system

Abstract

The system according to this embodiment aims to quickly locate a child who has gotten lost in a shopping center and reunite them with their parents. [Solution] The system according to the embodiment comprises a reception unit, a storage unit, an analysis unit, and a notification unit. The reception unit allows parents to register their child's face. The storage unit stores the face data registered by the reception unit. The analysis unit analyzes video footage from surveillance cameras in the shopping center to determine the child's location. The notification unit sends a notification to the parents and staff when a child is found missing.

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, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to quickly find a child who has become lost in a shopping center and reunite them with their parent.

[0005] The system according to the embodiment aims to quickly find a child who has become lost in a shopping center and reunite them with their parent.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a storage unit, an analysis unit, and a notification unit. The reception unit allows parents to register their child's face. The storage unit stores the face data registered by the reception unit. The analysis unit analyzes video footage from surveillance cameras within the shopping center to determine the child's location. The notification unit sends notifications to parents and staff when a child is found missing. [Effects of the Invention]

[0007] The system according to this embodiment can quickly locate a child who has gotten lost in a shopping center and reunite them with their parents. [Brief explanation of the drawing]

[0008] [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. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 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.

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

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

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

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) The lost child prevention system according to an embodiment of the present invention is a system for solving the problem of children getting lost in shopping centers. This system aims to quickly locate lost children and reunite them with their parents by combining facial recognition technology and real-time tracking functionality. First, when entering the shopping center, parents register their child's face in the system. This facial recognition registration system is designed to allow parents to easily register their child's face. For example, they can take a photo of their child's face using a dedicated terminal or smartphone app and upload it to the system. This stores the child's facial data in the system. Next, the system uses surveillance cameras installed in the shopping center to track the child's location in real time. Surveillance cameras are placed in various areas of the shopping center and determine the child's location by recognizing their face. For example, if a child enters a specific store, this information is sent to the system and notified to the parents and staff. Furthermore, if a child gets lost, the system immediately sends an alert notification to the parents and staff. This alert notification includes the child's current location and movement route, enabling parents and staff to respond quickly. For example, a notification is sent to the parent's smartphone, allowing them to check the child's location on a map. Staff are also notified via a dedicated terminal or app, enabling them to respond quickly. This system enhances parents' peace of mind while shopping and reduces the risk of children getting lost. It also reduces the burden of safety management at shopping centers, leading to improved overall customer satisfaction. For example, parents can enjoy shopping with peace of mind, potentially increasing visitor numbers and boosting sales. This system addresses the advancements in facial recognition technology and growing customer safety awareness, and demand is expected to increase significantly. For instance, the large shopping center market is projected to grow to $200 billion by 2025, and implementing this system can enhance competitiveness. Thus, this lost-child prevention system effectively solves the problem of children getting lost in shopping centers, improving the sense of security for both parents and children.

[0029] The lost child prevention system according to this embodiment comprises a reception unit, a storage unit, an analysis unit, and a notification unit. The reception unit allows parents to register their child's face. Methods for parents to register their child's face include, for example, using a dedicated terminal or a smartphone app. The reception unit, for example, takes a photo of the child's face using a dedicated terminal and uploads it to the system. Alternatively, the reception unit can take a photo of the child's face using a smartphone app and upload it to the system. The storage unit stores the face data registered by the reception unit. The storage unit, for example, encrypts and stores the registered face data. The storage unit can use encryption algorithms such as AES or RSA for data encryption. The analysis unit analyzes video from surveillance cameras in the shopping center to determine the child's location. The analysis unit, for example, analyzes video from surveillance cameras in real time to recognize the child's face and determine their location. The analysis unit can use deep learning technology for real-time analysis. The notification unit sends a notification to the parents and staff when a child is lost. The notification unit sends alert notifications to parents and staff, for example, including the child's current location and travel route. The notification unit can also send notifications to parents' smartphones, allowing them to check the child's location on a map. Furthermore, the notification unit can send notifications to staff via a dedicated terminal or app, enabling a quick response. As a result, the lost child prevention system according to this embodiment allows parents to register their child's face and respond quickly when the child goes missing.

[0030] The reception desk registers the child's face. There are several ways for parents to register their child's face, including using a dedicated terminal or a smartphone app. Specifically, dedicated terminals are installed at the entrances and information desks of shopping centers and public facilities. Parents use these terminals to take a photo of their child's face and upload it to the system. The terminals are equipped with high-resolution cameras that can capture facial features in detail. Alternatively, parents download the smartphone app, follow the in-app guides to take a photo of their child's face, and upload it to the system. The app includes features to guide parents on appropriate angles and lighting when taking photos, helping to improve the accuracy of face recognition. Furthermore, after the photo is uploaded, the reception desk automatically analyzes the facial data, extracts facial feature points, and registers them in the database. This process is designed to be easy for parents to use and can be completed quickly. This allows the reception desk to quickly and accurately register their child's face, enabling smooth initial setup of the lost child prevention system.

[0031] The storage unit stores the facial data registered by the reception unit. For example, the storage unit encrypts and stores the registered facial data. Specifically, before storing the facial data, the storage unit encrypts the data using strong encryption algorithms such as AES (Advanced Encryption Standard) or RSA (Rivest-Shamir-Adleman). AES is a symmetric-key cryptography method that can encrypt data quickly and securely. RSA, on the other hand, is a public-key cryptography method used to enhance data confidentiality. The storage unit stores the encrypted data on a secure server and implements security measures to prevent unauthorized access and data leakage. For example, the storage unit restricts access to the database, allowing only authenticated users to access it. The storage unit also regularly backs up the data to prepare for data loss or corruption. Furthermore, the storage unit sets data retention periods and appropriately deletes unnecessary data to ensure thorough data management. This allows the storage unit to manage registered facial data safely and efficiently, enhancing the reliability of the lost child prevention system.

[0032] The analysis unit analyzes video footage from surveillance cameras within the shopping center to pinpoint the child's location. For example, the analysis unit analyzes video footage from surveillance cameras in real time, recognizes the child's face, and determines their location. Specifically, the analysis unit uses deep learning technology to build a face recognition model and analyzes video footage from surveillance cameras. Deep learning technology trains the model using a large amount of face image data to achieve highly accurate face recognition. The analysis unit processes video footage from surveillance cameras in real time, detecting the child's face and acquiring their location information. Furthermore, the analysis unit can integrate video footage from multiple surveillance cameras to track the child's movement path. This allows the analysis unit to quickly locate a lost child and notify parents or staff. In addition, the analysis unit performs correction processing that takes into account environmental factors such as lighting conditions and camera angles to improve the accuracy of face recognition. For example, if the lighting is insufficient, it adjusts the brightness of the image to make facial features clearer. This enables the analysis unit to achieve highly accurate face recognition under various environmental conditions, improving the reliability of the lost child prevention system.

[0033] The notification unit sends notifications to parents and staff when a child goes missing. For example, the notification unit sends alert notifications to parents and staff that include the child's current location and travel route. Specifically, based on the child's location information obtained from the analysis unit, the notification unit sends push notifications to parents' smartphones. These notifications include a map showing the child's current location and information about the last place the child was seen. Parents can check their child's location in real time through a smartphone app and respond quickly. The notification unit also sends notifications to shopping center staff via dedicated terminals or apps. Upon receiving the notification, staff can check the child's current location and travel route and respond quickly. Furthermore, the notification unit saves a history of notifications sent for later review. This allows for recording the response to lost child incidents, which can be used for subsequent review and improvement. The notification unit can also reliably transmit information using multiple communication methods. For example, it uses not only push notifications but also SMS, email, and voice calls to ensure important information is delivered reliably. This allows the notification unit to provide information to parents and staff quickly and reliably, supporting responses to lost child incidents.

[0034] The reception desk can take a photo of a child's face using a dedicated terminal or smartphone app and upload it to the system. For example, the reception desk can take a photo of a child's face using a dedicated terminal and upload it to the system. The reception desk can also take a photo of a child's face using a smartphone app and upload it to the system. This makes it easy for parents to register their child's face. The dedicated terminal or smartphone app includes, for example, the functions of a compatible OS and app. Some or all of the above processing at the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can take a photo of a child's face, analyze the facial data using AI, and upload it to the system.

[0035] The storage unit can encrypt and store registered facial data. For example, the storage unit can encrypt and store registered facial data. The storage unit can use encryption algorithms such as AES or RSA for data encryption. This improves the security of facial data. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can use AI to select an algorithm for encrypting facial data and then perform the encryption.

[0036] The analysis unit can analyze video from surveillance cameras in real time and determine the child's location. For example, the analysis unit can analyze video from surveillance cameras in real time, recognize the child's face, and determine their location. The analysis unit can use deep learning technology for real-time analysis. This allows for rapid identification of the child's location. The method of real-time analysis includes, for example, the technology used and the latency. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input video from surveillance cameras into AI, which can recognize the child's face and determine their location.

[0037] The notification unit can send alert notifications to parents and staff, including the child's current location and travel route. For example, the notification unit can send alert notifications to parents and staff, including the child's current location and travel route. The notification unit can also send notifications to parents' smartphones, allowing them to view the child's location on a map. Furthermore, the notification unit can send notifications to staff via dedicated terminals or apps, enabling a quick response. This allows parents and staff to respond promptly. Alert notifications include, for example, the content and method of sending the notification. Some or all of the above processing in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can input the child's current location and travel route into an AI, which can then generate and send an alert notification.

[0038] The notification unit can send a notification to the parent's smartphone, allowing them to view the child's location on a map. For example, the notification unit can send a notification to the parent's smartphone, allowing them to view the child's location on a map. This allows the parent to view the child's location on a map. The method of viewing the location on a map includes, for example, the type of map application used. Some or all of the above processing in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can input the child's location data into AI, which can then generate a notification to display the location on a map.

[0039] The notification unit can send notifications to staff via dedicated terminals or apps to enable them to respond quickly. For example, the notification unit can send notifications to staff via dedicated terminals or apps to enable them to respond quickly. This allows staff to respond quickly. The dedicated terminals or apps include, for example, the functions of a corresponding OS or app. Some or all of the above-described processes in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can use AI to select the optimal method for sending notifications to staff terminals and then send the notifications.

[0040] The reception desk can suggest the optimal registration method by referring to the parent's past registration history during face registration. For example, the reception desk may prioritize suggesting registration methods previously used by the parent (e.g., smartphone apps or dedicated terminals). The reception desk can also suggest a similar procedure by referring to the child's face data previously registered by the parent. The reception desk can also suggest the optimal registration method by considering the time and location where the parent previously registered. This allows the reception desk to suggest the optimal registration method based on the parent's past registration history. The optimal registration method may include, for example, a method for analyzing past registration history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the parent's past registration history data into AI, which can then suggest the optimal registration method.

[0041] The reception desk can customize the registration process based on the child's age and gender during face registration. For example, if the child is a toddler, the reception desk provides a simple interface that parents can easily use. For example, if the child is a primary school student, the reception desk can also provide a customizable interface that allows parents to input more detailed information. For example, if the child is of a specific gender, the reception desk can provide an interface design appropriate for that gender. This allows for a registration process tailored to the child's age and gender. Methods for customizing the registration process include, for example, changing the procedure based on age and gender. Some or all of the above processes in the reception desk may be performed using AI, or not. For example, the reception desk can input the child's age and gender data into the AI, which can then customize the optimal registration process.

[0042] The reception desk can suggest the optimal registration location when registering a face, taking into account the parent's geographical location information. For example, if the parent is in a specific area within a shopping center, the reception desk can suggest the registration terminal closest to that area. If the parent is near the entrance of the shopping center, the reception desk can also suggest a registration terminal near the entrance. If the parent is in a specific store, the reception desk can also suggest the registration terminal closest to that store. This allows the system to suggest the optimal registration location based on the parent's geographical location information. Geographical location information includes, for example, GPS data and location services. Some or all of the above processing at the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the parent's geographical location data into the AI, which can then suggest the optimal registration location.

[0043] The reception desk can analyze the parent's social media activity during face registration and provide relevant registration information. For example, if the parent has posted about a shopping center on social media, the reception desk can provide registration information based on the content of that post. The reception desk can also provide registration information based on the content of the parent's posts about their child on social media. The reception desk can also provide registration information related to an event if the parent has participated in a specific event on social media. This allows the reception desk to provide relevant registration information based on the parent's social media activity. Social media activity includes, for example, posts and follower information. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the parent's social media data into an AI, and the AI ​​can provide relevant registration information.

[0044] The storage unit can apply the optimal storage algorithm by referring to previously stored data when saving data. For example, the storage unit can refer to the format of previously saved data and save the data in a similar format. The storage unit can also refer to previously used storage algorithms and apply the optimal algorithm. The storage unit can also analyze the history of past data storage and select the most efficient storage method. This allows the application of the optimal storage algorithm based on previously stored data. The optimal storage algorithm includes, for example, the type of data and the purpose of storage. Some or all of the above processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input previously stored data into AI, which can then select and apply the optimal storage algorithm.

[0045] The storage unit can adjust the level of detail in data storage based on the importance of the data. For example, the storage unit can store highly important data through a detailed storage procedure. For example, it can also store less important data through a simplified storage procedure. The storage unit can also dynamically adjust the level of detail in storage according to the importance of the data. This allows for a level of detail in storage that corresponds to the importance of the data. The level of detail in storage includes, for example, the importance of the data and the storage period. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the importance of the data into the AI, and the AI ​​can adjust the level of detail in storage.

[0046] The storage unit can select the optimal storage location when saving data, taking into account the parent's geographical location information. For example, if the parent is in a specific area within a shopping center, the storage unit can select the storage server closest to that area. If the parent is near the entrance of the shopping center, the storage unit can also select a storage server near the entrance. If the parent is in a specific store, the storage unit can also select the storage server closest to that store. This allows the storage unit to select the optimal storage location based on the parent's geographical location information. The optimal storage location includes, for example, geographical location information and data type. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the parent's geographical location data into AI, and the AI ​​can select the optimal storage location.

[0047] The storage unit can analyze the parent's social media activity when saving data and provide relevant stored information. For example, if a parent posts about a shopping center on social media, the storage unit can provide stored information based on the content of that post. For example, if a parent posts about their child on social media, the storage unit can also provide stored information based on the content of that post. For example, if a parent participates in a specific event on social media, the storage unit can also provide stored information related to that event. This allows the storage unit to provide relevant stored information based on the parent's social media activity. Social media activity includes, for example, post content and follower information. Some or all of the above processing in the storage unit may be performed using, for example, AI, or not using AI. For example, the storage unit can input the parent's social media data into AI, and the AI ​​can provide relevant stored information.

[0048] The analysis unit can optimize the analysis algorithm by referring to past analysis data during the analysis process. For example, the analysis unit can select the optimal analysis algorithm based on previously analyzed data. The analysis unit can also adjust the analysis algorithm by referring to past analysis results. For example, the analysis unit can analyze past analysis history and select the most efficient analysis method. This allows the optimal analysis algorithm to be applied based on past analysis data. The analysis algorithm includes, for example, methods for analyzing past data and criteria for selecting the algorithm. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past analysis data into AI, which can then select and apply the optimal analysis algorithm.

[0049] The analysis unit can improve the accuracy of its analysis based on the child's behavior patterns during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to the child's past behavior patterns. The analysis unit can also improve accuracy by analyzing the child's current behavior patterns in real time. The analysis unit can also improve the accuracy by learning the child's behavior patterns and optimizing the analysis algorithm. This allows the analysis accuracy to be improved based on the child's behavior patterns. Behavior patterns include, for example, movement history and behavior frequency. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the child's behavior pattern data into AI, which can learn the behavior patterns and improve the accuracy of the analysis.

[0050] The analysis unit can perform analysis while considering the geographical distribution of children. For example, if children are concentrated in a particular area, the analysis unit will focus its analysis on that area. For example, if children are widely distributed, the analysis unit can also perform an overall analysis. For example, the analysis unit can analyze the geographical distribution of children in real time and select the optimal analysis method. This allows for optimal analysis based on the geographical distribution of children. Geographical distribution includes, for example, location information and the range of distribution. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution data of children into AI, which can then select the optimal analysis method and perform the analysis.

[0051] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis unit can optimize its analysis algorithm by referring to relevant academic papers. The analysis unit can also improve the accuracy of its analysis by referring to relevant technical literature. The analysis unit can also improve its analysis method by referring to relevant patent documents. In this way, the accuracy of the analysis can be improved by referring to relevant literature. Relevant literature includes, for example, the type of literature and the method of reference. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature data into AI, and the AI ​​can refer to the literature to improve the accuracy of the analysis.

[0052] The notification unit can select the optimal notification method by referring to past notification history when a notification is sent. For example, the notification unit may prioritize notification methods used in the past (e.g., SMS, app notifications). The notification unit may also select the most effective notification method by referring to past notification history. The notification unit may also analyze past notification history and select the notification method that parents are most likely to respond to. This allows the optimal notification method to be selected based on past notification history. The optimal notification method may include, for example, past notification history and the purpose of the notification. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit may input past notification history data into AI, and the AI ​​may select the optimal notification method.

[0053] The notification unit can adjust the level of detail of a notification based on the child's current location and travel path. For example, if the child is in a specific area, the notification unit will notify the child of detailed information about that area. For example, if the child is on the move, the notification unit can also notify the child of detailed information about their travel path. The notification unit can also provide the most appropriate notification content based on the child's current location. This allows the notification unit to provide the most appropriate notification content based on the child's current location and travel path. The level of detail of the notification includes, for example, the current location and travel path. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the child's current location and travel path data into the AI, which can then generate the most appropriate notification content.

[0054] The notification unit can select the optimal notification method when sending a notification, taking into account the parent's geographical location information. For example, if the parent is in a specific area within a shopping center, the notification unit can select the most suitable notification method for that area. For example, if the parent is near the entrance of a shopping center, the notification unit can also select a notification method that is easily received near the entrance. For example, if the parent is in a specific store, the notification unit can also select the most suitable notification method for that store. This allows the system to select the optimal notification method based on the parent's geographical location information. Geographical location information includes, for example, GPS data and location services. Some or all of the processing described above in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can input the parent's geographical location data into an AI, which can then select the optimal notification method.

[0055] The notification unit can analyze the parent's social media activity and provide relevant notification information when issuing a notification. For example, if the parent has posted about a shopping center on social media, the notification unit can provide notification information based on the content of that post. The notification unit can also provide notification information based on the content of a parent's post about their child on social media. The notification unit can also provide notification information related to an event if the parent has participated in a specific event on social media. This allows the notification unit to provide relevant notification information based on the parent's social media activity. Social media activity includes, for example, posts and follower information. Some or all of the processing described above in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the parent's social media data into AI, and the AI ​​can provide relevant notification information.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The lost child prevention system can also be equipped with a voice recognition unit. When a parent or staff member calls out the child's name, the voice recognition unit analyzes the voice and can pinpoint the child's location. For example, if a parent calls out their child's name via a smartphone app, the voice recognition unit analyzes the voice and compares it with surveillance camera footage to pinpoint the child's location. The voice recognition unit can also work in conjunction with the shopping center's speaker system to broadcast the parent's voice to the lost child. This allows the child to hear their parent's voice and let them know their location. Furthermore, when a child gets lost, the voice recognition unit can automatically respond to voice instructions from a parent or staff member. For example, if a parent voice-instructs "My child is lost," the system can immediately issue an alert and begin the process of locating the child.

[0058] The lost child prevention system can also be equipped with a biometric authentication unit. This unit can register biometric information such as a child's fingerprints or iris scans and use it in conjunction with facial recognition to pinpoint the child's location. For example, if a parent registers their child's fingerprints using a dedicated terminal, the system stores the fingerprint data and compares it with surveillance camera footage to determine the child's location. Furthermore, the biometric authentication unit can work in conjunction with fingerprint or iris recognition devices installed in specific areas of a shopping center, automatically authenticating the child as they pass through those areas. This allows for accurate location identification using multiple authentication methods, even in cases where facial recognition alone is insufficient. Additionally, the biometric authentication unit can automatically suggest the most suitable authentication method when a parent registers their child's biometric information. For example, if a parent prefers fingerprint authentication, the system guides them through the fingerprint authentication process and supports registration.

[0059] The lost child prevention system can also be equipped with a behavior prediction unit. The behavior prediction unit can analyze a child's past behavioral data and predict areas where the child is likely to get lost. For example, if the system analyzes a child's past movement history and finds that the child tends to get lost in a particular area, it will focus its monitoring on that area. The behavior prediction unit can also predict areas where the child is at high risk of getting lost based on the child's age, gender, and interests. For example, if a young child is likely to get lost in a playground area, that area will be monitored preferentially. Furthermore, the behavior prediction unit can automatically suggest risk areas when parents register their child's behavioral patterns. For example, if parents register their child's favorite places or frequently visited areas, the system can use that information to predict areas where the child is at high risk of getting lost and strengthen monitoring.

[0060] The lost child prevention system can also be equipped with an emergency response unit. This unit can execute protocols for a rapid response when a child goes missing. For example, as soon as the system locates the child, it can send an emergency notification to all staff in the shopping center, instructing them to respond. The emergency response unit can also automatically notify the nearest staff when a parent reports a lost child, enabling a quick response. Furthermore, the emergency response unit can integrate with the shopping center's emergency broadcasting system to broadcast the parent's voice to the lost child, allowing the child to hear their parent and report their location. Additionally, the emergency response unit can be initiated by the parent via a smartphone app. For example, if the parent voice-commands "Initiate emergency response," the system can immediately execute the emergency response protocol and begin the process of locating the child.

[0061] The lost child prevention system can also be equipped with an environmental recognition unit. This unit can collect environmental information within the shopping center and use it to help locate children. For example, the environmental recognition unit can monitor the temperature, humidity, and lighting conditions within the shopping center in real time to increase the likelihood that a child is in a specific area. The environmental recognition unit can also analyze the flow of people and congestion levels within the shopping center to identify areas where children are at high risk of getting lost. For example, crowded areas are more likely to be where children get lost, so these areas can be monitored intensively. Furthermore, the environmental recognition unit can automatically suggest the optimal monitoring method when parents register their child's environmental information. For example, if parents register their child's preferred environmental conditions (e.g., bright or cool places), the system can enhance monitoring based on that information.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk registers the child's face. Parents can register their child's face using a dedicated terminal or a smartphone app. For example, they can take a photo of their child's face using a dedicated terminal and upload it to the system. Alternatively, they can take a photo of their child's face using a smartphone app and upload it to the system. Step 2: The storage unit stores the facial data registered by the reception unit. The storage unit encrypts and stores the registered facial data. Encryption algorithms such as AES or RSA can be used for data encryption. Step 3: The analysis unit analyzes the video from the security cameras in the shopping center to determine the child's location. The analysis unit analyzes the video from the security cameras in real time, recognizes the child's face, and determines their location. Deep learning technology can be used for real-time analysis. Step 4: The notification unit sends notifications to parents and staff when a child goes missing. The notification unit sends alert notifications to parents and staff that include the child's current location and travel route. Notifications can be sent to parents' smartphones so they can check the child's location on a map. Notifications can also be sent to staff via dedicated terminals or apps so they can respond quickly.

[0064] (Example of form 2) The lost child prevention system according to an embodiment of the present invention is a system for solving the problem of children getting lost in shopping centers. This system aims to quickly locate lost children and reunite them with their parents by combining facial recognition technology and real-time tracking functionality. First, when entering the shopping center, parents register their child's face in the system. This facial recognition registration system is designed to allow parents to easily register their child's face. For example, they can take a photo of their child's face using a dedicated terminal or smartphone app and upload it to the system. This stores the child's facial data in the system. Next, the system uses surveillance cameras installed in the shopping center to track the child's location in real time. Surveillance cameras are placed in various areas of the shopping center and determine the child's location by recognizing their face. For example, if a child enters a specific store, this information is sent to the system and notified to the parents and staff. Furthermore, if a child gets lost, the system immediately sends an alert notification to the parents and staff. This alert notification includes the child's current location and movement route, enabling parents and staff to respond quickly. For example, a notification is sent to the parent's smartphone, allowing them to check the child's location on a map. Staff are also notified via a dedicated terminal or app, enabling them to respond quickly. This system enhances parents' peace of mind while shopping and reduces the risk of children getting lost. It also reduces the burden of safety management at shopping centers, leading to improved overall customer satisfaction. For example, parents can enjoy shopping with peace of mind, potentially increasing visitor numbers and boosting sales. This system addresses the advancements in facial recognition technology and growing customer safety awareness, and demand is expected to increase significantly. For instance, the large shopping center market is projected to grow to $200 billion by 2025, and implementing this system can enhance competitiveness. Thus, this lost-child prevention system effectively solves the problem of children getting lost in shopping centers, improving the sense of security for both parents and children.

[0065] The lost child prevention system according to this embodiment comprises a reception unit, a storage unit, an analysis unit, and a notification unit. The reception unit allows parents to register their child's face. Methods for parents to register their child's face include, for example, using a dedicated terminal or a smartphone app. The reception unit, for example, takes a photo of the child's face using a dedicated terminal and uploads it to the system. Alternatively, the reception unit can take a photo of the child's face using a smartphone app and upload it to the system. The storage unit stores the face data registered by the reception unit. The storage unit, for example, encrypts and stores the registered face data. The storage unit can use encryption algorithms such as AES or RSA for data encryption. The analysis unit analyzes video from surveillance cameras in the shopping center to determine the child's location. The analysis unit, for example, analyzes video from surveillance cameras in real time to recognize the child's face and determine their location. The analysis unit can use deep learning technology for real-time analysis. The notification unit sends a notification to the parents and staff when a child is lost. The notification unit sends alert notifications to parents and staff, for example, including the child's current location and travel route. The notification unit can also send notifications to parents' smartphones, allowing them to check the child's location on a map. Furthermore, the notification unit can send notifications to staff via a dedicated terminal or app, enabling a quick response. As a result, the lost child prevention system according to this embodiment allows parents to register their child's face and respond quickly when the child goes missing.

[0066] The reception desk registers the child's face. There are several ways for parents to register their child's face, including using a dedicated terminal or a smartphone app. Specifically, dedicated terminals are installed at the entrances and information desks of shopping centers and public facilities. Parents use these terminals to take a photo of their child's face and upload it to the system. The terminals are equipped with high-resolution cameras that can capture facial features in detail. Alternatively, parents download the smartphone app, follow the in-app guides to take a photo of their child's face, and upload it to the system. The app includes features to guide parents on appropriate angles and lighting when taking photos, helping to improve the accuracy of face recognition. Furthermore, after the photo is uploaded, the reception desk automatically analyzes the facial data, extracts facial feature points, and registers them in the database. This process is designed to be easy for parents to use and can be completed quickly. This allows the reception desk to quickly and accurately register their child's face, enabling smooth initial setup of the lost child prevention system.

[0067] The storage unit stores the facial data registered by the reception unit. For example, the storage unit encrypts and stores the registered facial data. Specifically, before storing the facial data, the storage unit encrypts the data using strong encryption algorithms such as AES (Advanced Encryption Standard) or RSA (Rivest-Shamir-Adleman). AES is a symmetric-key cryptography method that can encrypt data quickly and securely. RSA, on the other hand, is a public-key cryptography method used to enhance data confidentiality. The storage unit stores the encrypted data on a secure server and implements security measures to prevent unauthorized access and data leakage. For example, the storage unit restricts access to the database, allowing only authenticated users to access it. The storage unit also regularly backs up the data to prepare for data loss or corruption. Furthermore, the storage unit sets data retention periods and appropriately deletes unnecessary data to ensure thorough data management. This allows the storage unit to manage registered facial data safely and efficiently, enhancing the reliability of the lost child prevention system.

[0068] The analysis unit analyzes video footage from surveillance cameras within the shopping center to pinpoint the child's location. For example, the analysis unit analyzes video footage from surveillance cameras in real time, recognizes the child's face, and determines their location. Specifically, the analysis unit uses deep learning technology to build a face recognition model and analyzes video footage from surveillance cameras. Deep learning technology trains the model using a large amount of face image data to achieve highly accurate face recognition. The analysis unit processes video footage from surveillance cameras in real time, detecting the child's face and acquiring their location information. Furthermore, the analysis unit can integrate video footage from multiple surveillance cameras to track the child's movement path. This allows the analysis unit to quickly locate a lost child and notify parents or staff. In addition, the analysis unit performs correction processing that takes into account environmental factors such as lighting conditions and camera angles to improve the accuracy of face recognition. For example, if the lighting is insufficient, it adjusts the brightness of the image to make facial features clearer. This enables the analysis unit to achieve highly accurate face recognition under various environmental conditions, improving the reliability of the lost child prevention system.

[0069] The notification unit sends notifications to parents and staff when a child goes missing. For example, the notification unit sends alert notifications to parents and staff that include the child's current location and travel route. Specifically, based on the child's location information obtained from the analysis unit, the notification unit sends push notifications to parents' smartphones. These notifications include a map showing the child's current location and information about the last place the child was seen. Parents can check their child's location in real time through a smartphone app and respond quickly. The notification unit also sends notifications to shopping center staff via dedicated terminals or apps. Upon receiving the notification, staff can check the child's current location and travel route and respond quickly. Furthermore, the notification unit saves a history of notifications sent for later review. This allows for recording the response to lost child incidents, which can be used for subsequent review and improvement. The notification unit can also reliably transmit information using multiple communication methods. For example, it uses not only push notifications but also SMS, email, and voice calls to ensure important information is delivered reliably. This allows the notification unit to provide information to parents and staff quickly and reliably, supporting responses to lost child incidents.

[0070] The reception desk can take a photo of a child's face using a dedicated terminal or smartphone app and upload it to the system. For example, the reception desk can take a photo of a child's face using a dedicated terminal and upload it to the system. The reception desk can also take a photo of a child's face using a smartphone app and upload it to the system. This makes it easy for parents to register their child's face. The dedicated terminal or smartphone app includes, for example, the functions of a compatible OS and app. Some or all of the above processing at the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can take a photo of a child's face, analyze the facial data using AI, and upload it to the system.

[0071] The storage unit can encrypt and store registered facial data. For example, the storage unit can encrypt and store registered facial data. The storage unit can use encryption algorithms such as AES or RSA for data encryption. This improves the security of facial data. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can use AI to select an algorithm for encrypting facial data and then perform the encryption.

[0072] The analysis unit can analyze video from surveillance cameras in real time and determine the child's location. For example, the analysis unit can analyze video from surveillance cameras in real time, recognize the child's face, and determine their location. The analysis unit can use deep learning technology for real-time analysis. This allows for rapid identification of the child's location. The method of real-time analysis includes, for example, the technology used and the latency. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input video from surveillance cameras into AI, which can recognize the child's face and determine their location.

[0073] The notification unit can send alert notifications to parents and staff, including the child's current location and travel route. For example, the notification unit can send alert notifications to parents and staff, including the child's current location and travel route. The notification unit can also send notifications to parents' smartphones, allowing them to view the child's location on a map. Furthermore, the notification unit can send notifications to staff via dedicated terminals or apps, enabling a quick response. This allows parents and staff to respond promptly. Alert notifications include, for example, the content and method of sending the notification. Some or all of the above processing in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can input the child's current location and travel route into an AI, which can then generate and send an alert notification.

[0074] The notification unit can send a notification to the parent's smartphone, allowing them to view the child's location on a map. For example, the notification unit can send a notification to the parent's smartphone, allowing them to view the child's location on a map. This allows the parent to view the child's location on a map. The method of viewing the location on a map includes, for example, the type of map application used. Some or all of the above processing in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can input the child's location data into AI, which can then generate a notification to display the location on a map.

[0075] The notification unit can send notifications to staff via dedicated terminals or apps to enable them to respond quickly. For example, the notification unit can send notifications to staff via dedicated terminals or apps to enable them to respond quickly. This allows staff to respond quickly. The dedicated terminals or apps include, for example, the functions of a corresponding OS or app. Some or all of the above-described processes in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can use AI to select the optimal method for sending notifications to staff terminals and then send the notifications.

[0076] The reception desk can estimate the parent's emotions and adjust the face registration procedure based on the estimated emotions. For example, if the parent is nervous, the reception desk can provide a simple and intuitive interface to simplify the registration procedure. For example, if the parent is relaxed, the reception desk can also provide an interface with detailed explanations to guide the registration procedure carefully. For example, if the parent is in a hurry, the reception desk can use voice guidance to allow for quick completion of face registration. This allows for the provision of a face registration procedure that is tailored to the parent's emotions. Methods for estimating the parent's emotions include, for example, facial recognition and voice analysis. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the parent's facial expression data into the AI, the AI ​​can estimate the emotions, and adjust the interface based on the estimation result.

[0077] The reception desk can suggest the optimal registration method by referring to the parent's past registration history during face registration. For example, the reception desk may prioritize suggesting registration methods previously used by the parent (e.g., smartphone apps or dedicated terminals). The reception desk can also suggest a similar procedure by referring to the child's face data previously registered by the parent. The reception desk can also suggest the optimal registration method by considering the time and location where the parent previously registered. This allows the reception desk to suggest the optimal registration method based on the parent's past registration history. The optimal registration method may include, for example, a method for analyzing past registration history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the parent's past registration history data into AI, which can then suggest the optimal registration method.

[0078] The reception desk can customize the registration process based on the child's age and gender during face registration. For example, if the child is a toddler, the reception desk provides a simple interface that parents can easily use. For example, if the child is a primary school student, the reception desk can also provide a customizable interface that allows parents to input more detailed information. For example, if the child is of a specific gender, the reception desk can provide an interface design appropriate for that gender. This allows for a registration process tailored to the child's age and gender. Methods for customizing the registration process include, for example, changing the procedure based on age and gender. Some or all of the above processes in the reception desk may be performed using AI, or not. For example, the reception desk can input the child's age and gender data into the AI, which can then customize the optimal registration process.

[0079] The reception desk can estimate the parent's emotions and determine registration priorities based on the estimated emotions. For example, if a parent is stressed, the reception desk will prioritize the registration process. If a parent is relaxed, the reception desk may also prioritize the registration process of other parents. If a parent is in a hurry, the reception desk may also set a priority for quick completion of the registration process. This allows for registration prioritization according to the parent's emotions. Methods for determining registration priority include, for example, the intensity and urgency of the emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input parent emotion data into an AI, the AI ​​can estimate the emotions, and the registration priority can be determined based on the estimation results.

[0080] The reception desk can suggest the optimal registration location when registering a face, taking into account the parent's geographical location information. For example, if the parent is in a specific area within a shopping center, the reception desk can suggest the registration terminal closest to that area. If the parent is near the entrance of the shopping center, the reception desk can also suggest a registration terminal near the entrance. If the parent is in a specific store, the reception desk can also suggest the registration terminal closest to that store. This allows the system to suggest the optimal registration location based on the parent's geographical location information. Geographical location information includes, for example, GPS data and location services. Some or all of the above processing at the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the parent's geographical location data into the AI, which can then suggest the optimal registration location.

[0081] The reception desk can analyze the parent's social media activity during face registration and provide relevant registration information. For example, if the parent has posted about a shopping center on social media, the reception desk can provide registration information based on the content of that post. The reception desk can also provide registration information based on the content of the parent's posts about their child on social media. The reception desk can also provide registration information related to an event if the parent has participated in a specific event on social media. This allows the reception desk to provide relevant registration information based on the parent's social media activity. Social media activity includes, for example, posts and follower information. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the parent's social media data into an AI, and the AI ​​can provide relevant registration information.

[0082] The storage unit can estimate the parent's emotions and adjust the data storage method based on the estimated parent's emotions. For example, if the parent is feeling anxious, the storage unit can simplify the data storage procedure and complete it quickly. For example, if the parent is relaxed, the storage unit can provide a detailed data storage procedure to reassure them. For example, if the parent is in a hurry, the storage unit can complete the data storage with minimal procedures. This allows for the provision of a data storage method that is appropriate to the parent's emotions. The data storage method includes, for example, the storage format and storage location. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI, for example, or not using AI. For example, the storage unit can input parent emotion data into an AI, the AI ​​can estimate the emotions, and the data storage method can be adjusted based on the estimation result.

[0083] The storage unit can apply the optimal storage algorithm by referring to previously stored data when saving data. For example, the storage unit can refer to the format of previously saved data and save the data in a similar format. The storage unit can also refer to previously used storage algorithms and apply the optimal algorithm. The storage unit can also analyze the history of past data storage and select the most efficient storage method. This allows the application of the optimal storage algorithm based on previously stored data. The optimal storage algorithm includes, for example, the type of data and the purpose of storage. Some or all of the above processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input previously stored data into AI, which can then select and apply the optimal storage algorithm.

[0084] The storage unit can adjust the level of detail in data storage based on the importance of the data. For example, the storage unit can store highly important data through a detailed storage procedure. For example, it can also store less important data through a simplified storage procedure. The storage unit can also dynamically adjust the level of detail in storage according to the importance of the data. This allows for a level of detail in storage that corresponds to the importance of the data. The level of detail in storage includes, for example, the importance of the data and the storage period. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the importance of the data into the AI, and the AI ​​can adjust the level of detail in storage.

[0085] The storage unit can estimate the parent's emotions and determine data storage priorities based on the estimated parent's emotions. For example, if the parent is feeling anxious, the storage unit will prioritize saving important data. For example, if the parent is relaxed, the storage unit may prioritize other data storage procedures. For example, if the parent is in a hurry, the storage unit may set priorities to complete the saving procedure quickly. This provides data storage priorities that correspond to the parent's emotions. Data storage priorities may include, for example, the intensity of the emotion or the importance of the data. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI or not using AI. For example, the storage unit can input parent emotion data into an AI, the AI ​​can estimate the emotions, and the storage unit can determine data storage priorities based on the estimation results.

[0086] The storage unit can select the optimal storage location when saving data, taking into account the parent's geographical location information. For example, if the parent is in a specific area within a shopping center, the storage unit can select the storage server closest to that area. If the parent is near the entrance of the shopping center, the storage unit can also select a storage server near the entrance. If the parent is in a specific store, the storage unit can also select the storage server closest to that store. This allows the storage unit to select the optimal storage location based on the parent's geographical location information. The optimal storage location includes, for example, geographical location information and data type. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the parent's geographical location data into AI, and the AI ​​can select the optimal storage location.

[0087] The storage unit can analyze the parent's social media activity when saving data and provide relevant stored information. For example, if a parent posts about a shopping center on social media, the storage unit can provide stored information based on the content of that post. For example, if a parent posts about their child on social media, the storage unit can also provide stored information based on the content of that post. For example, if a parent participates in a specific event on social media, the storage unit can also provide stored information related to that event. This allows the storage unit to provide relevant stored information based on the parent's social media activity. Social media activity includes, for example, post content and follower information. Some or all of the above processing in the storage unit may be performed using, for example, AI, or not using AI. For example, the storage unit can input the parent's social media data into AI, and the AI ​​can provide relevant stored information.

[0088] The analysis unit can estimate the parent's emotions and adjust the analysis criteria based on the estimated parent's emotions. For example, if the parent is feeling anxious, the analysis unit can perform a rapid analysis and provide results. For example, if the parent is relaxed, the analysis unit can also perform a detailed analysis and provide results. For example, if the parent is in a hurry, the analysis unit can perform a minimal analysis and provide results quickly. This allows for the provision of analysis criteria that are appropriate to the parent's emotions. The analysis criteria include, for example, the intensity of the emotion and the purpose of the analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input parent emotion data into an AI, the AI ​​can estimate the emotion, and the analysis criteria can be adjusted based on the estimation result.

[0089] The analysis unit can optimize the analysis algorithm by referring to past analysis data during the analysis process. For example, the analysis unit can select the optimal analysis algorithm based on previously analyzed data. The analysis unit can also adjust the analysis algorithm by referring to past analysis results. For example, the analysis unit can analyze past analysis history and select the most efficient analysis method. This allows the optimal analysis algorithm to be applied based on past analysis data. The analysis algorithm includes, for example, methods for analyzing past data and criteria for selecting the algorithm. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past analysis data into AI, which can then select and apply the optimal analysis algorithm.

[0090] The analysis unit can improve the accuracy of its analysis based on the child's behavior patterns during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to the child's past behavior patterns. The analysis unit can also improve accuracy by analyzing the child's current behavior patterns in real time. The analysis unit can also improve the accuracy by learning the child's behavior patterns and optimizing the analysis algorithm. This allows the analysis accuracy to be improved based on the child's behavior patterns. Behavior patterns include, for example, movement history and behavior frequency. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the child's behavior pattern data into AI, which can learn the behavior patterns and improve the accuracy of the analysis.

[0091] The analysis unit can estimate the parent's emotions and adjust the display method of the analysis results based on the estimated parent's emotions. For example, if the parent is feeling anxious, the analysis unit can provide a simple and highly visible display method. For example, if the parent is relaxed, the analysis unit can also provide a display method that includes detailed information. For example, if the parent is in a hurry, the analysis unit can also provide a display method that gets straight to the point. This allows for the provision of a display method of the analysis results that is appropriate to the parent's emotions. The display method of the analysis results may include, for example, the intensity of the emotion and the display format. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input parent emotion data into an AI, the AI ​​can estimate the emotion, and the display method of the analysis results can be adjusted based on the estimation result.

[0092] The analysis unit can perform analysis while considering the geographical distribution of children. For example, if children are concentrated in a particular area, the analysis unit will focus its analysis on that area. For example, if children are widely distributed, the analysis unit can also perform an overall analysis. For example, the analysis unit can analyze the geographical distribution of children in real time and select the optimal analysis method. This allows for optimal analysis based on the geographical distribution of children. Geographical distribution includes, for example, location information and the range of distribution. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution data of children into AI, which can then select the optimal analysis method and perform the analysis.

[0093] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis unit can optimize its analysis algorithm by referring to relevant academic papers. The analysis unit can also improve the accuracy of its analysis by referring to relevant technical literature. The analysis unit can also improve its analysis method by referring to relevant patent documents. In this way, the accuracy of the analysis can be improved by referring to relevant literature. Relevant literature includes, for example, the type of literature and the method of reference. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature data into AI, and the AI ​​can refer to the literature to improve the accuracy of the analysis.

[0094] The notification unit can estimate the parent's emotions and adjust the notification method based on the estimated emotions. For example, if the parent is feeling anxious, the notification unit can send a notification quickly and provide detailed information. For example, if the parent is relaxed, the notification unit can send a concise notification. For example, if the parent is in a hurry, the notification unit can send a to-the-point notification. This allows for notification methods that are tailored to the parent's emotions. The notification method may include, for example, the intensity of the emotion and the content of the notification. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input parent emotion data into an AI, the AI ​​can estimate the emotion, and adjust the notification method based on the estimation result.

[0095] The notification unit can select the optimal notification method by referring to past notification history when a notification is sent. For example, the notification unit may prioritize notification methods used in the past (e.g., SMS, app notifications). The notification unit may also select the most effective notification method by referring to past notification history. The notification unit may also analyze past notification history and select the notification method that parents are most likely to respond to. This allows the optimal notification method to be selected based on past notification history. The optimal notification method may include, for example, past notification history and the purpose of the notification. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit may input past notification history data into AI, and the AI ​​may select the optimal notification method.

[0096] The notification unit can adjust the level of detail of a notification based on the child's current location and travel path. For example, if the child is in a specific area, the notification unit will notify the child of detailed information about that area. For example, if the child is on the move, the notification unit can also notify the child of detailed information about their travel path. The notification unit can also provide the most appropriate notification content based on the child's current location. This allows the notification unit to provide the most appropriate notification content based on the child's current location and travel path. The level of detail of the notification includes, for example, the current location and travel path. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the child's current location and travel path data into the AI, which can then generate the most appropriate notification content.

[0097] The notification unit can estimate the parent's emotions and determine notification priorities based on the estimated emotions. For example, if the parent is feeling anxious, the notification unit will prioritize sending important notifications. For example, if the parent is relaxed, the notification unit may prioritize other notifications. For example, if the parent is in a hurry, the notification unit may set a priority for sending notifications quickly. This provides notification priorities that correspond to the parent's emotions. Notification priorities include, for example, the intensity of the emotion and the urgency of the notification. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input parent emotion data into an AI, the AI ​​can estimate the emotion, and the notification priority can be determined based on the estimation result.

[0098] The notification unit can select the optimal notification method when sending a notification, taking into account the parent's geographical location information. For example, if the parent is in a specific area within a shopping center, the notification unit can select the most suitable notification method for that area. For example, if the parent is near the entrance of a shopping center, the notification unit can also select a notification method that is easily received near the entrance. For example, if the parent is in a specific store, the notification unit can also select the most suitable notification method for that store. This allows the system to select the optimal notification method based on the parent's geographical location information. Geographical location information includes, for example, GPS data and location services. Some or all of the processing described above in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can input the parent's geographical location data into an AI, which can then select the optimal notification method.

[0099] The notification unit can analyze the parent's social media activity and provide relevant notification information when issuing a notification. For example, if the parent has posted about a shopping center on social media, the notification unit can provide notification information based on the content of that post. The notification unit can also provide notification information based on the content of a parent's post about their child on social media. The notification unit can also provide notification information related to an event if the parent has participated in a specific event on social media. This allows the notification unit to provide relevant notification information based on the parent's social media activity. Social media activity includes, for example, posts and follower information. Some or all of the processing described above in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the parent's social media data into AI, and the AI ​​can provide relevant notification information.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The lost child prevention system can also be equipped with a voice recognition unit. When a parent or staff member calls out the child's name, the voice recognition unit analyzes the voice and can pinpoint the child's location. For example, if a parent calls out their child's name via a smartphone app, the voice recognition unit analyzes the voice and compares it with surveillance camera footage to pinpoint the child's location. The voice recognition unit can also work in conjunction with the shopping center's speaker system to broadcast the parent's voice to the lost child. This allows the child to hear their parent's voice and let them know their location. Furthermore, when a child gets lost, the voice recognition unit can automatically respond to voice instructions from a parent or staff member. For example, if a parent voice-instructs "My child is lost," the system can immediately issue an alert and begin the process of locating the child.

[0102] The lost child prevention system can also be equipped with a biometric authentication unit. This unit can register biometric information such as a child's fingerprints or iris scans and use it in conjunction with facial recognition to pinpoint the child's location. For example, if a parent registers their child's fingerprints using a dedicated terminal, the system stores the fingerprint data and compares it with surveillance camera footage to determine the child's location. Furthermore, the biometric authentication unit can work in conjunction with fingerprint or iris recognition devices installed in specific areas of a shopping center, automatically authenticating the child as they pass through those areas. This allows for accurate location identification using multiple authentication methods, even in cases where facial recognition alone is insufficient. Additionally, the biometric authentication unit can automatically suggest the most suitable authentication method when a parent registers their child's biometric information. For example, if a parent prefers fingerprint authentication, the system guides them through the fingerprint authentication process and supports registration.

[0103] The lost child prevention system can also be equipped with a behavior prediction unit. The behavior prediction unit can analyze a child's past behavioral data and predict areas where the child is likely to get lost. For example, if the system analyzes a child's past movement history and finds that the child tends to get lost in a particular area, it will focus its monitoring on that area. The behavior prediction unit can also predict areas where the child is at high risk of getting lost based on the child's age, gender, and interests. For example, if a young child is likely to get lost in a playground area, that area will be monitored preferentially. Furthermore, the behavior prediction unit can automatically suggest risk areas when parents register their child's behavioral patterns. For example, if parents register their child's favorite places or frequently visited areas, the system can use that information to predict areas where the child is at high risk of getting lost and strengthen monitoring.

[0104] The lost child prevention system can also be equipped with an emergency response unit. This unit can execute protocols for a rapid response when a child goes missing. For example, as soon as the system locates the child, it can send an emergency notification to all staff in the shopping center, instructing them to respond. The emergency response unit can also automatically notify the nearest staff when a parent reports a lost child, enabling a quick response. Furthermore, the emergency response unit can integrate with the shopping center's emergency broadcasting system to broadcast the parent's voice to the lost child, allowing the child to hear their parent and report their location. Additionally, the emergency response unit can be initiated by the parent via a smartphone app. For example, if the parent voice-commands "Initiate emergency response," the system can immediately execute the emergency response protocol and begin the process of locating the child.

[0105] The lost child prevention system can also be equipped with an environmental recognition unit. This unit can collect environmental information within the shopping center and use it to help locate children. For example, the environmental recognition unit can monitor the temperature, humidity, and lighting conditions within the shopping center in real time to increase the likelihood that a child is in a specific area. The environmental recognition unit can also analyze the flow of people and congestion levels within the shopping center to identify areas where children are at high risk of getting lost. For example, crowded areas are more likely to be where children get lost, so these areas can be monitored intensively. Furthermore, the environmental recognition unit can automatically suggest the optimal monitoring method when parents register their child's environmental information. For example, if parents register their child's preferred environmental conditions (e.g., bright or cool places), the system can enhance monitoring based on that information.

[0106] The lost child prevention system can also be equipped with an emotion estimation unit. This unit can analyze a child's facial expressions and behavior to estimate the child's emotional state. For example, it can analyze surveillance camera footage and notify parents and staff if the child is feeling anxious or frightened. The emotion estimation unit can also automatically analyze the child's emotional state and suggest appropriate responses when a child gets lost. For example, if the child is crying, the system can suggest ways to reassure the child to the parents. Furthermore, the emotion estimation unit can automatically suggest the most appropriate response when parents register their child's emotional state. For example, if parents register their child's emotional state (e.g., easily nervous, easily frightened), the system can suggest a response based on that information.

[0107] The lost child prevention system can also be equipped with an emotional feedback unit. This unit allows parents and staff to provide feedback based on the child's emotional state. For example, when a parent checks the child's emotional state and inputs feedback into the system, the system adjusts its response based on that information. The emotional feedback unit also allows staff to check the child's emotional state and input feedback into the system, sharing appropriate response methods with other staff. Furthermore, when a parent registers the child's emotional state, the emotional feedback unit can automatically suggest the most appropriate feedback method. For example, when a parent registers the child's emotional state (e.g., easily anxious, easily frightened), the system suggests a feedback method based on that information.

[0108] The lost child prevention system can also be equipped with an emotion monitoring unit. The emotion monitoring unit can monitor the child's emotional state in real time and notify parents and staff. For example, it can analyze surveillance camera footage and notify parents and staff in real time if the child is feeling anxious or frightened. The emotion monitoring unit can also automatically monitor the child's emotional state and suggest appropriate responses when a child gets lost. For example, if the child is crying, the system can suggest ways to reassure the child to the parents. Furthermore, the emotion monitoring unit can automatically suggest the optimal monitoring method when parents register the child's emotional state. For example, if parents register the child's emotional state (e.g., easily stressed, easily frightened), the system can enhance monitoring based on that information.

[0109] The lost child prevention system can also be equipped with an emotion analysis unit. The emotion analysis unit can analyze a child's emotional state in detail and suggest specific response methods to parents and staff. For example, by analyzing surveillance camera footage, if a child is feeling anxious or frightened, it can identify the cause and suggest "specific ways to reassure the child" to the parents. Furthermore, when a child gets lost, the emotion analysis unit can automatically analyze the child's emotional state and suggest a response method. For example, if a child is crying, the system will suggest "specific ways to reassure the child" to the parents. In addition, when parents register their child's emotional state, the emotion analysis unit can automatically suggest the optimal analysis method. For example, if parents register their child's emotional state (e.g., easily nervous, easily frightened), the system will analyze that information and suggest a response method.

[0110] The lost child prevention system can also be equipped with an emotional support unit. The emotional support unit can help parents and staff provide appropriate support based on the child's emotional state. For example, it can analyze surveillance camera footage and, if the child is feeling anxious or frightened, notify parents and staff of this information and suggest appropriate support methods. The emotional support unit can also automatically analyze the child's emotional state when the child gets lost and suggest specific support methods to parents and staff. For example, if the child is crying, the system will suggest "specific support methods to reassure the child" to the parents. Furthermore, the emotional support unit can automatically suggest the most suitable support methods when parents register the child's emotional state. For example, if parents register the child's emotional state (e.g., easily nervous, easily frightened), the system will suggest support methods based on that information.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The reception desk registers the child's face. Parents can register their child's face using a dedicated terminal or a smartphone app. For example, they can take a photo of their child's face using a dedicated terminal and upload it to the system. Alternatively, they can take a photo of their child's face using a smartphone app and upload it to the system. Step 2: The storage unit stores the facial data registered by the reception unit. The storage unit encrypts and stores the registered facial data. Encryption algorithms such as AES or RSA can be used for data encryption. Step 3: The analysis unit analyzes the video from the security cameras in the shopping center to determine the child's location. The analysis unit analyzes the video from the security cameras in real time, recognizes the child's face, and determines their location. Deep learning technology can be used for real-time analysis. Step 4: The notification unit sends notifications to parents and staff when a child goes missing. The notification unit sends alert notifications to parents and staff that include the child's current location and travel route. Notifications can be sent to parents' smartphones so they can check the child's location on a map. Notifications can also be sent to staff via dedicated terminals or apps so they can respond quickly.

[0113] 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.

[0114] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0115] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the reception unit, storage unit, analysis unit, and notification unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit can use the reception device 38 of the smart device 14 to take a picture of the child's face and upload it to the system. The storage unit stores the face data in the database 24 of the data processing unit 12. The analysis unit analyzes the video from the surveillance camera using the identification processing unit 290 of the data processing unit 12 to determine the child's location. The notification unit sends a notification to the parent's smartphone via the output device 40 of the smart device 14, allowing the parent to check the child's location on a map. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0120] 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.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0122] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] 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.

[0124] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the reception unit, storage unit, analysis unit, and notification unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit can use the microphone 238 of the smart glasses 214 to take a picture of the child's face and upload it to the system. The storage unit stores the face data in the database 24 of the data processing unit 12. The analysis unit analyzes the video from the surveillance camera using the identification processing unit 290 of the data processing unit 12 to determine the child's location. The notification unit sends a notification to the parent's smartphone via the speaker 240 of the smart glasses 214, allowing the parent to check the child's location on a map. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0136] 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.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0138] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] 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.

[0140] 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.

[0141] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] 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.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the reception unit, storage unit, analysis unit, and notification unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit can use the microphone 238 of the headset terminal 314 to take a picture of the child's face and upload it to the system. The storage unit stores the face data in the database 24 of the data processing unit 12. The analysis unit analyzes the video from the surveillance camera using the identification processing unit 290 of the data processing unit 12 to determine the child's location. The notification unit sends a notification to the parent's smartphone via the display 343 of the headset terminal 314, allowing the parent to check the child's location on a map. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0152] 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.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0154] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] 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.

[0156] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0157] 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.

[0158] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] 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.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the reception unit, storage unit, analysis unit, and notification unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit can use the microphone 238 of the robot 414 to take a picture of the child's face and upload it to the system. The storage unit stores the face data in the database 24 of the data processing unit 12. The analysis unit analyzes the video from the surveillance camera using the identification processing unit 290 of the data processing unit 12 to determine the child's location. The notification unit sends a notification to the parent's smartphone via the speaker 240 of the robot 414, allowing the child's location to be viewed on a map. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0166] 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.

[0167] Figure 9 shows the 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.

[0168] 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.

[0169] 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.

[0170] 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, and motorcycles, 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 based, for example, 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.

[0171] 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."

[0172] 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.

[0173] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0182] 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 other things 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.

[0183] 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.

[0184] (Note 1) A reception area where parents register their child's face, A storage unit for storing facial data registered by the reception unit, An analysis unit analyzes footage from surveillance cameras inside the shopping center to determine the child's location, It includes a notification unit that sends notifications to parents and staff when a child gets lost. A system characterized by the following features. (Note 2) The aforementioned reception unit is A dedicated device or smartphone app is used to take a photo of the child's face, which is then uploaded to the system. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned storage unit is Registered facial data is encrypted and stored. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, The system analyzes video from surveillance cameras in real time to pinpoint the child's location. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned notification unit, Send alert notifications to parents and staff, including the child's current location and travel route. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned notification unit, It sends a notification to the parent's smartphone, allowing them to check the child's location on a map. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned notification unit, Notifications are sent to staff via dedicated terminals or apps to enable quick responses. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system estimates the parent's emotions and adjusts the face registration procedure based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When registering a face, the system will refer to the parent's past registration history to suggest the most suitable registration method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When registering a face, the registration process is customized based on the child's age and gender. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is The system estimates the parents' emotions and determines the priority of registration based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When registering a face, the system suggests the optimal registration location considering the parent's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When registering a face, the system analyzes the parent's social media activity and provides relevant registration information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned storage unit is We estimate the parents' emotions and adjust the data storage method based on the estimated parents' emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned storage unit is When saving data, the system refers to previously saved data to apply the optimal saving algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned storage unit is When saving data, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned storage unit is The system estimates the parents' emotions and determines the priority of data storage based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned storage unit is When saving data, the system selects the optimal storage location by considering the parent's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned storage unit is When saving data, the system analyzes the parents' social media activity and provides relevant saved information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, We estimate the parents' emotions and adjust the analysis criteria based on the estimated parents' emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to past analysis data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, During analysis, improve the accuracy of the analysis based on the child's behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, It estimates the parent's emotions and adjusts how the analysis results are displayed based on the estimated parent's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, During the analysis, the geographical distribution of children will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit, During analysis, we refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned notification unit, It estimates the parent's emotions and adjusts the notification method based on the estimated parent's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned notification unit, When sending a notification, the system will refer to past notification history to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned notification unit, When a notification is sent, adjust the level of detail based on the child's current location and travel route. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned notification unit, It estimates the parent's emotions and determines the priority of notifications based on the estimated parent's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned notification unit, When sending a notification, the system will select the most suitable notification method, taking into account the parent's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned notification unit, When sending a notification, the system analyzes the parent's social media activity and provides relevant notification information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where parents register their child's face, A storage unit for storing facial data registered by the reception unit, An analysis unit analyzes footage from surveillance cameras inside the shopping center to determine the child's location, It includes a notification unit that sends notifications to parents and staff when a child gets lost. A system characterized by the following features.

2. The aforementioned reception unit is A dedicated device or smartphone app is used to take a photo of the child's face, which is then uploaded to the system. The system according to feature 1.

3. The aforementioned storage unit is Registered facial data is encrypted and stored. The system according to feature 1.

4. The aforementioned analysis unit, The system analyzes video from surveillance cameras in real time to pinpoint the child's location. The system according to feature 1.

5. The aforementioned notification unit, Send alert notifications to parents and staff, including the child's current location and travel route. The system according to feature 1.

6. The aforementioned notification unit, It sends a notification to the parent's smartphone, allowing them to check the child's location on a map. The system according to feature 1.

7. The aforementioned notification unit, Notifications are sent to staff via dedicated terminals or apps to enable quick responses. The system according to feature 1.

8. The aforementioned reception unit is The system estimates the parent's emotions and adjusts the face registration procedure based on those estimated emotions. The system according to feature 1.

9. The aforementioned reception unit is When registering a face, the system will refer to the parent's past registration history to suggest the most suitable registration method. The system according to feature 1.

10. The aforementioned reception unit is When registering a face, the registration process is customized based on the child's age and gender. The system according to feature 1.

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