enabling a quick response. 【0764】 Step 9: 【0765】 The server, upon receiving a user's lost child status, contacts pre-configured emergency services. The server supports a rapid response by providing the child's last known location and activity history. 【0766】 This series of steps enables highly accurate security management by combining location monitoring with emotional data. 【0767】 (Example 2) 【0768】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0769】 While systems exist to acquire children's location information and manage their safety, these systems primarily rely on notifications based on deviations from behavioral patterns and fail to consider the user's emotional state. This makes them ineffective in prompting responses in situations where users feel anxious. Furthermore, the limited information provided to emergency communication devices during emergencies means there is insufficient information to make quick situational judgments. 【0770】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0771】 In this invention, the server includes means for acquiring the child's location information and transmitting said location information to an information processing device, means for storing past behavioral patterns and detecting deviations from a modeled range of behavior, and means for acquiring the user's emotional data using an emotion analysis device and transmitting that data to an information processing device. This enables effective notification in more urgent situations by simultaneously considering deviations in location information and the user's emotional state. Furthermore, it facilitates a rapid response by providing more detailed and comprehensive information to emergency communication devices. 【0772】 "Location information" refers to data on the longitude and latitude that indicates a child's current location, and is obtained using GPS or other location measurement technologies. 【0773】 An "information processing device" refers to a series of computer systems that receive and analyze location information and emotional data, and then perform defined actions. 【0774】 "Modeled range of movement" is a concept that represents the typical range of movement generated by a machine learning algorithm based on the behavioral patterns a child has exhibited in the past. 【0775】 "Deviation" refers to a physical departure from a modeled range of behavior, and describes a situation that deviates from normal behavioral patterns. 【0776】 An "emotion analysis device" is a hardware and software system that estimates an emotional state using input such as voice, facial expressions, and text from the user. 【0777】 A "user" refers to a parent or supervisor who uses this system, and is the entity that receives the necessary information and makes decisions about actions. 【0778】 An "emergency communication device" refers to a communication infrastructure used to contact designated emergency services and has the function of quickly transmitting information in critical situations. 【0779】 This invention provides a system for effectively managing child safety, integrating location information acquisition and emotional state recognition. The system is specifically implemented as follows: 【0780】 Terminal hardware and software: 【0781】 The device operates as a wearable device worn by children and acquires location information using a built-in GPS module. GPS data consists of longitude and latitude, is temporarily stored within the device, and then transmitted to a server. Wi-Fi and cellular network technologies are used for data transmission, enabling real-time location tracking. 【0782】 Server hardware and software: 【0783】 The server functions as a cloud-based processing unit, enabling efficient processing of large amounts of location and sentiment data. Received location data is analyzed by machine learning algorithms implemented using programming languages ​​such as Python. This analysis models daily behavioral patterns, and any deviations detected immediately notify parents. User sentiment data is also analyzed using libraries such as OpenCV and TensorFlow, taking emotional states into consideration. This allows for dynamic notification prioritization and highlighted notifications as needed. 【0784】 User actions: 【0785】 Users receive notifications through a dedicated application provided on their smartphones or tablets. The app provides detailed information about the child's current location and emotional state. If emotional data indicating anxiety is detected, a warning prompting immediate attention will be displayed on the user's device. 【0786】 Specific example: 【0787】 For example, suppose the server receives information that a device is taking a route the child doesn't normally take and is approaching a potentially dangerous area. Simultaneously, if the emotion analysis device suggests the child is experiencing anxiety, the server sends an highlighted notification to the parent's device. Through this, the user can quickly understand the child's dangerous situation and take prompt action. 【0788】 Example of a prompt: 【0789】 "Please explain how this system combines children's location data and emotional data to enhance safety management." 【0790】 This system, by using these means in combination with a dedicated application and cloud server, provides parents with the ability to properly monitor and manage their children's safety. 【0791】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0792】 Step 1: 【0793】 The device uses a built-in GPS module to obtain the child's location information. This location data (input) consists of longitude and latitude and is temporarily stored in the device. Storing the location information allows the device to prepare for subsequent data transmission. 【0794】 Step 2: 【0795】 The device sends the stored location data to the server. Specifically, it is sent (output) as encrypted data using the HTTPS protocol via Wi-Fi or a cellular network. This ensures secure communication, and the location information is received by the server. 【0796】 Step 3: 【0797】 The server receives location data as input and compares it with past behavioral patterns. A machine learning algorithm written in Python is used for this data analysis. As a result of the analysis, the modeled range of movement is updated, and it is determined whether the current location has deviated from the model (output). 【0798】 Step 4: 【0799】 The server receives emotion data sent from the terminal. This emotion data includes audio, facial expressions, and text input, captured using the smartphone's camera and microphone. OpenCV and TensorFlow are used to analyze the emotional state. The analysis results (output) are stored on the server as an indicator of the user's emotional state. 【0800】 Step 5: 【0801】 The server analyzes and integrates location deviations and emotional data. Based on this integration, notification priority is determined. For example, if a deviation is detected and the user indicates anxiety, the notification priority will be increased. The notification content (output) includes specific location information and emotional state. 【0802】 Step 6: 【0803】 Based on the analysis results described above, the server sends a notification to the parent's device. The notification is displayed on a dedicated app on the parent's smartphone, providing highlighted information. This allows the user to quickly understand the situation and take necessary actions. 【0804】 (Application Example 2) 【0805】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0806】 In modern society, ensuring children's safety requires not only monitoring their location but also sophisticated monitoring systems that take into account their emotional state. However, conventional systems only track location and lack sufficient notifications and emergency response that reflect the child's emotional state. 【0807】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0808】 In this invention, the server includes means for acquiring the child's location information and transmitting said location information to a central processing unit, means for storing past behavioral patterns and detecting deviations from the recorded range of behavior, and means for adjusting notification priority based on recognized emotional states. This enables flexible and rapid notification and response based on the child's location and emotional state. 【0809】 "Location information" refers to data that indicates the geographical coordinates of a specific object or individual. 【0810】 A "central processing unit" is a computer system that integrates and analyzes various types of data. 【0811】 "Behavioral patterns" refer to a collection of data that shows the characteristics of a user's movements and activities over a certain period of time. 【0812】 "Deviation" refers to a phenomenon that deviates from the usual range or pattern of behavior that has been recorded in advance. 【0813】 A "notification" is a message or alert used to inform a user of a specific event. 【0814】 "Emotions" refer to information that indicates an individual's affective state or psychological tendencies. 【0815】 "Priority" is an indicator that shows how important a particular task or notification is compared to other tasks or notifications. 【0816】 An "emergency organization" is a trained, specialized agency or group designed to respond to an emergency. 【0817】 A "detector" is a device used to detect specific signals from the environment. 【0818】 An "analysis engine" is a software or hardware system that organizes input data and produces a specific output. 【0819】 The system that realizes this invention aims to monitor a child's behavior and emotions using wearable devices and terminals, and to provide information to parents at the appropriate time. 【0820】 First, the device is equipped with a GPS module that periodically acquires the child's location information and sends it to the server. The server uses machine learning algorithms to analyze past behavioral patterns based on this data. Specifically, the server uses the PyTorch library to model these patterns and detect deviations from behavior. 【0821】 Next, regarding emotion recognition, the device is equipped with a camera and microphone, which are used to analyze the child's facial expressions and voice in real time. For the emotion analysis engine, YOLOv5 is used for image processing and OpenAI's Whisper is used for voice analysis. This allows the device to understand the child's emotional state and send that data to the server. 【0822】 Based on the emotional data it receives, the server adjusts the priority of notifications as needed. For example, if a child deviates from their normal range of activity and an anxious emotional state is detected, the server sends a higher-priority notification to the parent's device. This allows the parent to quickly check the situation and take appropriate action. 【0823】 For example, a child might move to a place they don't normally visit, and their expression might show signs of anxiety. In such a situation, the server would immediately send a warning to the parents, requiring a real-time response. 【0824】 An example of a prompt would be, "Create a notification message for when a child's location deviates from their usual behavioral pattern and they are showing signs of anxiety based on emotional data." This would allow the system to provide parents with specific and appropriate information. 【0825】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0826】 Step 1: 【0827】 The device uses a GPS module to obtain the child's location information. The input information is GPS data, which is sent to the device as location coordinates. The location coordinates are then ready to be sent directly to the server. 【0828】 Step 2: 【0829】 The device uses its built-in camera and microphone to capture and record the child's facial expressions and voice, collecting emotional data. Input information consists of image and audio data, which are analyzed using YOLOv5 and OpenAI's Whisper. Output consists of numerical data and tags representing emotions. 【0830】 Step 3: 【0831】 The device sends the collected location information and sentiment data to the server. The input is the location coordinates and sentiment data mentioned earlier, and the output is processed as a dataset for analysis on the server. 【0832】 Step 4: 【0833】 The server analyzes the received location information using a machine learning algorithm and retrieves past behavioral patterns from a database. The input is location coordinates, and the output is a judgment on whether those coordinates represent a deviation from the behavioral range. This allows for an assessment of the normality of the behavior. 【0834】 Step 5: 【0835】 The server analyzes the emotional state based on the received emotional data and a pre-configured emotional model. The input is emotional data, and the output is data indicating the intensity and type of emotion the user is experiencing. The analysis then sets the priority of the emotions. 【0836】 Step 6: 【0837】 Based on the analysis results, the server determines the necessity and priority of notifications to the parent's device. Inputs include data on behavioral deviations and emotional priority data. Outputs include information such as the content of the notification and the degree of warning. This prepares the notification to be sent to the parent. 【0838】 Step 7: 【0839】 Parents, as users, receive notifications, check their child's location as needed, and take appropriate action. Input is information in the form of notifications, and output is the next action based on that information. Parents can ensure their child's safety by reacting immediately. 【0840】 The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0841】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0842】 In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414. 【0843】 Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion. 【0844】 Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together. 【0845】 These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression. 【0846】 The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become. 【0847】 Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant. 【0848】 The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more." 【0849】 The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values. 【0850】 The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format. 【0851】 In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data. 【0852】 In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56. 【0853】 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. 【0854】 Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56. 【0855】 The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory. 【0856】 The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor. 【0857】 Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources. 【0858】 Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose. 【0859】 The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above. 【0860】 All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference. 【0861】 The following is further disclosed regarding the embodiments described above. 【0862】 (Claim 1) 【0863】 A means for acquiring the child's location information and transmitting said location information to a central processing unit, 【0864】 A means for memorizing past behavioral patterns and detecting deviations from a detailed range of behavior, 【0865】 A means of providing a notification to the parent's device when a deviation is detected, 【0866】 If a parent or guardian confirms a child is missing, what are the means to contact emergency services? 【0867】 A system that includes this. 【0868】 (Claim 2) 【0869】 The system according to claim 1, further comprising means for analyzing the aforementioned past behavioral patterns using a machine learning algorithm. 【0870】 (Claim 3) 【0871】 The system according to claim 1, wherein the emergency agency is provided with means for providing the last confirmed location information and activity history. 【0872】 "Example 1" 【0873】 (Claim 1) 【0874】 A means for acquiring the location information of an individual using a location information acquisition device and transmitting the location information to a central control unit, 【0875】 A means of accumulating past behavioral history and detecting deviations from the normal range of activity, 【0876】 A means of sending a notification to the supervisor's terminal when a deviation is detected, 【0877】 If the supervisor determines that an emergency has occurred, they will need a means of contacting rescue agencies, 【0878】 A system that includes this. 【0879】 (Claim 2) 【0880】 The system according to claim 1, further comprising means for using a learning algorithm to analyze the aforementioned past behavioral history. 【0881】 (Claim 3) 【0882】 The system according to claim 1, further comprising means for transmitting the last confirmed location and activity history to the rescue agency. 【0883】 "Application Example 1" 【0884】 (Claim 1) 【0885】 A means for acquiring a child's location information and transmitting said location information to an information processing device, 【0886】 A means for memorizing past behavioral patterns and detecting deviations from a detailed range of behavior, 【0887】 A means of providing a notification to the parent's device when a deviation is detected, 【0888】 If a parent or guardian confirms a child is missing, what is the means to contact rescue organizations? 【0889】 A means of alerting a person being monitored using voice communication via a home automated device, 【0890】 A system that includes this. 【0891】 (Claim 2) 【0892】 The system according to claim 1, further comprising means for analyzing the aforementioned past behavioral patterns using a statistical learning method. 【0893】 (Claim 3) 【0894】 The system according to claim 1, wherein the emergency agency is provided with means for providing the last confirmed location information and activity history. 【0895】 "Example 2 of combining an emotion engine" 【0896】 (Claim 1) 【0897】 A means for acquiring a child's location information and transmitting said location information to an information processing device, 【0898】 A means for memorizing past behavioral patterns and detecting deviations from a modeled range of behavior, 【0899】 A means for acquiring user emotion data using an emotion analysis device and transmitting that data to an information processing device, 【0900】 A means of providing highlighted notifications to parental devices when a deviation is detected and the user expresses concern, 【0901】 If a parent or guardian confirms a child is missing, a means of contacting the emergency communication device will be provided. 【0902】 A system that includes this. 【0903】 (Claim 2) 【0904】 The system according to claim 1, further comprising means for analyzing the aforementioned past behavioral patterns and emotional data using a machine learning algorithm and adjusting the priority of notifications. 【0905】 (Claim 3) 【0906】 The system according to claim 1, wherein the emergency communication device includes means for providing last confirmed location information, behavioral history, and emotion analysis results. 【0907】 "Application example 2 when combining with an emotional engine" 【0908】 (Claim 1) 【0909】 A means for acquiring the child's location information and transmitting said location information to a central processing unit, 【0910】 A means for memorizing past behavioral patterns and detecting deviations from the recorded range of behavior, 【0911】 A means of providing a notification to the parent's information terminal when a deviation is detected, 【0912】 A means of recognizing and analyzing the emotions of users, 【0913】 A means of adjusting notification priority based on recognized emotional states, 【0914】 If a parent or guardian confirms a child is missing, there are ways to contact emergency organizations. 【0915】 A system that includes this. 【0916】 (Claim 2) 【0917】 The system according to claim 1, further comprising means for analyzing past behavioral patterns using a machine learning algorithm, and generating user emotion data using a sensor and an analysis engine. 【0918】 (Claim 3) 【0919】 The system according to claim 1, comprising means for providing the emergency organization with last confirmed location information, activity history, and user emotion information. [Explanation of Symbols] 【0920】 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] A means for acquiring the child's location information and transmitting said location information to a central processing unit, A means for memorizing past behavioral patterns and detecting deviations from a detailed range of behavior, A means of providing a notification to the parent's device when a deviation is detected, If a parent or guardian confirms a child is missing, what are the means to contact emergency services? A system that includes this. [Claim 2] The system according to claim 1, further comprising means for analyzing the aforementioned past behavioral patterns using a machine learning algorithm. [Claim 3] The system according to claim 1, wherein the emergency agency is provided with means for providing the last confirmed location information and activity history.

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