system

Abstract

We provide the system. [Solution] A means for analyzing video data acquired by a camera at the entrance and generating facial information of visitors, A means for extracting predetermined facial information based on image data transmitted from a mobile device owned by a parent, A means for detecting objects that match extracted facial information using video data acquired in real time from multiple monitoring devices within the facility, and for determining their location, A means of notifying the nearest staff terminal of the location information of the detected target, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] The occurrence of lost children in large facilities can impose a mental burden on parents and facility users and may also have an adverse impact on the operation of the facility. In addition, there is a need for effective and efficient means to quickly find lost children and deliver them to their parents. However, conventional methods, such as having someone with acquaintance find them or having facility staff search with the naked eye, are time-consuming and laborious, and thus there is a problem that it is difficult to shorten the time to find lost children.

Means for Solving the Problems

[0005] This invention acquires visitor facial information using a camera at the entrance, registers this information in a database, and extracts predetermined facial information from image data transmitted from a mobile device carried by a parent. The system then compares the extracted facial information with video data from surveillance devices installed within the facility in real time, identifies the location of the detected individual, and notifies the nearest staff terminal of the location information, thereby enabling the rapid discovery and reunion of lost children. Furthermore, by comparing the generated visitor facial information with the extracted facial information in the database, and calculating and providing the optimal travel route based on surveillance device placement information and map information, the system achieves efficient tracking and a safe environment.

[0006] A "camera" is a device installed at entrances or within a facility to acquire video data.

[0007] "Facial information" refers to identifiable data generated based on a person's face acquired from a camera or mobile device.

[0008] A "mobile device" refers to a portable electronic device used by a parent to transmit image data, such as a smartphone or tablet.

[0009] "Image data" refers to visual data in the form of photographs or videos that are transmitted to a server via a mobile device.

[0010] A "database" is a collection of information that organizes and stores facial information, location information, and other data, and is used for matching as needed.

[0011] A "surveillance device" is a camera system that is placed within a facility to acquire and analyze video in real time.

[0012] A "staff terminal" is a communication-enabled electronic device carried by facility staff to receive notifications from a server.

[0013] "Location information" refers to data indicating the current location of a person identified as lost.

[0014] "Generative AI" is an artificial intelligence technology that calculates efficient search routes based on maps and camera placement information within a facility. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, when an emotion engine is combined. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention relates to a system for facilitating the rapid discovery of lost children and the reunion of parents and children. In this system, a server plays a central role. The operation of the system is described below in natural language.

[0037] First, the server receives video data acquired from a camera installed at the entrance and generates facial information of visitors. This facial information is registered in a database to prepare for facial data matching in the event of a lost child. When a user (parent) sends image data of their lost child using a mobile device, the server analyzes it and extracts the facial information.

[0038] Next, the server acquires video data in real time from monitoring devices within the facility. Based on this, it checks if it matches the extracted facial information and attempts to detect the missing person. If a match is found, the server acquires their location information. Subsequently, the server sends a notification to the nearest staff terminal based on the detected location information. This notification includes the missing person's location and instructions for what to do.

[0039] The generating AI calculates the optimal route for staff to move based on facility map information and monitoring device placement data. This allows staff to quickly and efficiently reach lost children and return them to their parents. For example, suppose a child gets lost in the amusement park area of ​​a theme park. The server identifies the child's location using real-time video and quickly notifies the nearest staff member. As a result, the child and parents are reunited in a short amount of time.

[0040] This system will significantly reduce the time required to process lost children, thereby reducing the emotional burden on staff and providing a safer facility environment.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server acquires video data of visitors from cameras installed at the entrance. Based on the acquired video data, a facial recognition algorithm is applied to generate facial information. This facial information, along with the visitor's identification information, is registered in the database.

[0044] Step 2:

[0045] When a child goes missing, the user (parent) launches a dedicated app on their mobile device and selects the most recent photo or video of the child. The selected image data is then sent to the server via the app.

[0046] Step 3:

[0047] The server receives image data sent by the user and extracts facial information. This extracted facial information is then compared with existing facial information in the database. This process identifies the facial information of the lost child.

[0048] Step 4:

[0049] The server establishes connections with each monitoring device within the facility and receives real-time video data. Using a detection algorithm, it analyzes people in the real-time video and verifies whether they match the facial information identified in step 3.

[0050] Step 5:

[0051] If a terminal (monitoring device) detects a lost child in real-time video, it sends its location information to a server. Based on this information, the server identifies the terminal of the nearest staff member.

[0052] Step 6:

[0053] The server pushes notifications to identified staff terminals regarding the location of the lost child and the surrounding environment. These notifications include instructions and recommended actions to take.

[0054] Step 7:

[0055] The AI ​​generates data using map data and monitoring device placement information within the facility to calculate the optimal route for staff to efficiently reach children. The calculated route information is then provided to staff terminals to support their response.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] The problem of lost or missing children within facilities requires swift and efficient search operations, but currently, this is time-consuming and burdensome for parents and facility operators, both mentally and operationally. Therefore, a system is needed to quickly identify individuals and enable staff to act efficiently to solve these problems.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes means for analyzing image information acquired by an entrance camera to generate visitor characteristic information, means for extracting predetermined characteristic information based on image information transmitted from a mobile information terminal carried by the user, means for detecting an object that matches the extracted characteristic information using image information acquired in real time from multiple observation devices within the facility and determining its location, and means for notifying the location information of the detected object to the nearest worker terminal. This enables rapid identification of the target and efficient search operations.

[0061] A "photography device" is a piece of equipment installed within a facility to acquire image information of visitors.

[0062] "Image information" refers to visual data acquired through imaging devices or mobile information terminals, and is fundamental data used to generate feature information through analysis.

[0063] "Feature information" refers to data that shows faces and other unique characteristics extracted from image information, and is used to identify individuals.

[0064] A "personal digital information terminal" is a portable device owned by a user and used to transmit image information.

[0065] An "observation device" is a system consisting of cameras and sensors installed within a facility, used to monitor and acquire image information in real time.

[0066] A "worker terminal" is a device used by the nearest worker to receive information from the server and to act according to the instructions.

[0067] "Real-time" refers to processing that is immediate and in line with real-world time, meaning that data is acquired, processed, and reported without delay.

[0068] This invention is a system to support the rapid discovery of lost children and the reunion of parents and children. Specific embodiments of the system are described below.

[0069] The server analyzes image information acquired from a camera installed at the entrance and generates visitor characteristic information. A high-resolution camera is used as the camera, and the captured image information is processed by analysis software on the server. At this stage, characteristic information is generated from the images using facial recognition technology and stored in a database. This facial recognition technology utilizes existing facial recognition algorithms to efficiently extract feature points.

[0070] Users utilize a dedicated application to send images of lost children to a server using their mobile devices. This application features a user-friendly interface, allowing for easy transmission of images to the server. The transmitted images are analyzed on the server, and the extracted features are compared with already registered features.

[0071] The server uses image information acquired in real time from observation devices within the facility and compares it with extracted feature information. Full HD surveillance cameras are used as observation devices to cover a wide area. The real-time image information obtained from these surveillance cameras is processed by the server via facial recognition software to identify subjects and acquire their location information.

[0072] For detected targets, the server notifies the nearest worker terminal of their location. The worker terminal is then provided with the optimal route calculated by a generative AI model, enabling the worker to act quickly and efficiently. This generative AI model uses facility map information and camera placement data to calculate the shortest and safest travel route.

[0073] As a concrete example, if a child gets lost in a theme park, the server can identify the child from real-time images and quickly notify the relevant personnel. As a result, the child and parent can be reunited in a short amount of time.

[0074] Examples of prompt messages include, "Please locate a child who has gotten lost in the amusement park and calculate the optimal route for staff to safely return the child to their parents." Through this system, safety management within the facility can be significantly improved, and users can feel more secure.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The server receives image information acquired from the camera at the entrance. The input is image data from the camera, and the server analyzes it to generate visitor characteristic information. Specifically, it uses an image processing algorithm to extract facial landmarks and registers them in the database as characteristic information. The output is visitor characteristic information.

[0078] Step 2:

[0079] The user takes a picture of a lost child with their mobile device and sends it to the server. The input is image data from the user, which the server receives and analyzes. The server applies a face recognition algorithm to extract feature information from the image and converts it into a format for matching with an existing database. The output is the extracted facial feature information.

[0080] Step 3:

[0081] The server acquires image information in real time from observation devices within the facility. The input is live image data from the observation devices, and the server performs face matching on this data. The server uses this data to search for objects that match the feature information extracted in step 2, and if a match is found, it obtains its location information. The output is the location information of the matched objects.

[0082] Step 4:

[0083] Based on the location information of the target detected in step 3, the server sends a notification to the nearest worker terminal. The input is location information, which the server formats as a notification message and sends to the worker terminal. This allows workers to know the location of the lost person in real time. The output is the notification sent to the worker terminal.

[0084] Step 5:

[0085] The AI ​​model installed on the terminal calculates the optimal route for the worker to take based on the facility's map information and the input location information. The input consists of location information and facility map data, and the AI ​​model calculates the shortest and safest route. The worker's terminal displays this route, assisting the worker in efficiently reaching the lost person. The output is the optimal route information.

[0086] (Application Example 1)

[0087] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0088] In large public facilities, it often takes a long time to find lost or missing children and reunite them with their guardians. Therefore, there is a need for methods to quickly and accurately locate lost children and facilitate their reunion. Reducing the burden on on-site staff and enabling efficient responses are also crucial issues.

[0089] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0090] In this invention, the server includes means for analyzing visual data acquired by a visual device at the entrance and generating image information of visitors; means for extracting predetermined image information based on visual data transmitted from a portable device held by a parent; and means for detecting a match with the extracted image information and identifying its location using visual data acquired in real time from multiple monitoring devices within the public facility. This enables the rapid discovery and reunion of lost children, providing a safe and secure environment within public facilities.

[0091] "Visual devices at entrances" are electronic devices installed at the entrances of public facilities to acquire video data of visitors' faces and bodies.

[0092] "Visual data" refers to digital data that includes information about the appearance of people and objects, acquired by cameras and sensors.

[0093] "Image information" refers to digital information that represents an individual's characteristics, generated by analyzing visual data, and is used in applications such as facial recognition.

[0094] "Portable device" refers to an electronic terminal owned by an individual that is capable of communication and sending / receiving information, particularly mobile phones and smartphones.

[0095] A "monitoring device" is a device installed in various locations within a facility that acquires video data of the surroundings in real time.

[0096] A "comparison" refers to a specific individual or object detected by visual data, specifically the target person.

[0097] A "data store" is an information management system that permanently stores information and allows for searching and matching as needed.

[0098] An "AI model" is a system that analyzes data based on machine learning and automatically generates solutions to specific problems.

[0099] A "prompt message" is a sentence used to convey instructions or suggestions generated by an AI model to a human.

[0100] To implement this invention, the system is constructed as follows:

[0101] The server first acquires video data from visual devices installed at the entrance of public facilities. This video data is then analyzed to generate visitor facial information. This process utilizes deep learning-based facial recognition software, specifically libraries such as TENSORFLOW® and OpenCV.

[0102] Next, the server extracts specific facial information using image data transmitted from the parent's mobile device. Similar facial recognition technology is used at this stage, specifically extracting feature points from the captured image data and comparing them against a database.

[0103] Subsequently, the server utilizes visual data obtained instantly from multiple monitoring devices installed within the facility to detect individuals matching the previously extracted facial information in real time and pinpoint their locations. This process uses, for example, video streaming analysis using AWS® Lambda.

[0104] The location information of detected individuals is sent from the server to the nearest worker's terminal. This notification utilizes services such as Firebase Cloud Messaging, enabling workers to take immediate action.

[0105] Furthermore, the server calculates the most efficient route for workers to move around based on the location information of each monitoring device within the facility and map information, and provides this information to the terminal. This process utilizes pathfinding algorithms built in R or Python.

[0106] As a concrete example, if a child gets lost in a theme park, the parent can send a photo of the child from their smartphone to a server. The server will then quickly determine the child's location based on the facial information and notify the nearest worker of the location and instructions on what to do. As a result, the worker can travel along the optimal route and safely return the child to their parents.

[0107] An example of a prompt message generated using AI is: "Create a step-by-step guide to find a lost child in a park and safely return them to their guardian. Please explain how to calculate the optimal route based on facility map information and real-time data, and how to support a safe reunion."

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The server acquires video data in real time from visual devices installed at the entrance of public facilities. This input data is analyzed using a deep learning model to generate visitor facial information. The output obtained as a result of the analysis is a vector containing individual facial features.

[0111] Step 2:

[0112] The user sends an image of the lost person to the server using a mobile device. The server receives this image data as input, applies a facial recognition algorithm, and extracts specific facial information. The output here is the extracted feature vector.

[0113] Step 3:

[0114] The server continuously acquires real-time video data from monitoring devices installed within the facility. Using this data as input, it performs facial recognition to detect individuals that match previously extracted facial information. The output is the location information of the matched individuals.

[0115] Step 4:

[0116] The server sends a notification to the nearest worker's terminal based on the location information of the detected person. Firebase Cloud Messaging is used to send the location information and action instructions to the terminal. This displays specific action instructions on the terminal, enabling the worker to respond immediately.

[0117] Step 5:

[0118] The server acquires local monitoring device placement information and map information within the facility as input. Using this information, it applies a pathfinding algorithm to calculate and provide the optimal travel route to the worker's terminal. The output of this calculation is the shortest and safest travel route.

[0119] Step 6:

[0120] Using a generative AI model, the server generates individual guides based on prompt messages and provides them to the user. The AI ​​model performs statistical analysis and simulations based on example prompt messages to output specific guides.

[0121] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0122] This invention relates to a system that, in addition to supporting the rapid discovery of lost children and the reunion of parents and children, also takes into account the emotional state of the parents. Specific embodiments of this invention are described below.

[0123] First, the server receives video data of visitors from cameras installed at the entrance and generates facial information. This facial information is registered in a database and used when a child gets lost. When a child gets lost, the user (parent) sends image data of the child's face to the server using a dedicated app on their mobile device. The server extracts facial information from the received data and compares it with the registered information in the database.

[0124] This system also incorporates an emotion engine that recognizes the parent's emotional state based on voice and facial expression data acquired from mobile devices. The server receives real-time video data from monitoring devices within the facility and detects targets that match the extracted facial information. If a target is detected, the server identifies its location and notifies the nearest staff terminal. The emotion engine analyzes the parent's emotional state and dynamically adjusts the notification content and support messages accordingly, providing them to the user.

[0125] The generating AI uses map information of the entire facility and the placement information of monitoring devices to calculate the optimal travel route. This route information is provided to staff terminals, enabling quick and efficient responses to lost children.

[0126] As a concrete example, if a child gets lost in a theme park and the parents feel anxious or worried, the emotion engine recognizes these emotions and generates and provides a message that offers greater reassurance. For example, it might say, "Staff are on their way, so please don't worry." In this way, by not only being able to locate the child but also responding with consideration for the parents' emotions, the overall service quality of the facility can be improved.

[0127] This invention makes it possible to provide faster and more attentive service than conventional lost child response systems, giving parents and children a sense of security and creating a safer environment.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The server acquires video data from a camera installed at the entrance. This generates facial information of visitors, and this information is registered in a database.

[0131] Step 2:

[0132] The user (parent) opens a dedicated app on their smartphone when their child gets lost. The app then sends the child's latest image data (photos and videos) to the server.

[0133] Step 3:

[0134] The server receives image data sent by the user, analyzes the data, and extracts the child's facial information. The extracted facial information is then compared with the facial information in the database to identify the corresponding information.

[0135] Step 4:

[0136] The device (the mobile device's emotion engine) uses the smartphone's microphone and camera to acquire the parent's voice and facial expression data. The emotion engine analyzes this data to recognize the parent's current emotional state.

[0137] Step 5:

[0138] The server receives real-time video from monitoring devices installed within the facility. Based on the video data, it searches for targets that match the facial information identified in step 3. If a target is detected, its location is determined.

[0139] Step 6:

[0140] The server identifies the nearest staff terminal based on the location information of the detected target. It then sends a notification to the staff terminal containing the lost child's location information and instructions for action.

[0141] Step 7:

[0142] The server customizes notifications and support messages sent to parents based on the parent's emotional state recognized by the emotion engine. It dynamically generates reassuring messages as needed.

[0143] Step 8:

[0144] The generating AI calculates the shortest possible route for staff to reach a child, based on facility map information and monitoring device placement data. This route information is then transmitted to staff terminals to support a quick response.

[0145] (Example 2)

[0146] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0147] Conventional lost child prevention systems have difficulty responding quickly and have not adequately addressed the anxiety and impatience of parents. Furthermore, locating lost children and providing information to guardians is inefficient, leading to a decline in the overall service quality of the facility. It is necessary to solve these problems to provide peace of mind to parents and children and to realize a safer and more efficient lost child prevention system.

[0148] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0149] In this invention, the server includes means for analyzing data from a display device acquired by a camera at the entrance and generating characteristic information of visitors; means for extracting predetermined characteristic information based on image data transmitted from an information terminal held by a guardian; means for detecting an object that matches the extracted characteristic information and identifying its location using data from display devices acquired in real time from multiple observation devices within the facility; and means for recognizing the emotional state of the guardian using voice data and facial expression data, and dynamically generating and providing support information according to the analysis results. This enables the rapid identification of the lost child's location, the provision of appropriate information to the guardian, and support that takes emotions into consideration.

[0150] An "entrance camera" is a camera installed at the entrance of a facility to capture video data of visitors.

[0151] "Data from a display device" refers to digital information about images and videos acquired by imaging or observation equipment.

[0152] "Visitor characteristic information" refers to facial and body characteristic information used to identify individuals, analyzed from data obtained from the display device.

[0153] "Information devices owned by guardians" refers to communication devices such as mobile phones and smartphones that are carried by parents or guardians and capable of transmitting images and audio.

[0154] An "observation device" is a surveillance camera system installed within a facility to acquire video data in real time and monitor specific targets.

[0155] A "data aggregation device" is an information storage system that stores generated characteristic information for comparison and retrieval.

[0156] "Support information" refers to advice and messages provided to parents and staff to help prevent children from getting lost.

[0157] A "person in charge terminal" is an information terminal carried by facility staff or other personnel to communicate and check on the situation within the facility.

[0158] "Emotional state" refers to information that indicates the psychological state of the caregiver, and is analyzed from voice and facial expression data.

[0159] This invention is a system designed to facilitate the rapid discovery of lost children and the reunion of parents and children, taking into account the emotional state of the parents. The specific configuration and method of use for implementing this invention are described below.

[0160] First, the server receives real-time video data of visitors from a camera installed at the facility's entrance. The server uses image processing software such as OpenCV or dlib to extract facial information from this video data and generate it as digital data. The facial information is stored in a data accumulator and used later for verification.

[0161] On the other hand, the parent, as the user, would use a mobile device (e.g., a smartphone) if their child gets lost. They would launch a dedicated application and either take a picture of their child's face or select an existing image and send it to the server. The mobile device would use a secure protocol to send the image data to the server.

[0162] The server analyzes the received facial image data of the child and compares it with real-time video data acquired from multiple observation devices installed within the facility. Once the target is identified, the server determines its location and notifies the staff member's terminal. This allows the staff member to quickly go to the child's location.

[0163] Furthermore, the server incorporates an emotion engine that analyzes voice and facial expression data acquired from the user's mobile device. The server uses the Natural Language Processing Toolkit (NLTK) and the Emotion API to understand the parent's emotional state. Based on the analysis results, the user is provided with dynamically generated support messages. For example, a message such as "Staff are on their way, so please don't worry" might be used.

[0164] The generated AI model calculates the optimal movement route using map information and observation device placement information within the facility. This route information is immediately provided to the staff member's terminal, enabling a quick response to lost children.

[0165] Examples of specific prompt messages include, "Generate a notification message for the parent based on the latest location information of the lost child at the scene," and "Create a reassuring recommendation message based on the parent's emotional state."

[0166] In this way, this system not only facilitates the rapid discovery of lost children and supports parent-child reunions, but also aims to provide greater peace of mind by taking into consideration the parents' feelings.

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] The server receives video data of visitors from the camera at the entrance. It takes video data from the camera as input. The server generates facial information using image processing software. The output is digital facial information data. Specifically, the server detects faces within the frame and formats their feature points for use in the database.

[0170] Step 2:

[0171] The user uses a dedicated app on their mobile device to send a facial image of their lost child to the server. The input is the facial image data of the child that has been photographed or selected. The mobile device transmits this data through a secure protocol. The output is the image data received by the server. Specifically, the parent captures an image of their child with the device's camera and presses the send button within the app.

[0172] Step 3:

[0173] The server analyzes the received image data and matches it based on feature information in the database. The input consists of face image data sent by the user and previously acquired face information in the database. The server performs the matching operation using algorithms such as FaceNet. The output is either the ID information of the identified individual or a list of candidates with a high probability of matching. Specifically, it calculates and compares feature vectors to list individuals with high similarity.

[0174] Step 4:

[0175] The server receives real-time video data from observation devices within the facility and identifies targets based on matched facial information. Inputs include video data from the observation devices and characteristic information of identified individuals. Outputs are the location information of the identified targets. Specifically, the server analyzes the streaming video and tracks the individual's location using a facial recognition algorithm.

[0176] Step 5:

[0177] The server incorporates an emotion engine that analyzes voice and facial expression data from the user's mobile device. Input consists of voice data and a video of the user's facial expressions. The server performs natural language processing and facial expression analysis to identify the emotional state. The output is an analysis result indicating the user's emotions. Specifically, it quantifies the emotional state through voice tone analysis and facial expression extraction.

[0178] Step 6:

[0179] The server generates support messages to provide to the user and calculates the optimal route based on the results of sentiment analysis. Inputs include sentiment analysis results, facility map information, and observation device placement information. A generation AI model dynamically generates the necessary support messages based on prompts. Outputs include support messages for the user and information on the staff member's movement route. Specifically, this process includes constructing message content and calculating route data.

[0180] Step 7:

[0181] The server notifies the nearest staff terminal of the generated information and sends a response to the parent. Inputs are support messages and routing information. The server transmits this information to each terminal via the network. Outputs are notifications to staff terminals and messages to the parent. Specifically, messages are displayed on a display device to help staff take immediate action to respond to the lost child.

[0182] (Application Example 2)

[0183] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0184] In public facilities and event venues, there is a need for the swift discovery and early reunion of lost children with their parents. However, conventional systems struggle to respond immediately to the anxiety and impatience of parents. Furthermore, the inability to conduct searches for lost children quickly and efficiently is a source of dissatisfaction and stress for facility users.

[0185] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0186] In this invention, the server includes means for analyzing image information acquired by an image acquisition device in the entrance area and generating facial data of people; means for extracting specific facial data based on image information transmitted from a mobile device held by a guardian; means for identifying an object that matches the extracted facial data and determining its location using image information acquired in real time from multiple monitoring mechanisms in the space; and means for analyzing the emotional state from the guardian's voice and facial information, generating a message corresponding to that emotional state, and transmitting it to the guardian's mobile device. This makes it possible to quickly detect and identify lost children, as well as provide reassuring support that corresponds to the parent's emotional state, thereby improving the overall service quality of the facility.

[0187] An "image acquisition device" is a device used to acquire image information of people in a specific area.

[0188] "Facial data" refers to a collection of specific feature information extracted to identify a person's face.

[0189] A "personal information terminal" is an electronic device that a user can carry with them and that is used for communication and information processing.

[0190] A "monitoring mechanism" is equipment installed to acquire image information in real time at a specific location within a space.

[0191] A "worker terminal" is an electronic device used by on-site workers for information processing and communication.

[0192] "Analyzing emotional state" is the process of determining the current psychological and emotional state of a parent or guardian based on information obtained from their voice and facial expressions.

[0193] "Generating a message" means creating an appropriate notification for the user based on the analysis results.

[0194] The system implementing this invention begins with an image acquisition device installed in the entrance area capturing image information of visitors and transmitting it to a server. The server then uses facial recognition software to generate facial data for each person and stores it in a database.

[0195] Next, when a parent reports a lost child using a mobile device, image information of the child is sent from the device to the server. Based on this information, the server extracts specific facial data and compares it with real-time image information constantly transmitted from the monitoring system. OpenCV and other similar technologies are used for facial recognition.

[0196] The server combines location information and map information provided by the monitoring mechanism and uses a generation AI platform to calculate the optimal travel route. As a result, the location information of the identified target is notified to the nearest worker terminal.

[0197] The parent's mobile device transmits voice and facial expression information to a server, which then analyzes their emotional state. Emotion analysis engines such as Microsoft® Azure® Emotion API are used. Based on the analysis results, the server generates an appropriate message corresponding to the emotional state and sends it to the parent's device.

[0198] For example, if a child gets lost in a theme park and the parents are worried, the server will quickly send a message such as, "Please rest assured, the nearest staff member is checking on your child." In this way, the service goes beyond simple location tracking and can provide emotionally resonant support.

[0199] Example prompt: "Generate a message to alleviate parental anxiety at a festival venue in a smart city."

[0200] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0201] Step 1:

[0202] Image acquisition devices in the entrance area capture images of visitors and send them to a server. The server receives this image information as input, extracts facial data using facial recognition software, and stores it in a database. This process identifies the facial information of each visitor.

[0203] Step 2:

[0204] Parents use a personal digital assistant (PDCA) to report their child as missing. The PDCA sends image information of the child as input to a server. The server receives this information, performs facial recognition again to extract specific facial data, and compares it with information in an existing database. This identifies the lost child.

[0205] Step 3:

[0206] The server receives real-time image information provided by the monitoring mechanism. Using this as input, it compares it with already extracted face data to identify matching targets. The server then generates and records the location information of the identified targets as output. This allows the child's current location to be determined.

[0207] Step 4:

[0208] The server receives location and map information of the monitoring system as input and uses a generated AI model to calculate the optimal travel route. This calculation outputs an efficient travel route and sends it to the worker's terminal. This enables rapid response to lost workers.

[0209] Step 5:

[0210] The parent's mobile device collects voice and facial expression information and sends it to a server. The server receives this as input and analyzes the emotional state using an emotion analysis engine. Based on the analysis results, it generates an appropriate message as output and sends it to the device. This process provides emotional support to alleviate parental anxiety.

[0211] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0212] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0213] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0214] [Second Embodiment]

[0215] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0216] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0217] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0218] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0219] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0220] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0221] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0222] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0223] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0224] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0225] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0226] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0227] This invention relates to a system for facilitating the rapid discovery of lost children and the reunion of parents and children. In this system, a server plays a central role. The operation of the system is described below in natural language.

[0228] First, the server receives video data acquired from a camera installed at the entrance and generates facial information of visitors. This facial information is registered in a database to prepare for facial data matching in the event of a lost child. When a user (parent) sends image data of their lost child using a mobile device, the server analyzes it and extracts the facial information.

[0229] Next, the server acquires video data in real time from monitoring devices within the facility. Based on this, it checks if it matches the extracted facial information and attempts to detect the missing person. If a match is found, the server acquires their location information. Subsequently, the server sends a notification to the nearest staff terminal based on the detected location information. This notification includes the missing person's location and instructions for what to do.

[0230] The generating AI calculates the optimal route for staff to move based on facility map information and monitoring device placement data. This allows staff to quickly and efficiently reach lost children and return them to their parents. For example, suppose a child gets lost in the amusement park area of ​​a theme park. The server identifies the child's location using real-time video and quickly notifies the nearest staff member. As a result, the child and parents are reunited in a short amount of time.

[0231] This system will significantly reduce the time required to process lost children, thereby reducing the emotional burden on staff and providing a safer facility environment.

[0232] The following describes the processing flow.

[0233] Step 1:

[0234] The server acquires video data of visitors from cameras installed at the entrance. Based on the acquired video data, a facial recognition algorithm is applied to generate facial information. This facial information, along with the visitor's identification information, is registered in the database.

[0235] Step 2:

[0236] When a child goes missing, the user (parent) launches a dedicated app on their mobile device and selects the most recent photo or video of the child. The selected image data is then sent to the server via the app.

[0237] Step 3:

[0238] The server receives image data sent by the user and extracts facial information. This extracted facial information is then compared with existing facial information in the database. This process identifies the facial information of the lost child.

[0239] Step 4:

[0240] The server establishes connections with each monitoring device within the facility and receives real-time video data. Using a detection algorithm, it analyzes people in the real-time video and verifies whether they match the facial information identified in step 3.

[0241] Step 5:

[0242] If a terminal (monitoring device) detects a lost child in real-time video, it sends its location information to a server. Based on this information, the server identifies the terminal of the nearest staff member.

[0243] Step 6:

[0244] The server pushes notifications to identified staff terminals regarding the location of the lost child and the surrounding environment. These notifications include instructions and recommended actions to take.

[0245] Step 7:

[0246] The AI ​​generates data using map data and monitoring device placement information within the facility to calculate the optimal route for staff to efficiently reach children. The calculated route information is then provided to staff terminals to support their response.

[0247] (Example 1)

[0248] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0249] The problem of lost or missing children within facilities requires swift and efficient search operations, but currently, this is time-consuming and burdensome for parents and facility operators, both mentally and operationally. Therefore, a system is needed to quickly identify individuals and enable staff to act efficiently to solve these problems.

[0250] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0251] In this invention, the server includes means for analyzing image information acquired by an entrance camera to generate visitor characteristic information, means for extracting predetermined characteristic information based on image information transmitted from a mobile information terminal carried by the user, means for detecting an object that matches the extracted characteristic information using image information acquired in real time from multiple observation devices within the facility and determining its location, and means for notifying the location information of the detected object to the nearest worker terminal. This enables rapid identification of the target and efficient search operations.

[0252] A "photography device" is a piece of equipment installed within a facility to acquire image information of visitors.

[0253] "Image information" refers to visual data acquired through imaging devices or mobile information terminals, and is fundamental data used to generate feature information through analysis.

[0254] "Feature information" refers to data that shows faces and other unique characteristics extracted from image information, and is used to identify individuals.

[0255] A "personal digital information terminal" is a portable device owned by a user and used to transmit image information.

[0256] An "observation device" is a system consisting of cameras and sensors installed within a facility, used to monitor and acquire image information in real time.

[0257] A "worker terminal" is a device used by the nearest worker to receive information from the server and to act according to the instructions.

[0258] "Real-time" refers to processing that is immediate and in line with real-world time, meaning that data is acquired, processed, and reported without delay.

[0259] This invention is a system to support the rapid discovery of lost children and the reunion of parents and children. Specific embodiments of the system are described below.

[0260] The server analyzes image information acquired from a camera installed at the entrance and generates visitor characteristic information. A high-resolution camera is used as the camera, and the captured image information is processed by analysis software on the server. At this stage, characteristic information is generated from the images using facial recognition technology and stored in a database. This facial recognition technology utilizes existing facial recognition algorithms to efficiently extract feature points.

[0261] Users utilize a dedicated application to send images of lost children to a server using their mobile devices. This application features a user-friendly interface, allowing for easy transmission of images to the server. The transmitted images are analyzed on the server, and the extracted features are compared with already registered features.

[0262] The server uses image information acquired in real time from observation devices within the facility and compares it with extracted feature information. Full HD surveillance cameras are used as observation devices to cover a wide area. The real-time image information obtained from these surveillance cameras is processed by the server via facial recognition software to identify subjects and acquire their location information.

[0263] For detected targets, the server notifies the nearest worker terminal of their location. The worker terminal is then provided with the optimal route calculated by a generative AI model, enabling the worker to act quickly and efficiently. This generative AI model uses facility map information and camera placement data to calculate the shortest and safest travel route.

[0264] As a concrete example, if a child gets lost in a theme park, the server can identify the child from real-time images and quickly notify the relevant personnel. As a result, the child and parent can be reunited in a short amount of time.

[0265] Examples of prompt messages include, "Please locate a child who has gotten lost in the amusement park and calculate the optimal route for staff to safely return the child to their parents." Through this system, safety management within the facility can be significantly improved, and users can feel more secure.

[0266] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0267] Step 1:

[0268] The server receives image information acquired from the camera at the entrance. The input is image data from the camera, and the server analyzes it to generate visitor characteristic information. Specifically, it uses an image processing algorithm to extract facial landmarks and registers them in the database as characteristic information. The output is visitor characteristic information.

[0269] Step 2:

[0270] The user takes a picture of a lost child with their mobile device and sends it to the server. The input is image data from the user, which the server receives and analyzes. The server applies a face recognition algorithm to extract feature information from the image and converts it into a format for matching with an existing database. The output is the extracted facial feature information.

[0271] Step 3:

[0272] The server acquires image information in real time from observation devices within the facility. The input is live image data from the observation devices, and the server performs face matching on this data. The server uses this data to search for objects that match the feature information extracted in step 2, and if a match is found, it obtains its location information. The output is the location information of the matched objects.

[0273] Step 4:

[0274] Based on the location information of the target detected in step 3, the server sends a notification to the nearest worker terminal. The input is location information, which the server formats as a notification message and sends to the worker terminal. This allows workers to know the location of the lost person in real time. The output is the notification sent to the worker terminal.

[0275] Step 5:

[0276] The AI ​​model installed on the terminal calculates the optimal route for the worker to take based on the facility's map information and the input location information. The input consists of location information and facility map data, and the AI ​​model calculates the shortest and safest route. The worker's terminal displays this route, assisting the worker in efficiently reaching the lost person. The output is the optimal route information.

[0277] (Application Example 1)

[0278] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0279] In large public facilities, it often takes a long time to find lost or missing children and reunite them with their guardians. Therefore, there is a need for methods to quickly and accurately locate lost children and facilitate their reunion. Reducing the burden on on-site staff and enabling efficient responses are also crucial issues.

[0280] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following means.

[0281] In this invention, the server includes means for analyzing visual data acquired by a visual device at an entrance and generating image information of a visitor, means for extracting predetermined image information based on visual data transmitted from a mobile device possessed by a parent, and means for detecting a match with the extracted image information and specifying the position thereof using visual data immediately acquired from a plurality of monitoring devices within a public facility. Thereby, it becomes possible to quickly find and reunite with a lost child, and a safe and secure environment within the public facility can be provided.

[0282] The "visual device at an entrance" is an electronic device installed at the entrance of a public facility for acquiring video data of the face and body of a visitor.

[0283] "Visual data" is digital data containing appearance information of people and objects, acquired by a camera or sensor.

[0284] "Image information" is digital information indicating personal characteristics analyzed and generated from visual data, and is used for face authentication and the like.

[0285] The "mobile device" refers to an electronic terminal owned by an individual and capable of communication and information transmission and reception, particularly a mobile phone or a smartphone. 4]

[0286] The "monitoring device" is a device installed at various locations within a facility for acquiring surrounding video data in real time.

[0287] The "match" is a specific individual or object detected by visual data and refers to the target person.

[0288] The "data store" is an information management system for permanently storing information and performing search and collation as needed.

[0289] An "AI model" is a system that analyzes data based on machine learning and automatically generates solutions to specific problems.

[0290] A "prompt message" is a sentence used to convey instructions or suggestions generated by an AI model to a human.

[0291] To implement this invention, the system is constructed as follows:

[0292] The server first acquires video data from visual devices installed at the entrance of public facilities. This video data is then analyzed to generate facial information of visitors. This process utilizes deep learning-based facial recognition software, specifically using libraries such as TensorFlow and OpenCV.

[0293] Next, the server extracts specific facial information using image data transmitted from the parent's mobile device. Similar facial recognition technology is used at this stage, specifically extracting feature points from the captured image data and comparing them against a database.

[0294] Subsequently, the server utilizes visual data obtained instantly from multiple monitoring devices installed within the facility to detect individuals matching the previously extracted facial information in real time and pinpoint their locations. This process uses, for example, video streaming analysis using AWS Lambda.

[0295] The location information of detected individuals is sent from the server to the nearest worker's terminal. This notification utilizes services such as Firebase Cloud Messaging, enabling workers to take immediate action.

[0296] Furthermore, the server calculates the most efficient route for workers to move around based on the location information of each monitoring device within the facility and map information, and provides this information to the terminal. This process utilizes pathfinding algorithms built in R or Python.

[0297] As a concrete example, if a child gets lost in a theme park, the parent can send a photo of the child from their smartphone to a server. The server will then quickly determine the child's location based on the facial information and notify the nearest worker of the location and instructions on what to do. As a result, the worker can travel along the optimal route and safely return the child to their parents.

[0298] An example of a prompt message generated using AI is: "Create a step-by-step guide to find a lost child in a park and safely return them to their guardian. Please explain how to calculate the optimal route based on facility map information and real-time data, and how to support a safe reunion."

[0299] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0300] Step 1:

[0301] The server acquires video data in real time from visual devices installed at the entrance of public facilities. This input data is analyzed using a deep learning model to generate visitor facial information. The output obtained as a result of the analysis is a vector containing individual facial features.

[0302] Step 2:

[0303] The user sends an image of the lost person to the server using a mobile device. The server receives this image data as input, applies a facial recognition algorithm, and extracts specific facial information. The output here is the extracted feature vector.

[0304] Step 3:

[0305] The server continuously acquires real-time video data from monitoring devices installed within the facility. Using this data as input, it performs facial recognition to detect individuals that match previously extracted facial information. The output is the location information of the matched individuals.

[0306] Step 4:

[0307] Based on the detected person's location information, the server sends a notification to the nearest operator terminal. The location information and action guidelines are sent to the terminal using Firebase Cloud Messaging. As a result, specific action guidelines for the operator to respond immediately are displayed on the terminal.

[0308] Step 5:

[0309] The server acquires the local monitoring device arrangement information and map information within the facility as inputs. Using these information, a pathfinding algorithm is applied to calculate and provide the optimal movement route to the operator's terminal. The output of this calculation is the shortest and safest movement route.

[0310] Step 6:

[0311] Using the generative AI model, the server generates an individual guide based on the prompt text and provides this to the operator. Based on examples of the prompt text, the AI model performs statistical analysis and simulation and outputs specific guides.

[0312] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0313] The present invention relates to a system that takes into account the emotional state of the parent in addition to assisting in the rapid discovery of a lost child and the reunion of parent and child. Hereinafter, specific embodiments of the present invention will be described.

[0314] First, the server receives video data of visitors from cameras installed at the entrance and generates facial information. This facial information is registered in a database and used when a child gets lost. When a child gets lost, the user (parent) sends image data of the child's face to the server using a dedicated app on their mobile device. The server extracts facial information from the received data and compares it with the registered information in the database.

[0315] This system also incorporates an emotion engine that recognizes the parent's emotional state based on voice and facial expression data acquired from mobile devices. The server receives real-time video data from monitoring devices within the facility and detects targets that match the extracted facial information. If a target is detected, the server identifies its location and notifies the nearest staff terminal. The emotion engine analyzes the parent's emotional state and dynamically adjusts the notification content and support messages accordingly, providing them to the user.

[0316] The generating AI uses map information of the entire facility and the placement information of monitoring devices to calculate the optimal travel route. This route information is provided to staff terminals, enabling quick and efficient responses to lost children.

[0317] As a concrete example, if a child gets lost in a theme park and the parents feel anxious or worried, the emotion engine recognizes these emotions and generates and provides a message that offers greater reassurance. For example, it might say, "Staff are on their way, so please don't worry." In this way, by not only being able to locate the child but also responding with consideration for the parents' emotions, the overall service quality of the facility can be improved.

[0318] This invention makes it possible to provide faster and more attentive service than conventional lost child response systems, giving parents and children a sense of security and creating a safer environment.

[0319] The following describes the processing flow.

[0320] Step 1:

[0321] The server acquires video data from a camera installed at the entrance. This generates facial information of visitors, and this information is registered in a database.

[0322] Step 2:

[0323] The user (parent) opens a dedicated app on their smartphone when their child gets lost. The app then sends the child's latest image data (photos and videos) to the server.

[0324] Step 3:

[0325] The server receives image data sent by the user, analyzes the data, and extracts the child's facial information. The extracted facial information is then compared with the facial information in the database to identify the corresponding information.

[0326] Step 4:

[0327] The device (the mobile device's emotion engine) uses the smartphone's microphone and camera to acquire the parent's voice and facial expression data. The emotion engine analyzes this data to recognize the parent's current emotional state.

[0328] Step 5:

[0329] The server receives real-time video from monitoring devices installed within the facility. Based on the video data, it searches for targets that match the facial information identified in step 3. If a target is detected, its location is determined.

[0330] Step 6:

[0331] The server identifies the nearest staff terminal based on the location information of the detected target. It then sends a notification to the staff terminal containing the lost child's location information and instructions for action.

[0332] Step 7:

[0333] The server customizes notifications and support messages sent to parents based on the parent's emotional state recognized by the emotion engine. It dynamically generates reassuring messages as needed.

[0334] Step 8:

[0335] The generating AI calculates the shortest possible route for staff to reach a child, based on facility map information and monitoring device placement data. This route information is then transmitted to staff terminals to support a quick response.

[0336] (Example 2)

[0337] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0338] Conventional lost child prevention systems have difficulty responding quickly and have not adequately addressed the anxiety and impatience of parents. Furthermore, locating lost children and providing information to guardians is inefficient, leading to a decline in the overall service quality of the facility. It is necessary to solve these problems to provide peace of mind to parents and children and to realize a safer and more efficient lost child prevention system.

[0339] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0340] In this invention, the server includes means for analyzing data from a display device acquired by a camera at the entrance and generating characteristic information of visitors; means for extracting predetermined characteristic information based on image data transmitted from an information terminal held by a guardian; means for detecting an object that matches the extracted characteristic information and identifying its location using data from display devices acquired in real time from multiple observation devices within the facility; and means for recognizing the emotional state of the guardian using voice data and facial expression data, and dynamically generating and providing support information according to the analysis results. This enables the rapid identification of the lost child's location, the provision of appropriate information to the guardian, and support that takes emotions into consideration.

[0341] An "entrance camera" is a camera installed at the entrance of a facility to capture video data of visitors.

[0342] "Data from a display device" refers to digital information about images and videos acquired by imaging or observation equipment.

[0343] "Visitor characteristic information" refers to facial and body characteristic information used to identify individuals, analyzed from data obtained from the display device.

[0344] "Information devices owned by guardians" refers to communication devices such as mobile phones and smartphones that are carried by parents or guardians and capable of transmitting images and audio.

[0345] An "observation device" is a surveillance camera system installed within a facility to acquire video data in real time and monitor specific targets.

[0346] A "data aggregation device" is an information storage system that stores generated characteristic information for comparison and retrieval.

[0347] "Support information" refers to advice and messages provided to parents and staff to help prevent children from getting lost.

[0348] A "person in charge terminal" is an information terminal carried by facility staff or other personnel to communicate and check on the situation within the facility.

[0349] "Emotional state" refers to information that indicates the psychological state of the caregiver, and is analyzed from voice and facial expression data.

[0350] This invention is a system designed to facilitate the rapid discovery of lost children and the reunion of parents and children, taking into account the emotional state of the parents. The specific configuration and method of use for implementing this invention are described below.

[0351] First, the server receives real-time video data of visitors from a camera installed at the facility's entrance. The server uses image processing software such as OpenCV or dlib to extract facial information from this video data and generate it as digital data. The facial information is stored in a data accumulator and used later for verification.

[0352] On the other hand, the parent, as the user, would use a mobile device (e.g., a smartphone) if their child gets lost. They would launch a dedicated application and either take a picture of their child's face or select an existing image and send it to the server. The mobile device would use a secure protocol to send the image data to the server.

[0353] The server analyzes the received facial image data of the child and compares it with real-time video data acquired from multiple observation devices installed within the facility. Once the target is identified, the server determines its location and notifies the staff member's terminal. This allows the staff member to quickly go to the child's location.

[0354] Furthermore, the server incorporates an emotion engine that analyzes voice and facial expression data acquired from the user's mobile device. The server uses the Natural Language Processing Toolkit (NLTK) and the Emotion API to understand the parent's emotional state. Based on the analysis results, the user is provided with dynamically generated support messages. For example, a message such as "Staff are on their way, so please don't worry" might be used.

[0355] The generated AI model calculates the optimal movement route using map information and observation device placement information within the facility. This route information is immediately provided to the staff member's terminal, enabling a quick response to lost children.

[0356] Examples of specific prompt messages include, "Generate a notification message for the parent based on the latest location information of the lost child at the scene," and "Create a reassuring recommendation message based on the parent's emotional state."

[0357] In this way, this system not only facilitates the rapid discovery of lost children and supports parent-child reunions, but also aims to provide greater peace of mind by taking into consideration the parents' feelings.

[0358] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0359] Step 1:

[0360] The server receives video data of visitors from the camera at the entrance. It takes video data from the camera as input. The server generates facial information using image processing software. The output is digital facial information data. Specifically, the server detects faces within the frame and formats their feature points for use in the database.

[0361] Step 2:

[0362] The user uses a dedicated app on their mobile device to send a facial image of their lost child to the server. The input is the facial image data of the child that has been photographed or selected. The mobile device transmits this data through a secure protocol. The output is the image data received by the server. Specifically, the parent captures an image of their child with the device's camera and presses the send button within the app.

[0363] Step 3:

[0364] The server analyzes the received image data and matches it based on feature information in the database. The input consists of face image data sent by the user and previously acquired face information in the database. The server performs the matching operation using algorithms such as FaceNet. The output is either the ID information of the identified individual or a list of candidates with a high probability of matching. Specifically, it calculates and compares feature vectors to list individuals with high similarity.

[0365] Step 4:

[0366] The server receives real-time video data from observation devices within the facility and identifies targets based on matched facial information. Inputs include video data from the observation devices and characteristic information of identified individuals. Outputs are the location information of the identified targets. Specifically, the server analyzes the streaming video and tracks the individual's location using a facial recognition algorithm.

[0367] Step 5:

[0368] The server incorporates an emotion engine that analyzes voice and facial expression data from the user's mobile device. Input consists of voice data and a video of the user's facial expressions. The server performs natural language processing and facial expression analysis to identify the emotional state. The output is an analysis result indicating the user's emotions. Specifically, it quantifies the emotional state through voice tone analysis and facial expression extraction.

[0369] Step 6:

[0370] The server generates support messages to provide to the user and calculates the optimal route based on the results of sentiment analysis. Inputs include sentiment analysis results, facility map information, and observation device placement information. A generation AI model dynamically generates the necessary support messages based on prompts. Outputs include support messages for the user and information on the staff member's movement route. Specifically, this process includes constructing message content and calculating route data.

[0371] Step 7:

[0372] The server notifies the nearest staff terminal of the generated information and sends a response to the parent. Inputs are support messages and routing information. The server transmits this information to each terminal via the network. Outputs are notifications to staff terminals and messages to the parent. Specifically, messages are displayed on a display device to help staff take immediate action to respond to the lost child.

[0373] (Application Example 2)

[0374] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0375] In public facilities and event venues, there is a need for the swift discovery and early reunion of lost children with their parents. However, conventional systems struggle to respond immediately to the anxiety and impatience of parents. Furthermore, the inability to conduct searches for lost children quickly and efficiently is a source of dissatisfaction and stress for facility users.

[0376] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0377] In this invention, the server includes means for analyzing image information acquired by an image acquisition device in the entrance area and generating facial data of people; means for extracting specific facial data based on image information transmitted from a mobile device held by a guardian; means for identifying an object that matches the extracted facial data and determining its location using image information acquired in real time from multiple monitoring mechanisms in the space; and means for analyzing the emotional state from the guardian's voice and facial information, generating a message corresponding to that emotional state, and transmitting it to the guardian's mobile device. This makes it possible to quickly detect and identify lost children, as well as provide reassuring support that corresponds to the parent's emotional state, thereby improving the overall service quality of the facility.

[0378] An "image acquisition device" is a device used to acquire image information of people in a specific area.

[0379] "Facial data" refers to a collection of specific feature information extracted to identify a person's face.

[0380] A "personal information terminal" is an electronic device that a user can carry with them and that is used for communication and information processing.

[0381] A "monitoring mechanism" is equipment installed to acquire image information in real time at a specific location within a space.

[0382] A "worker terminal" is an electronic device used by on-site workers for information processing and communication.

[0383] "Analyzing emotional state" is the process of determining the current psychological and emotional state of a parent or guardian based on information obtained from their voice and facial expressions.

[0384] "Generating a message" means creating an appropriate notification for the user based on the analysis results.

[0385] The system implementing this invention begins with an image acquisition device installed in the entrance area capturing image information of visitors and transmitting it to a server. The server then uses facial recognition software to generate facial data for each person and stores it in a database.

[0386] Next, when a parent reports a lost child using a mobile device, image information of the child is sent from the device to the server. Based on this information, the server extracts specific facial data and compares it with real-time image information constantly transmitted from the monitoring system. OpenCV and other similar technologies are used for facial recognition.

[0387] The server combines location information and map information provided by the monitoring mechanism and uses a generation AI platform to calculate the optimal travel route. As a result, the location information of the identified target is notified to the nearest worker terminal.

[0388] The parent's mobile device transmits voice and facial expression information to a server, which then analyzes their emotional state. An emotion analysis engine, such as the Microsoft Azure Emotion API, is used. Based on the analysis results, the server generates an appropriate message corresponding to the parent's emotional state and sends it to the parent's device.

[0389] For example, if a child gets lost in a theme park and the parents are worried, the server will quickly send a message such as, "Please rest assured, the nearest staff member is checking on your child." In this way, the service goes beyond simple location tracking and can provide emotionally resonant support.

[0390] Example prompt: "Generate a message to alleviate parental anxiety at a festival venue in a smart city."

[0391] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0392] Step 1:

[0393] Image acquisition devices in the entrance area capture images of visitors and send them to a server. The server receives this image information as input, extracts facial data using facial recognition software, and stores it in a database. This process identifies the facial information of each visitor.

[0394] Step 2:

[0395] Parents use a personal digital assistant (PDCA) to report their child as missing. The PDCA sends image information of the child as input to a server. The server receives this information, performs facial recognition again to extract specific facial data, and compares it with information in an existing database. This identifies the lost child.

[0396] Step 3:

[0397] The server receives real-time image information provided by the monitoring mechanism. Using this as input, it compares it with already extracted face data to identify matching targets. The server then generates and records the location information of the identified targets as output. This allows the child's current location to be determined.

[0398] Step 4:

[0399] The server receives location and map information of the monitoring system as input and uses a generated AI model to calculate the optimal travel route. This calculation outputs an efficient travel route and sends it to the worker's terminal. This enables rapid response to lost workers.

[0400] Step 5:

[0401] The parent's mobile device collects voice and facial expression information and sends it to a server. The server receives this as input and analyzes the emotional state using an emotion analysis engine. Based on the analysis results, it generates an appropriate message as output and sends it to the device. This process provides emotional support to alleviate parental anxiety.

[0402] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0403] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0404] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0405] [Third Embodiment]

[0406] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0407] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0408] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0409] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0410] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0411] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0412] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0413] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0414] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0415] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0416] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0417] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0418] This invention relates to a system for facilitating the rapid discovery of lost children and the reunion of parents and children. In this system, a server plays a central role. The operation of the system is described below in natural language.

[0419] First, the server receives video data acquired from a camera installed at the entrance and generates facial information of visitors. This facial information is registered in a database to prepare for facial data matching in the event of a lost child. When a user (parent) sends image data of their lost child using a mobile device, the server analyzes it and extracts the facial information.

[0420] Next, the server acquires video data in real time from monitoring devices within the facility. Based on this, it checks if it matches the extracted facial information and attempts to detect the missing person. If a match is found, the server acquires their location information. Subsequently, the server sends a notification to the nearest staff terminal based on the detected location information. This notification includes the missing person's location and instructions for what to do.

[0421] The generating AI calculates the optimal route for staff to move based on facility map information and monitoring device placement data. This allows staff to quickly and efficiently reach lost children and return them to their parents. For example, suppose a child gets lost in the amusement park area of ​​a theme park. The server identifies the child's location using real-time video and quickly notifies the nearest staff member. As a result, the child and parents are reunited in a short amount of time.

[0422] This system will significantly reduce the time required to process lost children, thereby reducing the emotional burden on staff and providing a safer facility environment.

[0423] The following describes the processing flow.

[0424] Step 1:

[0425] The server acquires video data of visitors from cameras installed at the entrance. Based on the acquired video data, a facial recognition algorithm is applied to generate facial information. This facial information, along with the visitor's identification information, is registered in the database.

[0426] Step 2:

[0427] When a child goes missing, the user (parent) launches a dedicated app on their mobile device and selects the most recent photo or video of the child. The selected image data is then sent to the server via the app.

[0428] Step 3:

[0429] The server receives image data sent by the user and extracts facial information. This extracted facial information is then compared with existing facial information in the database. This process identifies the facial information of the lost child.

[0430] Step 4:

[0431] The server establishes connections with each monitoring device within the facility and receives real-time video data. Using a detection algorithm, it analyzes people in the real-time video and verifies whether they match the facial information identified in step 3.

[0432] Step 5:

[0433] If a terminal (monitoring device) detects a lost child in real-time video, it sends its location information to a server. Based on this information, the server identifies the terminal of the nearest staff member.

[0434] Step 6:

[0435] The server pushes notifications to identified staff terminals regarding the location of the lost child and the surrounding environment. These notifications include instructions and recommended actions to take.

[0436] Step 7:

[0437] The AI ​​generates data using map data and monitoring device placement information within the facility to calculate the optimal route for staff to efficiently reach children. The calculated route information is then provided to staff terminals to support their response.

[0438] (Example 1)

[0439] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0440] The problem of lost or missing children within facilities requires swift and efficient search operations, but currently, this is time-consuming and burdensome for parents and facility operators, both mentally and operationally. Therefore, a system is needed to quickly identify individuals and enable staff to act efficiently to solve these problems.

[0441] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0442] In this invention, the server includes means for analyzing image information acquired by an entrance camera to generate visitor characteristic information, means for extracting predetermined characteristic information based on image information transmitted from a mobile information terminal carried by the user, means for detecting an object that matches the extracted characteristic information using image information acquired in real time from multiple observation devices within the facility and determining its location, and means for notifying the location information of the detected object to the nearest worker terminal. This enables rapid identification of the target and efficient search operations.

[0443] A "photography device" is a piece of equipment installed within a facility to acquire image information of visitors.

[0444] "Image information" refers to visual data acquired through imaging devices or mobile information terminals, and is fundamental data used to generate feature information through analysis.

[0445] "Feature information" refers to data that shows faces and other unique characteristics extracted from image information, and is used to identify individuals.

[0446] A "personal digital information terminal" is a portable device owned by a user and used to transmit image information.

[0447] An "observation device" is a system consisting of cameras and sensors installed within a facility, used to monitor and acquire image information in real time.

[0448] A "worker terminal" is a device used by the nearest worker to receive information from the server and to act according to the instructions.

[0449] "Real-time" refers to processing that is immediate and in line with real-world time, meaning that data is acquired, processed, and reported without delay.

[0450] This invention is a system to support the rapid discovery of lost children and the reunion of parents and children. Specific embodiments of the system are described below.

[0451] The server analyzes image information acquired from a camera installed at the entrance and generates visitor characteristic information. A high-resolution camera is used as the camera, and the captured image information is processed by analysis software on the server. At this stage, characteristic information is generated from the images using facial recognition technology and stored in a database. This facial recognition technology utilizes existing facial recognition algorithms to efficiently extract feature points.

[0452] Users utilize a dedicated application to send images of lost children to a server using their mobile devices. This application features a user-friendly interface, allowing for easy transmission of images to the server. The transmitted images are analyzed on the server, and the extracted features are compared with already registered features.

[0453] The server uses image information acquired in real time from observation devices within the facility and compares it with extracted feature information. Full HD surveillance cameras are used as observation devices to cover a wide area. The real-time image information obtained from these surveillance cameras is processed by the server via facial recognition software to identify subjects and acquire their location information.

[0454] For detected targets, the server notifies the nearest worker terminal of their location. The worker terminal is then provided with the optimal route calculated by a generative AI model, enabling the worker to act quickly and efficiently. This generative AI model uses facility map information and camera placement data to calculate the shortest and safest travel route.

[0455] As a concrete example, if a child gets lost in a theme park, the server can identify the child from real-time images and quickly notify the relevant personnel. As a result, the child and parent can be reunited in a short amount of time.

[0456] Examples of prompt messages include, "Please locate a child who has gotten lost in the amusement park and calculate the optimal route for staff to safely return the child to their parents." Through this system, safety management within the facility can be significantly improved, and users can feel more secure.

[0457] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0458] Step 1:

[0459] The server receives image information acquired from the camera at the entrance. The input is image data from the camera, and the server analyzes it to generate visitor characteristic information. Specifically, it uses an image processing algorithm to extract facial landmarks and registers them in the database as characteristic information. The output is visitor characteristic information.

[0460] Step 2:

[0461] The user takes a picture of a lost child with their mobile device and sends it to the server. The input is image data from the user, which the server receives and analyzes. The server applies a face recognition algorithm to extract feature information from the image and converts it into a format for matching with an existing database. The output is the extracted facial feature information.

[0462] Step 3:

[0463] The server acquires image information in real time from observation devices within the facility. The input is live image data from the observation devices, and the server performs face matching on this data. The server uses this data to search for objects that match the feature information extracted in step 2, and if a match is found, it obtains its location information. The output is the location information of the matched objects.

[0464] Step 4:

[0465] Based on the location information of the target detected in step 3, the server sends a notification to the nearest worker terminal. The input is location information, which the server formats as a notification message and sends to the worker terminal. This allows workers to know the location of the lost person in real time. The output is the notification sent to the worker terminal.

[0466] Step 5:

[0467] The AI ​​model installed on the terminal calculates the optimal route for the worker to take based on the facility's map information and the input location information. The input consists of location information and facility map data, and the AI ​​model calculates the shortest and safest route. The worker's terminal displays this route, assisting the worker in efficiently reaching the lost person. The output is the optimal route information.

[0468] (Application Example 1)

[0469] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0470] In large public facilities, it often takes a long time to find lost or missing children and reunite them with their guardians. Therefore, there is a need for methods to quickly and accurately locate lost children and facilitate their reunion. Reducing the burden on on-site staff and enabling efficient responses are also crucial issues.

[0471] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0472] In this invention, the server includes means for analyzing visual data acquired by a visual device at the entrance and generating image information of visitors; means for extracting predetermined image information based on visual data transmitted from a portable device held by a parent; and means for detecting a match with the extracted image information and identifying its location using visual data acquired in real time from multiple monitoring devices within the public facility. This enables the rapid discovery and reunion of lost children, providing a safe and secure environment within public facilities.

[0473] "Visual devices at entrances" are electronic devices installed at the entrances of public facilities to acquire video data of visitors' faces and bodies.

[0474] "Visual data" refers to digital data that includes information about the appearance of people and objects, acquired by cameras and sensors.

[0475] "Image information" refers to digital information that represents an individual's characteristics, generated by analyzing visual data, and is used in applications such as facial recognition.

[0476] "Portable device" refers to an electronic terminal owned by an individual that is capable of communication and sending / receiving information, particularly mobile phones and smartphones.

[0477] A "monitoring device" is a device installed in various locations within a facility that acquires video data of the surroundings in real time.

[0478] A "comparison" refers to a specific individual or object detected by visual data, specifically the target person.

[0479] A "data store" is an information management system that permanently stores information and allows for searching and matching as needed.

[0480] An "AI model" is a system that analyzes data based on machine learning and automatically generates solutions to specific problems.

[0481] A "prompt message" is a sentence used to convey instructions or suggestions generated by an AI model to a human.

[0482] To implement this invention, the system is constructed as follows:

[0483] The server first acquires video data from visual devices installed at the entrance of public facilities. This video data is then analyzed to generate facial information of visitors. This process utilizes deep learning-based facial recognition software, specifically using libraries such as TensorFlow and OpenCV.

[0484] Next, the server extracts specific facial information using image data transmitted from the parent's mobile device. Similar facial recognition technology is used at this stage, specifically extracting feature points from the captured image data and comparing them against a database.

[0485] Subsequently, the server utilizes visual data obtained instantly from multiple monitoring devices installed within the facility to detect individuals matching the previously extracted facial information in real time and pinpoint their locations. This process uses, for example, video streaming analysis using AWS Lambda.

[0486] The location information of detected individuals is sent from the server to the nearest worker's terminal. This notification utilizes services such as Firebase Cloud Messaging, enabling workers to take immediate action.

[0487] Furthermore, the server calculates the most efficient route for workers to move around based on the location information of each monitoring device within the facility and map information, and provides this information to the terminal. This process utilizes pathfinding algorithms built in R or Python.

[0488] As a concrete example, if a child gets lost in a theme park, the parent can send a photo of the child from their smartphone to a server. The server will then quickly determine the child's location based on the facial information and notify the nearest worker of the location and instructions on what to do. As a result, the worker can travel along the optimal route and safely return the child to their parents.

[0489] An example of a prompt message generated using AI is: "Create a step-by-step guide to find a lost child in a park and safely return them to their guardian. Please explain how to calculate the optimal route based on facility map information and real-time data, and how to support a safe reunion."

[0490] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0491] Step 1:

[0492] The server acquires video data in real time from visual devices installed at the entrance of public facilities. This input data is analyzed using a deep learning model to generate visitor facial information. The output obtained as a result of the analysis is a vector containing individual facial features.

[0493] Step 2:

[0494] The user sends an image of the lost person to the server using a mobile device. The server receives this image data as input, applies a facial recognition algorithm, and extracts specific facial information. The output here is the extracted feature vector.

[0495] Step 3:

[0496] The server continuously acquires real-time video data from monitoring devices installed within the facility. Using this data as input, it performs facial recognition to detect individuals that match previously extracted facial information. The output is the location information of the matched individuals.

[0497] Step 4:

[0498] The server sends a notification to the nearest worker's terminal based on the location information of the detected person. Firebase Cloud Messaging is used to send the location information and action instructions to the terminal. This displays specific action instructions on the terminal, enabling the worker to respond immediately.

[0499] Step 5:

[0500] The server acquires local monitoring device placement information and map information within the facility as input. Using this information, it applies a pathfinding algorithm to calculate and provide the optimal travel route to the worker's terminal. The output of this calculation is the shortest and safest travel route.

[0501] Step 6:

[0502] Using a generative AI model, the server generates individual guides based on prompt messages and provides them to the user. The AI ​​model performs statistical analysis and simulations based on example prompt messages to output specific guides.

[0503] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0504] This invention relates to a system that, in addition to supporting the rapid discovery of lost children and the reunion of parents and children, also takes into account the emotional state of the parents. Specific embodiments of this invention are described below.

[0505] First, the server receives video data of visitors from cameras installed at the entrance and generates facial information. This facial information is registered in a database and used when a child gets lost. When a child gets lost, the user (parent) sends image data of the child's face to the server using a dedicated app on their mobile device. The server extracts facial information from the received data and compares it with the registered information in the database.

[0506] This system also incorporates an emotion engine that recognizes the parent's emotional state based on voice and facial expression data acquired from mobile devices. The server receives real-time video data from monitoring devices within the facility and detects targets that match the extracted facial information. If a target is detected, the server identifies its location and notifies the nearest staff terminal. The emotion engine analyzes the parent's emotional state and dynamically adjusts the notification content and support messages accordingly, providing them to the user.

[0507] The generating AI uses map information of the entire facility and the placement information of monitoring devices to calculate the optimal travel route. This route information is provided to staff terminals, enabling quick and efficient responses to lost children.

[0508] As a concrete example, if a child gets lost in a theme park and the parents feel anxious or worried, the emotion engine recognizes these emotions and generates and provides a message that offers greater reassurance. For example, it might say, "Staff are on their way, so please don't worry." In this way, by not only being able to locate the child but also responding with consideration for the parents' emotions, the overall service quality of the facility can be improved.

[0509] This invention makes it possible to provide faster and more attentive service than conventional lost child response systems, giving parents and children a sense of security and creating a safer environment.

[0510] The following describes the processing flow.

[0511] Step 1:

[0512] The server acquires video data from a camera installed at the entrance. This generates facial information of visitors, and this information is registered in a database.

[0513] Step 2:

[0514] The user (parent) opens a dedicated app on their smartphone when their child gets lost. The app then sends the child's latest image data (photos and videos) to the server.

[0515] Step 3:

[0516] The server receives image data sent by the user, analyzes the data, and extracts the child's facial information. The extracted facial information is then compared with the facial information in the database to identify the corresponding information.

[0517] Step 4:

[0518] The device (the mobile device's emotion engine) uses the smartphone's microphone and camera to acquire the parent's voice and facial expression data. The emotion engine analyzes this data to recognize the parent's current emotional state.

[0519] Step 5:

[0520] The server receives real-time video from monitoring devices installed within the facility. Based on the video data, it searches for targets that match the facial information identified in step 3. If a target is detected, its location is determined.

[0521] Step 6:

[0522] The server identifies the nearest staff terminal based on the location information of the detected target. It then sends a notification to the staff terminal containing the lost child's location information and instructions for action.

[0523] Step 7:

[0524] The server customizes notifications and support messages sent to parents based on the parent's emotional state recognized by the emotion engine. It dynamically generates reassuring messages as needed.

[0525] Step 8:

[0526] The generating AI calculates the shortest possible route for staff to reach a child, based on facility map information and monitoring device placement data. This route information is then transmitted to staff terminals to support a quick response.

[0527] (Example 2)

[0528] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0529] Conventional lost child prevention systems have difficulty responding quickly and have not adequately addressed the anxiety and impatience of parents. Furthermore, locating lost children and providing information to guardians is inefficient, leading to a decline in the overall service quality of the facility. It is necessary to solve these problems to provide peace of mind to parents and children and to realize a safer and more efficient lost child prevention system.

[0530] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0531] In this invention, the server includes means for analyzing data from a display device acquired by a camera at the entrance and generating characteristic information of visitors; means for extracting predetermined characteristic information based on image data transmitted from an information terminal held by a guardian; means for detecting an object that matches the extracted characteristic information and identifying its location using data from display devices acquired in real time from multiple observation devices within the facility; and means for recognizing the emotional state of the guardian using voice data and facial expression data, and dynamically generating and providing support information according to the analysis results. This enables the rapid identification of the lost child's location, the provision of appropriate information to the guardian, and support that takes emotions into consideration.

[0532] An "entrance camera" is a camera installed at the entrance of a facility to capture video data of visitors.

[0533] "Data from a display device" refers to digital information about images and videos acquired by imaging or observation equipment.

[0534] "Visitor characteristic information" refers to facial and body characteristic information used to identify individuals, analyzed from data obtained from the display device.

[0535] "Information devices owned by guardians" refers to communication devices such as mobile phones and smartphones that are carried by parents or guardians and capable of transmitting images and audio.

[0536] An "observation device" is a surveillance camera system installed within a facility to acquire video data in real time and monitor specific targets.

[0537] A "data aggregation device" is an information storage system that stores generated characteristic information for comparison and retrieval.

[0538] "Support information" refers to advice and messages provided to parents and staff to help prevent children from getting lost.

[0539] A "person in charge terminal" is an information terminal carried by facility staff or other personnel to communicate and check on the situation within the facility.

[0540] "Emotional state" refers to information that indicates the psychological state of the caregiver, and is analyzed from voice and facial expression data.

[0541] This invention is a system designed to facilitate the rapid discovery of lost children and the reunion of parents and children, taking into account the emotional state of the parents. The specific configuration and method of use for implementing this invention are described below.

[0542] First, the server receives real-time video data of visitors from a camera installed at the facility's entrance. The server uses image processing software such as OpenCV or dlib to extract facial information from this video data and generate it as digital data. The facial information is stored in a data accumulator and used later for verification.

[0543] On the other hand, the parent, as the user, would use a mobile device (e.g., a smartphone) if their child gets lost. They would launch a dedicated application and either take a picture of their child's face or select an existing image and send it to the server. The mobile device would use a secure protocol to send the image data to the server.

[0544] The server analyzes the received facial image data of the child and compares it with real-time video data acquired from multiple observation devices installed within the facility. Once the target is identified, the server determines its location and notifies the staff member's terminal. This allows the staff member to quickly go to the child's location.

[0545] Furthermore, the server incorporates an emotion engine that analyzes voice and facial expression data acquired from the user's mobile device. The server uses the Natural Language Processing Toolkit (NLTK) and the Emotion API to understand the parent's emotional state. Based on the analysis results, the user is provided with dynamically generated support messages. For example, a message such as "Staff are on their way, so please don't worry" might be used.

[0546] The generated AI model calculates the optimal movement route using map information and observation device placement information within the facility. This route information is immediately provided to the staff member's terminal, enabling a quick response to lost children.

[0547] Examples of specific prompt messages include, "Generate a notification message for the parent based on the latest location information of the lost child at the scene," and "Create a reassuring recommendation message based on the parent's emotional state."

[0548] In this way, this system not only facilitates the rapid discovery of lost children and supports parent-child reunions, but also aims to provide greater peace of mind by taking into consideration the parents' feelings.

[0549] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0550] Step 1:

[0551] The server receives video data of visitors from the camera at the entrance. It takes video data from the camera as input. The server generates facial information using image processing software. The output is digital facial information data. Specifically, the server detects faces within the frame and formats their feature points for use in the database.

[0552] Step 2:

[0553] The user uses a dedicated app on their mobile device to send a facial image of their lost child to the server. The input is the facial image data of the child that has been photographed or selected. The mobile device transmits this data through a secure protocol. The output is the image data received by the server. Specifically, the parent captures an image of their child with the device's camera and presses the send button within the app.

[0554] Step 3:

[0555] The server analyzes the received image data and matches it based on feature information in the database. The input consists of face image data sent by the user and previously acquired face information in the database. The server performs the matching operation using algorithms such as FaceNet. The output is either the ID information of the identified individual or a list of candidates with a high probability of matching. Specifically, it calculates and compares feature vectors to list individuals with high similarity.

[0556] Step 4:

[0557] The server receives real-time video data from observation devices within the facility and identifies targets based on matched facial information. Inputs include video data from the observation devices and characteristic information of identified individuals. Outputs are the location information of the identified targets. Specifically, the server analyzes the streaming video and tracks the individual's location using a facial recognition algorithm.

[0558] Step 5:

[0559] The server incorporates an emotion engine that analyzes voice and facial expression data from the user's mobile device. Input consists of voice data and a video of the user's facial expressions. The server performs natural language processing and facial expression analysis to identify the emotional state. The output is an analysis result indicating the user's emotions. Specifically, it quantifies the emotional state through voice tone analysis and facial expression extraction.

[0560] Step 6:

[0561] The server generates support messages to provide to the user and calculates the optimal route based on the results of sentiment analysis. Inputs include sentiment analysis results, facility map information, and observation device placement information. A generation AI model dynamically generates the necessary support messages based on prompts. Outputs include support messages for the user and information on the staff member's movement route. Specifically, this process includes constructing message content and calculating route data.

[0562] Step 7:

[0563] The server notifies the nearest staff terminal of the generated information and sends a response to the parent. Inputs are support messages and routing information. The server transmits this information to each terminal via the network. Outputs are notifications to staff terminals and messages to the parent. Specifically, messages are displayed on a display device to help staff take immediate action to respond to the lost child.

[0564] (Application Example 2)

[0565] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0566] In public facilities and event venues, there is a need for the swift discovery and early reunion of lost children with their parents. However, conventional systems struggle to respond immediately to the anxiety and impatience of parents. Furthermore, the inability to conduct searches for lost children quickly and efficiently is a source of dissatisfaction and stress for facility users.

[0567] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0568] In this invention, the server includes means for analyzing image information acquired by an image acquisition device in the entrance area and generating facial data of people; means for extracting specific facial data based on image information transmitted from a mobile device held by a guardian; means for identifying an object that matches the extracted facial data and determining its location using image information acquired in real time from multiple monitoring mechanisms in the space; and means for analyzing the emotional state from the guardian's voice and facial information, generating a message corresponding to that emotional state, and transmitting it to the guardian's mobile device. This makes it possible to quickly detect and identify lost children, as well as provide reassuring support that corresponds to the parent's emotional state, thereby improving the overall service quality of the facility.

[0569] An "image acquisition device" is a device used to acquire image information of people in a specific area.

[0570] "Facial data" refers to a collection of specific feature information extracted to identify a person's face.

[0571] A "personal information terminal" is an electronic device that a user can carry with them and that is used for communication and information processing.

[0572] A "monitoring mechanism" is equipment installed to acquire image information in real time at a specific location within a space.

[0573] A "worker terminal" is an electronic device used by on-site workers for information processing and communication.

[0574] "Analyzing emotional state" is the process of determining the current psychological and emotional state of a parent or guardian based on information obtained from their voice and facial expressions.

[0575] "Generating a message" means creating an appropriate notification for the user based on the analysis results.

[0576] The system implementing this invention begins with an image acquisition device installed in the entrance area capturing image information of visitors and transmitting it to a server. The server then uses facial recognition software to generate facial data for each person and stores it in a database.

[0577] Next, when a parent reports a lost child using a mobile device, image information of the child is sent from the device to the server. Based on this information, the server extracts specific facial data and compares it with real-time image information constantly transmitted from the monitoring system. OpenCV and other similar technologies are used for facial recognition.

[0578] The server combines location information and map information provided by the monitoring mechanism and uses a generation AI platform to calculate the optimal travel route. As a result, the location information of the identified target is notified to the nearest worker terminal.

[0579] The parent's mobile device transmits voice and facial expression information to a server, which then analyzes their emotional state. An emotion analysis engine, such as the Microsoft Azure Emotion API, is used. Based on the analysis results, the server generates an appropriate message corresponding to the parent's emotional state and sends it to the parent's device.

[0580] For example, if a child gets lost in a theme park and the parents are worried, the server will quickly send a message such as, "Please rest assured, the nearest staff member is checking on your child." In this way, the service goes beyond simple location tracking and can provide emotionally resonant support.

[0581] Example prompt: "Generate a message to alleviate parental anxiety at a festival venue in a smart city."

[0582] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0583] Step 1:

[0584] Image acquisition devices in the entrance area capture images of visitors and send them to a server. The server receives this image information as input, extracts facial data using facial recognition software, and stores it in a database. This process identifies the facial information of each visitor.

[0585] Step 2:

[0586] Parents use a personal digital assistant (PDCA) to report their child as missing. The PDCA sends image information of the child as input to a server. The server receives this information, performs facial recognition again to extract specific facial data, and compares it with information in an existing database. This identifies the lost child.

[0587] Step 3:

[0588] The server receives real-time image information provided by the monitoring mechanism. Using this as input, it compares it with already extracted face data to identify matching targets. The server then generates and records the location information of the identified targets as output. This allows the child's current location to be determined.

[0589] Step 4:

[0590] The server receives location and map information of the monitoring system as input and uses a generated AI model to calculate the optimal travel route. This calculation outputs an efficient travel route and sends it to the worker's terminal. This enables rapid response to lost workers.

[0591] Step 5:

[0592] The parent's mobile device collects voice and facial expression information and sends it to a server. The server receives this as input and analyzes the emotional state using an emotion analysis engine. Based on the analysis results, it generates an appropriate message as output and sends it to the device. This process provides emotional support to alleviate parental anxiety.

[0593] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0594] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0595] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0596] [Fourth Embodiment]

[0597] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0598] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0599] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0600] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0601] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0602] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0603] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0604] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0605] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0606] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0607] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0608] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0609] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0610] This invention relates to a system for facilitating the rapid discovery of lost children and the reunion of parents and children. In this system, a server plays a central role. The operation of the system is described below in natural language.

[0611] First, the server receives video data acquired from a camera installed at the entrance and generates facial information of visitors. This facial information is registered in a database to prepare for facial data matching in the event of a lost child. When a user (parent) sends image data of their lost child using a mobile device, the server analyzes it and extracts the facial information.

[0612] Next, the server acquires video data in real time from monitoring devices within the facility. Based on this, it checks if it matches the extracted facial information and attempts to detect the missing person. If a match is found, the server acquires their location information. Subsequently, the server sends a notification to the nearest staff terminal based on the detected location information. This notification includes the missing person's location and instructions for what to do.

[0613] The generating AI calculates the optimal route for staff to move based on facility map information and monitoring device placement data. This allows staff to quickly and efficiently reach lost children and return them to their parents. For example, suppose a child gets lost in the amusement park area of ​​a theme park. The server identifies the child's location using real-time video and quickly notifies the nearest staff member. As a result, the child and parents are reunited in a short amount of time.

[0614] This system will significantly reduce the time required to process lost children, thereby reducing the emotional burden on staff and providing a safer facility environment.

[0615] The following describes the processing flow.

[0616] Step 1:

[0617] The server acquires video data of visitors from cameras installed at the entrance. Based on the acquired video data, a facial recognition algorithm is applied to generate facial information. This facial information, along with the visitor's identification information, is registered in the database.

[0618] Step 2:

[0619] When a child goes missing, the user (parent) launches a dedicated app on their mobile device and selects the most recent photo or video of the child. The selected image data is then sent to the server via the app.

[0620] Step 3:

[0621] The server receives image data sent by the user and extracts facial information. This extracted facial information is then compared with existing facial information in the database. This process identifies the facial information of the lost child.

[0622] Step 4:

[0623] The server establishes connections with each monitoring device within the facility and receives real-time video data. Using a detection algorithm, it analyzes people in the real-time video and verifies whether they match the facial information identified in step 3.

[0624] Step 5:

[0625] If a terminal (monitoring device) detects a lost child in real-time video, it sends its location information to a server. Based on this information, the server identifies the terminal of the nearest staff member.

[0626] Step 6:

[0627] The server pushes notifications to identified staff terminals regarding the location of the lost child and the surrounding environment. These notifications include instructions and recommended actions to take.

[0628] Step 7:

[0629] The AI ​​generates data using map data and monitoring device placement information within the facility to calculate the optimal route for staff to efficiently reach children. The calculated route information is then provided to staff terminals to support their response.

[0630] (Example 1)

[0631] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0632] The problem of lost or missing children within facilities requires swift and efficient search operations, but currently, this is time-consuming and burdensome for parents and facility operators, both mentally and operationally. Therefore, a system is needed to quickly identify individuals and enable staff to act efficiently to solve these problems.

[0633] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0634] In this invention, the server includes means for analyzing image information acquired by an entrance camera to generate visitor characteristic information, means for extracting predetermined characteristic information based on image information transmitted from a mobile information terminal carried by the user, means for detecting an object that matches the extracted characteristic information using image information acquired in real time from multiple observation devices within the facility and determining its location, and means for notifying the location information of the detected object to the nearest worker terminal. This enables rapid identification of the target and efficient search operations.

[0635] A "photography device" is a piece of equipment installed within a facility to acquire image information of visitors.

[0636] "Image information" refers to visual data acquired through imaging devices or mobile information terminals, and is fundamental data used to generate feature information through analysis.

[0637] "Feature information" refers to data that shows faces and other unique characteristics extracted from image information, and is used to identify individuals.

[0638] A "personal digital information terminal" is a portable device owned by a user and used to transmit image information.

[0639] An "observation device" is a system consisting of cameras and sensors installed within a facility, used to monitor and acquire image information in real time.

[0640] A "worker terminal" is a device used by the nearest worker to receive information from the server and to act according to the instructions.

[0641] "Real-time" refers to processing that is immediate and in line with real-world time, meaning that data is acquired, processed, and reported without delay.

[0642] This invention is a system to support the rapid discovery of lost children and the reunion of parents and children. Specific embodiments of the system are described below.

[0643] The server analyzes image information acquired from a camera installed at the entrance and generates visitor characteristic information. A high-resolution camera is used as the camera, and the captured image information is processed by analysis software on the server. At this stage, characteristic information is generated from the images using facial recognition technology and stored in a database. This facial recognition technology utilizes existing facial recognition algorithms to efficiently extract feature points.

[0644] Users utilize a dedicated application to send images of lost children to a server using their mobile devices. This application features a user-friendly interface, allowing for easy transmission of images to the server. The transmitted images are analyzed on the server, and the extracted features are compared with already registered features.

[0645] The server uses image information acquired in real time from observation devices within the facility and compares it with extracted feature information. Full HD surveillance cameras are used as observation devices to cover a wide area. The real-time image information obtained from these surveillance cameras is processed by the server via facial recognition software to identify subjects and acquire their location information.

[0646] For detected targets, the server notifies the nearest worker terminal of their location. The worker terminal is then provided with the optimal route calculated by a generative AI model, enabling the worker to act quickly and efficiently. This generative AI model uses facility map information and camera placement data to calculate the shortest and safest travel route.

[0647] As a concrete example, if a child gets lost in a theme park, the server can identify the child from real-time images and quickly notify the relevant personnel. As a result, the child and parent can be reunited in a short amount of time.

[0648] Examples of prompt messages include, "Please locate a child who has gotten lost in the amusement park and calculate the optimal route for staff to safely return the child to their parents." Through this system, safety management within the facility can be significantly improved, and users can feel more secure.

[0649] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0650] Step 1:

[0651] The server receives image information acquired from the camera at the entrance. The input is image data from the camera, and the server analyzes it to generate visitor characteristic information. Specifically, it uses an image processing algorithm to extract facial landmarks and registers them in the database as characteristic information. The output is visitor characteristic information.

[0652] Step 2:

[0653] The user takes a picture of a lost child with their mobile device and sends it to the server. The input is image data from the user, which the server receives and analyzes. The server applies a face recognition algorithm to extract feature information from the image and converts it into a format for matching with an existing database. The output is the extracted facial feature information.

[0654] Step 3:

[0655] The server acquires image information in real time from observation devices within the facility. The input is live image data from the observation devices, and the server performs face matching on this data. The server uses this data to search for objects that match the feature information extracted in step 2, and if a match is found, it obtains its location information. The output is the location information of the matched objects.

[0656] Step 4:

[0657] Based on the location information of the target detected in step 3, the server sends a notification to the nearest worker terminal. The input is location information, which the server formats as a notification message and sends to the worker terminal. This allows workers to know the location of the lost person in real time. The output is the notification sent to the worker terminal.

[0658] Step 5:

[0659] The AI ​​model installed on the terminal calculates the optimal route for the worker to take based on the facility's map information and the input location information. The input consists of location information and facility map data, and the AI ​​model calculates the shortest and safest route. The worker's terminal displays this route, assisting the worker in efficiently reaching the lost person. The output is the optimal route information.

[0660] (Application Example 1)

[0661] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0662] In large public facilities, it often takes a long time to find lost or missing children and reunite them with their guardians. Therefore, there is a need for methods to quickly and accurately locate lost children and facilitate their reunion. Reducing the burden on on-site staff and enabling efficient responses are also crucial issues.

[0663] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0664] In this invention, the server includes means for analyzing visual data acquired by a visual device at the entrance and generating image information of visitors; means for extracting predetermined image information based on visual data transmitted from a portable device held by a parent; and means for detecting a match with the extracted image information and identifying its location using visual data acquired in real time from multiple monitoring devices within the public facility. This enables the rapid discovery and reunion of lost children, providing a safe and secure environment within public facilities.

[0665] "Visual devices at entrances" are electronic devices installed at the entrances of public facilities to acquire video data of visitors' faces and bodies.

[0666] "Visual data" refers to digital data that includes information about the appearance of people and objects, acquired by cameras and sensors.

[0667] "Image information" refers to digital information that represents an individual's characteristics, generated by analyzing visual data, and is used in applications such as facial recognition.

[0668] "Portable device" refers to an electronic terminal owned by an individual that is capable of communication and sending / receiving information, particularly mobile phones and smartphones.

[0669] A "monitoring device" is a device installed in various locations within a facility that acquires video data of the surroundings in real time.

[0670] A "comparison" refers to a specific individual or object detected by visual data, specifically the target person.

[0671] A "data store" is an information management system that permanently stores information and allows for searching and matching as needed.

[0672] An "AI model" is a system that analyzes data based on machine learning and automatically generates solutions to specific problems.

[0673] A "prompt message" is a sentence used to convey instructions or suggestions generated by an AI model to a human.

[0674] To implement this invention, the system is constructed as follows:

[0675] The server first acquires video data from visual devices installed at the entrance of public facilities. This video data is then analyzed to generate facial information of visitors. This process utilizes deep learning-based facial recognition software, specifically using libraries such as TensorFlow and OpenCV.

[0676] Next, the server extracts specific facial information using image data transmitted from the parent's mobile device. Similar facial recognition technology is used at this stage, specifically extracting feature points from the captured image data and comparing them against a database.

[0677] Subsequently, the server utilizes visual data obtained instantly from multiple monitoring devices installed within the facility to detect individuals matching the previously extracted facial information in real time and pinpoint their locations. This process uses, for example, video streaming analysis using AWS Lambda.

[0678] The location information of detected individuals is sent from the server to the nearest worker's terminal. This notification utilizes services such as Firebase Cloud Messaging, enabling workers to take immediate action.

[0679] Furthermore, the server calculates the most efficient route for workers to move around based on the location information of each monitoring device within the facility and map information, and provides this information to the terminal. This process utilizes pathfinding algorithms built in R or Python.

[0680] As a concrete example, if a child gets lost in a theme park, the parent can send a photo of the child from their smartphone to a server. The server will then quickly determine the child's location based on the facial information and notify the nearest worker of the location and instructions on what to do. As a result, the worker can travel along the optimal route and safely return the child to their parents.

[0681] An example of a prompt message generated using AI is: "Create a step-by-step guide to find a lost child in a park and safely return them to their guardian. Please explain how to calculate the optimal route based on facility map information and real-time data, and how to support a safe reunion."

[0682] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0683] Step 1:

[0684] The server acquires video data in real time from visual devices installed at the entrance of public facilities. This input data is analyzed using a deep learning model to generate visitor facial information. The output obtained as a result of the analysis is a vector containing individual facial features.

[0685] Step 2:

[0686] The user sends an image of the lost person to the server using a mobile device. The server receives this image data as input, applies a facial recognition algorithm, and extracts specific facial information. The output here is the extracted feature vector.

[0687] Step 3:

[0688] The server continuously acquires real-time video data from monitoring devices installed within the facility. Using this data as input, it performs facial recognition to detect individuals that match previously extracted facial information. The output is the location information of the matched individuals.

[0689] Step 4:

[0690] The server sends a notification to the nearest worker's terminal based on the location information of the detected person. Firebase Cloud Messaging is used to send the location information and action instructions to the terminal. This displays specific action instructions on the terminal, enabling the worker to respond immediately.

[0691] Step 5:

[0692] The server acquires local monitoring device placement information and map information within the facility as input. Using this information, it applies a pathfinding algorithm to calculate and provide the optimal travel route to the worker's terminal. The output of this calculation is the shortest and safest travel route.

[0693] Step 6:

[0694] Using a generative AI model, the server generates individual guides based on prompt messages and provides them to the user. The AI ​​model performs statistical analysis and simulations based on example prompt messages to output specific guides.

[0695] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0696] This invention relates to a system that, in addition to supporting the rapid discovery of lost children and the reunion of parents and children, also takes into account the emotional state of the parents. Specific embodiments of this invention are described below.

[0697] First, the server receives video data of visitors from cameras installed at the entrance and generates facial information. This facial information is registered in a database and used when a child gets lost. When a child gets lost, the user (parent) sends image data of the child's face to the server using a dedicated app on their mobile device. The server extracts facial information from the received data and compares it with the registered information in the database.

[0698] This system also incorporates an emotion engine that recognizes the parent's emotional state based on voice and facial expression data acquired from mobile devices. The server receives real-time video data from monitoring devices within the facility and detects targets that match the extracted facial information. If a target is detected, the server identifies its location and notifies the nearest staff terminal. The emotion engine analyzes the parent's emotional state and dynamically adjusts the notification content and support messages accordingly, providing them to the user.

[0699] The generating AI uses map information of the entire facility and the placement information of monitoring devices to calculate the optimal travel route. This route information is provided to staff terminals, enabling quick and efficient responses to lost children.

[0700] As a concrete example, if a child gets lost in a theme park and the parents feel anxious or worried, the emotion engine recognizes these emotions and generates and provides a message that offers greater reassurance. For example, it might say, "Staff are on their way, so please don't worry." In this way, by not only being able to locate the child but also responding with consideration for the parents' emotions, the overall service quality of the facility can be improved.

[0701] This invention makes it possible to provide faster and more attentive service than conventional lost child response systems, giving parents and children a sense of security and creating a safer environment.

[0702] The following describes the processing flow.

[0703] Step 1:

[0704] The server acquires video data from a camera installed at the entrance. This generates facial information of visitors, and this information is registered in a database.

[0705] Step 2:

[0706] The user (parent) opens a dedicated app on their smartphone when their child gets lost. The app then sends the child's latest image data (photos and videos) to the server.

[0707] Step 3:

[0708] The server receives image data sent by the user, analyzes the data, and extracts the child's facial information. The extracted facial information is then compared with the facial information in the database to identify the corresponding information.

[0709] Step 4:

[0710] The device (the mobile device's emotion engine) uses the smartphone's microphone and camera to acquire the parent's voice and facial expression data. The emotion engine analyzes this data to recognize the parent's current emotional state.

[0711] Step 5:

[0712] The server receives real-time video from monitoring devices installed within the facility. Based on the video data, it searches for targets that match the facial information identified in step 3. If a target is detected, its location is determined.

[0713] Step 6:

[0714] The server identifies the nearest staff terminal based on the location information of the detected target. It then sends a notification to the staff terminal containing the lost child's location information and instructions for action.

[0715] Step 7:

[0716] The server customizes notifications and support messages sent to parents based on the parent's emotional state recognized by the emotion engine. It dynamically generates reassuring messages as needed.

[0717] Step 8:

[0718] The generating AI calculates the shortest possible route for staff to reach a child, based on facility map information and monitoring device placement data. This route information is then transmitted to staff terminals to support a quick response.

[0719] (Example 2)

[0720] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0721] Conventional lost child prevention systems have difficulty responding quickly and have not adequately addressed the anxiety and impatience of parents. Furthermore, locating lost children and providing information to guardians is inefficient, leading to a decline in the overall service quality of the facility. It is necessary to solve these problems to provide peace of mind to parents and children and to realize a safer and more efficient lost child prevention system.

[0722] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0723] In this invention, the server includes means for analyzing data from a display device acquired by a camera at the entrance and generating characteristic information of visitors; means for extracting predetermined characteristic information based on image data transmitted from an information terminal held by a guardian; means for detecting an object that matches the extracted characteristic information and identifying its location using data from display devices acquired in real time from multiple observation devices within the facility; and means for recognizing the emotional state of the guardian using voice data and facial expression data, and dynamically generating and providing support information according to the analysis results. This enables the rapid identification of the lost child's location, the provision of appropriate information to the guardian, and support that takes emotions into consideration.

[0724] An "entrance camera" is a camera installed at the entrance of a facility to capture video data of visitors.

[0725] "Data from a display device" refers to digital information about images and videos acquired by imaging or observation equipment.

[0726] "Visitor characteristic information" refers to facial and body characteristic information used to identify individuals, analyzed from data obtained from the display device.

[0727] "Information devices owned by guardians" refers to communication devices such as mobile phones and smartphones that are carried by parents or guardians and capable of transmitting images and audio.

[0728] An "observation device" is a surveillance camera system installed within a facility to acquire video data in real time and monitor specific targets.

[0729] A "data aggregation device" is an information storage system that stores generated characteristic information for comparison and retrieval.

[0730] "Support information" refers to advice and messages provided to parents and staff to help prevent children from getting lost.

[0731] A "person in charge terminal" is an information terminal carried by facility staff or other personnel to communicate and check on the situation within the facility.

[0732] "Emotional state" refers to information that indicates the psychological state of the caregiver, and is analyzed from voice and facial expression data.

[0733] This invention is a system designed to facilitate the rapid discovery of lost children and the reunion of parents and children, taking into account the emotional state of the parents. The specific configuration and method of use for implementing this invention are described below.

[0734] First, the server receives real-time video data of visitors from a camera installed at the facility's entrance. The server uses image processing software such as OpenCV or dlib to extract facial information from this video data and generate it as digital data. The facial information is stored in a data accumulator and used later for verification.

[0735] On the other hand, the parent, as the user, would use a mobile device (e.g., a smartphone) if their child gets lost. They would launch a dedicated application and either take a picture of their child's face or select an existing image and send it to the server. The mobile device would use a secure protocol to send the image data to the server.

[0736] The server analyzes the received facial image data of the child and compares it with real-time video data acquired from multiple observation devices installed within the facility. Once the target is identified, the server determines its location and notifies the staff member's terminal. This allows the staff member to quickly go to the child's location.

[0737] Furthermore, the server incorporates an emotion engine that analyzes voice and facial expression data acquired from the user's mobile device. The server uses the Natural Language Processing Toolkit (NLTK) and the Emotion API to understand the parent's emotional state. Based on the analysis results, the user is provided with dynamically generated support messages. For example, a message such as "Staff are on their way, so please don't worry" might be used.

[0738] The generated AI model calculates the optimal movement route using map information and observation device placement information within the facility. This route information is immediately provided to the staff member's terminal, enabling a quick response to lost children.

[0739] Examples of specific prompt messages include, "Generate a notification message for the parent based on the latest location information of the lost child at the scene," and "Create a reassuring recommendation message based on the parent's emotional state."

[0740] In this way, this system not only facilitates the rapid discovery of lost children and supports parent-child reunions, but also aims to provide greater peace of mind by taking into consideration the parents' feelings.

[0741] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0742] Step 1:

[0743] The server receives video data of visitors from the camera at the entrance. It takes video data from the camera as input. The server generates facial information using image processing software. The output is digital facial information data. Specifically, the server detects faces within the frame and formats their feature points for use in the database.

[0744] Step 2:

[0745] The user uses a dedicated app on their mobile device to send a facial image of their lost child to the server. The input is the facial image data of the child that has been photographed or selected. The mobile device transmits this data through a secure protocol. The output is the image data received by the server. Specifically, the parent captures an image of their child with the device's camera and presses the send button within the app.

[0746] Step 3:

[0747] The server analyzes the received image data and matches it based on feature information in the database. The input consists of face image data sent by the user and previously acquired face information in the database. The server performs the matching operation using algorithms such as FaceNet. The output is either the ID information of the identified individual or a list of candidates with a high probability of matching. Specifically, it calculates and compares feature vectors to list individuals with high similarity.

[0748] Step 4:

[0749] The server receives real-time video data from observation devices within the facility and identifies targets based on matched facial information. Inputs include video data from the observation devices and characteristic information of identified individuals. Outputs are the location information of the identified targets. Specifically, the server analyzes the streaming video and tracks the individual's location using a facial recognition algorithm.

[0750] Step 5:

[0751] The server incorporates an emotion engine that analyzes voice and facial expression data from the user's mobile device. Input consists of voice data and a video of the user's facial expressions. The server performs natural language processing and facial expression analysis to identify the emotional state. The output is an analysis result indicating the user's emotions. Specifically, it quantifies the emotional state through voice tone analysis and facial expression extraction.

[0752] Step 6:

[0753] The server generates support messages to provide to the user and calculates the optimal route based on the results of sentiment analysis. Inputs include sentiment analysis results, facility map information, and observation device placement information. A generation AI model dynamically generates the necessary support messages based on prompts. Outputs include support messages for the user and information on the staff member's movement route. Specifically, this process includes constructing message content and calculating route data.

[0754] Step 7:

[0755] The server notifies the nearest staff terminal of the generated information and sends a response to the parent. Inputs are support messages and routing information. The server transmits this information to each terminal via the network. Outputs are notifications to staff terminals and messages to the parent. Specifically, messages are displayed on a display device to help staff take immediate action to respond to the lost child.

[0756] (Application Example 2)

[0757] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0758] In public facilities and event venues, there is a need for the swift discovery and early reunion of lost children with their parents. However, conventional systems struggle to respond immediately to the anxiety and impatience of parents. Furthermore, the inability to conduct searches for lost children quickly and efficiently is a source of dissatisfaction and stress for facility users.

[0759] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0760] In this invention, the server includes means for analyzing image information acquired by an image acquisition device in the entrance area and generating facial data of people; means for extracting specific facial data based on image information transmitted from a mobile device held by a guardian; means for identifying an object that matches the extracted facial data and determining its location using image information acquired in real time from multiple monitoring mechanisms in the space; and means for analyzing the emotional state from the guardian's voice and facial information, generating a message corresponding to that emotional state, and transmitting it to the guardian's mobile device. This makes it possible to quickly detect and identify lost children, as well as provide reassuring support that corresponds to the parent's emotional state, thereby improving the overall service quality of the facility.

[0761] An "image acquisition device" is a device used to acquire image information of people in a specific area.

[0762] "Facial data" refers to a collection of specific feature information extracted to identify a person's face.

[0763] A "personal information terminal" is an electronic device that a user can carry with them and that is used for communication and information processing.

[0764] A "monitoring mechanism" is equipment installed to acquire image information in real time at a specific location within a space.

[0765] A "worker terminal" is an electronic device used by on-site workers for information processing and communication.

[0766] "Analyzing emotional state" is the process of determining the current psychological and emotional state of a parent or guardian based on information obtained from their voice and facial expressions.

[0767] "Generating a message" means creating an appropriate notification for the user based on the analysis results.

[0768] The system implementing this invention begins with an image acquisition device installed in the entrance area capturing image information of visitors and transmitting it to a server. The server then uses facial recognition software to generate facial data for each person and stores it in a database.

[0769] Next, when a parent reports a lost child using a mobile device, image information of the child is sent from the device to the server. Based on this information, the server extracts specific facial data and compares it with real-time image information constantly transmitted from the monitoring system. OpenCV and other similar technologies are used for facial recognition.

[0770] The server combines location information and map information provided by the monitoring mechanism and uses a generation AI platform to calculate the optimal travel route. As a result, the location information of the identified target is notified to the nearest worker terminal.

[0771] The parent's mobile device transmits voice and facial expression information to a server, which then analyzes their emotional state. An emotion analysis engine, such as the Microsoft Azure Emotion API, is used. Based on the analysis results, the server generates an appropriate message corresponding to the parent's emotional state and sends it to the parent's device.

[0772] For example, if a child gets lost in a theme park and the parents are worried, the server will quickly send a message such as, "Please rest assured, the nearest staff member is checking on your child." In this way, the service goes beyond simple location tracking and can provide emotionally resonant support.

[0773] Example prompt: "Generate a message to alleviate parental anxiety at a festival venue in a smart city."

[0774] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0775] Step 1:

[0776] Image acquisition devices in the entrance area capture images of visitors and send them to a server. The server receives this image information as input, extracts facial data using facial recognition software, and stores it in a database. This process identifies the facial information of each visitor.

[0777] Step 2:

[0778] Parents use a personal digital assistant (PDCA) to report their child as missing. The PDCA sends image information of the child as input to a server. The server receives this information, performs facial recognition again to extract specific facial data, and compares it with information in an existing database. This identifies the lost child.

[0779] Step 3:

[0780] The server receives real-time image information provided by the monitoring mechanism. Using this as input, it compares it with already extracted face data to identify matching targets. The server then generates and records the location information of the identified targets as output. This allows the child's current location to be determined.

[0781] Step 4:

[0782] The server receives location and map information of the monitoring system as input and uses a generated AI model to calculate the optimal travel route. This calculation outputs an efficient travel route and sends it to the worker's terminal. This enables rapid response to lost workers.

[0783] Step 5:

[0784] The parent's mobile device collects voice and facial expression information and sends it to a server. The server receives this as input and analyzes the emotional state using an emotion analysis engine. Based on the analysis results, it generates an appropriate message as output and sends it to the device. This process provides emotional support to alleviate parental anxiety.

[0785] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0786] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0787] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0788] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0789] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0790] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0791] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0792] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0793] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0794] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0795] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0796] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0797] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0798] 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.

[0799] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0800] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0801] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0802] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0803] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0804] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0805] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0806] The following is further disclosed regarding the embodiments described above.

[0807] (Claim 1)

[0808] A means for analyzing video data acquired by a camera at the entrance and generating facial information of visitors,

[0809] A means for extracting predetermined facial information based on image data transmitted from a mobile device owned by a parent,

[0810] A means for detecting objects that match extracted facial information using video data acquired in real time from multiple monitoring devices within the facility, and for determining their location,

[0811] A means of notifying the nearest staff terminal of the location information of the detected target,

[0812] A system that includes this.

[0813] (Claim 2)

[0814] The system according to claim 1, which matches the generated visitor facial information with the extracted facial information in a database.

[0815] (Claim 3)

[0816] The system according to claim 1, which calculates the optimal travel route based on the location information and map information of each monitoring device within the facility and provides it to the staff terminal.

[0817] "Example 1"

[0818] (Claim 1)

[0819] A means for analyzing image information acquired by a camera at the entrance and generating characteristic information of visitors,

[0820] A means for extracting predetermined characteristic information based on image information transmitted from a mobile device owned by the user,

[0821] A means for detecting objects that match extracted feature information using image information acquired in real time from multiple observation devices within the facility, and for determining their location,

[0822] A means of notifying the nearest worker's terminal of the location information of the detected target,

[0823] A system that includes this.

[0824] (Claim 2)

[0825] The system according to claim 1, which compares the generated visitor characteristic information with the extracted characteristic information in a storage device.

[0826] (Claim 3)

[0827] The system according to claim 1, which calculates the optimal travel route based on the location information and map information of each observation device within the facility and provides it to the worker's terminal.

[0828] "Application Example 1"

[0829] (Claim 1)

[0830] A means for analyzing visual data acquired by a visual device at the entrance and generating image information of visitors,

[0831] A means for extracting predetermined image information based on visual data transmitted from a portable device owned by the parent,

[0832] A means for detecting objects that match extracted image information using visual data acquired in real time from multiple monitoring devices within a public facility, and for determining their location,

[0833] A means for notifying the nearest worker terminal of the location information of the detected target,

[0834] A means for calculating the optimal movement route based on the detailed placement and map information of each monitoring device within a public facility and providing it to the worker's terminal,

[0835] A system that includes this.

[0836] (Claim 2)

[0837] The system according to claim 1, which matches generated visitor image information with extracted image information in a data store.

[0838] (Claim 3)

[0839] The system according to claim 1, which generates a movement route for workers to reach their destinations safely and quickly within a facility using an AI model and provides it to the worker along with a prompt message.

[0840] "Example 2 of combining an emotion engine"

[0841] (Claim 1)

[0842] A means for analyzing data from a display device acquired by a camera at the entrance and generating characteristic information of visitors,

[0843] A means for extracting predetermined characteristic information based on image data transmitted from an information terminal owned by a guardian,

[0844] A means for detecting an object that matches the extracted feature information using data from display devices acquired in real time from multiple observation devices within the facility, and for determining its location,

[0845] A means for recognizing the emotional state of guardians using voice data and facial expression data, and dynamically generating and providing support information according to the analysis results,

[0846] A means of notifying the nearest staff member's terminal of the generated support information,

[0847] A system that includes this.

[0848] (Claim 2)

[0849] The system according to claim 1, wherein the generated visitor characteristic information and the extracted characteristic information are compared within a data aggregation device.

[0850] (Claim 3)

[0851] The system according to claim 1, which calculates the optimal travel route based on the location information and map information of each observation device within the facility and provides it to the terminal of the person in charge.

[0852] "Application example 2 when combining with an emotional engine"

[0853] (Claim 1)

[0854] A means for analyzing image information acquired by an image acquisition device in the entrance area and generating facial data of individuals,

[0855] A means for extracting specific facial data based on image information transmitted from a mobile device owned by a guardian,

[0856] A means for identifying an object that matches extracted face data and determining its position, using image information acquired in real time from multiple monitoring mechanisms within a space,

[0857] A means of notifying the nearest worker's terminal of the extracted target's location information,

[0858] A means for analyzing the emotional state of a parent from their voice and facial expression information, generating a message corresponding to that emotional state, and sending it to the parent's mobile device,

[0859] A system that includes this.

[0860] (Claim 2)

[0861] The system according to claim 1, wherein the generated facial information of a person and the extracted facial data are compared within the data management unit.

[0862] (Claim 3)

[0863] The system according to claim 1, which calculates the optimal movement route based on the placement information and map information of each monitoring mechanism in the space and provides it to the worker's terminal. [Explanation of Symbols]

[0864] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for analyzing video data acquired by a camera at the entrance and generating facial information of visitors, A means for extracting predetermined facial information based on image data transmitted from a mobile device owned by a parent, A means for detecting objects that match extracted facial information using video data acquired in real time from multiple monitoring devices within the facility, and for determining their location, A means of notifying the nearest staff terminal of the location information of the detected target, A system that includes this.

2. The system according to claim 1, which matches the generated visitor facial information with the extracted facial information in a database.

3. The system according to claim 1, which calculates the optimal travel route based on the location information and map information of each monitoring device within the facility and provides it to the staff terminal.

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