System

Abstract

An object of a system according to an embodiment is to quickly and accurately identify the position of a lost child.SOLUTION: A system according to an embodiment includes a face authentication unit, a video analysis unit, and a notification unit. The face authentication part inputs a face image of the lost child. The video analysis unit analyzes the monitoring camera video data based on the face image of the lost child input by the face authentication unit. The notification part notifies the protector and the person concerned of the position information of the lost child specified by the video analysis part.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 techniques have had the problem of making it difficult to quickly and accurately locate a lost child.

[0005] The system according to the embodiment aims to quickly and accurately locate a lost child. [Means for solving the problem]

[0006] The system according to the embodiment includes a face authentication unit, a video analysis unit, and a notification unit. The face authentication unit inputs a facial image of the lost child. The video analysis unit analyzes surveillance camera video data based on the facial image of the lost child input by the face authentication unit. The notification unit notifies the guardian and other relevant parties of the location information of the lost child identified by the video analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and accurately locate a lost child. [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) A lost child tracking system according to an embodiment of the present invention utilizes facial recognition technology and surveillance camera video data to track a lost child. This system inputs a facial image of the lost child into the system and analyzes the surveillance camera video data to identify the child's location and quickly notify the child's guardian and other relevant parties. This allows the lost child tracking system to quickly find the lost child and notify the guardian.

[0029] A lost child tracking system according to an embodiment includes a face authentication unit, a video analysis unit, and a notification unit. The face authentication unit inputs a facial image of the lost child. For example, a facial image taken by a guardian using a smartphone or tablet may be input. Alternatively, a pre-registered facial image may be used. Furthermore, the face authentication unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to perform facial recognition based on the facial image of the lost child and extract the characteristics of the lost child. For example, the generation AI analyzes the facial image of the lost child and extracts facial feature points. The video analysis unit analyzes surveillance camera video data based on the facial image of the lost child input by the face authentication unit. For example, the generation AI analyzes the surveillance camera video data in real time to identify a person matching the facial image of the lost child. The generation AI can also determine the location of the lost child based on the surveillance camera video data. For example, the generation AI analyzes the surveillance camera video data to identify the location of a person matching the facial image of the lost child. The notification unit notifies the guardian and relevant parties of the location information of the lost child identified by the video analysis unit. For example, a smartphone app can display the lost child's current location on a map and notify the guardian. Notifications are also sent to facility staff, enabling a prompt response. For example, the notification unit inputs a prompt containing the lost child's location information into the generation AI, which then sends a notification based on that prompt. This enables the lost child tracking system according to the embodiment to quickly locate the lost child and notify the guardian. For example, even if a child gets lost in a large facility such as a shopping mall or amusement park, the lost child can be quickly found and the guardian notified by utilizing facial recognition technology and surveillance camera video data.

[0030] The facial recognition unit can extract additional information such as age, gender, and facial expression from the lost child's facial image, improving authentication accuracy. For example, the facial recognition unit inputs the lost child's facial image, and the generation AI extracts additional information such as age, gender, and facial expression from the image. For example, it analyzes facial contours and skin texture to estimate age. The generation AI also analyzes the lost child's facial image to identify gender. For example, it determines gender based on facial features, hairstyle, clothing, etc. The generation AI also analyzes facial expressions from the lost child's facial image to estimate their emotional state. For example, it recognizes facial expressions such as smiling and crying faces to understand the lost child's psychological state. This improves authentication accuracy.

[0031] The facial recognition unit can analyze the lost child's past movement patterns based on past surveillance camera video data and the lost child's facial image. For example, the facial recognition unit inputs the lost child's facial image, and the generation AI analyzes the movement pattern by referring to the past surveillance camera video data. For example, it identifies which routes the lost child has taken in the past. The generation AI also extracts the movement history of the same person from past surveillance camera video data based on the lost child's facial image. For example, it analyzes which areas the child was in at specific times of day. The generation AI also analyzes past surveillance camera video data based on the lost child's facial image to identify the lost child's behavior pattern. For example, it identifies a tendency for the child to frequently visit specific places. This makes it easier to identify the lost child's location by analyzing their past movement patterns.

[0032] The facial recognition unit can register facial images of not only the lost child but also the guardian, and can support the reunion of the guardian and the lost child. For example, the facial recognition unit registers the facial images of the lost child and guardian in the system, and the generation AI performs facial recognition of both. For example, when a guardian searches for a lost child, the position of the lost child is identified based on the guardian's facial image. The generation AI also analyzes the facial images of the lost child and guardian to build a system to support the reunion. For example, it notifies the guardian if they are near the lost child. The generation AI also registers facial images of the lost child and guardian, and performs facial recognition of both to support the reunion. For example, when a guardian finds a lost child, the system automatically notifies them. This helps reunite the guardian and the lost child, enabling a quick reunion.

[0033] The facial recognition unit can recognize the characteristics of the lost child's clothing or belongings along with the lost child's facial image, improving tracking accuracy. For example, the facial recognition unit inputs the characteristics of the lost child's clothing and belongings along with the lost child's facial image, and the generation AI recognizes them. For example, the lost child can be identified based on clothing of a specific color or design. The generation AI can also analyze the lost child's facial image and clothing characteristics to improve tracking accuracy. For example, the lost child can be located based on a specific item the lost child is carrying. The generation AI can also input the lost child's facial image and the characteristics of their belongings and recognize them to improve tracking accuracy. For example, the lost child can be identified based on the design of the backpack the lost child is carrying. This improves tracking accuracy.

[0034] The video analysis unit can analyze the speed or direction of a lost child's movement from surveillance camera video data and predict their movement. For example, the video analysis unit inputs surveillance camera video data, and the generation AI analyzes the speed and direction of the lost child's movement. For example, it determines which direction the lost child is heading. The generation AI also analyzes the lost child's movement speed based on the surveillance camera video data and predicts their movement. For example, it evaluates how fast the lost child is moving. The generation AI also analyzes the direction of the lost child's movement from the surveillance camera video data and predicts their movement. For example, it predicts which area the lost child is likely to head to next. This makes it possible to predict the lost child's movement.

[0035] The video analysis unit can remove background noise from surveillance camera video data, improving the accuracy of lost child detection. For example, the video analysis unit inputs surveillance camera video data, and the generation AI removes background noise. For example, it removes unnecessary objects and shadows from the video. The generation AI also analyzes the surveillance camera video data and removes background noise to improve the accuracy of lost child detection. For example, it identifies moving objects in the video and distinguishes them from lost children. The generation AI also removes background noise from surveillance camera video data to improve the accuracy of lost child detection. For example, it removes stationary objects in the video and identifies moving people. This improves the accuracy of lost child detection.

[0036] The video analysis unit can simultaneously analyze surveillance camera video data and audio data to recognize the voice of a lost child. For example, the video analysis unit inputs surveillance camera video data and audio data, and the generation AI analyzes both. For example, it identifies the lost child's voice and links the video and audio. The generation AI can also simultaneously analyze surveillance camera video data and audio data to detect the lost child's voice. For example, it can recognize the lost child's cry for help. The generation AI can also build a system to detect the lost child's voice based on surveillance camera video data and audio data. For example, it can identify the voice if the lost child is screaming. This makes it possible to detect the lost child's voice.

[0037] The video analysis unit can analyze the movements of people around the lost child from surveillance camera video data and determine the location of the lost child. For example, the video analysis unit inputs surveillance camera video data, and the generation AI analyzes the movements of people around the lost child. For example, it identifies the movements of people near the lost child. The generation AI also analyzes the movements of people around the lost child based on the surveillance camera video data and determines the location of the lost child. For example, it identifies the location of the lost child if the lost child is in a crowd. The generation AI also analyzes the movements of people around the lost child from surveillance camera video data and determines the location of the lost child. For example, it identifies the location of the lost child if the lost child belongs to a specific group. This makes it possible to determine the location of the lost child.

[0038] The notification unit updates the lost child's location information in real time and can continuously notify the guardian. For example, the notification unit inputs the lost child's location information, and the generation AI updates that information in real time. For example, it updates the location information every time the lost child moves and notifies the guardian. The generation AI also builds a system that notifies the guardian in real time based on the lost child's location information. For example, it notifies when the lost child enters a specific area. The generation AI also updates the lost child's location information in real time and notifies the guardian accordingly. For example, it sends a notification to a smartphone every time the lost child moves. This allows the guardian to be notified of the lost child's location information in real time.

[0039] The notification unit can analyze the movement pattern of the lost child based on past location data and the lost child's location information. For example, the notification unit inputs the lost child's location information, and the generation AI analyzes the movement pattern by referring to the past location data. For example, it identifies which routes the lost child has taken in the past. The generation AI also extracts movement patterns from the past location data based on the lost child's location information. For example, it identifies whether the lost child tends to frequently visit certain areas. The generation AI also analyzes the lost child's location information and past location data to analyze the movement pattern. For example, it predicts which area the lost child will be in at a certain time of day. This makes it easier to identify the lost child's location by analyzing their movement pattern.

[0040] The notification unit can notify the location information of a lost child not only to a smartphone app but also to a smartwatch or smartglasses. For example, the notification unit inputs the location information of the lost child, and the generation AI notifies the information to a smartwatch. For example, this allows a guardian to check the location of the lost child on a smartwatch. The generation AI also builds a system that notifies smartglasses based on the lost child's location information. For example, this allows a guardian to visually check the location of the lost child on smartglasses. The generation AI also notifies the location information of a lost child not only to a smartphone app but also to a smartwatch or smartglasses. For example, this allows a guardian to check the location of a lost child on multiple devices. This allows a guardian to check the location of a lost child on multiple devices.

[0041] When displaying the location information of a lost child on a map, the notification unit can also simultaneously display the child's movement history. For example, the notification unit inputs the lost child's location information, and the generation AI displays that information on a map. For example, it displays the lost child's past movement history along with its current location. The generation AI also builds a system that analyzes the lost child's location information and movement history and displays them on a map. For example, it visually displays the route the lost child took. The generation AI also displays the lost child's location information and movement history on a map and notifies the guardian. For example, it simultaneously displays the lost child's current location and past movement routes. This allows the lost child's current location and movement history to be checked at the same time.

[0042] The notification unit can organize the tracking history of the lost child in chronological order, allowing parents to intuitively understand the history. For example, the notification unit inputs the tracking history of the lost child, and the generation AI organizes the information in chronological order. For example, it identifies what time of day and what area the lost child was in. The generation AI also organizes the tracking history of the lost child in chronological order, building a system that allows parents to intuitively understand the history. For example, it displays the route the lost child took in chronological order. The generation AI also organizes the tracking history of the lost child in chronological order and notifies the parents. For example, it displays the route the lost child took in chronological order. This allows parents to intuitively understand the tracking history of the lost child.

[0043] The notification unit analyzes the movement patterns of the lost child's tracking history and can understand the behavioral trends of the lost child. For example, the notification unit inputs the tracking history of the lost child, and the generation AI analyzes the movement patterns based on that information. For example, it identifies the tendency for lost children to frequently visit specific areas. The generation AI also analyzes the tracking history of the lost child and builds a system that analyzes the movement patterns. For example, it identifies what time of day and what area the lost child is in. The generation AI also analyzes the movement patterns based on the tracking history of the lost child and understands the behavioral trends of the lost child. For example, it identifies the tendency for lost children to travel along specific routes. This makes it possible to understand the behavioral trends of the lost child.

[0044] The notification unit can make it possible to share the tracking history of a lost child not only with the guardian but also with the facility manager or the police. For example, the notification unit inputs the tracking history of a lost child, and the generation AI shares that information not only with the guardian but also with the facility manager and the police. For example, the location information of the lost child is shared in real time. The generation AI also builds a system for sharing the tracking history of a lost child with the facility manager and the police. For example, the route the lost child traveled is shared. The generation AI also shares the tracking history of a lost child not only with the guardian but also with the facility manager and the police and notifies them. For example, a notification is sent if the lost child is in a specific area. This allows the tracking history of a lost child to be shared with multiple parties.

[0045] The notification unit can display the tracking history of the lost child on a map so that the guardian can visually understand it. For example, the notification unit inputs the tracking history of the lost child, and the generation AI displays the information on a map. For example, the lost child's movement route is displayed on the map so that the guardian can visually confirm it. The generation AI also builds a system that displays on a map based on the tracking history of the lost child. For example, it displays on the map the route the lost child took. The generation AI also displays the tracking history of the lost child on a map and notifies the guardian. For example, it displays the lost child's movement route on a map so that the guardian can visually confirm it. This makes it possible to visually confirm the tracking history of the lost child.

[0046] When a lost child is captured on a surveillance camera again, the notification unit compares the child's past tracking history to improve the accuracy of re-recognition. For example, when a lost child is captured on a surveillance camera again, the notification unit causes the generation AI to compare the child's past tracking history. For example, when a lost child is captured on a surveillance camera again, the generation AI compares the child's past movement patterns to improve the accuracy of re-recognition. Furthermore, when a lost child is re-recognized, the generation AI builds a system that improves the accuracy of re-recognition based on the past tracking history. For example, when a lost child reappears in a specific area, the generation AI compares the child's past data. Furthermore, when a lost child is re-recognized, the generation AI compares the child's past tracking history to improve the accuracy of re-recognition. For example, when a lost child is captured on a surveillance camera again, the generation AI compares the child's past movement routes. This improves the accuracy of re-recognition.

[0047] The notification unit can improve the accuracy of re-recognition by also basing the re-recognition of a lost child on information about the surrounding environment. For example, when re-recognizing a lost child, the generation AI takes into account information about the surrounding environment. For example, when a lost child is captured on a surveillance camera again, the re-recognition accuracy is improved based on information about the surrounding environment. The generation AI also builds a system that analyzes information about the surrounding environment when re-recognizing a lost child and improves the accuracy of re-recognition. For example, when a lost child reappears in a specific area, the generation AI also takes into account information about the surrounding environment when re-recognizing a lost child and improves the accuracy of re-recognition. For example, when a lost child is captured on a surveillance camera again, the generation AI re-recognizes the lost child based on information about the surrounding environment. This improves the accuracy of re-recognition.

[0048] The notification unit can not only notify the guardian of the lost child's re-identification information, but also facility staff or the police at the same time. For example, the notification unit inputs the lost child's re-identification information, and the generation AI notifies the guardian of the lost child's re-identification information, and notifies all relevant parties, including facility staff and the police. For example, if a lost child is seen on a surveillance camera again, a notification is sent to all relevant parties. The generation AI also builds a system that notifies facility staff and the police based on the lost child's re-identification information. For example, if a lost child reappears in a specific area, a notification is sent to all relevant parties. The generation AI also notifies the lost child's re-identification information not only to the guardian, but also to facility staff and the police, allowing for a prompt response. For example, if a lost child is seen on a surveillance camera again, a notification is sent to all relevant parties. This allows the lost child's re-identification information to be sent to multiple relevant parties at the same time.

[0049] The notification unit can display the re-identification information of the lost child on a map so that the guardian can visually understand it. For example, the notification unit inputs the re-identification information of the lost child, and the generation AI displays the information on a map. For example, when the lost child is captured on a surveillance camera again, the location is displayed on the map. The generation AI also builds a system that displays the information on a map based on the re-identification information of the lost child. For example, when the lost child reappears in a specific area, the location is displayed on the map. The generation AI also displays the re-identification information of the lost child on a map and notifies the guardian. For example, when the lost child is captured on a surveillance camera again, the location is displayed on a map so that the guardian can visually confirm it. This allows the re-identification information of the lost child to be visually confirmed.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The lost child tracking system can further include a voice recognition unit. The voice recognition unit can identify the voice of the lost child and determine the location of the lost child based on that voice. For example, if a lost child calls for help, the voice can be analyzed and compared with surveillance camera video data to determine the child's location. The voice recognition unit can also analyze the characteristics of the lost child's voice and identify the lost child based on a specific voice pattern. For example, if the lost child repeatedly utters a specific phrase, the lost child can be identified based on that phrase. Furthermore, the voice recognition unit can analyze the tone and volume of the lost child's voice to estimate the lost child's psychological state. For example, if the lost child is in a panic, it can be estimated from the tone and volume of the voice. This makes it possible to identify the lost child's location based on their voice and respond quickly.

[0052] The lost child tracking system can further include a temperature sensor unit. The temperature sensor unit detects the body temperature of the lost child and can use this information to understand the lost child's health condition. For example, if the lost child's body temperature is abnormally high, it indicates the risk of heatstroke. The temperature sensor unit can also monitor changes in the lost child's body temperature in real time and notify parents and other relevant parties if an abnormality is detected. For example, if the lost child's body temperature rises suddenly, this information will be notified promptly. Furthermore, the temperature sensor unit can accumulate the lost child's body temperature data and compare it with past data to understand changes in the child's health condition. This allows the lost child's health condition to be monitored in real time and enables prompt response.

[0053] The lost child tracking system can further include a vibration sensor unit. The vibration sensor unit can detect the movement of the lost child and identify the lost child's location based on that information. For example, if the lost child is running, the vibrations can be detected and compared with surveillance camera video data to identify the lost child's location. The vibration sensor unit can also analyze the movement pattern of the lost child and identify the lost child based on a specific movement pattern. For example, if the lost child is walking at a specific rhythm, the lost child can be identified based on that rhythm. Furthermore, the vibration sensor unit can analyze the intensity of the lost child's movement and estimate the lost child's psychological state. For example, if the lost child is moving violently, the intensity of the movement can be used to estimate that the child is in a panic. This allows the location of the lost child to be identified based on their movement, enabling a rapid response.

[0054] The lost child tracking system can also be equipped with a GPS unit. The GPS unit can acquire the lost child's location information in real time and identify the lost child's location based on that information. For example, if a lost child is moving around within a large facility, the location information can be acquired by GPS and compared with surveillance camera video data to identify the lost child's location. The GPS unit can also accumulate the lost child's movement history and identify movement patterns by comparing it with past data. For example, it can identify whether lost children tend to frequently visit specific areas. Furthermore, the GPS unit can notify parents and other relevant parties in real time based on the lost child's location information. This allows the lost child's location to be accurately identified and a prompt response can be made.

[0055] The lost child tracking system can further include an environmental sensor unit. The environmental sensor unit acquires environmental information about the lost child's surroundings and can identify the child's location based on that information. For example, if the lost child is in a noisy place, the environmental sound can be detected and compared with surveillance camera video data to identify the child's location. The environmental sensor unit can also detect the temperature and humidity around the lost child and use that information to understand the child's health condition. For example, if the lost child is in a hot and humid place, it suggests that there is a risk of heatstroke. Furthermore, the environmental sensor unit can notify parents and other relevant parties in real time based on the environmental information about the lost child's surroundings. This allows the location of the lost child to be identified based on the environmental information about the lost child's surroundings, enabling rapid response.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The facial recognition unit inputs an image of the lost child's face. For example, a facial image taken by a parent or guardian using a smartphone or tablet is input. A pre-registered facial image can also be used. The facial recognition unit then uses a generation AI (for example, a text generation AI or a multimodal generation AI) to perform facial recognition based on the lost child's facial image and extract the lost child's features. For example, the generation AI analyzes the lost child's facial image and extracts facial features. Step 2: The video analysis unit analyzes the surveillance camera video data based on the facial image of the lost child input by the facial recognition unit. For example, the generation AI analyzes the surveillance camera video data in real time and identifies the person whose facial image matches the lost child's. The generation AI can also identify the location of the lost child based on the surveillance camera video data. For example, the generation AI analyzes the surveillance camera video data and identifies the location of the person whose facial image matches the lost child's. Step 3: The notification unit notifies the guardian and other relevant parties of the location information of the lost child identified by the video analysis unit. For example, the current location of the lost child may be displayed on a map via a smartphone app, and the guardian may be notified. A notification may also be sent to facility staff, enabling them to respond promptly. For example, the notification unit may input a prompt containing the lost child's location information into the generation AI, which then issues a notification based on that prompt.

[0058] (Example 2) A lost child tracking system according to an embodiment of the present invention utilizes facial recognition technology and surveillance camera video data to track a lost child. This system inputs a facial image of the lost child into the system and analyzes the surveillance camera video data to identify the child's location and quickly notify the child's guardian and other relevant parties. This allows the lost child tracking system to quickly find the lost child and notify the guardian.

[0059] A lost child tracking system according to an embodiment includes a face authentication unit, a video analysis unit, and a notification unit. The face authentication unit inputs a facial image of the lost child. For example, a facial image taken by a guardian using a smartphone or tablet may be input. Alternatively, a pre-registered facial image may be used. Furthermore, the face authentication unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to perform facial recognition based on the facial image of the lost child and extract the characteristics of the lost child. For example, the generation AI analyzes the facial image of the lost child and extracts facial feature points. The video analysis unit analyzes surveillance camera video data based on the facial image of the lost child input by the face authentication unit. For example, the generation AI analyzes the surveillance camera video data in real time to identify a person matching the facial image of the lost child. The generation AI can also determine the location of the lost child based on the surveillance camera video data. For example, the generation AI analyzes the surveillance camera video data to identify the location of a person matching the facial image of the lost child. The notification unit notifies the guardian and relevant parties of the location information of the lost child identified by the video analysis unit. For example, a smartphone app can display the lost child's current location on a map and notify the guardian. Notifications are also sent to facility staff, enabling a prompt response. For example, the notification unit inputs a prompt containing the lost child's location information into the generation AI, which then sends a notification based on that prompt. This enables the lost child tracking system according to the embodiment to quickly locate the lost child and notify the guardian. For example, even if a child gets lost in a large facility such as a shopping mall or amusement park, the lost child can be quickly found and the guardian notified by utilizing facial recognition technology and surveillance camera video data.

[0060] The facial recognition unit can extract additional information such as age, gender, and facial expression from the lost child's facial image, improving authentication accuracy. For example, the facial recognition unit inputs the lost child's facial image, and the generation AI extracts additional information such as age, gender, and facial expression from the image. For example, it analyzes facial contours and skin texture to estimate age. The generation AI also analyzes the lost child's facial image to identify gender. For example, it determines gender based on facial features, hairstyle, clothing, etc. The generation AI also analyzes facial expressions from the lost child's facial image to estimate their emotional state. For example, it recognizes facial expressions such as smiling and crying faces to understand the lost child's psychological state. This improves authentication accuracy.

[0061] The facial recognition unit can analyze the lost child's past movement patterns based on past surveillance camera video data and the lost child's facial image. For example, the facial recognition unit inputs the lost child's facial image, and the generation AI analyzes the movement pattern by referring to the past surveillance camera video data. For example, it identifies which routes the lost child has taken in the past. The generation AI also extracts the movement history of the same person from past surveillance camera video data based on the lost child's facial image. For example, it analyzes which areas the child was in at specific times of day. The generation AI also analyzes past surveillance camera video data based on the lost child's facial image to identify the lost child's behavior pattern. For example, it identifies a tendency for the child to frequently visit specific places. This makes it easier to identify the lost child's location by analyzing their past movement patterns.

[0062] The facial recognition unit can estimate emotions from facial images of the lost child and propose countermeasures according to the child's psychological state. For example, the facial recognition unit inputs an image of the lost child's face, and the generation AI estimates emotions from that image. For example, it analyzes facial expressions such as smiling or crying and calculates an emotion score. The generation AI also estimates emotions based on the lost child's facial image to understand the child's psychological state. For example, it evaluates the level of stress and anxiety. The generation AI also estimates emotions from the lost child's facial image and proposes countermeasures based on the results. For example, if the lost child is anxious, it generates a message to reassure them. This increases the lost child's sense of security by proposing countermeasures according to their psychological state.

[0063] The facial recognition unit can register facial images of not only the lost child but also the guardian, and can support the reunion of the guardian and the lost child. For example, the facial recognition unit registers the facial images of the lost child and guardian in the system, and the generation AI performs facial recognition of both. For example, when a guardian searches for a lost child, the position of the lost child is identified based on the guardian's facial image. The generation AI also analyzes the facial images of the lost child and guardian to build a system to support the reunion. For example, it notifies the guardian if they are near the lost child. The generation AI also registers facial images of the lost child and guardian, and performs facial recognition of both to support the reunion. For example, when a guardian finds a lost child, the system automatically notifies them. This helps reunite the guardian and the lost child, enabling a quick reunion.

[0064] The facial recognition unit can recognize the characteristics of the lost child's clothing or belongings along with the lost child's facial image, improving tracking accuracy. For example, the facial recognition unit inputs the characteristics of the lost child's clothing and belongings along with the lost child's facial image, and the generation AI recognizes them. For example, the lost child can be identified based on clothing of a specific color or design. The generation AI can also analyze the lost child's facial image and clothing characteristics to improve tracking accuracy. For example, the lost child can be located based on a specific item the lost child is carrying. The generation AI can also input the lost child's facial image and the characteristics of their belongings and recognize them to improve tracking accuracy. For example, the lost child can be identified based on the design of the backpack the lost child is carrying. This improves tracking accuracy.

[0065] The facial recognition unit analyzes the facial image of the lost child and generates a voice message according to the child's emotions, reassuring the child. For example, the facial recognition unit inputs an image of the lost child's face, and the generation AI infers the child's emotions from the image and generates an appropriate voice message. For example, if the lost child is anxious, a reassuring message is generated. The generation AI also builds a system that analyzes the lost child's facial image and generates a voice message according to the child's emotions. For example, if the lost child is crying, an encouraging message is generated. The generation AI also infers the child's emotions from the facial image and generates a voice message based on the result. For example, if the lost child is smiling, a praising message is generated. This reassures the lost child.

[0066] The video analysis unit can analyze the speed or direction of a lost child's movement from surveillance camera video data and predict their movement. For example, the video analysis unit inputs surveillance camera video data, and the generation AI analyzes the speed and direction of the lost child's movement. For example, it determines which direction the lost child is heading. The generation AI also analyzes the lost child's movement speed based on the surveillance camera video data and predicts their movement. For example, it evaluates how fast the lost child is moving. The generation AI also analyzes the direction of the lost child's movement from the surveillance camera video data and predicts their movement. For example, it predicts which area the lost child is likely to head to next. This makes it possible to predict the lost child's movement.

[0067] The video analysis unit can remove background noise from surveillance camera video data, improving the accuracy of lost child detection. For example, the video analysis unit inputs surveillance camera video data, and the generation AI removes background noise. For example, it removes unnecessary objects and shadows from the video. The generation AI also analyzes the surveillance camera video data and removes background noise to improve the accuracy of lost child detection. For example, it identifies moving objects in the video and distinguishes them from lost children. The generation AI also removes background noise from surveillance camera video data to improve the accuracy of lost child detection. For example, it removes stationary objects in the video and identifies moving people. This improves the accuracy of lost child detection.

[0068] The video analysis unit can analyze the facial expression of a lost child from surveillance camera video data and understand the lost child's psychological state. For example, the video analysis unit inputs surveillance camera video data, and the generation AI analyzes the lost child's facial expression. For example, it recognizes facial expressions such as smiling and crying faces and calculates an emotion score. The generation AI also analyzes the lost child's facial expression based on the surveillance camera video data and understands the lost child's psychological state. For example, it evaluates the level of stress and anxiety. The generation AI also analyzes the lost child's facial expression from surveillance camera video data and understands the lost child's psychological state. For example, it determines the level of anxiety the lost child is feeling. This makes it possible to understand the lost child's psychological state.

[0069] The video analysis unit can simultaneously analyze surveillance camera video data and audio data to recognize the voice of a lost child. For example, the video analysis unit inputs surveillance camera video data and audio data, and the generation AI analyzes both. For example, it identifies the lost child's voice and links the video and audio. The generation AI can also simultaneously analyze surveillance camera video data and audio data to detect the lost child's voice. For example, it can recognize the lost child's cry for help. The generation AI can also build a system to detect the lost child's voice based on surveillance camera video data and audio data. For example, it can identify the voice if the lost child is screaming. This makes it possible to detect the lost child's voice.

[0070] The video analysis unit can analyze the movements of people around the lost child from surveillance camera video data and determine the location of the lost child. For example, the video analysis unit inputs surveillance camera video data, and the generation AI analyzes the movements of people around the lost child. For example, it identifies the movements of people near the lost child. The generation AI also analyzes the movements of people around the lost child based on the surveillance camera video data and determines the location of the lost child. For example, it identifies the location of the lost child if the lost child is in a crowd. The generation AI also analyzes the movements of people around the lost child from surveillance camera video data and determines the location of the lost child. For example, it identifies the location of the lost child if the lost child belongs to a specific group. This makes it possible to determine the location of the lost child.

[0071] The video analysis unit can estimate the emotions of a lost child from surveillance camera video data and provide countermeasures according to the lost child's psychological state. For example, the video analysis unit inputs surveillance camera video data, and the generation AI estimates the emotions of the lost child. For example, it analyzes facial expressions such as smiling and crying faces and calculates an emotion score. The generation AI also estimates the emotions of the lost child based on the surveillance camera video data and proposes countermeasures according to their psychological state. For example, if the lost child is anxious, it generates a message to reassure them. The generation AI also estimates the emotions of the lost child from surveillance camera video data and proposes countermeasures based on the results. For example, if the lost child is crying, it generates an encouraging message. This suggests countermeasures according to the lost child's psychological state, increasing the lost child's sense of security.

[0072] The notification unit updates the lost child's location information in real time and can continuously notify the guardian. For example, the notification unit inputs the lost child's location information, and the generation AI updates that information in real time. For example, it updates the location information every time the lost child moves and notifies the guardian. The generation AI also builds a system that notifies the guardian in real time based on the lost child's location information. For example, it notifies when the lost child enters a specific area. The generation AI also updates the lost child's location information in real time and notifies the guardian accordingly. For example, it sends a notification to a smartphone every time the lost child moves. This allows the guardian to be notified of the lost child's location information in real time.

[0073] The notification unit can analyze the movement pattern of the lost child based on past location data and the lost child's location information. For example, the notification unit inputs the lost child's location information, and the generation AI analyzes the movement pattern by referring to the past location data. For example, it identifies which routes the lost child has taken in the past. The generation AI also extracts movement patterns from the past location data based on the lost child's location information. For example, it identifies whether the lost child tends to frequently visit certain areas. The generation AI also analyzes the lost child's location information and past location data to analyze the movement pattern. For example, it predicts which area the lost child will be in at a certain time of day. This makes it easier to identify the lost child's location by analyzing their movement pattern.

[0074] The notification unit can notify the guardian of the lost child's emotional state along with the lost child's location information. For example, the notification unit inputs the lost child's location information, and the generation AI estimates the child's emotional state along with that information. For example, if the lost child is anxious, it notifies the guardian of that information. The generation AI also analyzes the lost child's location information and emotional state and builds a system to notify the guardian. For example, if the lost child is crying, it sends that information to the guardian. The generation AI also analyzes the lost child's location information and emotional state in real time and notifies the guardian. For example, if the lost child is smiling, it conveys that information to the guardian. In this way, by notifying the guardian of the lost child's emotional state, the guardian can take appropriate action.

[0075] The notification unit can notify the location information of a lost child not only to a smartphone app but also to a smartwatch or smartglasses. For example, the notification unit inputs the location information of the lost child, and the generation AI notifies the information to a smartwatch. For example, this allows a guardian to check the location of the lost child on a smartwatch. The generation AI also builds a system that notifies smartglasses based on the lost child's location information. For example, this allows a guardian to visually check the location of the lost child on smartglasses. The generation AI also notifies the location information of a lost child not only to a smartphone app but also to a smartwatch or smartglasses. For example, this allows a guardian to check the location of a lost child on multiple devices. This allows a guardian to check the location of a lost child on multiple devices.

[0076] When displaying the location information of a lost child on a map, the notification unit can also simultaneously display the child's movement history. For example, the notification unit inputs the lost child's location information, and the generation AI displays that information on a map. For example, it displays the lost child's past movement history along with its current location. The generation AI also builds a system that analyzes the lost child's location information and movement history and displays them on a map. For example, it visually displays the route the lost child took. The generation AI also displays the lost child's location information and movement history on a map and notifies the guardian. For example, it simultaneously displays the lost child's current location and past movement routes. This allows the lost child's current location and movement history to be checked at the same time.

[0077] The notification unit can provide parents with countermeasures based on the lost child's emotional state along with the lost child's location information. For example, the notification unit inputs the lost child's location information, and the generation AI estimates the lost child's emotional state along with that information and proposes countermeasures. For example, if the lost child is anxious, it proposes a message to reassure the parent. The generation AI also analyzes the lost child's location information and emotional state, and builds a system that proposes countermeasures to the parent. For example, if the lost child is crying, it proposes an encouraging message to the parent. The generation AI also analyzes the lost child's location information and emotional state in real time and proposes countermeasures to the parent. For example, if the lost child is smiling, it proposes a message of praise to the parent. This makes it possible to propose countermeasures to the parent based on the lost child's emotional state, enabling appropriate responses.

[0078] The notification unit can organize the tracking history of the lost child in chronological order, allowing parents to intuitively understand the history. For example, the notification unit inputs the tracking history of the lost child, and the generation AI organizes the information in chronological order. For example, it identifies what time of day and what area the lost child was in. The generation AI also organizes the tracking history of the lost child in chronological order, building a system that allows parents to intuitively understand the history. For example, it displays the route the lost child took in chronological order. The generation AI also organizes the tracking history of the lost child in chronological order and notifies the parents. For example, it displays the route the lost child took in chronological order. This allows parents to intuitively understand the tracking history of the lost child.

[0079] The notification unit analyzes the movement patterns of the lost child's tracking history and can understand the behavioral trends of the lost child. For example, the notification unit inputs the tracking history of the lost child, and the generation AI analyzes the movement patterns based on that information. For example, it identifies the tendency for lost children to frequently visit specific areas. The generation AI also analyzes the tracking history of the lost child and builds a system that analyzes the movement patterns. For example, it identifies what time of day and what area the lost child is in. The generation AI also analyzes the movement patterns based on the tracking history of the lost child and understands the behavioral trends of the lost child. For example, it identifies the tendency for lost children to travel along specific routes. This makes it possible to understand the behavioral trends of the lost child.

[0080] The notification unit can store changes in the lost child's emotional state along with the lost child's tracking history. For example, the notification unit inputs the lost child's tracking history, and the generation AI estimates and records the emotional state along with that information. For example, it records what time of day the lost child was in, what area, and what emotional state the lost child was in. The generation AI also builds a system that analyzes and records the lost child's tracking history and emotional state. For example, it records the lost child's emotional state every time the lost child moves. The generation AI also analyzes and records the lost child's tracking history and emotional state in real time. For example, it records changes in the lost child's emotional state every time the lost child moves. This makes it possible to record changes in the lost child's emotional state.

[0081] The notification unit can make it possible to share the tracking history of a lost child not only with the guardian but also with the facility manager or the police. For example, the notification unit inputs the tracking history of a lost child, and the generation AI shares that information not only with the guardian but also with the facility manager and the police. For example, the location information of the lost child is shared in real time. The generation AI also builds a system for sharing the tracking history of a lost child with the facility manager and the police. For example, the route the lost child traveled is shared. The generation AI also shares the tracking history of a lost child not only with the guardian but also with the facility manager and the police and notifies them. For example, a notification is sent if the lost child is in a specific area. This allows the tracking history of a lost child to be shared with multiple parties.

[0082] The notification unit can display the tracking history of the lost child on a map so that the guardian can visually understand it. For example, the notification unit inputs the tracking history of the lost child, and the generation AI displays the information on a map. For example, the lost child's movement route is displayed on the map so that the guardian can visually confirm it. The generation AI also builds a system that displays on a map based on the tracking history of the lost child. For example, it displays on the map the route the lost child took. The generation AI also displays the tracking history of the lost child on a map and notifies the guardian. For example, it displays the lost child's movement route on a map so that the guardian can visually confirm it. This makes it possible to visually confirm the tracking history of the lost child.

[0083] The notification unit can graph changes in the lost child's emotional state along with the lost child's tracking history and show this to the guardian. For example, the notification unit inputs the lost child's tracking history, and the generation AI estimates and graphs the emotional state along with that information. For example, it graphs changes in the lost child's emotional state over time and provides this to the guardian. The generation AI also builds a system that analyzes the lost child's tracking history and emotional state and graphs them. For example, it graphs the lost child's movement route and changes in emotional state. The generation AI also analyzes the lost child's tracking history and emotional state in real time and graphs them to provide to the guardian. For example, it visually displays changes in the lost child's emotional state. This allows the lost child's changes in emotional state to be visually confirmed.

[0084] When a lost child is captured on a surveillance camera again, the notification unit compares the child's past tracking history to improve the accuracy of re-recognition. For example, when a lost child is captured on a surveillance camera again, the notification unit causes the generation AI to compare the child's past tracking history. For example, when a lost child is captured on a surveillance camera again, the generation AI compares the child's past movement patterns to improve the accuracy of re-recognition. Furthermore, when a lost child is re-recognized, the generation AI builds a system that improves the accuracy of re-recognition based on the past tracking history. For example, when a lost child reappears in a specific area, the generation AI compares the child's past data. Furthermore, when a lost child is re-recognized, the generation AI compares the child's past tracking history to improve the accuracy of re-recognition. For example, when a lost child is captured on a surveillance camera again, the generation AI compares the child's past movement routes. This improves the accuracy of re-recognition.

[0085] The notification unit can improve the accuracy of re-recognition by also basing the re-recognition of a lost child on information about the surrounding environment. For example, when re-recognizing a lost child, the generation AI takes into account information about the surrounding environment. For example, when a lost child is captured on a surveillance camera again, the re-recognition accuracy is improved based on information about the surrounding environment. The generation AI also builds a system that analyzes information about the surrounding environment when re-recognizing a lost child and improves the accuracy of re-recognition. For example, when a lost child reappears in a specific area, the generation AI also takes into account information about the surrounding environment when re-recognizing a lost child and improves the accuracy of re-recognition. For example, when a lost child is captured on a surveillance camera again, the generation AI re-recognizes the lost child based on information about the surrounding environment. This improves the accuracy of re-recognition.

[0086] The notification unit can estimate emotions when a lost child is re-recognized and notify the guardian of the results. For example, when a lost child is re-recognized, the generation AI estimates emotions and notifies the guardian of the results. For example, when a lost child is again captured on a surveillance camera, the generation AI estimates the emotional state and notifies the guardian. The generation AI also builds a system that estimates emotions when a lost child is re-recognized and notifies the guardian. For example, if the lost child is anxious, that information is sent to the guardian. The generation AI also estimates emotions when a lost child is re-recognized and notifies the guardian of the results. For example, if the lost child is smiling, that information is communicated to the guardian. This allows the guardian to be notified of the lost child's emotional state, enabling appropriate action to be taken.

[0087] The notification unit can not only notify the guardian of the lost child's re-identification information, but also facility staff or the police at the same time. For example, the notification unit inputs the lost child's re-identification information, and the generation AI notifies the guardian of the lost child's re-identification information, and notifies all relevant parties, including facility staff and the police. For example, if a lost child is seen on a surveillance camera again, a notification is sent to all relevant parties. The generation AI also builds a system that notifies facility staff and the police based on the lost child's re-identification information. For example, if a lost child reappears in a specific area, a notification is sent to all relevant parties. The generation AI also notifies the lost child's re-identification information not only to the guardian, but also to facility staff and the police, allowing for a prompt response. For example, if a lost child is seen on a surveillance camera again, a notification is sent to all relevant parties. This allows the lost child's re-identification information to be sent to multiple relevant parties at the same time.

[0088] The notification unit can display the re-identification information of the lost child on a map so that the guardian can visually understand it. For example, the notification unit inputs the re-identification information of the lost child, and the generation AI displays the information on a map. For example, when the lost child is captured on a surveillance camera again, the location is displayed on the map. The generation AI also builds a system that displays the information on a map based on the re-identification information of the lost child. For example, when the lost child reappears in a specific area, the location is displayed on the map. The generation AI also displays the re-identification information of the lost child on a map and notifies the guardian. For example, when the lost child is captured on a surveillance camera again, the location is displayed on a map so that the guardian can visually confirm it. This allows the re-identification information of the lost child to be visually confirmed.

[0089] The notification unit can estimate emotions when the lost child is re-recognized, and provide the guardian with countermeasures based on the results. For example, when the lost child is re-recognized, the generation AI estimates emotions and suggests countermeasures based on the results. For example, if the lost child is anxious, it suggests a message to reassure the guardian. The generation AI also estimates emotions when the lost child is re-recognized, and builds a system that suggests countermeasures to the guardian. For example, if the lost child is crying, it suggests a message of encouragement to the guardian. The generation AI also estimates emotions when the lost child is re-recognized, and suggests countermeasures based on the results. For example, if the lost child is smiling, it suggests a message of praise to the guardian. This makes it possible to suggest countermeasures according to the emotional state of the lost child, enabling appropriate responses.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The lost child tracking system can further include a voice recognition unit. The voice recognition unit can identify the voice of the lost child and determine the location of the lost child based on that voice. For example, if a lost child calls for help, the voice can be analyzed and compared with surveillance camera video data to determine the child's location. The voice recognition unit can also analyze the characteristics of the lost child's voice and identify the lost child based on a specific voice pattern. For example, if the lost child repeatedly utters a specific phrase, the lost child can be identified based on that phrase. Furthermore, the voice recognition unit can analyze the tone and volume of the lost child's voice to estimate the lost child's psychological state. For example, if the lost child is in a panic, it can be estimated from the tone and volume of the voice. This makes it possible to identify the lost child's location based on their voice and respond quickly.

[0092] The lost child tracking system can further include a temperature sensor unit. The temperature sensor unit detects the body temperature of the lost child and can use this information to understand the lost child's health condition. For example, if the lost child's body temperature is abnormally high, it indicates the risk of heatstroke. The temperature sensor unit can also monitor changes in the lost child's body temperature in real time and notify parents and other relevant parties if an abnormality is detected. For example, if the lost child's body temperature rises suddenly, this information will be notified promptly. Furthermore, the temperature sensor unit can accumulate the lost child's body temperature data and compare it with past data to understand changes in the child's health condition. This allows the lost child's health condition to be monitored in real time and enables prompt response.

[0093] The lost child tracking system can further include a vibration sensor unit. The vibration sensor unit can detect the movement of the lost child and identify the lost child's location based on that information. For example, if the lost child is running, the vibrations can be detected and compared with surveillance camera video data to identify the lost child's location. The vibration sensor unit can also analyze the movement pattern of the lost child and identify the lost child based on a specific movement pattern. For example, if the lost child is walking at a specific rhythm, the lost child can be identified based on that rhythm. Furthermore, the vibration sensor unit can analyze the intensity of the lost child's movement and estimate the lost child's psychological state. For example, if the lost child is moving violently, the intensity of the movement can be used to estimate that the child is in a panic. This allows the location of the lost child to be identified based on their movement, enabling a rapid response.

[0094] The lost child tracking system can also be equipped with a GPS unit. The GPS unit can acquire the lost child's location information in real time and identify the lost child's location based on that information. For example, if a lost child is moving around within a large facility, the location information can be acquired by GPS and compared with surveillance camera video data to identify the lost child's location. The GPS unit can also accumulate the lost child's movement history and identify movement patterns by comparing it with past data. For example, it can identify whether lost children tend to frequently visit specific areas. Furthermore, the GPS unit can notify parents and other relevant parties in real time based on the lost child's location information. This allows the lost child's location to be accurately identified and a prompt response can be made.

[0095] The lost child tracking system can further include an environmental sensor unit. The environmental sensor unit acquires environmental information about the lost child's surroundings and can identify the child's location based on that information. For example, if the lost child is in a noisy place, the environmental sound can be detected and compared with surveillance camera video data to identify the child's location. The environmental sensor unit can also detect the temperature and humidity around the lost child and use that information to understand the child's health condition. For example, if the lost child is in a hot and humid place, it suggests that there is a risk of heatstroke. Furthermore, the environmental sensor unit can notify parents and other relevant parties in real time based on the environmental information about the lost child's surroundings. This allows the location of the lost child to be identified based on the environmental information about the lost child's surroundings, enabling rapid response.

[0096] The lost child tracking system can also estimate the emotions of the lost child and provide countermeasures according to those emotions. For example, it analyzes an image of the lost child's face and the generation AI infers the emotion from that image. For example, it analyzes facial expressions such as smiling or crying and calculates an emotion score. The generation AI also estimates the emotion based on the lost child's facial image and understands the lost child's psychological state. For example, it evaluates the level of stress and anxiety. The generation AI also infers the emotion from the lost child's facial image and suggests countermeasures based on the results. For example, if the lost child is anxious, it generates a message to reassure them. This makes it possible to suggest countermeasures according to the lost child's psychological state, thereby increasing the lost child's sense of security.

[0097] The lost child tracking system can further estimate the emotions of the lost child and generate a voice message according to those emotions. For example, an image of the lost child's face can be analyzed, and the generation AI can estimate the emotion from the image and generate an appropriate voice message. For example, if the lost child is anxious, a reassuring message can be generated. The generation AI can also build a system that analyzes the lost child's facial image and generates a voice message according to the emotion. For example, if the lost child is crying, an encouraging message can be generated. The generation AI can also estimate the emotion from the lost child's facial image and generate a voice message based on the result. For example, if the lost child is smiling, a praising message can be generated. This can reassure the lost child.

[0098] The lost child tracking system can also estimate the emotions of the lost child and provide the guardian with countermeasures based on those emotions. For example, by analyzing an image of the lost child's face, the generation AI can infer the emotion from the image and suggest countermeasures based on the results. For example, if the lost child is anxious, it can suggest a message to reassure the guardian. The generation AI can also estimate the emotion based on the lost child's facial image and build a system that suggests countermeasures to the guardian. For example, if the lost child is crying, it can suggest an encouraging message to the guardian. The generation AI can also infer the emotion from the lost child's facial image and suggest countermeasures based on the results. For example, if the lost child is smiling, it can suggest a message of praise to the guardian. This makes it possible to suggest countermeasures to the guardian based on the lost child's emotional state, enabling appropriate responses.

[0099] The lost child tracking system can also estimate the emotions of the lost child and send a notification based on those emotions. For example, the generation AI analyzes an image of the lost child's face, infers the emotion from that image, and sends a notification based on the results. For example, if the lost child is anxious, the parent or guardian is notified of that information. The generation AI can also estimate the emotion based on the lost child's facial image and build a system to notify the parent or guardian. For example, if the lost child is crying, that information is sent to the parent or guardian. The generation AI can also estimate the emotion from the lost child's facial image and send a notification based on the results. For example, if the lost child is smiling, that information is conveyed to the parent or guardian. This allows the parent or guardian to be notified of the lost child's emotional state, enabling appropriate action to be taken.

[0100] The lost child tracking system can also estimate the emotions of the lost child and provide facility staff with countermeasures based on those emotions. For example, by analyzing an image of the lost child's face, the generation AI can infer the emotion from the image and suggest countermeasures based on the results. For example, if the lost child is anxious, it can suggest a message to reassure the staff. The generation AI can also estimate the emotion based on the lost child's facial image and build a system that suggests countermeasures to the staff. For example, if the lost child is crying, it can suggest an encouraging message to the staff. The generation AI can also infer the emotion from the lost child's facial image and suggest countermeasures based on the results. For example, if the lost child is smiling, it can suggest a message of praise to the staff. This makes it possible to suggest countermeasures to the staff based on the lost child's emotional state, enabling appropriate responses.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The facial recognition unit inputs an image of the lost child's face. For example, a facial image taken by a parent or guardian using a smartphone or tablet is input. A pre-registered facial image can also be used. The facial recognition unit then uses a generation AI (for example, a text generation AI or a multimodal generation AI) to perform facial recognition based on the lost child's facial image and extract the lost child's features. For example, the generation AI analyzes the lost child's facial image and extracts facial features. Step 2: The video analysis unit analyzes the surveillance camera video data based on the facial image of the lost child input by the facial recognition unit. For example, the generation AI analyzes the surveillance camera video data in real time and identifies the person whose facial image matches the lost child's. The generation AI can also identify the location of the lost child based on the surveillance camera video data. For example, the generation AI analyzes the surveillance camera video data and identifies the location of the person whose facial image matches the lost child's. Step 3: The notification unit notifies the guardian and other relevant parties of the location information of the lost child identified by the video analysis unit. For example, the current location of the lost child may be displayed on a map via a smartphone app, and the guardian may be notified. A notification may also be sent to facility staff, enabling them to respond promptly. For example, the notification unit may input a prompt containing the lost child's location information into the generation AI, which then issues a notification based on that prompt.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A face recognition unit that inputs a face image of the lost child; a video analysis unit that analyzes surveillance camera video data based on the facial image of the lost child input by the face authentication unit; a notification unit that notifies a guardian and other relevant parties of the location information of the lost child identified by the video analysis unit. A system characterized by:

2. The face authentication unit Extracting additional information such as age, gender, and facial expression from the lost child's facial image to improve authentication accuracy 2. The system of claim 1.

3. The face authentication unit Along with the face image of the lost child, the system also recognizes the characteristics of the child's clothing or belongings, improving tracking accuracy.

2. The system of claim 1.

4. The video analysis unit The speed or direction of the movement of the lost child is analyzed from the surveillance camera image data, and movement prediction is performed.

2. The system of claim 1.

5. The notification unit The location information of the lost child is updated in real time and the guardian is continuously notified.

2. The system of claim 1.

6. The face authentication unit Estimating the emotion of the lost child from the facial image and proposing a countermeasure according to the lost child's psychological state 2. The system of claim 1.

7. The video analysis unit Analyzing the facial expression of the lost child from the surveillance camera video data to understand the mental state of the lost child 2. The system of claim 1.

8. The notification unit Informing the guardian of the lost child's emotional state along with the location information of the lost child 2. The system of claim 1.

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