system

Abstract

The system according to this embodiment aims to achieve more secure access management through facial recognition and matching with multiple certificates. [Solution] The system according to the embodiment comprises a face recognition unit, a matching unit, a determination unit, and a provision unit. The face recognition unit recognizes the face of the subject. The matching unit matches the face information recognized by the face recognition unit with a plurality of certificate databases. The determination unit determines the subject's range of movement based on the results of the matching unit. The provision unit permits passage within the range of movement determined by the determination unit.

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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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, access management using keys and security cards is performed, but there is a risk that these may be lost or stolen, and there is room for improvement in security enhancement.

[0005] The system according to the embodiment aims to achieve more secure access management through collation of face authentication and multiple certificates.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a face recognition unit, a matching unit, a determination unit, and a provision unit. The face recognition unit recognizes the face of the subject. The matching unit matches the face information recognized by the face recognition unit with a plurality of certificate databases. The determination unit determines the subject's range of movement based on the results of the matching unit. The provision unit permits passage within the range of movement determined by the determination unit. [Effects of the Invention]

[0007] The system according to this embodiment can achieve more secure access management through facial recognition and matching with multiple certificates. [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, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applicable 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 security management system according to an embodiment of the present invention is a system that automates security management using AI. This security management system enables accurate and secure operation by having AI perform facial recognition and matching with multiple identification documents at the entrance of a home, the reception or gate of an office, and within the restricted area of ​​movement within a company. This eliminates the need for keys and security cards, and eliminates the risk of loss or theft. It also reduces the labor costs associated with security management, allowing people to concentrate on creative activities. For example, the AI ​​recognizes the face of a person through a camera. Next, it matches the recognized facial information with multiple identification document databases to verify the person's identity. For example, by matching it with identification data such as employee ID cards or driver's licenses, accurate identity verification is possible. Once the matching is complete, the AI ​​determines the person's range of movement and automatically permits passage within the permitted area. This system can be used in various locations such as the entrance of a home, the reception or gate of an office, and restricted access areas within a company. For example, at the entrance of a home, it can recognize the faces of family members and automatically unlock the door. At the reception of an office, it can recognize the faces of employees and permit entry, and at gates and restricted access areas, it can ensure that only authorized employees can pass through. Thus, an AI-powered security management system eliminates the need to issue and manage security cards, thus eliminating the risk of loss or theft. It also reduces labor costs associated with security management, allowing humans to focus on creative activities. Furthermore, AI enables accurate and secure security management through facial recognition and identification verification. As a result, the security management system can automate security management and ensure accurate and secure operation.

[0029] The security management system according to the embodiment comprises a face recognition unit, a matching unit, a determination unit, and a provision unit. The face recognition unit recognizes the face of a subject. The face recognition unit performs face recognition using, for example, AI. The face recognition unit photographs the face of a subject through a camera and recognizes the face using a face recognition algorithm. The face recognition unit detects the face using, for example, a face detection algorithm and extracts facial features using a feature extraction method. The matching unit compares the face information recognized by the face recognition unit with multiple certificate databases. The matching unit performs matching using, for example, AI. The matching unit compares the certificate information stored in the certificate database with the face information to verify the identity of the person. The matching unit compares the face information with the certificate information using, for example, a matching algorithm and evaluates the accuracy of the matching. The determination unit determines the subject's range of movement based on the results of the matching by the matching unit. The determination unit determines the range of movement using, for example, AI. The determination unit determines the subject's range of movement based on the matching results according to criteria such as geographical range and accessible area. The provision unit permits passage within the range of movement determined by the determination unit. The providing unit, for example, uses AI to grant permission for passage. The providing unit provides results such as unlocking doors or opening gates. The providing unit controls, for example, door unlocking devices or gate opening devices to grant permission for passage. As a result, the security management system according to the embodiment can automate facial recognition of the subject, matching with a certificate database, determination of the range of movement, and permission for passage, thereby achieving accurate and safe operation.

[0030] The face recognition unit recognizes the subject's face. The face recognition unit performs face recognition using, for example, AI. The face recognition unit captures the subject's face through a camera and recognizes the face using a face recognition algorithm. Specifically, the face recognition unit first captures the subject's face using a camera and acquires the image data. Next, it identifies the face region in the image using a face detection algorithm. The face detection algorithm detects the position and size of the face in the image and cuts out the face region. Subsequently, it extracts facial features using a feature extraction method. Feature extraction methods include, for example, convolutional neural networks (CNNs) using deep learning and principal component analysis (PCA). Using these methods, facial features are extracted as numerical data and input into the face recognition algorithm. The face recognition algorithm recognizes the subject's face based on the extracted features. Face recognition algorithms include, for example, support vector machines (SVMs) and k-nearest neighbors (k-NNs). As a result, the face recognition unit can recognize the subject's face with high accuracy and provide face information to the subsequent matching unit. Furthermore, the facial recognition unit can perform facial recognition in real time, instantly recognizing a person's face as they pass in front of the camera. This allows the facial recognition unit to efficiently and accurately recognize a person's face, improving the overall performance of the security management system.

[0031] The matching unit compares the facial information recognized by the facial recognition unit with multiple certificate databases. The matching unit performs matching using, for example, AI. The matching unit compares the certificate information stored in the certificate database with the facial information to verify the identity of the individual. Specifically, the matching unit receives the facial information provided by the facial recognition unit and accesses the certificate database. The certificate database stores the facial information and other personal information of the individual, and the matching unit compares this information with the facial information provided by the facial recognition unit. Using a matching algorithm, the degree of agreement between the facial information and the certificate information is evaluated to improve the accuracy of identity verification. Matching algorithms include, for example, cosine similarity and Euclidean distance, which are used to calculate the similarity of the facial information. Based on the matching results, the matching unit determines whether the individual is a person registered in the certificate database. The matching unit processes the matching results in real time, enabling rapid identity verification. Furthermore, the matching unit can refer to multiple certificate databases and improve the accuracy of identity verification by performing matching across different databases. This allows the matching unit to perform accurate and rapid identity verification, improving the reliability of the entire security management system.

[0032] The determination unit determines the range of the subject's movements based on the results verified by the matching unit. The determination unit uses AI, for example, to determine the range of movements. Specifically, the determination unit receives the verification results provided by the verification unit and determines the subject's range of movements according to criteria such as geographical range and accessible areas. Based on the verification results, the determination unit identifies areas that the subject can access and routes that they can travel. For example, if the subject has permission to access a specific area, the determination unit grants access to that area. On the other hand, it restricts access to areas that the subject does not have access to. The determination unit can analyze the verification results using AI and dynamically determine the subject's range of movements. The AI ​​can learn from past data and behavioral patterns to predict the subject's range of movements. This allows the determination unit to determine the subject's range of movements with high accuracy and improve the security of the entire security management system. Furthermore, the determination unit can update the range of movements in real time, enabling flexible responses according to the subject's situation. This allows the determination unit to efficiently and accurately determine the subject's range of movements and improve the performance of the entire security management system.

[0033] The service provider grants permission to pass within the range of movement determined by the judgment unit. The service provider grants permission using, for example, AI. Specifically, the service provider receives the range of movement information provided by the judgment unit and grants permission to pass within the area accessible to the subject. The service provider provides results such as unlocking doors or opening gates. For example, the service provider controls door unlocking devices or gate opening devices to allow the subject to enter the accessible area. The service provider can use AI to make decisions on granting permission to pass and control passage in real time. The AI ​​can learn the subject's behavior patterns and past data to optimize decisions on granting permission to pass. This allows the service provider to grant permission efficiently and accurately, improving the overall security of the security management system. Furthermore, the service provider records the history of permission to pass and can refer to it later. This allows the service provider to understand the operational status of the entire security management system based on the history of permission to pass and make improvements as needed. This allows the service provider to grant permission efficiently and accurately, improving the overall performance of the security management system.

[0034] The face recognition unit performs face recognition using multiple cameras. For example, the face recognition unit improves the accuracy of face recognition by taking pictures of faces from different angles using multiple cameras. By using multiple cameras, the face recognition unit can capture facial features in more detail. For example, the face recognition unit improves the accuracy of face recognition by optimizing the number and placement of cameras. The face recognition unit improves the accuracy of face recognition by selecting the type and performance of the cameras to be used. As a result, the accuracy of face recognition is improved by using multiple cameras. Some or all of the above processing in the face recognition unit may be performed using AI, for example, or without AI. For example, the face recognition unit can input image data acquired from multiple cameras into a generating AI and have the generating AI perform processing to improve the accuracy of face recognition.

[0035] The matching unit includes a database management unit that manages the certificate database. The matching unit optimizes the update frequency and management method of the certificate database, for example, to improve the accuracy of matching. The matching unit uses the database management unit to evaluate the reliability of the certificate database and prioritizes the use of highly reliable certificate information for matching. The matching unit optimizes the management method of the certificate database, for example, to improve the accuracy of matching. The matching unit uses the database management unit to adjust the update frequency of the certificate database and use the latest certificate information for matching. This makes the management of the certificate database easier and improves the accuracy of matching. Some or all of the above processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can have a generating AI perform the management of the certificate database to improve the accuracy of matching.

[0036] The service provider provides results such as unlocking doors or opening gates. The service provider controls, for example, door unlocking devices or gate opening devices to allow passage. The service provider uses AI to automate door unlocking and gate opening. The service provider sets, for example, door unlocking conditions or gate opening conditions, and allows passage when the conditions are met. The service provider provides the results of door unlocking and gate opening in real time to improve the convenience of passage. This improves the convenience of passage by automating door unlocking and gate opening. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can have a generating AI perform the control of door unlocking and gate opening to improve the convenience of passage.

[0037] The face recognition unit optimizes the recognition algorithm by referring to the subject's past face recognition history during face recognition. For example, the face recognition unit analyzes past face recognition history and adjusts parameters to improve recognition accuracy. The face recognition unit improves recognition accuracy for specific time periods and locations based on the subject's past face recognition history. The face recognition unit updates the learning data for the recognition algorithm based on the subject's past face recognition history. This improves the accuracy of the recognition algorithm by referring to past face recognition history. Some or all of the above processes in the face recognition unit may be performed using AI, for example, or without AI. For example, the face recognition unit can input past face recognition history data into a generating AI and have the generating AI perform the optimization of the recognition algorithm.

[0038] The face recognition unit performs correction processing to accommodate changes in the angle and expression of the subject's face during face recognition. For example, if the angle of the face changes, the face recognition unit uses a correction algorithm to perform accurate face recognition. If the subject's expression changes, the face recognition unit uses an expression recognition algorithm to perform correction. If the subject is wearing a mask, the face recognition unit performs correction processing that takes the mask into account. This improves the accuracy of face recognition by accommodating changes in the angle and expression of the face. Some or all of the above processing in the face recognition unit may be performed using AI, for example, or without AI. For example, the face recognition unit can input data on changes in the angle of the face and expression into a generating AI and have the generating AI perform the correction processing.

[0039] The face recognition unit improves recognition accuracy by considering the subject's height and body type information during face recognition. For example, the face recognition unit improves recognition accuracy by adjusting the camera angle based on the subject's height information. The face recognition unit adjusts the parameters of the face recognition algorithm based on the subject's body type information. The face recognition unit updates the learning data of the recognition algorithm based on the subject's height and body type information. As a result, the accuracy of face recognition is improved by considering height and body type information. Some or all of the above processes in the face recognition unit may be performed using AI, for example, or without AI. For example, the face recognition unit can input the subject's height and body type information into a generating AI and have the generating AI perform the improvement of recognition accuracy.

[0040] The face recognition unit improves recognition accuracy by referring to information about the subject's clothing and accessories during face recognition. For example, the face recognition unit adjusts the parameters of the face recognition algorithm based on the subject's clothing information. The face recognition unit adjusts the parameters of the face recognition algorithm based on the subject's accessory information. The face recognition unit updates the training data of the recognition algorithm based on the subject's clothing and accessory information. As a result, the accuracy of face recognition is improved by referring to information about clothing and accessories. Some or all of the above processes in the face recognition unit may be performed using AI, for example, or without AI. For example, the face recognition unit can input the subject's clothing and accessory information into a generating AI and have the generating AI perform the improvement of recognition accuracy.

[0041] The matching unit improves the accuracy of the matching results by evaluating the reliability of multiple certificate databases during matching. For example, the matching unit evaluates the reliability of multiple certificate databases and prioritizes matching with highly reliable databases. The matching unit evaluates the reliability of certificate databases and excludes less reliable databases from matching. The matching unit evaluates the reliability of multiple certificate databases and optimizes the matching results based on highly reliable databases. This improves the accuracy of the matching results by evaluating the reliability of multiple certificate databases. Some or all of the above processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can have a generating AI perform the reliability evaluation of multiple certificate databases to improve the accuracy of the matching results.

[0042] The matching unit optimizes the matching results by considering the certificate's expiration date and issuer information during the matching process. For example, the matching unit checks the certificate's expiration date and uses only valid certificates for matching. The matching unit checks the certificate's issuer information and prioritizes matching certificates from highly reliable issuers. The matching unit optimizes the matching results based on the certificate's expiration date and issuer information. This improves the accuracy of the matching results by considering the certificate's expiration date and issuer information. Some or all of the above processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input the certificate's expiration date and issuer information into a generating AI and have the generating AI perform the optimization of the matching results.

[0043] The matching unit improves the reliability of the matching results by considering the geographical location information of the subject during matching. The matching unit evaluates the reliability of the matching results based on the geographical location information of the subject, for example. The matching unit provides highly reliable matching results by considering the geographical location information of the subject. The matching unit improves the accuracy of the matching results based on the geographical location information of the subject. As a result, the reliability of the matching results is improved by considering geographical location information. Some or all of the above processing in the matching unit may be performed using AI, for example, or without using AI. For example, the matching unit can input the geographical location information of the subject into a generating AI and have the generating AI perform the improvement of the reliability of the matching results.

[0044] The matching unit optimizes the matching results by referring to the subject's past behavioral history during matching. The matching unit improves the reliability of the matching results based on the subject's past behavioral history, for example. The matching unit improves the accuracy of the matching results by referring to the subject's past behavioral history. The matching unit optimizes the matching results based on the subject's past behavioral history. As a result, the accuracy of the matching results is improved by referring to past behavioral history. Some or all of the above processing in the matching unit may be performed using AI, for example, or without using AI. For example, the matching unit can input the subject's past behavioral history data into a generating AI and have the generating AI perform the optimization of the matching results.

[0045] The judgment unit optimizes the judgment algorithm by referring to the subject's past behavioral history when making a judgment. The judgment unit adjusts the parameters of the judgment algorithm based on the subject's past behavioral history, for example. The judgment unit improves the accuracy of determining the range of movement by referring to the subject's past behavioral history. The judgment unit updates the learning data of the judgment algorithm based on the subject's past behavioral history. As a result, the accuracy of the judgment algorithm is improved by referring to past behavioral history. Some or all of the above processes in the judgment unit may be performed using AI, for example, or without using AI. For example, the judgment unit can input the subject's past behavioral history data into a generating AI and have the generating AI perform the optimization of the judgment algorithm.

[0046] The determination unit customizes the scope of action based on the subject's job title and duties during the determination process. For example, the determination unit customizes the scope of action based on the subject's job title information. The determination unit customizes the scope of action based on the subject's duties. The determination unit adjusts the criteria for determining the scope of action based on the subject's job title and duties. This improves security by customizing the scope of action based on job title and duties. Some or all of the above processing in the determination unit may be performed using AI, for example, or without AI. For example, the determination unit can input the subject's job title and duties data into a generating AI and have the generating AI perform the customization of the scope of action.

[0047] The determination unit optimizes the range of activity by considering the subject's geographical location information during the determination process. The determination unit optimizes the range of activity based on the subject's geographical location information, for example. The determination unit improves the accuracy of determining the range of activity by considering the subject's geographical location information. The determination unit adjusts the criteria for determining the range of activity based on the subject's geographical location information. As a result, the accuracy of determining the range of activity is improved by considering geographical location information. Some or all of the above processes in the determination unit may be performed using AI, for example, or without using AI. For example, the determination unit can input the subject's geographical location information into a generating AI and have the generating AI perform the optimization of the range of activity.

[0048] The determination unit adjusts the range of activity by referring to the subject's working hours and shift information when making a determination. For example, the determination unit adjusts the range of activity based on the subject's working hours information. The determination unit adjusts the range of activity based on the subject's shift information. The determination unit adjusts the criteria for determining the range of activity based on the subject's working hours and shift information. This improves the accuracy of determining the range of activity by referring to working hours and shift information. Some or all of the above processing in the determination unit may be performed using AI, for example, or without using AI. For example, the determination unit can input the subject's working hours and shift information into a generating AI and have the generating AI perform the adjustment of the range of activity.

[0049] The service delivery unit optimizes the delivery results by referring to the target user's past usage history at the time of delivery. The service delivery unit improves the accuracy of the delivery results based on the target user's past usage history. The service delivery unit improves the reliability of the delivery results by referring to the target user's past usage history. The service delivery unit optimizes the delivery results based on the target user's past usage history. As a result, the accuracy of the delivery results is improved by referring to past usage history. Some or all of the above processing in the service delivery unit may be performed using AI, for example, or without using AI. For example, the service delivery unit can input the target user's past usage history data into a generating AI and have the generating AI perform the optimization of the delivery results.

[0050] The delivery unit customizes the delivery results based on the recipient's current situation at the time of delivery. The delivery unit customizes the delivery results based on the recipient's current situation, for example. The delivery unit improves the accuracy of the delivery results by considering the recipient's current situation. The delivery unit optimizes the delivery results based on the recipient's current situation. As a result, the accuracy of the delivery results is improved by customizing the delivery results based on the current situation. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without using AI. For example, the delivery unit can input the recipient's current situation data into a generating AI and have the generating AI perform the customization of the delivery results.

[0051] The delivery unit optimizes the delivery results by considering the geographical location information of the recipient at the time of delivery. The delivery unit optimizes the delivery results based on the geographical location information of the recipient, for example. The delivery unit improves the accuracy of the delivery results by considering the geographical location information of the recipient. The delivery unit improves the reliability of the delivery results based on the geographical location information of the recipient. As a result, the accuracy of the delivery results is improved by considering geographical location information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without using AI. For example, the delivery unit can input the geographical location information of the recipient into a generating AI and have the generating AI perform the optimization of the delivery results.

[0052] The service provider analyzes the target's social media activity at the time of delivery and customizes the delivery results. The service provider customizes the delivery results based on the target's social media activity, for example. The service provider analyzes the target's social media activity to improve the accuracy of the delivery results. The service provider improves the reliability of the delivery results based on the target's social media activity. As a result, the accuracy of the delivery results is improved by analyzing social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the target's social media activity data into a generating AI and have the generating AI perform the customization of the delivery results.

[0053] The Database Management Department improves the accuracy of the database by evaluating the reliability of certificates during database management. For example, the Database Management Department evaluates the reliability of certificates and prioritizes registering highly reliable certificates in the database. The Database Management Department evaluates the reliability of certificates and manages the database by excluding less reliable certificates. The Database Management Department evaluates the reliability of certificates and improves the accuracy of the database based on highly reliable certificates. In this way, the accuracy of the database is improved by evaluating the reliability of certificates. Some or all of the above processes in the Database Management Department may be performed using AI, for example, or not using AI. For example, the Database Management Department can improve the accuracy of the database by having a generating AI perform the certificate reliability evaluation.

[0054] The Database Management Department improves the reliability of the database by considering the issuer and expiration date of certificates during database management. For example, the Database Management Department prioritizes registering highly reliable certificates in the database based on the issuer information. The Database Management Department checks the expiration date of certificates and registers only valid certificates in the database. The Database Management Department improves the reliability of the database based on the issuer and expiration date of certificates. As a result, the reliability of the database is improved by considering the issuer and expiration date of certificates. Some or all of the above processes in the Database Management Department may be performed using AI, for example, or not using AI. For example, the Database Management Department can input certificate issuer and expiration date information into a generating AI and have the generating AI perform the database reliability improvement.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The facial recognition unit can improve recognition accuracy by using the subject's voiceprint information in conjunction with facial recognition. For example, the facial recognition unit collects the subject's spoken voice and extracts voiceprint information using a voiceprint recognition algorithm. Next, the facial recognition unit integrates the facial information and voiceprint information to improve recognition accuracy. Furthermore, the facial recognition unit can analyze the subject's voice tone and speaking style characteristics and use them as auxiliary information for facial recognition. In this way, the facial recognition unit can improve recognition accuracy by combining facial information and voiceprint information.

[0057] The matching unit can improve the accuracy of the matching results by considering the subject's health status. For example, the matching unit collects vital data such as the subject's heart rate and body temperature and evaluates their health status. Next, the matching unit adjusts the parameters of the matching algorithm based on the health status to improve the accuracy of the matching results. Furthermore, if the health status is unstable, the matching unit evaluates the reliability of the matching results and can perform re-matching as needed. In this way, the matching unit can improve the accuracy of the matching results by considering the health status.

[0058] The facial recognition unit can improve recognition accuracy by using the subject's walking pattern in conjunction with facial recognition. For example, the facial recognition unit collects the subject's walking pattern and extracts walking information using a walking recognition algorithm. Next, the facial recognition unit integrates the facial information and walking information to improve recognition accuracy. Furthermore, the facial recognition unit can analyze the characteristics of the subject's walking speed and stride length and use this as auxiliary information for facial recognition. In this way, the facial recognition unit can improve recognition accuracy by combining facial information and walking information.

[0059] The matching unit can improve the accuracy of the matching results by considering the job content of the subject. For example, the matching unit can adjust the parameters of the matching algorithm based on the job content of the subject to improve the accuracy of the matching results. Next, the matching unit can evaluate the reliability of the matching results according to the job content and perform re-matching as necessary. Furthermore, if the job content changes, the matching unit can update the training data of the matching algorithm to improve the accuracy of the matching results. In this way, the matching unit can improve the accuracy of the matching results by considering the job content.

[0060] The determination unit can adjust the criteria for determining the range of activity by referring to the subject's past behavioral history. For example, the determination unit analyzes the subject's past behavioral history and optimizes the criteria for determining the range of activity. Next, the determination unit improves the accuracy of determining the range of activity based on the past behavioral history. Furthermore, the determination unit can update the training data for the range of activity determination algorithm based on the past behavioral history. In this way, the determination unit can improve the accuracy of determining the range of activity by referring to the past behavioral history.

[0061] The database management department can adjust its database management methods by considering the geographical location information of the subjects. For example, the database management department optimizes its database management methods based on the geographical location information of the subjects. Next, the database management department adjusts the update frequency of the database based on the geographical location information to maintain up-to-date information. Furthermore, the database management department can evaluate the reliability of the database based on the geographical location information and prioritize the management of highly reliable information. In this way, the database management department can improve the accuracy of database management by considering geographical location information.

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

[0063] Step 1: The face recognition unit recognizes the subject's face. The face recognition unit captures the subject's face through the camera and recognizes the face using a face recognition algorithm. For example, it detects the face using a face detection algorithm and extracts facial features using a feature extraction method. Step 2: The matching unit compares the facial information recognized by the facial recognition unit with multiple certificate databases. The matching unit compares the facial information with the certificate information stored in the certificate databases to verify the identity of the person. For example, it uses a matching algorithm to compare the facial information with the certificate information and evaluates the accuracy of the matching. Step 3: The determination unit determines the subject's range of activity based on the results matched by the matching unit. The determination unit determines the subject's range of activity based on the matching results according to criteria such as geographical range and accessible area. Step 4: The providing unit permits passage within the range of action determined by the determining unit. The providing unit provides results such as unlocking doors or opening gates. For example, it controls door unlocking devices or gate opening devices to permit passage.

[0064] (Example of form 2) The security management system according to an embodiment of the present invention is a system that automates security management using AI. This security management system enables accurate and secure operation by having AI perform facial recognition and matching with multiple identification documents at the entrance of a home, the reception or gate of an office, and within the restricted area of ​​movement within a company. This eliminates the need for keys and security cards, and eliminates the risk of loss or theft. It also reduces the labor costs associated with security management, allowing people to concentrate on creative activities. For example, the AI ​​recognizes the face of a person through a camera. Next, it matches the recognized facial information with multiple identification document databases to verify the person's identity. For example, by matching it with identification data such as employee ID cards or driver's licenses, accurate identity verification is possible. Once the matching is complete, the AI ​​determines the person's range of movement and automatically permits passage within the permitted area. This system can be used in various locations such as the entrance of a home, the reception or gate of an office, and restricted access areas within a company. For example, at the entrance of a home, it can recognize the faces of family members and automatically unlock the door. At the reception of an office, it can recognize the faces of employees and permit entry, and at gates and restricted access areas, it can ensure that only authorized employees can pass through. Thus, an AI-powered security management system eliminates the need to issue and manage security cards, thus eliminating the risk of loss or theft. It also reduces labor costs associated with security management, allowing humans to focus on creative activities. Furthermore, AI enables accurate and secure security management through facial recognition and identification verification. As a result, the security management system can automate security management and ensure accurate and secure operation.

[0065] The security management system according to the embodiment comprises a face recognition unit, a matching unit, a determination unit, and a provision unit. The face recognition unit recognizes the face of a subject. The face recognition unit performs face recognition using, for example, AI. The face recognition unit photographs the face of a subject through a camera and recognizes the face using a face recognition algorithm. The face recognition unit detects the face using, for example, a face detection algorithm and extracts facial features using a feature extraction method. The matching unit compares the face information recognized by the face recognition unit with multiple certificate databases. The matching unit performs matching using, for example, AI. The matching unit compares the certificate information stored in the certificate database with the face information to verify the identity of the person. The matching unit compares the face information with the certificate information using, for example, a matching algorithm and evaluates the accuracy of the matching. The determination unit determines the subject's range of movement based on the results of the matching by the matching unit. The determination unit determines the range of movement using, for example, AI. The determination unit determines the subject's range of movement based on the matching results according to criteria such as geographical range and accessible area. The provision unit permits passage within the range of movement determined by the determination unit. The providing unit, for example, uses AI to grant permission for passage. The providing unit provides results such as unlocking doors or opening gates. The providing unit controls, for example, door unlocking devices or gate opening devices to grant permission for passage. As a result, the security management system according to the embodiment can automate facial recognition of the subject, matching with a certificate database, determination of the range of movement, and permission for passage, thereby achieving accurate and safe operation.

[0066] The face recognition unit recognizes the subject's face. The face recognition unit performs face recognition using, for example, AI. The face recognition unit captures the subject's face through a camera and recognizes the face using a face recognition algorithm. Specifically, the face recognition unit first captures the subject's face using a camera and acquires the image data. Next, it identifies the face region in the image using a face detection algorithm. The face detection algorithm detects the position and size of the face in the image and cuts out the face region. Subsequently, it extracts facial features using a feature extraction method. Feature extraction methods include, for example, convolutional neural networks (CNNs) using deep learning and principal component analysis (PCA). Using these methods, facial features are extracted as numerical data and input into the face recognition algorithm. The face recognition algorithm recognizes the subject's face based on the extracted features. Face recognition algorithms include, for example, support vector machines (SVMs) and k-nearest neighbors (k-NNs). As a result, the face recognition unit can recognize the subject's face with high accuracy and provide face information to the subsequent matching unit. Furthermore, the facial recognition unit can perform facial recognition in real time, instantly recognizing a person's face as they pass in front of the camera. This allows the facial recognition unit to efficiently and accurately recognize a person's face, improving the overall performance of the security management system.

[0067] The matching unit compares the facial information recognized by the facial recognition unit with multiple certificate databases. The matching unit performs matching using, for example, AI. The matching unit compares the certificate information stored in the certificate database with the facial information to verify the identity of the individual. Specifically, the matching unit receives the facial information provided by the facial recognition unit and accesses the certificate database. The certificate database stores the facial information and other personal information of the individual, and the matching unit compares this information with the facial information provided by the facial recognition unit. Using a matching algorithm, the degree of agreement between the facial information and the certificate information is evaluated to improve the accuracy of identity verification. Matching algorithms include, for example, cosine similarity and Euclidean distance, which are used to calculate the similarity of the facial information. Based on the matching results, the matching unit determines whether the individual is a person registered in the certificate database. The matching unit processes the matching results in real time, enabling rapid identity verification. Furthermore, the matching unit can refer to multiple certificate databases and improve the accuracy of identity verification by performing matching across different databases. This allows the matching unit to perform accurate and rapid identity verification, improving the reliability of the entire security management system.

[0068] The determination unit determines the range of the subject's movements based on the results verified by the matching unit. The determination unit uses AI, for example, to determine the range of movements. Specifically, the determination unit receives the verification results provided by the verification unit and determines the subject's range of movements according to criteria such as geographical range and accessible areas. Based on the verification results, the determination unit identifies areas that the subject can access and routes that they can travel. For example, if the subject has permission to access a specific area, the determination unit grants access to that area. On the other hand, it restricts access to areas that the subject does not have access to. The determination unit can analyze the verification results using AI and dynamically determine the subject's range of movements. The AI ​​can learn from past data and behavioral patterns to predict the subject's range of movements. This allows the determination unit to determine the subject's range of movements with high accuracy and improve the security of the entire security management system. Furthermore, the determination unit can update the range of movements in real time, enabling flexible responses according to the subject's situation. This allows the determination unit to efficiently and accurately determine the subject's range of movements and improve the performance of the entire security management system.

[0069] The service provider grants permission to pass within the range of movement determined by the judgment unit. The service provider grants permission using, for example, AI. Specifically, the service provider receives the range of movement information provided by the judgment unit and grants permission to pass within the area accessible to the subject. The service provider provides results such as unlocking doors or opening gates. For example, the service provider controls door unlocking devices or gate opening devices to allow the subject to enter the accessible area. The service provider can use AI to make decisions on granting permission to pass and control passage in real time. The AI ​​can learn the subject's behavior patterns and past data to optimize decisions on granting permission to pass. This allows the service provider to grant permission efficiently and accurately, improving the overall security of the security management system. Furthermore, the service provider records the history of permission to pass and can refer to it later. This allows the service provider to understand the operational status of the entire security management system based on the history of permission to pass and make improvements as needed. This allows the service provider to grant permission efficiently and accurately, improving the overall performance of the security management system.

[0070] The face recognition unit performs face recognition using multiple cameras. For example, the face recognition unit improves the accuracy of face recognition by taking pictures of faces from different angles using multiple cameras. By using multiple cameras, the face recognition unit can capture facial features in more detail. For example, the face recognition unit improves the accuracy of face recognition by optimizing the number and placement of cameras. The face recognition unit improves the accuracy of face recognition by selecting the type and performance of the cameras to be used. As a result, the accuracy of face recognition is improved by using multiple cameras. Some or all of the above processing in the face recognition unit may be performed using AI, for example, or without AI. For example, the face recognition unit can input image data acquired from multiple cameras into a generating AI and have the generating AI perform processing to improve the accuracy of face recognition.

[0071] The matching unit includes a database management unit that manages the certificate database. The matching unit optimizes the update frequency and management method of the certificate database, for example, to improve the accuracy of matching. The matching unit uses the database management unit to evaluate the reliability of the certificate database and prioritizes the use of highly reliable certificate information for matching. The matching unit optimizes the management method of the certificate database, for example, to improve the accuracy of matching. The matching unit uses the database management unit to adjust the update frequency of the certificate database and use the latest certificate information for matching. This makes the management of the certificate database easier and improves the accuracy of matching. Some or all of the above processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can have a generating AI perform the management of the certificate database to improve the accuracy of matching.

[0072] The service provider provides results such as unlocking doors or opening gates. The service provider controls, for example, door unlocking devices or gate opening devices to allow passage. The service provider uses AI to automate door unlocking and gate opening. The service provider sets, for example, door unlocking conditions or gate opening conditions, and allows passage when the conditions are met. The service provider provides the results of door unlocking and gate opening in real time to improve the convenience of passage. This improves the convenience of passage by automating door unlocking and gate opening. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can have a generating AI perform the control of door unlocking and gate opening to improve the convenience of passage.

[0073] The facial recognition unit estimates the subject's emotions and adjusts the accuracy of facial recognition based on the estimated emotions. The facial recognition unit estimates the subject's emotions using, for example, an emotion estimation algorithm. The facial recognition unit adjusts the accuracy of facial recognition according to the subject's emotions. For example, if the subject is nervous, it integrates images from multiple cameras to improve the accuracy of facial recognition. If the subject is relaxed, the facial recognition unit uses a normal facial recognition algorithm for recognition. If the subject is in a hurry, the facial recognition unit prioritizes the speed of facial recognition and performs recognition quickly. This improves recognition accuracy by adjusting the accuracy of facial recognition according to the subject's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 facial recognition unit may be performed using, for example, AI, or not using AI. For example, the facial recognition unit can input the subject's emotional data into the generating AI, which can then perform adjustments to the accuracy of facial recognition based on those emotions.

[0074] The face recognition unit optimizes the recognition algorithm by referring to the subject's past face recognition history during face recognition. For example, the face recognition unit analyzes past face recognition history and adjusts parameters to improve recognition accuracy. The face recognition unit improves recognition accuracy for specific time periods and locations based on the subject's past face recognition history. The face recognition unit updates the learning data for the recognition algorithm based on the subject's past face recognition history. This improves the accuracy of the recognition algorithm by referring to past face recognition history. Some or all of the above processes in the face recognition unit may be performed using AI, for example, or without AI. For example, the face recognition unit can input past face recognition history data into a generating AI and have the generating AI perform the optimization of the recognition algorithm.

[0075] The face recognition unit performs correction processing to accommodate changes in the angle and expression of the subject's face during face recognition. For example, if the angle of the face changes, the face recognition unit uses a correction algorithm to perform accurate face recognition. If the subject's expression changes, the face recognition unit uses an expression recognition algorithm to perform correction. If the subject is wearing a mask, the face recognition unit performs correction processing that takes the mask into account. This improves the accuracy of face recognition by accommodating changes in the angle and expression of the face. Some or all of the above processing in the face recognition unit may be performed using AI, for example, or without AI. For example, the face recognition unit can input data on changes in the angle of the face and expression into a generating AI and have the generating AI perform the correction processing.

[0076] The facial recognition unit estimates the subject's emotions and adjusts the timing of facial recognition based on the estimated emotions. The facial recognition unit estimates the subject's emotions using, for example, an emotion estimation algorithm. The facial recognition unit adjusts the timing of facial recognition according to the subject's emotions. For example, if the subject is tense, the timing of facial recognition is delayed to help them relax. If the subject is relaxed, the facial recognition unit performs facial recognition at the normal timing. If the subject is in a hurry, the facial recognition unit speeds up the timing of facial recognition for faster recognition. By adjusting the timing of facial recognition according to the subject's emotions, recognition accuracy is improved. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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 facial recognition unit may be performed using, for example, AI, or not using AI. For example, the facial recognition unit can input the subject's emotional data into the generating AI, which can then perform adjustments to the timing of facial recognition based on those emotions.

[0077] The face recognition unit improves recognition accuracy by considering the subject's height and body type information during face recognition. For example, the face recognition unit improves recognition accuracy by adjusting the camera angle based on the subject's height information. The face recognition unit adjusts the parameters of the face recognition algorithm based on the subject's body type information. The face recognition unit updates the learning data of the recognition algorithm based on the subject's height and body type information. As a result, the accuracy of face recognition is improved by considering height and body type information. Some or all of the above processes in the face recognition unit may be performed using AI, for example, or without AI. For example, the face recognition unit can input the subject's height and body type information into a generating AI and have the generating AI perform the improvement of recognition accuracy.

[0078] The face recognition unit improves recognition accuracy by referring to information about the subject's clothing and accessories during face recognition. For example, the face recognition unit adjusts the parameters of the face recognition algorithm based on the subject's clothing information. The face recognition unit adjusts the parameters of the face recognition algorithm based on the subject's accessory information. The face recognition unit updates the training data of the recognition algorithm based on the subject's clothing and accessory information. As a result, the accuracy of face recognition is improved by referring to information about clothing and accessories. Some or all of the above processes in the face recognition unit may be performed using AI, for example, or without AI. For example, the face recognition unit can input the subject's clothing and accessory information into a generating AI and have the generating AI perform the improvement of recognition accuracy.

[0079] The matching unit estimates the subject's emotions and determines matching priorities based on the estimated emotions. The matching unit estimates the subject's emotions using, for example, an emotion estimation algorithm. The matching unit determines matching priorities according to the subject's emotions. For example, if the subject is nervous, the matching priority is increased and matching is performed quickly. If the subject is relaxed, the matching unit performs matching with normal priority. If the subject is in a hurry, the matching unit increases the matching priority and performs matching quickly. This improves the accuracy of matching by determining matching priorities according to the subject's emotions. 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 matching unit may be performed using, for example, AI, or not using AI. For example, the matching unit can input the subject's emotion data into a generative AI and have the generative AI perform emotion-based matching priority determination.

[0080] The matching unit improves the accuracy of the matching results by evaluating the reliability of multiple certificate databases during matching. For example, the matching unit evaluates the reliability of multiple certificate databases and prioritizes matching with highly reliable databases. The matching unit evaluates the reliability of certificate databases and excludes less reliable databases from matching. The matching unit evaluates the reliability of multiple certificate databases and optimizes the matching results based on highly reliable databases. This improves the accuracy of the matching results by evaluating the reliability of multiple certificate databases. Some or all of the above processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can have a generating AI perform the reliability evaluation of multiple certificate databases to improve the accuracy of the matching results.

[0081] The matching unit optimizes the matching results by considering the certificate's expiration date and issuer information during the matching process. For example, the matching unit checks the certificate's expiration date and uses only valid certificates for matching. The matching unit checks the certificate's issuer information and prioritizes matching certificates from highly reliable issuers. The matching unit optimizes the matching results based on the certificate's expiration date and issuer information. This improves the accuracy of the matching results by considering the certificate's expiration date and issuer information. Some or all of the above processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input the certificate's expiration date and issuer information into a generating AI and have the generating AI perform the optimization of the matching results.

[0082] The matching unit estimates the subject's emotions and adjusts the display method of the matching results based on the estimated emotions. The matching unit estimates the subject's emotions using, for example, an emotion estimation algorithm. The matching unit adjusts the display method of the matching results according to the subject's emotions. For example, if the subject is tense, it provides a simple and highly visible display method. If the subject is relaxed, it provides a display method that includes detailed information. If the subject is in a hurry, it provides a concise display method. This improves visibility by adjusting the display method of the matching results according to the subject's emotions. 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 matching unit may be performed using, for example, AI, or not using AI. For example, the matching unit can input the subject's emotion data into the generative AI and have the generative AI perform the emotion-based adjustment of the display method of the matching results.

[0083] The matching unit improves the reliability of the matching results by considering the geographical location information of the subject during matching. The matching unit evaluates the reliability of the matching results based on the geographical location information of the subject, for example. The matching unit provides highly reliable matching results by considering the geographical location information of the subject. The matching unit improves the accuracy of the matching results based on the geographical location information of the subject. As a result, the reliability of the matching results is improved by considering geographical location information. Some or all of the above processing in the matching unit may be performed using AI, for example, or without using AI. For example, the matching unit can input the geographical location information of the subject into a generating AI and have the generating AI perform the improvement of the reliability of the matching results.

[0084] The matching unit optimizes the matching results by referring to the subject's past behavioral history during matching. The matching unit improves the reliability of the matching results based on the subject's past behavioral history, for example. The matching unit improves the accuracy of the matching results by referring to the subject's past behavioral history. The matching unit optimizes the matching results based on the subject's past behavioral history. As a result, the accuracy of the matching results is improved by referring to past behavioral history. Some or all of the above processing in the matching unit may be performed using AI, for example, or without using AI. For example, the matching unit can input the subject's past behavioral history data into a generating AI and have the generating AI perform the optimization of the matching results.

[0085] The Judgment Unit estimates the subject's emotions and adjusts the criteria for determining the range of action based on the estimated emotions. The Judgment Unit estimates the subject's emotions using, for example, an emotion estimation algorithm. The Judgment Unit adjusts the criteria for determining the range of action according to the subject's emotions. For example, if the subject is tense, the Judgment Unit tightens the criteria for determining the range of action to enhance security. If the subject is relaxed, the Judgment Unit determines the range of action using the normal criteria. If the subject is in a hurry, the Judgment Unit loosens the criteria for determining the range of action to allow for quick passage. This improves security by adjusting the criteria for determining the range of action according to the subject's emotions. 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, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Judgment Unit may be performed using, for example, AI, or not using AI. For example, the judgment unit can input the subject's emotional data into the generating AI, and have the generating AI perform adjustments to the criteria for determining the range of actions based on those emotions.

[0086] The judgment unit optimizes the judgment algorithm by referring to the subject's past behavioral history when making a judgment. The judgment unit adjusts the parameters of the judgment algorithm based on the subject's past behavioral history, for example. The judgment unit improves the accuracy of determining the range of movement by referring to the subject's past behavioral history. The judgment unit updates the learning data of the judgment algorithm based on the subject's past behavioral history. As a result, the accuracy of the judgment algorithm is improved by referring to past behavioral history. Some or all of the above processes in the judgment unit may be performed using AI, for example, or without using AI. For example, the judgment unit can input the subject's past behavioral history data into a generating AI and have the generating AI perform the optimization of the judgment algorithm.

[0087] The determination unit customizes the scope of action based on the subject's job title and duties during the determination process. For example, the determination unit customizes the scope of action based on the subject's job title information. The determination unit customizes the scope of action based on the subject's duties. The determination unit adjusts the criteria for determining the scope of action based on the subject's job title and duties. This improves security by customizing the scope of action based on job title and duties. Some or all of the above processing in the determination unit may be performed using AI, for example, or without AI. For example, the determination unit can input the subject's job title and duties data into a generating AI and have the generating AI perform the customization of the scope of action.

[0088] The Judgment Unit estimates the subject's emotions and determines the priority of the range of actions based on the estimated emotions. The Judgment Unit estimates the subject's emotions using, for example, an emotion estimation algorithm. The Judgment Unit determines the priority of the range of actions according to the subject's emotions. For example, if the subject is nervous, the Judgment Unit increases the priority of the range of actions and makes a quick decision. If the subject is relaxed, the Judgment Unit determines the range of actions with normal priority. If the subject is in a hurry, the Judgment Unit increases the priority of the range of actions and makes a quick decision. This improves the accuracy of the decision by determining the priority of the range of actions according to the subject's emotions. 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 Judgment Unit may be performed using, for example, an AI, or not using an AI. For example, the Judgment Unit can input the subject's emotion data into a generative AI and have the generative AI perform the determination of priority of the range of actions based on emotions.

[0089] The determination unit optimizes the range of activity by considering the subject's geographical location information during the determination process. The determination unit optimizes the range of activity based on the subject's geographical location information, for example. The determination unit improves the accuracy of determining the range of activity by considering the subject's geographical location information. The determination unit adjusts the criteria for determining the range of activity based on the subject's geographical location information. As a result, the accuracy of determining the range of activity is improved by considering geographical location information. Some or all of the above processes in the determination unit may be performed using AI, for example, or without using AI. For example, the determination unit can input the subject's geographical location information into a generating AI and have the generating AI perform the optimization of the range of activity.

[0090] The determination unit adjusts the range of activity by referring to the subject's working hours and shift information when making a determination. For example, the determination unit adjusts the range of activity based on the subject's working hours information. The determination unit adjusts the range of activity based on the subject's shift information. The determination unit adjusts the criteria for determining the range of activity based on the subject's working hours and shift information. This improves the accuracy of determining the range of activity by referring to working hours and shift information. Some or all of the above processing in the determination unit may be performed using AI, for example, or without using AI. For example, the determination unit can input the subject's working hours and shift information into a generating AI and have the generating AI perform the adjustment of the range of activity.

[0091] The service provider estimates the subject's emotions and adjusts the display method of the results based on the estimated emotions. The service provider estimates the subject's emotions using, for example, an emotion estimation algorithm. The service provider adjusts the display method of the results according to the subject's emotions. For example, if the subject is nervous, it provides a simple and highly visible display method. If the subject is relaxed, it provides a display method that includes detailed information. If the subject is in a hurry, it provides a display method that gets straight to the point. By adjusting the display method of the results according to the subject's emotions, visibility is improved. 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 service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the subject's emotion data into a generative AI and have the generative AI perform the adjustment of the display method of the results based on the emotions.

[0092] The service delivery unit optimizes the delivery results by referring to the target user's past usage history at the time of delivery. The service delivery unit improves the accuracy of the delivery results based on the target user's past usage history. The service delivery unit improves the reliability of the delivery results by referring to the target user's past usage history. The service delivery unit optimizes the delivery results based on the target user's past usage history. As a result, the accuracy of the delivery results is improved by referring to past usage history. Some or all of the above processing in the service delivery unit may be performed using AI, for example, or without using AI. For example, the service delivery unit can input the target user's past usage history data into a generating AI and have the generating AI perform the optimization of the delivery results.

[0093] The delivery unit customizes the delivery results based on the recipient's current situation at the time of delivery. The delivery unit customizes the delivery results based on the recipient's current situation, for example. The delivery unit improves the accuracy of the delivery results by considering the recipient's current situation. The delivery unit optimizes the delivery results based on the recipient's current situation. As a result, the accuracy of the delivery results is improved by customizing the delivery results based on the current situation. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without using AI. For example, the delivery unit can input the recipient's current situation data into a generating AI and have the generating AI perform the customization of the delivery results.

[0094] The delivery unit estimates the subject's emotions and determines the priority of the results to be delivered based on the estimated emotions. The delivery unit estimates the subject's emotions using, for example, an emotion estimation algorithm. The delivery unit determines the priority of the results to be delivered according to the subject's emotions. For example, if the subject is nervous, the delivery unit increases the priority of the results and delivers them quickly. If the subject is relaxed, the delivery unit delivers them with the normal priority. If the subject is in a hurry, the delivery unit increases the priority of the results and delivers them quickly. This improves the accuracy of the delivery by determining the priority of the results according to the subject's emotions. 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 delivery unit may be performed using, for example, AI, or not using AI. For example, the delivery unit can input the subject's emotion data into a generative AI and have the generative AI perform the priority determination of the results to be delivered based on emotions.

[0095] The delivery unit optimizes the delivery results by considering the geographical location information of the recipient at the time of delivery. The delivery unit optimizes the delivery results based on the geographical location information of the recipient, for example. The delivery unit improves the accuracy of the delivery results by considering the geographical location information of the recipient. The delivery unit improves the reliability of the delivery results based on the geographical location information of the recipient. As a result, the accuracy of the delivery results is improved by considering geographical location information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without using AI. For example, the delivery unit can input the geographical location information of the recipient into a generating AI and have the generating AI perform the optimization of the delivery results.

[0096] The service provider analyzes the target's social media activity at the time of delivery and customizes the delivery results. The service provider customizes the delivery results based on the target's social media activity, for example. The service provider analyzes the target's social media activity to improve the accuracy of the delivery results. The service provider improves the reliability of the delivery results based on the target's social media activity. As a result, the accuracy of the delivery results is improved by analyzing social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the target's social media activity data into a generating AI and have the generating AI perform the customization of the delivery results.

[0097] The Database Management Department estimates the emotions of the subject and adjusts the database management method based on the estimated emotions. The Database Management Department estimates the emotions of the subject using, for example, an emotion estimation algorithm. The Database Management Department adjusts the database management method according to the emotions of the subject. For example, if the subject is tense, the database management method is simplified for faster management. If the subject is relaxed, the Database Management Department manages the database using the normal management method. If the subject is in a hurry, the Database Management Department simplifies the database management method for faster management. This improves the accuracy of management by adjusting the database management method according to the emotions of the subject. 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 Database Management Department may be performed using, for example, AI, or not using AI. For example, the Database Management Department can input the subject's emotion data into a generative AI and have the generative AI perform the emotion-based adjustment of the database management method.

[0098] The Database Management Department improves the accuracy of the database by evaluating the reliability of certificates during database management. For example, the Database Management Department evaluates the reliability of certificates and prioritizes registering highly reliable certificates in the database. The Database Management Department evaluates the reliability of certificates and manages the database by excluding less reliable certificates. The Database Management Department evaluates the reliability of certificates and improves the accuracy of the database based on highly reliable certificates. In this way, the accuracy of the database is improved by evaluating the reliability of certificates. Some or all of the above processes in the Database Management Department may be performed using AI, for example, or not using AI. For example, the Database Management Department can improve the accuracy of the database by having a generating AI perform the certificate reliability evaluation.

[0099] The Database Management Department estimates the emotions of the subject and adjusts the database update frequency based on the estimated emotions. The Database Management Department estimates the emotions of the subject using, for example, an emotion estimation algorithm. The Database Management Department adjusts the database update frequency according to the emotions of the subject. For example, if the subject is tense, the database update frequency is increased to update quickly. If the subject is relaxed, the Database Management Department updates the database at the normal update frequency. If the subject is in a hurry, the Database Management Department increases the database update frequency to update quickly. This improves the accuracy of updates by adjusting the database update frequency according to the emotions of the subject. 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 Database Management Department may be performed using, for example, AI, or not using AI. For example, the Database Management Department can input the emotions of the subject into a generative AI and have the generative AI perform the emotion-based adjustment of the database update frequency.

[0100] The Database Management Department improves the reliability of the database by considering the issuer and expiration date of certificates during database management. For example, the Database Management Department prioritizes registering highly reliable certificates in the database based on the issuer information. The Database Management Department checks the expiration date of certificates and registers only valid certificates in the database. The Database Management Department improves the reliability of the database based on the issuer and expiration date of certificates. As a result, the reliability of the database is improved by considering the issuer and expiration date of certificates. Some or all of the above processes in the Database Management Department may be performed using AI, for example, or not using AI. For example, the Database Management Department can input certificate issuer and expiration date information into a generating AI and have the generating AI perform the database reliability improvement.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The facial recognition unit can improve recognition accuracy by using the subject's voiceprint information in conjunction with facial recognition. For example, the facial recognition unit collects the subject's spoken voice and extracts voiceprint information using a voiceprint recognition algorithm. Next, the facial recognition unit integrates the facial information and voiceprint information to improve recognition accuracy. Furthermore, the facial recognition unit can analyze the subject's voice tone and speaking style characteristics and use them as auxiliary information for facial recognition. In this way, the facial recognition unit can improve recognition accuracy by combining facial information and voiceprint information.

[0103] The matching unit can improve the accuracy of the matching results by considering the subject's health status. For example, the matching unit collects vital data such as the subject's heart rate and body temperature and evaluates their health status. Next, the matching unit adjusts the parameters of the matching algorithm based on the health status to improve the accuracy of the matching results. Furthermore, if the health status is unstable, the matching unit evaluates the reliability of the matching results and can perform re-matching as needed. In this way, the matching unit can improve the accuracy of the matching results by considering the health status.

[0104] The information provider can estimate the subject's emotions and adjust the content of the information provided based on those emotions. For example, the provider can use an emotion estimation algorithm to estimate the subject's emotions and, if the subject is tense, provide information to help them relax. Next, if the subject is relaxed, the provider provides normal information. Furthermore, if the subject is in a hurry, the provider can quickly provide the necessary information. In this way, the information provider improves the accuracy of information provision by adjusting the content of the information provided according to the subject's emotions.

[0105] The judgment unit can estimate the subject's emotions and adjust the method of notifying the subject of the action range determination based on the estimated emotions. For example, the judgment unit uses an emotion estimation algorithm to estimate the subject's emotions and provides a simple and easy-to-understand notification method if the subject is tense. Next, if the judgment unit is relaxed, it provides a notification method that includes detailed information. Furthermore, if the judgment unit is in a hurry, it can provide a concise and rapid notification method. In this way, the judgment unit improves the accuracy of notifications by adjusting the notification method according to the subject's emotions.

[0106] The database management unit can estimate the emotions of individuals and adjust database access permissions based on those estimated emotions. For example, the database management unit can use an emotion estimation algorithm to estimate an individual's emotions and, if the individual is stressed, restrict access permissions to enhance security. Next, if the individual is relaxed, the database management unit provides normal access permissions. Furthermore, if the individual is in a hurry, the database management unit can adjust permissions to allow for quick access. In this way, the database management unit can improve security and convenience by adjusting access permissions according to the emotions of individuals.

[0107] The facial recognition unit can improve recognition accuracy by using the subject's walking pattern in conjunction with facial recognition. For example, the facial recognition unit collects the subject's walking pattern and extracts walking information using a walking recognition algorithm. Next, the facial recognition unit integrates the facial information and walking information to improve recognition accuracy. Furthermore, the facial recognition unit can analyze the characteristics of the subject's walking speed and stride length and use this as auxiliary information for facial recognition. In this way, the facial recognition unit can improve recognition accuracy by combining facial information and walking information.

[0108] The matching unit can improve the accuracy of the matching results by considering the job content of the subject. For example, the matching unit can adjust the parameters of the matching algorithm based on the job content of the subject to improve the accuracy of the matching results. Next, the matching unit can evaluate the reliability of the matching results according to the job content and perform re-matching as necessary. Furthermore, if the job content changes, the matching unit can update the training data of the matching algorithm to improve the accuracy of the matching results. In this way, the matching unit can improve the accuracy of the matching results by considering the job content.

[0109] The delivery unit can estimate the subject's emotions and determine the priority of the results to be delivered based on the estimated emotions. For example, the delivery unit uses an emotion estimation algorithm to estimate the subject's emotions and, if the subject is tense, increases the priority of the results to be delivered quickly. Next, if the subject is relaxed, the delivery unit delivers the results with the normal priority. Furthermore, if the subject is in a hurry, the delivery unit can increase the priority of the results to be delivered quickly. In this way, the delivery unit improves the accuracy of its deliveries by determining the priority of the results according to the subject's emotions.

[0110] The determination unit can adjust the criteria for determining the range of activity by referring to the subject's past behavioral history. For example, the determination unit analyzes the subject's past behavioral history and optimizes the criteria for determining the range of activity. Next, the determination unit improves the accuracy of determining the range of activity based on the past behavioral history. Furthermore, the determination unit can update the training data for the range of activity determination algorithm based on the past behavioral history. In this way, the determination unit can improve the accuracy of determining the range of activity by referring to the past behavioral history.

[0111] The database management department can adjust its database management methods by considering the geographical location information of the subjects. For example, the database management department optimizes its database management methods based on the geographical location information of the subjects. Next, the database management department adjusts the update frequency of the database based on the geographical location information to maintain up-to-date information. Furthermore, the database management department can evaluate the reliability of the database based on the geographical location information and prioritize the management of highly reliable information. In this way, the database management department can improve the accuracy of database management by considering geographical location information.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The face recognition unit recognizes the subject's face. The face recognition unit captures the subject's face through the camera and recognizes the face using a face recognition algorithm. For example, it detects the face using a face detection algorithm and extracts facial features using a feature extraction method. Step 2: The matching unit compares the facial information recognized by the facial recognition unit with multiple certificate databases. The matching unit compares the facial information with the certificate information stored in the certificate databases to verify the identity of the person. For example, it uses a matching algorithm to compare the facial information with the certificate information and evaluates the accuracy of the matching. Step 3: The determination unit determines the subject's range of activity based on the results matched by the matching unit. The determination unit determines the subject's range of activity based on the matching results according to criteria such as geographical range and accessible area. Step 4: The providing unit permits passage within the range of action determined by the determining unit. The providing unit provides results such as unlocking doors or opening gates. For example, it controls door unlocking devices or gate opening devices to permit passage.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the face recognition unit, matching unit, determination unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the face recognition unit uses the camera 42 of the smart device 14 to capture the subject's face and the processor 46 executes a face recognition algorithm. The matching unit uses the identification processing unit 290 of the data processing unit 12 to match the face information with the certificate database 24. The determination unit uses the identification processing unit 290 of the data processing unit 12 to determine the range of movement based on the matching result. The provision unit uses the control unit 46A of the smart device 14 to control unlocking doors or opening gates. 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.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the face recognition unit, matching unit, determination unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the face recognition unit uses the camera 42 of the smart glasses 214 to capture the subject's face and the processor 46 executes a face recognition algorithm. The matching unit uses the identification processing unit 290 of the data processing unit 12 to match the face information with the certificate database 24. The determination unit uses the identification processing unit 290 of the data processing unit 12 to determine the range of movement based on the matching result. The provision unit uses the control unit 46A of the smart glasses 214 to control unlocking doors or opening gates. 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.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the face recognition unit, matching unit, determination unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the face recognition unit uses the camera 42 of the headset terminal 314 to capture the subject's face and the processor 46 executes a face recognition algorithm. The matching unit uses the identification processing unit 290 of the data processing unit 12 to match the face information with the certificate database 24. The determination unit uses the identification processing unit 290 of the data processing unit 12 to determine the range of movement based on the matching result. The provision unit uses the control unit 46A of the headset terminal 314 to control unlocking doors or opening gates. 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.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] Each of the multiple elements described above, including the face recognition unit, matching unit, determination unit, and provision unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the face recognition unit uses the camera 42 of the robot 414 to photograph the subject's face and the processor 46 executes a face recognition algorithm. The matching unit uses the identification processing unit 290 of the data processing unit 12 to match the face information with the certificate database 24. The determination unit uses the identification processing unit 290 of the data processing unit 12 to determine the range of action based on the matching result. The provision unit uses the control unit 46A of the robot 414 to control the unlocking of doors or the opening of gates. 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] (Note 1) A facial recognition unit that recognizes the face of the subject, A matching unit that compares the facial information recognized by the facial recognition unit with multiple certificate databases, A determination unit that determines the range of movement of the subject based on the results of the verification unit, The system includes a provisioning unit that permits passage within the range of action determined by the determination unit. A system characterized by the following features. (Note 2) The aforementioned face recognition unit, Perform facial recognition using multiple cameras. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned verification unit is It includes a database management unit that manages the certificate database. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provides results such as unlocking doors and opening gates. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned face recognition unit, The system estimates the emotions of the subject and adjusts the accuracy of facial recognition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned face recognition unit, During face recognition, the recognition algorithm is optimized by referring to the subject's past face recognition history. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned face recognition unit, During face recognition, correction processing is performed to accommodate changes in the angle and expression of the subject's face. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned face recognition unit, The system estimates the subject's emotions and adjusts the timing of facial recognition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned face recognition unit, When performing facial recognition, the recognition accuracy is improved by taking into account the subject's height and body type information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned face recognition unit, During facial recognition, the system improves recognition accuracy by referencing information about the subject's clothing and accessories. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned verification unit is The system estimates the emotions of the subjects and determines the matching priority based on the estimated emotions of the subjects. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned verification unit is During the matching process, the reliability of multiple certificate databases is evaluated to improve the accuracy of the matching results. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned verification unit is During verification, the verification results are optimized by considering the certificate's expiration date and issuing authority information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned verification unit is The system estimates the emotions of the subjects and adjusts the display method of the matching results based on the estimated emotions of the subjects. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned verification unit is During matching, the reliability of the matching results is improved by considering the geographical location information of the subject. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned verification unit is During the matching process, the matching results are optimized by referring to the subject's past behavioral history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The determination unit, The system estimates the emotions of the subjects and adjusts the criteria for determining the scope of their actions based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The determination unit, During the assessment process, the assessment algorithm is optimized by referring to the subject's past behavioral history. The system described in Appendix 1, characterized by the features described herein. (Note 19) The determination unit, During the assessment, the scope of action is customized based on the subject's position and job responsibilities. The system described in Appendix 1, characterized by the features described herein. (Note 20) The determination unit, The system estimates the emotions of the target individuals and determines the priority of actions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The determination unit, When making a decision, the range of activity is optimized by considering the subject's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The determination unit, During the assessment, the scope of activity is adjusted by referring to the subject's working hours and shift information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, The system estimates the emotions of the subjects and adjusts how the results are displayed based on the estimated emotions of the subjects. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing the service, the service results are optimized by referring to the user's past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, At the time of delivery, the results will be customized based on the current situation of the target audience. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, The system estimates the emotions of the target audience and prioritizes the results provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, the delivery results are optimized by considering the geographical location information of the target audience. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, the social media activity of the target audience is analyzed to customize the results. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned database management unit, The system estimates the emotions of the subjects and adjusts the database management method based on the estimated emotions of the subjects. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned database management unit, When managing a database, evaluate the reliability of certificates to improve the accuracy of the database. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned database management unit, The system estimates the emotions of the subjects and adjusts the database update frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned database management unit, When managing a database, consider the issuer and expiration date of the certificate to improve the reliability of the database. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0186] 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 facial recognition unit that recognizes the face of the subject, A matching unit that compares the facial information recognized by the facial recognition unit with multiple certificate databases, A determination unit that determines the range of movement of the subject based on the results of the verification unit, The system includes a provisioning unit that permits passage within the range of action determined by the determination unit. A system characterized by the following features.

2. The aforementioned face recognition unit, Perform facial recognition using multiple cameras. The system according to feature 1.

3. The aforementioned verification unit is It includes a database management unit that manages the certificate database. The system according to feature 1.

4. The aforementioned supply unit is, Provides results such as unlocking doors and opening gates. The system according to feature 1.

5. The aforementioned face recognition unit, The system estimates the emotions of the subject and adjusts the accuracy of facial recognition based on the estimated emotions. The system according to feature 1.

6. The aforementioned face recognition unit, During face recognition, the recognition algorithm is optimized by referring to the subject's past face recognition history. The system according to feature 1.

7. The aforementioned face recognition unit, During face recognition, correction processing is performed to accommodate changes in the angle and expression of the subject's face. The system according to feature 1.

8. The aforementioned face recognition unit, The system estimates the subject's emotions and adjusts the timing of facial recognition based on the estimated emotions. The system according to feature 1.

9. The aforementioned face recognition unit, When performing facial recognition, the recognition accuracy is improved by taking into account the subject's height and body type information. The system according to feature 1.

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