System

Abstract

An object of a system according to an embodiment is to monitor a range of activities of a child and to promptly handle an abnormality when the abnormality occurs.SOLUTION: A system according to an embodiment includes an activity range storage part, an abnormality detection part, an alert transmission part, a lost child recognition part, and a police notification part. The movement area storage unit stores a movement area of the child. The abnormality detection unit detects an abnormality by comparing the movement area stored by the movement area storage unit with the real-time position information. The alert transmission unit notifies the protector of the abnormality detected by the abnormality detection unit. The lost child certification part accepts the lost child certification of the protector. The police notifier notifies the police of the lost child information recognized by the lost child recognizer.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to monitor a child's range of movement in real time and to respond quickly when an abnormality occurs.

[0005] The system according to the embodiment aims to monitor the range of a child's activities and to respond quickly when an abnormality occurs. [Means for solving the problem]

[0006] The system according to the embodiment includes a movement range storage unit, an abnormality detection unit, an alert sending unit, a lost child recognition unit, and a police notification unit. The movement range storage unit stores the child's movement range. The abnormality detection unit detects abnormalities by comparing the movement range stored by the movement range storage unit with real-time location information. The alert sending unit notifies the guardian of the abnormality detected by the abnormality detection unit. The lost child recognition unit accepts the guardian's recognition of the lost child. The police notification unit notifies the police of the lost child information recognized by the lost child recognition unit. [Effects of the Invention]

[0007] The system according to the embodiment can monitor the range of a child's movements and respond quickly when an abnormality occurs. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The child safety system according to an embodiment of the present invention stores a child's range of movement in advance and issues an alert if the child moves in a way that is different from normal. Furthermore, it has a function that allows the local police to quickly protect the child if the child is identified as lost by the guardian. This allows the child safety system to ensure the safety of the child by storing the child's range of movement, detecting abnormalities and notifying the guardian, and accepting the child's identification and notifying the police.

[0029] A child safety system according to an embodiment includes a movement range storage unit, an abnormality detection unit, an alert transmission unit, a lost child identification unit, and a police notification unit. The movement range storage unit stores a child's movement range. For example, the movement range and route of a child's daily movements are collected using a GPS device, and AI analyzes and stores the data. The movement range storage unit can also record the child's route from school to home, the area where the child plays in the park, and so on. The abnormality detection unit detects an abnormality by comparing the movement range stored by the movement range storage unit with real-time location information. For example, the AI ​​detects an abnormality when a child deviates from their usual route or travels far beyond their usual movement range. The alert transmission unit notifies the guardian of the abnormality detected by the abnormality detection unit. For example, the alert is sent via a smartphone app, email, SMS, or the like. The lost child identification unit accepts the guardian's recognition of a lost child. For example, when a guardian presses a "recognize lost child" button on the app, the child's current location and last confirmed location information are sent to the police. The police notification unit notifies the police of the lost child information recognized by the lost child identification unit. For example, the police can check the child's current location and rush to the scene to protect them. This allows the child safety system to record the child's movement range, detect abnormalities and notify the parents, and accept the child as lost and notify the police, thereby ensuring the child's safety.

[0030] The activity range memory unit can learn activity patterns and take into account fluctuations due to seasonal and weather changes. For example, the activity range memory unit uses AI to learn children's activity patterns and store seasonal fluctuations in activity range. For example, it compares activity ranges during summer vacation with those during winter vacation and records the different activity patterns. The activity range memory unit also collects weather data, and the AI ​​analyzes the differences in activity ranges on rainy and sunny days. For example, it stores the activity range taking into account that children spend more time indoors on rainy days. The activity range memory unit also predicts fluctuations in activity range due to seasonal and weather changes, and the AI ​​stores the activity range based on that data. For example, it predicts that children will spend more time playing in the park in spring and records the activity range. This allows for more accurate activity range storage by taking into account fluctuations in activity range due to seasonal and weather changes.

[0031] The activity range memory unit simultaneously learns the activity ranges of friends and family and can predict the activity range based on the relationships. For example, the activity range memory unit uses AI to learn the activity ranges of a child's friends and family and predicts the activity range based on the mutual relationships. For example, it records places where the child plays with friends and places where the child goes out with family. The activity range memory unit also analyzes family activity patterns and identifies factors that affect the child's activity range. For example, it predicts the activity range based on how the family spends their holidays and where they go out. The activity range memory unit also integrates activity range data of friends and family to build a system that more accurately predicts the child's activity range. For example, it stores the activity range taking into account the time of day and places the child spends with friends. This allows for more accurate activity range predictions by taking into account the activity ranges of friends and family.

[0032] The activity range memory unit can record health conditions and analyze fluctuations according to the health conditions. For example, the activity range memory unit collects step count and heart rate data to record a child's health condition, and AI analyzes the activity range based on that data. For example, it records the activity range when the child is in good health. The activity range memory unit also builds a system that predicts fluctuations in activity range based on health condition data. For example, when the heart rate is high, it records the tendency to go to places where the child gets more exercise. The activity range memory unit also analyzes step count and heart rate data and stores fluctuations in activity range according to the health condition. For example, it records the tendency to move over a wide area on days when the child takes a lot of steps. This makes it possible to analyze fluctuations in activity range according to the health condition by taking the health condition into consideration.

[0033] The activity range memory unit can analyze the device usage history and predict the activity range. The activity range memory unit, for example, analyzes the usage history of devices used by children and builds a system that predicts the activity range. For example, it stores the activity range based on the location information and activity data of a smartwatch. The activity range memory unit also analyzes the usage history of a tablet and predicts the child's activity range. For example, it records the activity range based on the time and location of use of a specific app. The activity range memory unit also integrates device usage history data and develops a system in which AI predicts the activity range. For example, it combines data from a smartwatch and a tablet to store the activity range. This makes it possible to predict the activity range by taking into account the device usage history.

[0034] The anomaly detection unit can detect anomalies by taking into account the speed of movement and the means of transportation. For example, the anomaly detection unit builds a system that analyzes a child's speed of movement and detects movements that are different from normal. For example, an anomaly is detected when the child is moving at a faster speed than normal. The anomaly detection unit also develops a system that detects anomalies by taking into account the means of transportation. For example, an anomaly is detected when a child is traveling by bicycle in a place where they should be traveling on foot. The anomaly detection unit also integrates data on the speed of movement and the means of transportation and builds a system in which AI detects anomalies. For example, an anomaly is detected when a child is traveling by car outside their normal range of movement. In this way, by taking into account the speed of movement and the means of transportation, anomalies can be detected more accurately.

[0035] The anomaly detection unit can determine abnormalities by taking into account events within the range of movement. For example, the anomaly detection unit will build a system that determines abnormalities by taking into account specific events within a child's range of movement. For example, it will detect an abnormality if the child is behaving differently than normal on the day of a school event. The anomaly detection unit will also collect information on local festivals and events and develop a system in which AI will determine abnormalities. For example, it will detect an abnormality if the child is moving beyond the normal range of movement on a festival day. The anomaly detection unit will also build a system in which AI will determine abnormalities based on specific event data. For example, it will detect an abnormality if the child is behaving differently than normal on the day of an event. This allows for more accurate determination of abnormalities by taking specific events into account.

[0036] The anomaly detection unit can determine abnormalities by taking into account environmental changes within the range of movement. For example, the anomaly detection unit collects traffic congestion data within a child's range of movement and builds a system in which AI determines abnormalities. For example, it detects abnormal behavior when traffic congestion occurs. The anomaly detection unit also develops a system in which AI determines abnormalities based on weather data. For example, it detects abnormal behavior when the weather worsens. The anomaly detection unit also integrates environmental change data and builds a system in which AI determines abnormalities. For example, it detects abnormalities by taking traffic congestion and weather changes into account. This allows for more accurate determination of abnormalities by taking environmental changes into account.

[0037] The anomaly detection unit can also analyze the behavioral patterns of other children within a child's range of movement and detect abnormalities. The anomaly detection unit, for example, analyzes the behavioral patterns of other children within a child's range of movement and builds a system to detect abnormalities. For example, it detects abnormalities when a child moves differently from other children. The anomaly detection unit also collects behavioral data of other children and develops a system in which AI determines abnormalities. For example, it detects abnormalities when a child moves differently from normal behavior patterns. The anomaly detection unit also builds a system in which AI detects abnormalities based on the behavioral patterns of other children. For example, it detects abnormalities when a child has a different range of movement from other children. This allows for more accurate detection of abnormalities by taking into account the behavioral patterns of other children.

[0038] The lost child recognition unit can analyze behavioral history and current situation, and automatically evaluate the possibility of a child being lost. For example, the lost child recognition unit builds a system in which AI analyzes a child's behavioral history and automatically evaluates the possibility of a child being lost. For example, it evaluates the possibility of a child being lost if the child is moving in a way that differs from their usual behavioral pattern. The lost child recognition unit also develops a system in which AI evaluates the possibility of a child being lost based on current situation data. For example, it analyzes current location information and the surrounding environment to determine the possibility of a child being lost. The lost child recognition unit also integrates behavioral history and current situation data, and builds a system in which AI automatically evaluates the possibility of a child being lost. For example, it evaluates the possibility of a child being lost if the child is moving beyond their usual range of movement. In this way, it is possible to automatically evaluate the possibility of a child being lost by analyzing behavioral history and current situation.

[0039] The lost child recognition unit can integrate information from devices within the child's range of movement and assess the possibility of the child being lost. For example, the lost child recognition unit analyzes video data from surveillance cameras and builds a system in which AI assesses the possibility of the child being lost. For example, it identifies the child's current location from camera footage and determines the possibility of the child being lost. The lost child recognition unit also develops a system in which AI assesses the possibility of the child being lost based on data from smart city sensors. For example, it analyzes sensor information to confirm the child's range of movement and determine the possibility of the child being lost. The lost child recognition unit also integrates information from other devices and builds a system in which AI assesses the possibility of the child being lost. For example, it combines data from surveillance cameras and sensors to assess the possibility of the child being lost. This allows for a more accurate assessment of the possibility of the child being lost by integrating information from other devices.

[0040] The lost child recognition department can collect information from parents and local residents within the child's range of movement and assess the possibility that a child is lost. For example, the lost child recognition department collects information from other parents and local residents and builds a system in which AI assesses the possibility that a child is lost. For example, it determines the possibility that a child is lost based on sightings and reports. The lost child recognition department also develops a system in which AI assesses the possibility of a child being lost based on information from local residents. For example, it analyzes reports from nearby residents to determine the possibility that a child is lost. The lost child recognition department also integrates information from other parents and local residents and builds a system in which AI assesses the possibility of a child being lost. For example, it combines sightings and reports to assess the possibility that a child is lost. In this way, by collecting information from other parents and local residents, it is possible to more accurately assess the possibility of a child being lost.

[0041] The lost child recognition unit can refer to past cases of children getting lost within the child's range of movement and assess the possibility of the child being lost. For example, the lost child recognition unit references past cases of children getting lost and builds a system in which AI assesses the possibility of the child being lost. For example, it compares past cases with the current situation to determine the possibility of the child being lost. The lost child recognition unit also develops a system in which AI assesses the possibility of the child being lost based on past cases of children getting lost within the child's range of movement. For example, it analyzes past case data to determine the possibility of the child being lost. The lost child recognition unit also integrates data on past cases of children getting lost and builds a system in which AI assesses the possibility of the child being lost. For example, it combines past cases with the current situation to assess the possibility of the child being lost. This allows the possibility of the child being lost to be assessed more accurately by referring to past cases of children getting lost.

[0042] The police notification unit can analyze behavioral history and current situation and propose the optimal protection method. For example, the police notification unit will build a system in which AI analyzes a child's behavioral history and the police propose the optimal protection method. For example, the protection method will be proposed based on past behavioral patterns. The police notification unit will also develop a system in which AI proposes the optimal protection method to the police based on current situation data. For example, the protection method will be proposed by analyzing current location information and the surrounding environment. The police notification unit will also build a system in which AI integrates behavioral history and current situation data and proposes the optimal protection method to the police. For example, the optimal protection method will be proposed if the child is moving outside of their normal range of movement. In this way, the optimal protection method can be proposed by analyzing behavioral history and current situation.

[0043] The police notification unit can integrate information from devices within the child's range of movement and support rapid protection. For example, the police notification unit analyzes video data from surveillance cameras and builds a system in which AI supports the police in providing rapid protection. For example, it identifies the child's current location from camera footage and notifies the police. The police notification unit also develops a system in which AI supports the police in providing rapid protection based on data from smart city sensors. For example, it analyzes sensor information to confirm the child's range of movement and notifies the police. The police notification unit also integrates information from other devices and builds a system in which AI supports the police in providing rapid protection. For example, it combines data from surveillance cameras and sensors and notifies the police. This allows for rapid protection to be supported by integrating information from other devices.

[0044] The police notification unit collects information from parents and local residents within the area of ​​movement and can support rapid protection. For example, the police notification unit collects information from other parents and local residents and builds a system in which AI supports the police in providing rapid protection. For example, it notifies the police based on sightings and reports. The police notification unit also develops a system in which AI supports the police in providing rapid protection based on information from local residents. For example, it analyzes reports from nearby residents and notifies the police. The police notification unit also integrates information from other parents and local residents and builds a system in which AI supports the police in providing rapid protection. For example, it combines sightings and reports and notifies the police. In this way, by collecting information from other parents and local residents, it can support rapid protection.

[0045] The police notification unit can refer to past cases of protection within the child's range of movement and suggest the most appropriate protection method. For example, the police notification unit can refer to past cases of protection and build a system in which AI suggests the most appropriate protection method to the police. For example, it can compare past cases with the current situation and suggest a protection method. The police notification unit can also develop a system in which AI suggests the most appropriate protection method to the police based on past cases of protection within the child's range of movement. For example, it can analyze past case data and suggest a protection method. The police notification unit can also integrate data from past cases of protection and build a system in which AI suggests the most appropriate protection method to the police. For example, it can propose a protection method by combining past cases with the current situation. In this way, the most appropriate protection method can be suggested by referring to past cases of protection.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The activity range memory unit stores the child's activity range. For example, the GPS device can collect the child's daily travel range and route, and the AI ​​analyzes and stores that data. The activity range memory unit can also record the child's route from school to home, the area where the child plays in the park, and so on. The anomaly detection unit detects anomalies by comparing the activity range stored by the activity range memory unit with real-time location information. For example, if the child deviates from their usual route or travels far beyond their usual activity range, the AI ​​detects an anomaly. The alert sending unit notifies the parent of any abnormalities detected by the anomaly detection unit. For example, alerts are sent via a smartphone app, email, SMS, etc. The lost child recognition unit accepts the parent's recognition of the child as lost. For example, when a parent presses the "Recognize Lost Child" button on the app, the child's current location and last known location information are sent to the police. The police notification unit notifies the police of the lost child information recognized by the lost child recognition unit. For example, the police can confirm the child's current location and rush to the scene to protect the child. This allows the child safety system to ensure the safety of children by recording the child's range of movement, detecting abnormalities and notifying the parents, and accepting the child's status as lost and notifying the police.

[0048] The activity range memory unit can learn activity patterns and take into account changes in activity range due to seasonal and weather changes. For example, the AI ​​learns a child's activity patterns and memorizes seasonal changes in activity range. For example, it compares activity ranges during summer vacation with those during winter vacation and records the different activity patterns. The activity range memory unit also collects weather data, and the AI ​​analyzes the differences in activity ranges on rainy and sunny days. For example, it memorizes activity ranges taking into account that children spend more time indoors on rainy days. The activity range memory unit also predicts changes in activity range due to seasonal and weather changes, and the AI ​​memorizes activity ranges based on that data. For example, it predicts that children will spend more time playing in the park in spring and records the activity ranges. This allows for more accurate memorization of activity ranges by taking into account changes in activity range due to seasonal and weather changes.

[0049] The activity range memory unit simultaneously learns the activity ranges of friends and family members, and can predict the activity range based on the relationships. For example, the AI ​​learns the activity ranges of a child's friends and family members, and predicts the activity range based on the relationships between them. For example, it records the places where the child plays with friends and the places where the child goes out with family. The activity range memory unit also analyzes the family's activity patterns and identifies factors that affect the child's activity range. For example, it predicts the activity range based on how the family spends their holidays and where they go out. The activity range memory unit also integrates the activity range data of friends and family members to build a system that more accurately predicts the child's activity range. For example, it memorizes the activity range taking into account the time of day and places the child spends with friends. This allows for more accurate activity range predictions by taking into account the activity ranges of friends and family members.

[0050] The activity range memory unit can record health status and analyze fluctuations according to health status. For example, to record a child's health status, step count and heart rate data are collected, and AI analyzes the activity range based on that data. For example, the activity range when the child is in good health is recorded. The activity range memory unit also builds a system that predicts fluctuations in activity range based on health status data. For example, when the heart rate is high, it records the tendency to go to places where the child gets more exercise. The activity range memory unit also analyzes step count and heart rate data and stores fluctuations in activity range according to health status. For example, it records the tendency to move over a wide area on days when the child takes a lot of steps. This makes it possible to analyze fluctuations in activity range according to health status by taking health status into consideration.

[0051] The activity range memory unit can analyze the device usage history and predict the activity range. For example, a system can be built that analyzes the usage history of devices used by children and predicts the activity range. For example, the activity range can be stored based on the location information and activity data of a smartwatch. The activity range memory unit can also analyze the tablet usage history and predict the child's activity range. For example, the activity range can be recorded based on the time and location of use of a specific app. The activity range memory unit can also integrate device usage history data and develop a system in which AI predicts the activity range. For example, the activity range can be stored by combining data from a smartwatch and a tablet. This makes it possible to predict the activity range by taking into account the device usage history.

[0052] The anomaly detection unit can detect anomalies by taking into account the speed of movement and the means of transportation. For example, a system can be built that analyzes a child's speed of movement and detects movements that are different from normal. For example, an anomaly is detected when the child is moving at a faster speed than normal. The anomaly detection unit can also develop a system that takes into account the means of transportation to detect anomalies. For example, an anomaly is detected when a child is traveling by bicycle in a place where they should be traveling on foot. The anomaly detection unit can also integrate data on the speed of movement and the means of transportation to build a system where AI detects anomalies. For example, an anomaly is detected when a child is traveling by car outside their normal range of movement. This allows for more accurate detection of anomalies by taking into account the speed of movement and the means of transportation.

[0053] The anomaly detection unit is able to determine abnormalities by taking into account events within the range of movement. For example, a system can be constructed that determines abnormalities by taking into account specific events within a child's range of movement. For example, an abnormality can be detected if the child is behaving differently than normal on the day of a school event. The anomaly detection unit can also collect information on local festivals and events and develop a system in which AI can determine abnormalities. For example, an abnormality can be detected if the child is moving outside the normal range of movement on a festival day. The anomaly detection unit can also build a system in which AI can determine abnormalities based on specific event data. For example, an abnormality can be detected if the child is behaving differently than normal on the day of an event. This allows for more accurate determination of abnormalities by taking specific events into account.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The activity range memory unit stores the child's activity range. For example, the range and route that the child travels on a daily basis is collected via a GPS device, and the AI ​​analyzes and stores this data. The activity range memory unit can also record the child's route from school to home, the area where they play in the park, etc. Step 2: The anomaly detection unit detects anomalies by comparing the movement range stored in the movement range memory unit with real-time location information. For example, if a child deviates from their usual route or travels far beyond their usual movement range, the AI ​​will detect an anomaly. Step 3: The alert sending unit notifies the parent or guardian of any abnormalities detected by the abnormality detection unit. For example, the alert may be sent via a smartphone app, email, SMS, etc. Step 4: The lost child certification department accepts the parent's certification of the lost child. For example, when a parent presses the "certify lost child" button on the app, the child's current location and last confirmed location information are sent to the police. Step 5: The police notification department notifies the police of the lost child information recognized by the lost child recognition department. For example, the police will confirm the child's current location and rush to the scene to protect the child.

[0056] (Example 2) The child safety system according to an embodiment of the present invention stores a child's range of movement in advance and issues an alert if the child moves in a way that is different from normal. Furthermore, it has a function that allows the local police to quickly protect the child if the child is identified as lost by the guardian. This allows the child safety system to ensure the safety of the child by storing the child's range of movement, detecting abnormalities and notifying the guardian, and accepting the child's identification and notifying the police.

[0057] A child safety system according to an embodiment includes a movement range storage unit, an abnormality detection unit, an alert transmission unit, a lost child identification unit, and a police notification unit. The movement range storage unit stores a child's movement range. For example, the movement range and route of a child's daily movements are collected using a GPS device, and AI analyzes and stores the data. The movement range storage unit can also record the child's route from school to home, the area where the child plays in the park, and so on. The abnormality detection unit detects an abnormality by comparing the movement range stored by the movement range storage unit with real-time location information. For example, the AI ​​detects an abnormality when a child deviates from their usual route or travels far beyond their usual movement range. The alert transmission unit notifies the guardian of the abnormality detected by the abnormality detection unit. For example, the alert is sent via a smartphone app, email, SMS, or the like. The lost child identification unit accepts the guardian's recognition of a lost child. For example, when a guardian presses a "recognize lost child" button on the app, the child's current location and last confirmed location information are sent to the police. The police notification unit notifies the police of the lost child information recognized by the lost child identification unit. For example, the police can check the child's current location and rush to the scene to protect them. This allows the child safety system to record the child's movement range, detect abnormalities and notify the parents, and accept the child as lost and notify the police, thereby ensuring the child's safety.

[0058] The activity range memory unit can learn activity patterns and take into account fluctuations due to seasonal and weather changes. For example, the activity range memory unit uses AI to learn children's activity patterns and store seasonal fluctuations in activity range. For example, it compares activity ranges during summer vacation with those during winter vacation and records the different activity patterns. The activity range memory unit also collects weather data, and the AI ​​analyzes the differences in activity ranges on rainy and sunny days. For example, it stores the activity range taking into account that children spend more time indoors on rainy days. The activity range memory unit also predicts fluctuations in activity range due to seasonal and weather changes, and the AI ​​stores the activity range based on that data. For example, it predicts that children will spend more time playing in the park in spring and records the activity range. This allows for more accurate activity range storage by taking into account fluctuations in activity range due to seasonal and weather changes.

[0059] The activity range memory unit simultaneously learns the activity ranges of friends and family and can predict the activity range based on the relationships. For example, the activity range memory unit uses AI to learn the activity ranges of a child's friends and family and predicts the activity range based on the mutual relationships. For example, it records places where the child plays with friends and places where the child goes out with family. The activity range memory unit also analyzes family activity patterns and identifies factors that affect the child's activity range. For example, it predicts the activity range based on how the family spends their holidays and where they go out. The activity range memory unit also integrates activity range data of friends and family to build a system that more accurately predicts the child's activity range. For example, it stores the activity range taking into account the time of day and places the child spends with friends. This allows for more accurate activity range predictions by taking into account the activity ranges of friends and family.

[0060] The activity range memory unit can use the emotion estimation function to analyze the emotional state and store fluctuations when stress and anxiety increase. The activity range memory unit, for example, uses the emotion estimation function to analyze the child's emotional state in real time and store fluctuations in the activity range when stress or anxiety increases. For example, it records the child's tendency to go to a specific place when stress increases. The activity range memory unit also analyzes the child's emotional state and builds a system that predicts changes in the activity range in response to emotional fluctuations. For example, it records the child's tendency to return home when anxiety increases. The activity range memory unit also stores fluctuations in the child's activity range based on the emotion estimation data and identifies behavioral patterns when stress or anxiety increases. For example, it records the child's tendency to go to a specific place when the emotional state worsens. In this way, it is possible to store fluctuations in the activity range when stress or anxiety increases by taking the emotional state into consideration.

[0061] The activity range memory unit can record health conditions and analyze fluctuations according to the health conditions. For example, the activity range memory unit collects step count and heart rate data to record a child's health condition, and AI analyzes the activity range based on that data. For example, it records the activity range when the child is in good health. The activity range memory unit also builds a system that predicts fluctuations in activity range based on health condition data. For example, when the heart rate is high, it records the tendency to go to places where the child gets more exercise. The activity range memory unit also analyzes step count and heart rate data and stores fluctuations in activity range according to the health condition. For example, it records the tendency to move over a wide area on days when the child takes a lot of steps. This makes it possible to analyze fluctuations in activity range according to the health condition by taking the health condition into consideration.

[0062] The activity range memory unit can analyze the device usage history and predict the activity range. The activity range memory unit, for example, analyzes the usage history of devices used by children and builds a system that predicts the activity range. For example, it stores the activity range based on the location information and activity data of a smartwatch. The activity range memory unit also analyzes the usage history of a tablet and predicts the child's activity range. For example, it records the activity range based on the time and location of use of a specific app. The activity range memory unit also integrates device usage history data and develops a system in which AI predicts the activity range. For example, it combines data from a smartwatch and a tablet to store the activity range. This makes it possible to predict the activity range by taking into account the device usage history.

[0063] The activity range memory unit uses the emotion estimation function to record the emotional state of the child when he or she is in a specific location and can suggest an activity range for eliciting positive emotions. The activity range memory unit, for example, uses the emotion estimation function to record the emotional state of the child when he or she is in a specific location. For example, the activity range memory unit analyzes the emotional state when playing in a park and suggests an activity range for eliciting positive emotions. The activity range memory unit also builds a system that suggests an activity range for eliciting positive emotions based on the child's emotional state. For example, it preferentially suggests places with high emotion scores as activity ranges. The activity range memory unit also identifies places where the child feels positive emotions based on the emotion estimation data and suggests those places as activity ranges. For example, it records places where the emotional state is good and suggests them as activity ranges. In this way, it is possible to support the child's psychological health by suggesting activity ranges for eliciting positive emotions.

[0064] The anomaly detection unit can detect anomalies by taking into account the speed of movement and the means of transportation. For example, the anomaly detection unit builds a system that analyzes a child's speed of movement and detects movements that are different from normal. For example, an anomaly is detected when the child is moving at a faster speed than normal. The anomaly detection unit also develops a system that detects anomalies by taking into account the means of transportation. For example, an anomaly is detected when a child is traveling by bicycle in a place where they should be traveling on foot. The anomaly detection unit also integrates data on the speed of movement and the means of transportation and builds a system in which AI detects anomalies. For example, an anomaly is detected when a child is traveling by car outside their normal range of movement. In this way, by taking into account the speed of movement and the means of transportation, anomalies can be detected more accurately.

[0065] The anomaly detection unit can determine abnormalities by taking into account events within the range of movement. For example, the anomaly detection unit will build a system that determines abnormalities by taking into account specific events within a child's range of movement. For example, it will detect an abnormality if the child is behaving differently than normal on the day of a school event. The anomaly detection unit will also collect information on local festivals and events and develop a system in which AI will determine abnormalities. For example, it will detect an abnormality if the child is moving beyond the normal range of movement on a festival day. The anomaly detection unit will also build a system in which AI will determine abnormalities based on specific event data. For example, it will detect an abnormality if the child is behaving differently than normal on the day of an event. This allows for more accurate determination of abnormalities by taking specific events into account.

[0066] The anomaly detection unit can use the emotion estimation function to detect a sudden change in the emotional state as an anomaly and issue an alert. The anomaly detection unit, for example, uses the emotion estimation function to build a system that detects an anomaly when a child's emotional state changes suddenly. For example, an alert is issued if the emotion score suddenly drops. The anomaly detection unit also analyzes the child's emotional state in real time and detects sudden changes as an anomaly. For example, an alert is issued if the emotional state suddenly becomes unstable. The anomaly detection unit also develops a system that detects sudden changes in the emotional state as an anomaly and issues an alert based on the emotion estimation data. For example, an alert is issued if the emotion score suddenly fluctuates. This enables a rapid response by detecting a sudden change in the emotional state as an anomaly and issuing an alert.

[0067] The anomaly detection unit can determine abnormalities by taking into account environmental changes within the range of movement. For example, the anomaly detection unit collects traffic congestion data within a child's range of movement and builds a system in which AI determines abnormalities. For example, it detects abnormal behavior when traffic congestion occurs. The anomaly detection unit also develops a system in which AI determines abnormalities based on weather data. For example, it detects abnormal behavior when the weather worsens. The anomaly detection unit also integrates environmental change data and builds a system in which AI determines abnormalities. For example, it detects abnormalities by taking traffic congestion and weather changes into account. This allows for more accurate determination of abnormalities by taking environmental changes into account.

[0068] The anomaly detection unit can also analyze the behavioral patterns of other children within a child's range of movement and detect abnormalities. The anomaly detection unit, for example, analyzes the behavioral patterns of other children within a child's range of movement and builds a system to detect abnormalities. For example, it detects abnormalities when a child moves differently from other children. The anomaly detection unit also collects behavioral data of other children and develops a system in which AI determines abnormalities. For example, it detects abnormalities when a child moves differently from normal behavior patterns. The anomaly detection unit also builds a system in which AI detects abnormalities based on the behavioral patterns of other children. For example, it detects abnormalities when a child has a different range of movement from other children. This allows for more accurate detection of abnormalities by taking into account the behavioral patterns of other children.

[0069] The lost child recognition unit can analyze behavioral history and current situation, and automatically evaluate the possibility of a child being lost. For example, the lost child recognition unit builds a system in which AI analyzes a child's behavioral history and automatically evaluates the possibility of a child being lost. For example, it evaluates the possibility of a child being lost if the child is moving in a way that differs from their usual behavioral pattern. The lost child recognition unit also develops a system in which AI evaluates the possibility of a child being lost based on current situation data. For example, it analyzes current location information and the surrounding environment to determine the possibility of a child being lost. The lost child recognition unit also integrates behavioral history and current situation data, and builds a system in which AI automatically evaluates the possibility of a child being lost. For example, it evaluates the possibility of a child being lost if the child is moving beyond their usual range of movement. In this way, it is possible to automatically evaluate the possibility of a child being lost by analyzing behavioral history and current situation.

[0070] The lost child recognition unit can integrate information from devices within the child's range of movement and assess the possibility of the child being lost. For example, the lost child recognition unit analyzes video data from surveillance cameras and builds a system in which AI assesses the possibility of the child being lost. For example, it identifies the child's current location from camera footage and determines the possibility of the child being lost. The lost child recognition unit also develops a system in which AI assesses the possibility of the child being lost based on data from smart city sensors. For example, it analyzes sensor information to confirm the child's range of movement and determine the possibility of the child being lost. The lost child recognition unit also integrates information from other devices and builds a system in which AI assesses the possibility of the child being lost. For example, it combines data from surveillance cameras and sensors to assess the possibility of the child being lost. This allows for a more accurate assessment of the possibility of the child being lost by integrating information from other devices.

[0071] The lost child recognition unit can use the emotion estimation function to analyze the emotional state of the guardian and determine whether it is appropriate to recognize a child as lost. For example, the lost child recognition unit uses the emotion estimation function to analyze the emotional state of the guardian and build a system to determine whether it is appropriate to recognize a child as lost. For example, if the guardian's emotion score is high, the lost child recognition unit will prompt the guardian to recognize the child as lost. The lost child recognition unit also analyzes the guardian's emotional state in real time and determines whether it is appropriate to recognize a child as lost. For example, if the emotional state is unstable, the lost child recognition unit will reconfirm the recognition of the child as lost. The lost child recognition unit also develops a system that analyzes the guardian's emotional state based on the emotion estimation data and determines whether it is appropriate to recognize a child as lost. For example, if the emotion score is low, the lost child recognition unit will reconsider the recognition of the child as lost. In this way, it is possible to determine whether it is appropriate to recognize a child as lost by taking the guardian's emotional state into consideration.

[0072] The lost child recognition department can collect information from parents and local residents within the child's range of movement and assess the possibility that a child is lost. For example, the lost child recognition department collects information from other parents and local residents and builds a system in which AI assesses the possibility that a child is lost. For example, it determines the possibility that a child is lost based on sightings and reports. The lost child recognition department also develops a system in which AI assesses the possibility of a child being lost based on information from local residents. For example, it analyzes reports from nearby residents to determine the possibility that a child is lost. The lost child recognition department also integrates information from other parents and local residents and builds a system in which AI assesses the possibility of a child being lost. For example, it combines sightings and reports to assess the possibility that a child is lost. In this way, by collecting information from other parents and local residents, it is possible to more accurately assess the possibility of a child being lost.

[0073] The lost child recognition unit can refer to past cases of children getting lost within the child's range of movement and assess the possibility of the child being lost. For example, the lost child recognition unit references past cases of children getting lost and builds a system in which AI assesses the possibility of the child being lost. For example, it compares past cases with the current situation to determine the possibility of the child being lost. The lost child recognition unit also develops a system in which AI assesses the possibility of the child being lost based on past cases of children getting lost within the child's range of movement. For example, it analyzes past case data to determine the possibility of the child being lost. The lost child recognition unit also integrates data on past cases of children getting lost and builds a system in which AI assesses the possibility of the child being lost. For example, it combines past cases with the current situation to assess the possibility of the child being lost. This allows the possibility of the child being lost to be assessed more accurately by referring to past cases of children getting lost.

[0074] The lost child recognition unit can use the emotion estimation function to provide a support function to reduce stress and anxiety felt by guardians when they recognize a lost child. The lost child recognition unit, for example, uses the emotion estimation function to build a system that provides a support function to reduce stress and anxiety felt by guardians when they recognize a lost child. For example, it displays a message that provides a sense of security. The lost child recognition unit also analyzes the guardian's emotional state in real time and provides a support function to reduce stress and anxiety. For example, it displays advice on how to relax. The lost child recognition unit also develops a system that provides a support function to reduce stress and anxiety felt by guardians based on the emotion estimation data. For example, it displays a support message according to the emotional state. This provides a support function to reduce stress and anxiety felt by guardians, allowing for more appropriate recognition of lost children.

[0075] The police notification unit can analyze behavioral history and current situation and propose the optimal protection method. For example, the police notification unit will build a system in which AI analyzes a child's behavioral history and the police propose the optimal protection method. For example, the protection method will be proposed based on past behavioral patterns. The police notification unit will also develop a system in which AI proposes the optimal protection method to the police based on current situation data. For example, the protection method will be proposed by analyzing current location information and the surrounding environment. The police notification unit will also build a system in which AI integrates behavioral history and current situation data and proposes the optimal protection method to the police. For example, the optimal protection method will be proposed if the child is moving outside of their normal range of movement. In this way, the optimal protection method can be proposed by analyzing behavioral history and current situation.

[0076] The police notification unit can integrate information from devices within the child's range of movement and support rapid protection. For example, the police notification unit analyzes video data from surveillance cameras and builds a system in which AI supports the police in providing rapid protection. For example, it identifies the child's current location from camera footage and notifies the police. The police notification unit also develops a system in which AI supports the police in providing rapid protection based on data from smart city sensors. For example, it analyzes sensor information to confirm the child's range of movement and notifies the police. The police notification unit also integrates information from other devices and builds a system in which AI supports the police in providing rapid protection. For example, it combines data from surveillance cameras and sensors and notifies the police. This allows for rapid protection to be supported by integrating information from other devices.

[0077] The police notification unit can use the emotion estimation function to provide a support function for reducing stress and anxiety experienced by police when protecting children. The police notification unit, for example, uses the emotion estimation function to build a system that provides a support function for reducing stress and anxiety experienced by police when protecting children. For example, a message that provides a sense of security is displayed. The police notification unit also analyzes the emotional state of police in real time and provides a support function for reducing stress and anxiety. For example, advice on how to relax is displayed. The police notification unit also develops a system that provides a support function for reducing stress and anxiety experienced by police based on the emotion estimation data. For example, a support message according to the emotional state is displayed. This provides a support function for reducing stress and anxiety experienced by police, allowing for more appropriate child protection.

[0078] The police notification unit collects information from parents and local residents within the area of ​​movement and can support rapid protection. For example, the police notification unit collects information from other parents and local residents and builds a system in which AI supports the police in providing rapid protection. For example, it notifies the police based on sightings and reports. The police notification unit also develops a system in which AI supports the police in providing rapid protection based on information from local residents. For example, it analyzes reports from nearby residents and notifies the police. The police notification unit also integrates information from other parents and local residents and builds a system in which AI supports the police in providing rapid protection. For example, it combines sightings and reports and notifies the police. In this way, by collecting information from other parents and local residents, it can support rapid protection.

[0079] The police notification unit can refer to past cases of protection within the child's range of movement and suggest the most appropriate protection method. For example, the police notification unit can refer to past cases of protection and build a system in which AI suggests the most appropriate protection method to the police. For example, it can compare past cases with the current situation and suggest a protection method. The police notification unit can also develop a system in which AI suggests the most appropriate protection method to the police based on past cases of protection within the child's range of movement. For example, it can analyze past case data and suggest a protection method. The police notification unit can also integrate data from past cases of protection and build a system in which AI suggests the most appropriate protection method to the police. For example, it can propose a protection method by combining past cases with the current situation. In this way, the most appropriate protection method can be suggested by referring to past cases of protection.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The activity range memory unit stores the child's activity range. For example, the GPS device can collect the child's daily travel range and route, and the AI ​​analyzes and stores that data. The activity range memory unit can also record the child's route from school to home, the area where the child plays in the park, and so on. The anomaly detection unit detects anomalies by comparing the activity range stored by the activity range memory unit with real-time location information. For example, if the child deviates from their usual route or travels far beyond their usual activity range, the AI ​​detects an anomaly. The alert sending unit notifies the parent of any abnormalities detected by the anomaly detection unit. For example, alerts are sent via a smartphone app, email, SMS, etc. The lost child recognition unit accepts the parent's recognition of the child as lost. For example, when a parent presses the "Recognize Lost Child" button on the app, the child's current location and last known location information are sent to the police. The police notification unit notifies the police of the lost child information recognized by the lost child recognition unit. For example, the police can confirm the child's current location and rush to the scene to protect the child. This allows the child safety system to ensure the safety of children by recording the child's range of movement, detecting abnormalities and notifying the parents, and accepting the child's status as lost and notifying the police.

[0082] The activity range memory unit can learn activity patterns and take into account changes in activity range due to seasonal and weather changes. For example, the AI ​​learns a child's activity patterns and memorizes seasonal changes in activity range. For example, it compares activity ranges during summer vacation with those during winter vacation and records the different activity patterns. The activity range memory unit also collects weather data, and the AI ​​analyzes the differences in activity ranges on rainy and sunny days. For example, it memorizes activity ranges taking into account that children spend more time indoors on rainy days. The activity range memory unit also predicts changes in activity range due to seasonal and weather changes, and the AI ​​memorizes activity ranges based on that data. For example, it predicts that children will spend more time playing in the park in spring and records the activity ranges. This allows for more accurate memorization of activity ranges by taking into account changes in activity range due to seasonal and weather changes.

[0083] The activity range memory unit simultaneously learns the activity ranges of friends and family members, and can predict the activity range based on the relationships. For example, the AI ​​learns the activity ranges of a child's friends and family members, and predicts the activity range based on the relationships between them. For example, it records the places where the child plays with friends and the places where the child goes out with family. The activity range memory unit also analyzes the family's activity patterns and identifies factors that affect the child's activity range. For example, it predicts the activity range based on how the family spends their holidays and where they go out. The activity range memory unit also integrates the activity range data of friends and family members to build a system that more accurately predicts the child's activity range. For example, it memorizes the activity range taking into account the time of day and places the child spends with friends. This allows for more accurate activity range predictions by taking into account the activity ranges of friends and family members.

[0084] The activity range memory unit can use the emotion estimation function to analyze the emotional state and store fluctuations when stress and anxiety increase. For example, the emotion estimation function can be used to analyze a child's emotional state in real time and store fluctuations in the activity range when stress or anxiety increases. For example, the emotion estimation function can be used to record a tendency for the child to go to a specific place when stress increases. The activity range memory unit also analyzes the child's emotional state and builds a system that predicts changes in the activity range in response to emotional fluctuations. For example, the activity range memory unit can record a tendency for the child to return home when anxiety increases. The activity range memory unit also stores fluctuations in the child's activity range based on the emotion estimation data and identifies behavioral patterns when stress or anxiety increases. For example, the activity range memory unit can record a tendency for the child to go to a specific place when the emotional state worsens. In this way, by taking the emotional state into consideration, it is possible to store fluctuations in the activity range when stress or anxiety increases.

[0085] The activity range memory unit can record health status and analyze fluctuations according to health status. For example, to record a child's health status, step count and heart rate data are collected, and AI analyzes the activity range based on that data. For example, the activity range when the child is in good health is recorded. The activity range memory unit also builds a system that predicts fluctuations in activity range based on health status data. For example, when the heart rate is high, it records the tendency to go to places where the child gets more exercise. The activity range memory unit also analyzes step count and heart rate data and stores fluctuations in activity range according to health status. For example, it records the tendency to move over a wide area on days when the child takes a lot of steps. This makes it possible to analyze fluctuations in activity range according to health status by taking health status into consideration.

[0086] The activity range memory unit can analyze the device usage history and predict the activity range. For example, a system can be built that analyzes the usage history of devices used by children and predicts the activity range. For example, the activity range can be stored based on the location information and activity data of a smartwatch. The activity range memory unit can also analyze the tablet usage history and predict the child's activity range. For example, the activity range can be recorded based on the time and location of use of a specific app. The activity range memory unit can also integrate device usage history data and develop a system in which AI predicts the activity range. For example, the activity range can be stored by combining data from a smartwatch and a tablet. This makes it possible to predict the activity range by taking into account the device usage history.

[0087] The activity range memory unit can use the emotion estimation function to record the emotional state of a child when he or she is in a specific location and suggest an activity range that will elicit positive emotions. For example, the emotion estimation function is used to record the emotional state of a child when he or she is in a specific location. For example, the emotional state when playing in a park is analyzed and an activity range that will elicit positive emotions is suggested. The activity range memory unit also builds a system that suggests an activity range that will elicit positive emotions based on the child's emotional state. For example, it prioritizes suggesting places with high emotion scores as activity ranges. The activity range memory unit also identifies places where the child feels positive emotions based on the emotion estimation data and suggests those places as activity ranges. For example, it records places where the emotional state is good and suggests them as activity ranges. In this way, it is possible to support the child's psychological health by suggesting activity ranges that will elicit positive emotions.

[0088] The anomaly detection unit can detect anomalies by taking into account the speed of movement and the means of transportation. For example, a system can be built that analyzes a child's speed of movement and detects movements that are different from normal. For example, an anomaly is detected when the child is moving at a faster speed than normal. The anomaly detection unit can also develop a system that takes into account the means of transportation to detect anomalies. For example, an anomaly is detected when a child is traveling by bicycle in a place where they should be traveling on foot. The anomaly detection unit can also integrate data on the speed of movement and the means of transportation to build a system where AI detects anomalies. For example, an anomaly is detected when a child is traveling by car outside their normal range of movement. This allows for more accurate detection of anomalies by taking into account the speed of movement and the means of transportation.

[0089] The anomaly detection unit is able to determine abnormalities by taking into account events within the range of movement. For example, a system can be constructed that determines abnormalities by taking into account specific events within a child's range of movement. For example, an abnormality can be detected if the child is behaving differently than normal on the day of a school event. The anomaly detection unit can also collect information on local festivals and events and develop a system in which AI can determine abnormalities. For example, an abnormality can be detected if the child is moving outside the normal range of movement on a festival day. The anomaly detection unit can also build a system in which AI can determine abnormalities based on specific event data. For example, an abnormality can be detected if the child is behaving differently than normal on the day of an event. This allows for more accurate determination of abnormalities by taking specific events into account.

[0090] The anomaly detection unit can use the emotion estimation function to detect a sudden change in the emotional state as an anomaly and issue an alert. For example, a system is constructed that uses the emotion estimation function to detect an anomaly when a child's emotional state changes suddenly. For example, an alert is issued if the emotion score suddenly drops. The anomaly detection unit also analyzes the child's emotional state in real time and detects sudden changes as an anomaly. For example, an alert is issued if the emotional state suddenly becomes unstable. The anomaly detection unit is also developed to detect a sudden change in the emotional state as an anomaly and issue an alert based on the emotion estimation data. For example, an alert is issued if the emotion score suddenly fluctuates. This enables a rapid response by detecting a sudden change in the emotional state as an anomaly and issuing an alert.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The activity range memory unit stores the child's activity range. For example, the range and route that the child travels on a daily basis is collected via a GPS device, and the AI ​​analyzes and stores this data. The activity range memory unit can also record the child's route from school to home, the area where they play in the park, etc. Step 2: The anomaly detection unit detects anomalies by comparing the movement range stored in the movement range memory unit with real-time location information. For example, if a child deviates from their usual route or travels far beyond their usual movement range, the AI ​​will detect an anomaly. Step 3: The alert sending unit notifies the parent or guardian of any abnormalities detected by the abnormality detection unit. For example, the alert may be sent via a smartphone app, email, SMS, etc. Step 4: The lost child certification department accepts the parent's certification of the lost child. For example, when a parent presses the "certify lost child" button on the app, the child's current location and last confirmed location information are sent to the police. Step 5: The police notification department notifies the police of the lost child information recognized by the lost child recognition department. For example, the police will confirm the child's current location and rush to the scene to protect the child.

[0093] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0099] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0100] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0101] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0102] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0103] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0104] 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.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0134] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0143] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0144] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0145] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0146] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0147] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0148] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0149] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0150] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0151] 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.

[0152] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0153] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0154] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0155] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0156] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0157] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0158] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an activity range storage unit that stores the child's activity range; an anomaly detection unit that compares the movement range stored by the movement range storage unit with real-time position information to detect an anomaly; an alert sending unit that notifies a guardian of an abnormality detected by the abnormality detection unit; The Lost Child Certification Department accepts lost child certifications from guardians; a police notification unit that notifies the police of the lost child information recognized by the lost child recognition unit; A system characterized by:

2. The movement range storage unit Analyzes emotional states and remembers fluctuations during periods of stress and anxiety 2. The system of claim 1.

3. The abnormality detection unit Detect the anomaly by taking into account the speed of movement and the means of movement.

2. The system of claim 1.

4. The lost child identification unit Analyzes behavioral history and current situation to automatically assess the likelihood of a child being lost 2. The system of claim 1.

5. The police notification department: Analyzes behavioral history and current situation to suggest optimal protection methods 2. The system of claim 1.

6. The abnormality detection unit When a sudden change in emotional state occurs, it is detected as an abnormality and an alert is issued.

2. The system of claim 1.

7. The lost child identification unit Analyzing the emotional state of the parent and determining whether the identification of the child as lost is appropriate 2. The system of claim 1.

8. The police notification department: Provide support functions to reduce stress and anxiety when the police are protecting the children 2. The system of claim 1.

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