System

Abstract

A system is provided.SOLUTION: The system includes a monitoring device attached to an old communication infrastructure, a central processing unit for receiving video data transmitted from the monitoring device, an artificial intelligence means for analyzing the video data in the central processing unit, a notification means for notifying a user of abnormality when the abnormality is detected, and a terminal for the user to confirm the video data.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] The present invention relates to a security system that effectively utilizes old communication infrastructure (e.g., old PHS antennas) to perform wide-area real-time video monitoring and anomaly detection. While much of the old communication infrastructure has been left abandoned due to the evolution of the mobile communication environment, improving security has become an important issue in urban and residential areas. However, existing security systems have problems such as installation costs, limited monitoring ranges, and inaccuracy in anomaly detection. Therefore, the present invention aims to solve these problems and provide an efficient and highly accurate security system. [Means for solving the problem]

[0005] The present invention provides a system that includes a monitoring device attached to an old communication infrastructure, a central processing unit that receives video data transmitted from the monitoring device, an artificial intelligence (AI) means in the central processing unit that analyzes the video data, a notification means that notifies the user when an abnormality is detected, and a terminal on which the user can view the video data. Specifically, by reusing the old communication infrastructure, installation costs are reduced and a monitoring network that covers a wide area is constructed. The video data received by the central processing unit is analyzed using AI to detect an abnormality through movement patterns and object recognition. If an abnormality is detected, the user is immediately notified, and the user can view the video data in real time via the terminal. The central processing unit also has a maintenance means that manages a periodic maintenance schedule and notifies the user when necessary. This makes it possible to effectively utilize the old communication infrastructure and provide an efficient, highly accurate security system.

[0006] "Old communications infrastructure" refers to communications facilities and equipment that were used in the past but are no longer in use.

[0007] "Surveillance equipment" refers to devices such as cameras and sensors used to monitor specific areas via video.

[0008] "Central processing unit" refers to a computer system that receives, analyzes, and stores video data transmitted from a monitoring device.

[0009] "Artificial intelligence means" refers to a system that includes algorithms and programs for analyzing video data and performing anomaly detection and motion pattern recognition.

[0010] "Notification means" refers to a system that sends alerts or notifications to users when an abnormality is detected.

[0011] "Terminal" refers to a device such as a smartphone, tablet, or PC that a user uses to check video data or configure the system.

[0012] "Maintenance means" refers to the function of carrying out regular maintenance of the system and monitoring device and notifying the user when necessary. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0014] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0017] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0018] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0019] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0026] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0033] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0034] The present invention provides a security system that covers a wide area by reusing the old communication infrastructure. Specific embodiments of the present invention are described below.

[0035] 1. Camera installation and initial settings

[0036] Camera installation

[0037] The user physically installs a security camera near the old communication infrastructure (old PHS antenna, etc.). The camera is weatherproof and designed to withstand the external environment.

[0038] Network Connection

[0039] The server obtains the camera's IP address and configures the network settings. The server also authenticates the camera's MAC address and registers it in its internal database.

[0040] Initial Setup

[0041] The user accesses the camera's settings page through a dedicated app or web interface. The user enters initial settings such as the camera name (e.g., front door camera) and monitoring area, and the server confirms these settings and applies them to the camera.

[0042] 2. Real-time video monitoring

[0043] Video capture and transmission

[0044] The camera captures video of the monitored area in real time, compresses and encrypts the video data, and sends it to the server.

[0045] Receiving and storing video

[0046] The server receives the video data sent from the camera, stores it in a temporary buffer, and also backs it up in a database for permanent storage.

[0047] User monitoring

[0048] Users access the system via a dedicated app or web interface on their smartphone or PC and view footage in real time.

[0049] 3. Anomaly Detection

[0050] Video analysis

[0051] The server uses an AI model to analyze the received video data, recognize movement patterns and objects, and detect abnormalities.

[0052] Sending alerts

[0053] Any abnormalities detected by the server will be immediately notified to the user via push notification or email.

[0054] User Support

[0055] The user receives a notification and can view detailed video footage via a dedicated app or web interface. If an abnormality is detected, the user can take action, such as reporting the incident to the police.

[0056] 4. Maintenance Notice

[0057] Maintenance schedule management

[0058] The server manages the regular maintenance schedule for each camera in a database and has reminder logic.

[0059] Sending notifications

[0060] Check when the server needs maintenance and send a notification to users.

[0061] User Response

[0062] Users receive a notification, check the maintenance schedule using a dedicated app or web interface, and contact the person in charge to arrange a schedule.

[0063] 5. Data analysis and security consulting

[0064] Data collection

[0065] The server periodically backs up past video data and stores it in a database for analysis.

[0066] Data analysis

[0067] The server uses AI and data analysis tools to analyze the collected video data and extract abnormal behavior patterns and security vulnerabilities.

[0068] Reporting

[0069] The server automatically creates a security assessment report based on the analysis results, and in some cases adds additional comments from security experts.

[0070] User Feedback

[0071] The server sends the completed security report to the user, who receives the report and implements the suggested remediation measures.

[0072] Specific examples

[0073] For example, let's say a user installs a camera at the entrance of their home. The camera captures video in front of the entrance and sends it to a server. The server uses an AI model to analyze the video and detect suspicious activity. If a suspicious activity is detected, the server immediately sends an alert to the user. The user receives the notification, checks the video on their smartphone, and takes action, such as notifying the police, depending on the situation. The server also regularly notifies the user monthly about system maintenance, and the user contacts the person in charge to carry out the maintenance. The server then analyzes the data it collects, identifies trends such as suspicious activity patterns, and security specialists create a report based on the analysis results, proposing specific improvement measures. The user can then strengthen their security measures based on this report.

[0074] The above is an embodiment of the present invention, which makes it possible to effectively utilize the old communication infrastructure and provide a wide-area, highly accurate security system.

[0075] The processing flow will be explained below.

[0076] Camera installation and initial setup

[0077] Step 1: Camera installation

[0078] The user physically installs security cameras into the old communications infrastructure.

[0079] Step 2: Turn on the camera and connect to the network

[0080] The user powers on the camera and it connects to the network.

[0081] Step 3: Obtaining and registering an IP address

[0082] The server obtains the camera's IP address and recognizes it within the network.

[0083] Step 4: MAC Address Authentication

[0084] The server authenticates the camera's MAC address and registers it in its internal database.

[0085] Step 5: Initial Setup

[0086] Users access a dedicated app or web interface to set the camera name and monitoring area.

[0087] Step 6: Apply the settings

[0088] The server reflects the setting information received from the user in the camera.

[0089] Real-time video monitoring

[0090] Step 1: Capture the footage

[0091] The camera captures real-time images of the surveillance area.

[0092] Step 2: Sending video data

[0093] The video data captured by the camera is compressed, encrypted, and sent to the server.

[0094] Step 3: Receiving video data

[0095] The server receives the video data sent from the camera.

[0096] Step 4: Save

[0097] The video data received by the server is temporarily stored in a buffer.

[0098] Step 5: Permanent storage

[0099] The server backs up the video data in the temporary buffer to a database.

[0100] Step 6: User monitoring

[0101] Users access the system from their device (smartphone or PC) via a dedicated app or web interface and view the footage in real time.

[0102] Anomaly detection

[0103] Step 1: Analyzing the footage

[0104] The video data received by the server is input into an AI model to recognize movement patterns and objects.

[0105] Step 2: Anomaly detection

[0106] The server uses an AI model to detect anomalies.

[0107] Step 3: Alert Generation

[0108] If the server detects an abnormality, it generates alert data.

[0109] Step 4: Sending notifications

[0110] Send server-generated alerts to users via push notifications.

[0111] Step 5: User interaction

[0112] Users receive a notification and can view detailed footage in a dedicated app or web interface, and if necessary, call the police.

[0113] Maintenance Notification

[0114] Step 1: Schedule Management

[0115] The server stores the regular maintenance schedule for each camera in a management database.

[0116] Step 2: Trigger the reminder logic

[0117] The server will trigger reminder logic during maintenance periods.

[0118] Step 3: Sending notifications

[0119] Check when the server needs maintenance and send a notification to users.

[0120] Step 4: User response

[0121] Users will receive a notification and can check the maintenance schedule via a dedicated app or web interface.

[0122] Step 5: Scheduling

[0123] The user contacts the maintenance staff and schedules the maintenance.

[0124] Data analysis and security consulting

[0125] Step 1: Data collection

[0126] The server periodically backs up past video data and stores it in a database for analysis.

[0127] Step 2: Data analysis

[0128] The server uses AI and data analysis tools to analyze the collected video data and extract abnormal behavior patterns and security vulnerabilities.

[0129] Step 3: Reporting

[0130] The server automatically creates a security assessment report based on the analysis results.

[0131] Step 4: Staff review

[0132] Security professionals will review the report and provide additional comments and suggestions as needed.

[0133] Step 5: Send your feedback

[0134] The server sends the completed security report to the user.

[0135] Step 6: User execution

[0136] The user receives the report and implements the suggested improvements.

[0137] Example 1

[0138] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0139] Conventional security systems require the construction of new infrastructure, which is expensive, making it difficult to cover a wide area. Real-time anomaly detection and maintenance management are also inadequate. This leads to issues such as delays in rapid response and difficulty in long-term system operation.

[0140] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0141] In this invention, the server includes a monitoring device attached to the old communication infrastructure, a central processing unit that receives video data transmitted from the monitoring device, artificial intelligence means in the central processing unit that analyzes the video data, notification means that notifies the user when an abnormality is detected, a terminal for the user to check the video data, setting means that performs initial configuration of the monitoring device through the terminal, means that compress and encrypt the video data and transmit it, means that temporarily store the video data and back it up in a database for permanent storage, means that manage a periodic maintenance schedule, means that send reminders based on the maintenance schedule, means that periodically analyze the video data and create a security evaluation report, and means that feed back the security evaluation report to the user. This makes it possible to effectively utilize the old communication infrastructure to cover a wide area, and to enable real-time abnormality detection, prompt notification, and maintenance management.

[0142] "Old communications infrastructure" refers to communications technologies and equipment that were used in the past and are no longer in use but can be reused.

[0143] "Monitoring equipment" refers to equipment installed to monitor a specific area, and has the function of capturing video data and transmitting it to a central processing unit.

[0144] "Video data" refers to the data that can be stored and transmitted in digital format from images captured by surveillance equipment.

[0145] "Central processing unit" refers to a computer device that receives and analyzes video data transmitted from a monitoring device.

[0146] "Artificial intelligence means" refers to artificial intelligence (AI) technology used to analyze received video data and detect anomalies and recognize behavioral patterns.

[0147] "Notification means" refers to technology or devices that immediately notify the user when an abnormality is detected.

[0148] "Terminal" refers to the device used by the user to view video data and change system settings. Examples include smartphones and personal computers.

[0149] "Setting means" refers to a system that has the function of setting the initial settings of the monitoring device and various parameters through a terminal.

[0150] "Compression and encryption methods" refers to technologies that reduce the data size so that video data can be transmitted efficiently and encrypt it to maintain security.

[0151] "Temporary storage means" refers to a technology that has the function of temporarily storing video data in a buffer after receiving it.

[0152] A "permanent storage database" refers to a database system for safely storing important video data for a long period of time.

[0153] "Means for managing maintenance schedules" refers to a system that has the function of managing regular maintenance of monitoring devices and systems in a planned manner and encouraging maintenance to be carried out.

[0154] "Means for sending reminders" refers to technology that has a notification function to notify users of upcoming maintenance or other important events.

[0155] A "security assessment report" is a report that evaluates the security status of a system based on collected and analyzed data and proposes improvement measures.

[0156] "Feedback means" refers to a function that provides the created security evaluation report to the user and provides information for the user to take further measures.

[0157] The present invention provides a security system that covers a wide area by reusing old communication infrastructure. The system includes a monitoring device, a central processing unit, an artificial intelligence means, a notification means, a terminal, and various means related thereto.

[0158] Camera installation and initial setup

[0159] Camera installation

[0160] The user installs an all-weather surveillance device (security camera) near the old communication infrastructure (e.g., old PHS antenna). Specifically, the user fixes the camera using screws and brackets and connects a power cable. This allows the camera to be used in external environments.

[0161] Network Connection

[0162] The server obtains the camera's IP address via the DHCP server and configures the camera's network settings. The server also authenticates the camera's MAC address and registers it in its internal database. During this process, command line tools can be used to verify the network connection if necessary.

[0163] Initial Setup

[0164] The user accesses the camera's settings page using a dedicated app or web interface and enters initial settings such as the camera name and monitoring area. The server confirms these settings and applies them to the camera. The server then generates a configuration file in JSON or XML format and sends it to the camera to complete the setup.

[0165] Real-time video monitoring

[0166] Video capture and transmission

[0167] The camera captures video of the monitored area in real time, compresses the video data in H.264 format, encrypts it with AES, and sends it to the server.

[0168] Receiving and storing video

[0169] The server receives the video data sent from the camera, stores it in a temporary buffer, and also backs it up in a database for permanent storage. MySQL or PostgreSQL is used as the database management system.

[0170] User monitoring

[0171] Users access the system via a dedicated app or web interface on their smartphone or PC and view the video in real time. The video is displayed on the device using WebRTC and streaming technology.

[0172] Anomaly detection

[0173] Video analysis

[0174] The server analyzes the received video data using an AI model (e.g., YOLOv4, OpenCV), recognizing movement patterns and objects to detect abnormalities.

[0175] Sending alerts

[0176] If the server detects an abnormality, it immediately notifies the user via push notification or email using Firebase Cloud Messaging (FCM) or Twilio.

[0177] User Support

[0178] The user receives a notification and can view detailed video footage via a dedicated app or web interface. If an abnormality is detected, the user can take action, such as reporting the incident to the police.

[0179] Maintenance Notification

[0180] Maintenance schedule management

[0181] The server manages the regular maintenance schedule for each camera in a database and has reminder logic.

[0182] Sending notifications

[0183] Check when the server needs maintenance and send a notification to users.

[0184] User Response

[0185] Users receive a notification, check the maintenance schedule using a dedicated app or web interface, and contact the person in charge to arrange a schedule.

[0186] Data analysis and security consulting

[0187] Data collection

[0188] The server periodically backs up past video data and stores it in a database for analysis.

[0189] Data analysis

[0190] The server analyzes the collected video data using AI and data analysis tools (e.g., TensorFlow, PyTorch) to extract abnormal behavior patterns and security vulnerabilities.

[0191] Reporting

[0192] The server automatically creates a security assessment report based on the analysis results, and security specialists add comments as needed.

[0193] User Feedback

[0194] The server sends the completed security report to the user, who receives the report and implements the suggested remediation measures.

[0195] Specific examples

[0196] For example, let's say a user installs a camera at the entrance of their home. This camera constantly captures video of the area in front of the entrance and sends it to a server. The server then uses an AI model to analyze the video in real time and detect any suspicious activity. If a suspicious person is detected, the server immediately sends a push notification to the user's smartphone or PC, allowing the user to review the detailed video and then report the incident to the police.

[0197] The server also periodically manages camera and system maintenance schedules and sends timely reminders to users. Furthermore, the server analyzes the collected data, creates reports suggesting security improvements, and provides feedback to users, allowing users to continuously strengthen their system security.

[0198] An example prompt might be, "How do I configure an AI model to receive video data captured by a camera and detect abnormal behavior patterns?"

[0199] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0200] Step 1: Camera installation

[0201] The user physically installs an all-weather surveillance device (security camera) near the old communications infrastructure (e.g., old PHS antenna). The camera is fixed in place using screws and brackets, and a power cable is connected. After installation, the camera starts up by turning on the power. The inputs required are the camera's installation position and power source, and the output is a properly installed camera.

[0202] Step 2: Network Connection

[0203] The server obtains the camera's IP address via the DHCP server and configures the network settings. It also authenticates the camera's MAC address and registers it in its internal database. It checks the network connection using command line tools (e.g., ipconfig, ifconfig). The input is the camera's MAC address and network information, and the authenticated camera's IP address is obtained as output.

[0204] Step 3: Initial Setup

[0205] The user accesses the camera's settings page using a dedicated app or web interface and sets the camera's name (e.g., front door camera) and monitoring area. The server checks these settings and reflects them on the camera. A configuration file in JSON or XML format is generated and sent to the camera. The input is the configuration information entered by the user, and the output is the initial settings reflected on the camera.

[0206] Step 4: Record and send footage

[0207] The camera captures real-time video of the monitored area. The video data is compressed in H.264 format, encrypted with AES, and sent to the server. The input is real-time video of the monitored area, and the output is compressed and encrypted video data.

[0208] Step 5: Receive and save the footage

[0209] The server receives the video data sent from the camera and stores it in a temporary buffer. At the same time, it also creates a backup in a database for permanent storage. MySQL or PostgreSQL is used as the database management system. The input is compressed and encrypted video data, and the output is the temporarily stored and backed up video data.

[0210] Step 6: User monitoring

[0211] Users access the system on their smartphones or PCs through a dedicated app or web interface and view the video in real time. WebRTC and streaming technology are used to display the video on the device. The input is video data retrieved from the database, and the output is real-time video displayed on the user's device.

[0212] Step 7: Analyzing the footage

[0213] The server uses an AI model (e.g., YOLOv4, OpenCV) to analyze the received video data, recognizing movement patterns and objects to detect anomalies. The input is the received video data, and the output is anomaly detection information obtained as the analysis result.

[0214] Step 8: Sending an alert

[0215] If the server detects an anomaly, it immediately sends a notification to the user. Notifications are sent in the form of push notifications or emails using Firebase Cloud Messaging (FCM) or Twilio. The input is the anomaly detection information, and the output is a notification to the user.

[0216] Step 9: User interaction

[0217] The user receives a notification and checks the detailed video on a dedicated app or web interface. If an abnormality is detected, the user can take action, such as reporting the incident to the police. The input is the notification and detailed video data, and the output is the appropriate user response.

[0218] Step 10: Maintenance Schedule Management

[0219] The server manages the periodic maintenance schedule of each camera in a database and has reminder logic. The input is the camera's operational data and maintenance schedule, and the output is the managed maintenance schedule.

[0220] Step 11: Sending notifications

[0221] The server checks when maintenance is required and sends a notification to the user. The input is the maintenance schedule, and the output is the maintenance notification to the user.

[0222] Step 12: User Response

[0223] The user receives the notification, checks the maintenance schedule using a dedicated app or web interface, and contacts the person in charge to adjust the schedule. The input is the maintenance notification and schedule adjustment information, and the output is the adjusted maintenance schedule.

[0224] Step 13: Data collection

[0225] The server periodically backs up past video data and stores it in an analytical database. The input is past video data, and the output is the backup data.

[0226] Step 14: Data analysis

[0227] The server analyzes the collected video data using AI and data analysis tools (e.g., TensorFlow, PyTorch). It extracts abnormal behavior patterns and security vulnerabilities. The input is the backup data, and the output is the analysis results.

[0228] Step 15: Reporting

[0229] The server automatically creates a security assessment report based on the analysis results, and security experts add comments as needed. The input is the analysis results, and the created security assessment report is obtained as the output.

[0230] Step 16: User Feedback

[0231] The server sends the completed security assessment report to the user, who receives the report and implements the suggested remediation measures. The input is the security assessment report, and the output is the user's implementation of the security remediation measures.

[0232] (Application example 1)

[0233] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0234] In wide-area security systems that reuse old communication infrastructure, there is a need to provide new methods for users to remotely check camera footage in real time and quickly detect and respond to abnormalities. Another challenge is to centralize security management and reduce the burden on users by providing periodic maintenance notifications and security reports based on analysis results.

[0235] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0236] In this invention, the server includes a monitoring device attached to the old communication infrastructure, a central processing unit that receives video data transmitted from the monitoring device, artificial intelligence means in the central processing unit that analyzes the video data, notification means that notifies the user when an abnormality is detected, a terminal for the user to check the video data, a terminal having a user interface for changing the settings of the monitoring device, means for the central processing unit to send periodic maintenance notifications, and means for the central processing unit to generate security reports based on the analysis results. This allows the user to check the video in real time from a remote location, quickly detect and respond to abnormalities, and receive periodic maintenance notifications and security reports based on the analysis results.

[0237] "Old communications infrastructure" refers to the communications equipment and infrastructure that was previously used, including those that have now been replaced primarily by new technologies and equipment.

[0238] A "monitoring device" is a device such as a camera or sensor that has the function of acquiring images and data of a specific area and transmitting them to a central processing unit.

[0239] "Central processing unit" refers to a computer system that analyzes received video data and performs processes such as detecting abnormalities, saving data, and notifying users.

[0240] "Artificial intelligence means" refers to technology that uses algorithms such as machine learning and deep learning to analyze video data and recognize movement patterns and objects to detect abnormalities.

[0241] "Notification means" has the function of sending an alert to the user when an abnormality is detected, and includes push notifications and email notifications.

[0242] A "terminal" is a device such as a smartphone or PC used by a user, and is used to check video data from a surveillance device in real time and change settings.

[0243] "User interface" refers to an interface that allows a user to access a monitoring device or a central processing unit to change settings, check maintenance, and so on.

[0244] "Maintenance Notification" means a reminder sent to a user when scheduled maintenance of a monitoring device or system is required.

[0245] A "security report" is a document generated based on the results of video data analysis, and provides information including the security status and improvement measures.

[0246] In this invention, a specific method for realizing a wide-area security system that utilizes the old communication infrastructure will be described.

[0247] 1. System Configuration

[0248] The system is configured using the following hardware and software:

[0249] Hardware

[0250] Old communication infrastructure: Old PHS antennas, etc.

[0251] Surveillance equipment: Weatherproof security cameras.

[0252] User device: Smartphone or PC.

[0253] software

[0254] Central processing unit: Runs as a server and includes a web framework using Flask and a real-time image processing system using OpenCV.

[0255] Artificial intelligence means: A video data analysis system that utilizes machine learning and deep learning models.

[0256] Notification methods: Push notifications and email notification systems.

[0257] 2. Program Processing

[0258] The central processing unit (server) receives the video data transmitted from the monitoring device and analyzes it in real time. Specifically, the server includes the following means:

[0259] 1. Acquiring video data

[0260] The monitoring device (camera) captures images of the designated monitoring area in real time and transmits the image data to a central processing unit, which receives the image data and begins analyzing it.

[0261] 2. Anomaly detection

[0262] Video data is analyzed using artificial intelligence tools such as machine learning and deep learning to detect anomalies based on movement patterns and object recognition. For example, if suspicious movements or specific objects are detected, they will be flagged as an anomaly.

[0263] 3. Sending notifications

[0264] When an abnormality is detected, the central processing unit uses the notification means to send a push notification or email notification to the user, allowing the user to quickly confirm the abnormality and take the necessary action.

[0265] 4. User Interface

[0266] Users can access a dedicated user interface via a device (smartphone or PC) to check video data in real time, and can also change the settings of the monitoring device and set maintenance schedules.

[0267] 5. Generate security reports

[0268] The central processing unit periodically backs up the collected video data and analyzes it using AI. A security report based on the analysis results is provided to the user, suggesting improvement measures.

[0269] 3. Specific Examples

[0270] For example, if a user installs a surveillance camera at the entrance of their home, they can remotely check the situation at the entrance in real time via their smartphone. If the camera detects a suspicious person, a push notification is sent immediately, allowing the user to confirm the abnormality. In addition, regular maintenance notifications and detailed security reports are provided, strengthening security measures.

[0271] Prompt Sentence Examples

[0272] Write a Python program that analyzes video data from a home security camera in real time to detect suspicious individuals. The program should have a web interface using Flask, video analysis functionality using OpenCV, and the ability to send a push notification when an anomaly is detected.

[0273] This completes the description of the embodiment of the invention. This system makes it possible to realize wide-area, highly accurate security management that effectively utilizes the old communication infrastructure.

[0274] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0275] Step 1:

[0276] The server receives the video data transmitted from the monitoring device.

[0277] Specific behavior:

[0278] The monitoring device (camera) captures real-time video of the designated monitoring area and sends it as an RTSP stream to the server. The server receives the video data using a web framework such as Flask and stores it in a buffer frame by frame.

[0279] Input: Real-time video data as an RTSP stream.

[0280] Output: Video frame data waiting to be analyzed.

[0281] Step 2:

[0282] The server analyzes the received video data.

[0283] Specific behavior:

[0284] Video data is analyzed in real time using OpenCV and generative AI models, allowing the server to recognize motion patterns and objects in the video frames and determine whether anomalies have occurred.

[0285] Input: Buffered video frame data.

[0286] Output: Anomaly detection result (flagged as normal or abnormal).

[0287] Step 3:

[0288] If the server detects an abnormality, it will notify the user.

[0289] Specific behavior:

[0290] If the anomaly detection result indicates an abnormal state, the server will use notification means to send a push notification or email to the user's device, allowing the user to immediately check the situation.

[0291] Input: Anomaly detection result (anomaly status flag).

[0292] Output: Push notification or email notification.

[0293] Step 4:

[0294] The user checks the video data on a smartphone or PC.

[0295] Specific behavior:

[0296] Users can access real-time video data stored on the server through a dedicated app or web interface, allowing them to directly check the current situation and identify any abnormalities.

[0297] Input: Real-time video data stored on the server.

[0298] Output: Real-time video displayed on the user's device.

[0299] Step 5:

[0300] The server sends periodic maintenance notifications.

[0301] Specific behavior:

[0302] The server manages the maintenance schedule for the monitoring device and sends reminders to the user when maintenance is required.

[0303] Input: Maintenance schedule data.

[0304] Output: Reminder notification.

[0305] Step 6:

[0306] The server generates a security report based on the analysis results.

[0307] Specific behavior:

[0308] The server analyzes the collected video data to identify abnormal behavior patterns and security vulnerabilities, and automatically generates a security report that is sent to the user.

[0309] Input: Analyzed video data and anomaly detection results.

[0310] Output: Security report.

[0311] Step 7:

[0312] The user implements countermeasures based on the security report.

[0313] Specific behavior:

[0314] The user reviews the security report and implements the suggested improvements, changing the settings of monitoring devices and strengthening physical security as necessary.

[0315] Input: Security report.

[0316] Output: Results of the implementation of the improvement measures.

[0317] The above are the specific processing steps in this invention.

[0318] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0319] The present invention provides a system that reuses old communication infrastructure and combines a wide-area security system with an emotion engine that recognizes user emotions. Specific embodiments of this system are described below.

[0320] 1. Camera installation and initial settings

[0321] Camera installation

[0322] The user physically installs a security camera near the old communication infrastructure (old PHS antenna, etc.). The camera is weatherproof and designed to withstand the external environment.

[0323] Network Connection

[0324] The server obtains the camera's IP address and configures the network settings. The server also authenticates the camera's MAC address and registers it in its internal database.

[0325] Initial Setup

[0326] The user accesses the camera's settings page through a dedicated app or web interface. The user enters initial settings such as the camera name (e.g., front door camera) and monitoring area, and the server confirms these settings and applies them to the camera.

[0327] 2. Real-time video monitoring

[0328] Video capture and transmission

[0329] The camera captures video of the monitored area in real time, compresses and encrypts the video data, and sends it to the server.

[0330] Receiving and storing video

[0331] The server receives the video data sent from the camera, stores it in a temporary buffer, and also backs it up in a database for permanent storage.

[0332] User monitoring

[0333] Users access the system via a dedicated app or web interface on their smartphone or PC and view footage in real time.

[0334] 3. Anomaly Detection

[0335] Video analysis

[0336] The video data received by the server is input into an AI model to recognize movement patterns and objects.

[0337] Anomaly detection

[0338] The server uses an AI model to detect anomalies.

[0339] Alert Generation

[0340] If the server detects an abnormality, it generates alert data.

[0341] Sending notifications

[0342] Send server-generated alerts to users via push notifications.

[0343] User Support

[0344] Users receive a notification and can view detailed footage in a dedicated app or web interface, and if necessary, call the police.

[0345] 4. Maintenance Notice

[0346] Maintenance schedule management

[0347] The server manages the regular maintenance schedule for each camera in a database and has reminder logic.

[0348] Sending notifications

[0349] Check when the server needs maintenance and send a notification to users.

[0350] User Response

[0351] Users will receive notifications and can check the maintenance schedule via a dedicated app or web interface.

[0352] Schedule adjustment

[0353] The user contacts the maintenance staff and schedules the maintenance.

[0354] 5. Data analysis and security consulting

[0355] Data collection

[0356] The server periodically backs up past video data and stores it in a database for analysis.

[0357] Data analysis

[0358] The server uses AI and data analysis tools to analyze the collected video data and extract abnormal behavior patterns and security vulnerabilities.

[0359] Reporting

[0360] The server automatically creates a security assessment report based on the analysis results.

[0361] Staff Reviews

[0362] Security professionals will review the report and provide additional comments and suggestions as needed.

[0363] Send Feedback

[0364] The server sends the completed security report to the user.

[0365] User execution

[0366] The user receives the report and implements the suggested improvements.

[0367] 6. Adding an Emotion Engine

[0368] Collecting Emotional Data

[0369] The device's built-in camera and microphone analyze the user's facial expressions and tone of voice to collect emotional data.

[0370] emotion recognition

[0371] The server analyzes the collected data through the emotion engine and recognizes the user's emotional state.

[0372] Feedback of sentiment analysis results

[0373] The server provides feedback to the device based on the user's emotional state. For example, if the user is feeling anxious, it displays a comforting or encouraging message.

[0374] Alert Tuning

[0375] The server adjusts the frequency and content of alerts based on the user's emotional state: for example, if the user exhibits high anxiety levels, alerts are sent more quickly than usual.

[0376] Specific examples

[0377] For example, a user installs a camera at the entrance of their home, which captures footage of the area in front of the entrance and sends it to a server. The server then uses an AI model to analyze the footage and detect suspicious activity. If a suspicious person is detected, the server immediately sends an alert to the user. The user receives a notification, checks the footage on their smartphone, and, if necessary, calls the police.

[0378] Furthermore, if the user's emotion engine detects stress, the system will send a notification encouraging the user to relax. For example, if the user shows high anxiety levels, the system will provide safety advice in addition to camera footage, allowing the user to feel psychologically at ease and take appropriate action.

[0379] The above is an embodiment of the present invention, which makes it possible to effectively utilize the old communication infrastructure, provide a wide-area, highly accurate security system, and also enable flexible responses that take user emotions into consideration.

[0380] The processing flow will be explained below.

[0381] 1. Camera installation and initial settings

[0382] Step 1: Camera installation

[0383] The user physically installs security cameras into the old communications infrastructure.

[0384] Step 2: Power on the camera

[0385] The user supplies power to the camera and starts the system.

[0386] Step 3: Network Connection

[0387] The server obtains the camera's IP address and checks the network connection.

[0388] Step 4: MAC address registration

[0389] The server registers the camera's MAC address in its internal database and performs authentication.

[0390] Step 5: Initial Setup

[0391] The user opens a dedicated app or web interface and enters settings such as the camera name and monitoring area.

[0392] Step 6: Apply the settings

[0393] The server reflects the setting information received from the user in the camera.

[0394] 2. Real-time video monitoring

[0395] Step 1: Capture the footage

[0396] The camera captures real-time images of the surveillance area.

[0397] Step 2: Sending video data

[0398] The video data captured by the camera is compressed, encrypted, and sent to the server.

[0399] Step 3: Receiving video data

[0400] The server receives the video data sent from the camera.

[0401] Step 4: Save

[0402] The video data received by the server is temporarily stored in a buffer.

[0403] Step 5: Permanent storage

[0404] The server backs up the video data in the temporary buffer to a database.

[0405] Step 6: User monitoring

[0406] Users access the system from their device (smartphone or PC) via a dedicated app or web interface and view the footage in real time.

[0407] 3. Anomaly Detection

[0408] Step 1: Analyzing the footage

[0409] The video data received by the server is input into an AI model to recognize movement patterns and objects.

[0410] Step 2: Anomaly detection

[0411] The server uses an AI model to detect anomalies.

[0412] Step 3: Alert Generation

[0413] If the server detects an abnormality, it generates alert data.

[0414] Step 4: Sending notifications

[0415] Send server-generated alerts to users via push notifications.

[0416] Step 5: User interaction

[0417] Users receive a notification and can view detailed footage in a dedicated app or web interface, and if necessary, call the police.

[0418] 4. Maintenance Notice

[0419] Step 1: Schedule Management

[0420] The server manages the regular maintenance schedule for each camera.

[0421] Step 2: Trigger the reminder logic

[0422] The server will trigger reminder logic during maintenance periods.

[0423] Step 3: Sending notifications

[0424] Check when the server needs maintenance and send a notification to users.

[0425] Step 4: User response

[0426] Users will receive notifications and can check maintenance schedules via a dedicated app or web interface.

[0427] Step 5: Scheduling

[0428] The user contacts the maintenance staff and schedules the maintenance.

[0429] 5. Data analysis and security consulting

[0430] Step 1: Data collection

[0431] The server periodically backs up past video data and stores it in a database for analysis.

[0432] Step 2: Data analysis

[0433] The server uses AI and data analysis tools to analyze the collected video data and extract abnormal behavior patterns and security vulnerabilities.

[0434] Step 3: Reporting

[0435] The server automatically creates a security assessment report based on the analysis results.

[0436] Step 4: Staff review

[0437] Security professionals will review the report and provide additional comments and suggestions as needed.

[0438] Step 5: Send your feedback

[0439] The server sends the completed security report to the user.

[0440] Step 6: User execution

[0441] The user receives the report and implements the suggested improvements.

[0442] 6. Adding an Emotion Engine

[0443] Step 1: Collecting emotion data

[0444] The device's built-in camera and microphone analyze the user's facial expressions and tone of voice to collect emotional data.

[0445] Step 2: Emotion Recognition

[0446] The server analyzes the collected data through the emotion engine and recognizes the user's emotional state.

[0447] Step 3: Feedback of sentiment analysis results

[0448] The server provides feedback to the device based on the user's emotional state. For example, if the user is feeling anxious, it displays a comforting or encouraging message.

[0449] Step 4: Adjust your alerts

[0450] The server adjusts the frequency and content of alerts based on the user's emotional state: for example, if the user exhibits high anxiety levels, alerts are sent more quickly than usual.

[0451] Specific examples

[0452] For example, a user installs a camera at the entrance of their home, which captures footage of the area in front of the entrance and sends it to a server. The server then uses an AI model to analyze the footage and detect suspicious activity. If a suspicious person is detected, the server immediately sends an alert to the user. The user receives a notification, checks the footage on their smartphone, and, if necessary, calls the police.

[0453] Furthermore, if the user's emotion engine detects stress, the system will send a notification encouraging the user to relax. For example, if the user shows high anxiety levels, the system will provide safety advice in addition to camera footage, allowing the user to feel psychologically at ease and take appropriate action.

[0454] The above is an embodiment of the present invention, which makes it possible to effectively utilize the old communication infrastructure, provide a wide-area, highly accurate security system, and also enable flexible responses that take user emotions into consideration.

[0455] Example 2

[0456] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0457] Conventional monitoring systems face the challenges of making it difficult to reuse old communication infrastructure, and the complex installation and network connection required to cover a wide area. Furthermore, while there is a demand for improved accuracy in anomaly detection and flexible responses based on the user's emotional state, current systems have difficulty meeting these demands. Furthermore, managing regular maintenance and effectively notifying users is also an issue.

[0458] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0459] In this invention, the server includes a monitoring device attached to the old communication infrastructure, a central processing unit that receives video data transmitted from the monitoring device, and artificial intelligence (AI) means in the central processing unit that analyzes the video data. This enables high-precision monitoring that covers a wide area by reusing the old communication infrastructure. The server also includes AI means that analyzes the video data, a notification means that notifies the user when an abnormality is detected, a terminal on which the user can view the video data, a means for the central processing unit to manage the periodic maintenance schedule of the monitoring device and send reminders to the user, and an emotion recognition means that analyzes the user's emotional state and adjusts the frequency and content of alerts when an abnormality is detected. This enables flexible responses based on the user's emotional state and enables efficient periodic maintenance management.

[0460] "Old communications infrastructure" refers to communications equipment and facilities that were used in the past but are no longer in use.

[0461] "Surveillance equipment" means equipment installed to monitor a specific area, including cameras and sensors.

[0462] "Central Processing Unit" means a computer system or server that analyzes and processes data received from monitoring devices.

[0463] "Artificial intelligence means" refers to technologies that use machine learning models and algorithms to analyze data and perform anomaly detection and object recognition.

[0464] "Notification means" refers to a means for sending an alert or notification to the user when an abnormality is detected, and includes push notifications and emails.

[0465] A "terminal" is a device that allows a user to view video data and operate a surveillance device, and includes smartphones and PCs.

[0466] "Maintenance Schedule Management" refers to the function of planning and managing regular maintenance of monitoring devices and sending reminders.

[0467] "Emotion recognition means" refers to technology that analyzes a user's facial expressions and tone of voice to recognize their emotional state.

[0468] "Motion pattern" refers to the pattern of movement of people or objects detected within the monitored area.

[0469] "Object recognition" refers to the technology of detecting and identifying specific objects from video data.

[0470] The present invention provides a system that reuses old communication infrastructure and combines a wide-coverage security system with an emotion engine that recognizes user emotions. Detailed embodiments of this system are described below.

[0471] Camera installation and initial setup

[0472] Camera installation

[0473] A user installs an all-weather security camera near an old communication infrastructure (for example, an old PHS antenna). The camera is configured as an IP camera and is designed to withstand external environments.

[0474] Network Connection

[0475] The server scans and identifies the IP addresses of IP cameras connected to the network, then authenticates the camera's MAC address using an authentication server and registers the camera information in an internal database.

[0476] Initial Setup

[0477] Users access the camera settings page via a dedicated app or web interface on their smartphone or PC. For initial setup, the user enters the camera name (e.g., front door camera) and monitoring area, and the server confirms the settings and updates them on the camera.

[0478] Real-time video monitoring

[0479] Video capture and transmission

[0480] The cameras continuously capture images of the designated surveillance area, compress the video data into Motion JPEG format in real time, and then encrypt the video data using the SSL / TLS protocol before sending it to the server.

[0481] Receiving and storing video

[0482] The server receives the video data sent from the camera using the SSL / TLS protocol and temporarily stores it in buffer memory. At the same time, the video data is permanently stored in a database for backup purposes.

[0483] User monitoring

[0484] Users can access the server via a dedicated app or web interface on their smartphone or PC to view the streaming video in real time. Users can also use zoom and recording / playback functions as needed.

[0485] Anomaly detection

[0486] Video analysis

[0487] The server uses Python's OpenCV library to analyze the received video data frame by frame, and an AI model (e.g., YOLO or SSD) detects motion patterns and objects.

[0488] Anomaly detection

[0489] The server uses an anomaly detection AI model using TensorFlow or PyTorch to detect suspicious behavior or objects, such as sudden movements of a suspicious person or abnormal vehicle behavior.

[0490] Alert Generation

[0491] When the server detects an abnormality, it generates alert data, which includes a timestamp, location, and details of the abnormality.

[0492] Sending notifications

[0493] Server-generated alerts are sent to users via push notifications, using Firebase Cloud Messaging (FCM) or Apple Push Notification Service (APNS).

[0494] User Support

[0495] Users can check the notification they receive on their smartphone, view detailed footage through a dedicated app or web interface, and, if necessary, report the incident to the police.

[0496] Maintenance Notification

[0497] Maintenance Schedule Management

[0498] The server monitors the operating status and operating time of each camera, and manages regular maintenance schedules in a database. It has a reminder function and checks them regularly.

[0499] Sending notifications

[0500] The server checks when maintenance is required and sends reminder notifications to users.

[0501] User Response

[0502] Users will receive notifications and can view recommended maintenance dates via a dedicated app or web interface.

[0503] Schedule adjustment

[0504] The user contacts the maintenance staff and schedules the maintenance.

[0505] Data analysis and security consulting

[0506] Data collection

[0507] The server regularly backs up past video data and anomaly detection data and stores it in a database for analysis.

[0508] Data analysis

[0509] The server uses Python to analyze large amounts of video data using AI models and data analysis tools (e.g., scikit-learn, pandas) to extract abnormal behavior patterns and security vulnerabilities.

[0510] Reporting

[0511] The server automatically creates a security assessment report based on the analysis results, which includes an overview of detected anomalies and recommended remediation measures.

[0512] Staff Reviews

[0513] Security professionals review the automatically generated reports and provide additional comments and specific suggestions as needed.

[0514] Send Feedback

[0515] The server generates a completed security report in PDF format and sends it to the user.

[0516] User execution

[0517] The user reviews the report and implements the suggested improvements.

[0518] Adding an Emotion Engine

[0519] Collecting Emotional Data

[0520] The device's built-in camera and microphone collect real-time facial expression data and tone of voice, using facial recognition and voice analysis APIs.

[0521] emotion recognition

[0522] The server analyzes the collected data using an emotion engine. An emotion recognition algorithm is used to identify the user's current emotional state (e.g., joy, anxiety, anger, etc.).

[0523] Feedback of sentiment analysis results

[0524] Based on the analysis results of the emotion engine, the server provides feedback such as messages to the user on the device. For example, if the user is feeling anxious, a message such as "Take a deep breath to relax" will be displayed.

[0525] Alert Tuning

[0526] The server adjusts the frequency and content of alerts depending on the user's emotional state: if the user shows high anxiety levels, alerts are sent more quickly than usual.

[0527] Specific examples

[0528] For example, a user installs a camera at the entrance of their home, which captures footage of the area in front of the entrance and sends it to a server. The server then uses an AI model to analyze the footage and detect suspicious activity. If a suspicious person is detected, the server immediately sends an alert to the user. The user receives a notification, checks the footage on their smartphone, and, if necessary, calls the police.

[0529] Furthermore, if the user's emotion engine detects stress, the system will send a notification encouraging the user to relax. For example, if the user shows high anxiety levels, the system will provide safety advice in addition to camera footage, allowing the user to feel psychologically at ease and take appropriate action.

[0530] Prompt Sentence Examples

[0531] "The entrance camera has detected suspicious activity. Please review the footage immediately."

[0532] "Our systems have detected high levels of anxiety. Please take a deep breath to calm yourself and review our latest safety information."

[0533] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0534] Step 1: Camera installation and initial setup

[0535] Camera installation

[0536] The user physically installs a weatherproof IP camera near the old communication infrastructure (for example, an old PHS antenna), thus physically installing the surveillance device.

[0537] Input: IP camera, old communication infrastructure

[0538] Output: Physically installed monitoring equipment

[0539] Network Connection

[0540] The server scans and identifies the IP addresses of cameras connected to the network, then authenticates the MAC addresses of the cameras and registers the camera information in its internal database.

[0541] Input: Camera IP address, MAC address

[0542] Output: Authenticated cameras, camera information registered in the internal database

[0543] Initial Setup

[0544] The user accesses the dedicated app or web interface using a smartphone or PC. They log in to the camera setting page and enter the camera name (e.g., front door camera) and monitoring area. The server checks these settings and applies them to the camera.

[0545] Input: Camera name, monitoring area information

[0546] Output: Camera with settings reflected

[0547] Step 2: Real-time video monitoring

[0548] Video capture and transmission

[0549] The cameras continuously capture video of the configured surveillance area, compress the video into Motion JPEG format, and then send it to the server using the SSL / TLS protocol.

[0550] Input: Video of the monitored area

[0551] Output: Compressed video data, data encrypted with SSL / TLS protocol

[0552] Receiving and storing video

[0553] The server receives the video data sent from the camera using the SSL / TLS protocol, stores it in a temporary buffer memory, and also backs it up in a database for permanent storage.

[0554] Input: Encrypted video data

[0555] Output: Data stored in temporary buffer, data stored in database

[0556] User monitoring

[0557] Users can access the server via a dedicated app or web interface on their smartphone or PC, check the video in real time, and also use zoom and recording / playback functions.

[0558] Input: Streaming video

[0559] Output: Real-time video, user-controlled zoom and recording data

[0560] Step 3: Anomaly detection

[0561] Video analysis

[0562] The server uses Python's OpenCV library to analyze the received video data frame by frame, and an AI model (e.g., YOLO or SSD) detects motion patterns and objects.

[0563] Input: Received video data

[0564] Output: Object detection results, movement patterns

[0565] Anomaly detection

[0566] The server uses an anomaly detection AI model using TensorFlow or PyTorch to detect suspicious behavior or objects.

[0567] Input: Object detection results, movement patterns

[0568] Output: Anomaly detection results

[0569] Alert Generation

[0570] When the server detects an abnormality, it generates alert data, which includes a timestamp, location, and details of the abnormality.

[0571] Input: Anomaly detection result

[0572] Output: Generated alert data

[0573] Sending notifications

[0574] Server-generated alerts are sent to users via push notifications, using Firebase Cloud Messaging (FCM) or Apple Push Notification Service (APNS).

[0575] Input: Alert data

[0576] Output: Notification sent to the user

[0577] User Support

[0578] The user receives a notification on their smartphone, checks the video in detail using a dedicated app or web interface, and, if necessary, reports the incident to the police.

[0579] Input: Received notification, detailed video data

[0580] Output: User confirmation result, possibly police report

[0581] Step 4: Maintenance Notification

[0582] Maintenance Schedule Management

[0583] The server monitors the operating status and operating time of each camera, and manages regular maintenance schedules in a database. It has a reminder function and performs regular checks.

[0584] Input: Operation data of each camera

[0585] Output: Managed maintenance schedule

[0586] Sending notifications

[0587] The server checks when maintenance is required and sends reminder notifications to users.

[0588] Input: Managed Maintenance Schedule

[0589] Output: Notification sent to the user

[0590] User Response

[0591] Users will receive notifications and can view recommended maintenance dates via a dedicated app or web interface.

[0592] Input: Reminder

[0593] Output: Maintenance dates confirmed by the user

[0594] Schedule adjustment

[0595] The user contacts the maintenance staff and schedules the maintenance.

[0596] Input: User request

[0597] Output: Adjusted maintenance schedule

[0598] Step 5: Data analysis and security consulting

[0599] Data collection

[0600] The server regularly backs up past video data and anomaly detection data and stores it in a database for analysis.

[0601] Input: Past video data, anomaly detection data

[0602] Output: Backed up data, saved database

[0603] Data analysis

[0604] The server uses Python to analyze large amounts of video data using AI models and data analysis tools (e.g., scikit-learn, pandas) to extract abnormal behavior patterns and security vulnerabilities.

[0605] Input: Saved database

[0606] Output: Analyzed data, anomalous patterns, security vulnerabilities

[0607] Reporting

[0608] The server automatically creates a security assessment report based on the analysis results, which includes an overview of detected anomalies and recommended remediation measures.

[0609] Input: Analyzed data, anomalous patterns, security vulnerabilities

[0610] Output: Security Assessment Report

[0611] Staff Reviews

[0612] Security professionals review the automatically generated reports and provide additional comments and specific suggestions as needed.

[0613] Input: Security Assessment Report

[0614] Output: Reviewed report

[0615] Send Feedback

[0616] The server generates a completed security report in PDF format and sends it to the user.

[0617] Input:Reviewed report

[0618] Output: PDF report sent to the user

[0619] User execution

[0620] The user reviews the report and implements the suggested improvements.

[0621] Input: PDF report

[0622] Output: Implemented remedial measures

[0623] Step 6: Adding an Emotion Engine

[0624] Collecting Emotional Data

[0625] The device's built-in camera and microphone collect real-time facial expression data and tone of voice, using facial recognition and voice analysis APIs.

[0626] Input: User's facial expression data, tone of voice

[0627] Output: Collected emotion data

[0628] emotion recognition

[0629] The server analyzes the collected data using an emotion engine. An emotion recognition algorithm is used to identify the user's current emotional state (e.g., joy, anxiety, anger, etc.).

[0630] Input: Collected emotion data

[0631] Output: Identified emotional state

[0632] Feedback of sentiment analysis results

[0633] Based on the analysis results of the emotion engine, the server provides feedback such as messages to the user on the device. For example, if the user is feeling anxious, a message such as "Take a deep breath to relax" will be displayed.

[0634] Input: Identified emotional state

[0635] Output: The feedback message provided to the user

[0636] Alert Tuning

[0637] The server adjusts the frequency and content of alerts depending on the user's emotional state: if the user shows high anxiety levels, alerts are sent more quickly than usual.

[0638] Input: Identified emotional state

[0639] Output: Adjusted alert notification

[0640] (Application example 2)

[0641] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0642] In recent years, security systems using surveillance cameras have been introduced in many places, but these systems have difficulty simultaneously ensuring the safety and psychological security of all users. Furthermore, because there is no established method for effectively utilizing existing communication infrastructure, high costs and operational effort have become problems. Furthermore, few systems can provide feedback based on the user's emotional state, limiting the user's response in emergencies. To solve these issues, it is necessary to provide flexible responses that take user emotions into account while performing high-precision monitoring over a wide area.

[0643] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for managing monitoring devices attached to the old communication infrastructure, artificial intelligence means for receiving and analyzing video data transmitted from the monitoring devices, and emotion engine means for recognizing the user's emotions and providing feedback based on them. This enables high-precision monitoring over a wide area and flexible feedback according to the user's emotional state while making effective use of the old communication infrastructure.

[0644] "Old communications infrastructure" refers to physical infrastructure such as communications equipment and antennas that were used in the past, and are currently unused but can be reused.

[0645] "Surveillance equipment" means devices such as cameras and sensors that are installed to monitor a designated area.

[0646] A "central processing unit" is a computer system that receives, analyzes, and manages data sent from the monitoring device.

[0647] "Artificial intelligence means" refers to machine learning models and algorithms used to analyze received video data and recognize movement patterns and objects.

[0648] "Notification means" refers to a system or device that sends an alert or notification to the user when an abnormality is detected.

[0649] A "terminal" is a device such as a smartphone or PC that a user uses to check video data and notifications.

[0650] An "emotion engine" refers to software or algorithms that analyze a user's facial expressions, voice, etc. to recognize their emotional state.

[0651] A "means for providing feedback" is a system or application that sends messages or notifications based on the user's emotional state.

[0652] The present invention combines an emotion engine with a highly accurate security system that covers a wide area by reusing old communication infrastructure. Specific embodiments of the present invention are described below.

[0653] 1. Camera installation and initial settings

[0654] Camera installation

[0655] Users install weatherproof security cameras near old communication infrastructure (old PHS antennas, etc.) The cameras are designed to withstand the external environment.

[0656] Network Connection

[0657] The central processing unit (server) acquires the camera's IP address and configures the network settings. The server also authenticates the camera's MAC address and registers it in its internal database.

[0658] Initial Setup

[0659] Users access the camera's settings page through a dedicated application or web interface. They enter initial settings such as the camera's name (e.g., front door camera) and monitoring area, and the server updates the camera with these settings.

[0660] 2. Real-time video monitoring

[0661] Video capture and transmission

[0662] The camera captures video of the designated monitoring area in real time, compresses and encrypts the video data, and sends it to the server.

[0663] Receiving and storing video

[0664] The server receives the video data sent from the camera, stores it in a temporary buffer, and also backs it up in a database for permanent storage.

[0665] User monitoring

[0666] Users access the system via a dedicated application or web interface on their smartphone or PC and view footage in real time.

[0667] 3. Anomaly Detection and Emotion Recognition

[0668] Video analysis

[0669] The server inputs the received video data into a generative AI model to recognize movement patterns and objects.

[0670] Anomaly detection

[0671] The server uses an AI model to detect anomalies.

[0672] Collecting Emotional Data

[0673] The device's built-in camera and microphone analyze the user's facial expressions and tone of voice to collect emotional data.

[0674] emotion recognition

[0675] The server analyzes the collected data through an emotion engine and recognizes the user's emotional state.

[0676] Providing Feedback

[0677] The server then provides feedback to the device based on the user's emotional state. For example, if the user is feeling anxious, it displays a comforting or encouraging message.

[0678] 4. Notifications and User Response

[0679] Alert Generation

[0680] The server generates alert data when an abnormality is detected.

[0681] Sending notifications

[0682] The server then sends the generated alerts to the user via push notifications, and adjusts the frequency and content of the alerts based on the user's emotional state. For example, if the user shows a high level of anxiety, the alerts will be sent more quickly than usual.

[0683] User Support

[0684] Users receive a notification and can review the video in detail in a dedicated app or web interface, and if necessary, call the police.

[0685] 5. Maintenance notifications and data analysis

[0686] Maintenance Schedule Management

[0687] The server manages the regular maintenance schedule for each camera in a database and is equipped with reminder logic.

[0688] Sending notifications

[0689] The server checks when maintenance is required and sends a notification to the user.

[0690] User Response

[0691] Users will receive notifications and can check maintenance dates via a dedicated application or web interface.

[0692] Data collection and analysis

[0693] The server periodically backs up past video data and analyzes it using AI and data analysis tools to identify abnormal behavior patterns and security vulnerabilities.

[0694] Reporting

[0695] The server automatically creates a security assessment report based on the analysis results and sends it to the user.

[0696] Specific examples

[0697] For example, a user installs a camera at the entrance of their home, which captures footage of the area in front of the entrance and sends it to a server. The server then uses a generative AI model to analyze the footage and detect suspicious activity. If a suspicious activity is detected, the server immediately sends an alert to the user. The user receives the notification, checks the footage on their smartphone, and, if necessary, calls the police. If the user's emotion engine detects stress in the user, the system sends a notification encouraging the user to relax. For example, if the user shows a high level of anxiety, the system will provide safety advice in addition to the camera footage. This provides the user with psychological peace of mind and allows them to take appropriate action.

[0698] Prompt Sentence Examples

[0699] A suspicious person is standing in front of the house.

[0700] Addresses high anxiety levels in users.

[0701] The user is prompted to relax and given instructions on how to report the incident to the police.

[0702] The above is a specific embodiment for carrying out the present invention, which makes it possible to effectively utilize the old communication infrastructure, provide a wide-area, highly accurate security system, and also enable flexible responses that take user emotions into consideration.

[0703] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0704] Step 1: Camera Settings

[0705] Users install weatherproof security cameras near the old communication infrastructure, then enter the camera name (for example, front door camera) and monitoring area through a dedicated application or web interface, and the server updates the camera with this information.

[0706] Input: Camera IP address, monitoring area settings

[0707] Output: The camera setting information is registered on the server.

[0708] Specific operation: The server obtains the IP address of the camera, configures the network settings, and then reflects the camera name and monitoring area settings on the camera.

[0709] Step 2: Real-time video monitoring

[0710] The cameras capture video of the designated surveillance area in real time, compressing and encrypting the video data before sending it to the server. The server temporarily stores the received video data in a buffer and also backs it up in a database for permanent storage. Users can view the video in real time on their smartphones or PCs using a dedicated application or web interface.

[0711] Input: Real-time video data

[0712] Output: Video data stored on the server, real-time video confirmation by the user

[0713] How it works: The camera captures video, compresses and encrypts it, and sends it to the server. The server receives the video data, stores it in a temporary buffer, and also stores it in a database for permanent storage. The user can view the video in real time via a dedicated application or web interface.

[0714] Step 3: Video analysis and anomaly detection

[0715] The server inputs the received video data into a generative AI model to recognize movement patterns and objects. If an abnormality is detected based on the analysis, the server generates alert data.

[0716] Input: Received video data

[0717] Output: Alert data

[0718] Specific operation: The server inputs the video data into the AI ​​model, analyzes movement patterns and object recognition, and generates alert data indicating the abnormality if an abnormality is detected.

[0719] Step 4: Collect and recognize emotion data

[0720] The device's built-in camera and microphone analyze the user's facial expressions and tone of voice to collect emotional data. The server then analyzes the collected data through an emotion engine to recognize the user's emotional state.

[0721] Input: User's facial expression and voice data

[0722] Output: User's emotional state

[0723] Specific operation: The system collects the user's emotions in real time using a camera and microphone, and analyzes them using the server's emotion engine. The system recognizes the user's emotional state based on the analysis results.

[0724] Step 5: Provide emotional feedback

[0725] The server then provides feedback to the device based on the user's emotional state. For example, if the user is feeling anxious, it displays a comforting or encouraging message.

[0726] Input: User's emotional state

[0727] Output: Feedback message

[0728] Specific operation: The server generates feedback (e.g., a message to ease anxiety) according to the user's emotional state and sends it to the device.

[0729] Step 6: Send notifications and respond to users

[0730] The server sends a push notification to the user using the generated alert data and feedback based on the user's emotional state. The user receives the notification and can view detailed footage in a dedicated application or web interface. If necessary, the user can report the incident to the police.

[0731] Input: Alert data, feedback based on emotional state

[0732] Output: Notify user, notify police

[0733] Specific operation: After receiving a notification from the server, the user checks the detailed video using a dedicated application or web interface and, if necessary, reports the incident to the police.

[0734] Step 7: Maintenance notifications and data analysis

[0735] The server sets a regular maintenance schedule for each camera and notifies the user of the required date and time. It also periodically backs up past video data and analyzes it using AI and data analysis tools to identify abnormal behavior patterns and security vulnerabilities. Based on the analysis results, it sends a security assessment report to the user.

[0736] Input: Maintenance schedule, past video data

[0737] Output: Maintenance notification, security assessment report

[0738] Specific operations: The server manages maintenance schedules, sends notifications to users, analyzes regularly backed-up video data, and generates and sends security assessment reports to users.

[0739] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0740] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0741] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0742] [Second embodiment]

[0743] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0744] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0745] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0746] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0747] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0748] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0749] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0750] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0751] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0752] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0753] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0754] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0755] The present invention provides a security system that covers a wide area by reusing the old communication infrastructure. Specific embodiments of the present invention are described below.

[0756] 1. Camera installation and initial settings

[0757] Camera installation

[0758] The user physically installs a security camera near the old communication infrastructure (old PHS antenna, etc.). The camera is weatherproof and designed to withstand the external environment.

[0759] Network Connection

[0760] The server obtains the camera's IP address and configures the network settings. The server also authenticates the camera's MAC address and registers it in its internal database.

[0761] Initial Setup

[0762] The user accesses the camera's settings page through a dedicated app or web interface. The user enters initial settings such as the camera name (e.g., front door camera) and monitoring area, and the server confirms these settings and applies them to the camera.

[0763] 2. Real-time video monitoring

[0764] Video capture and transmission

[0765] The camera captures video of the monitored area in real time, compresses and encrypts the video data, and sends it to the server.

[0766] Receiving and storing video

[0767] The server receives the video data sent from the camera, stores it in a temporary buffer, and also backs it up in a database for permanent storage.

[0768] User monitoring

[0769] Users access the system via a dedicated app or web interface on their smartphone or PC and view footage in real time.

[0770] 3. Anomaly Detection

[0771] Video analysis

[0772] The server uses an AI model to analyze the received video data, recognize movement patterns and objects, and detect abnormalities.

[0773] Sending alerts

[0774] Any abnormalities detected by the server will be immediately notified to the user via push notification or email.

[0775] User Support

[0776] The user receives a notification and can view detailed video footage via a dedicated app or web interface. If an abnormality is detected, the user can take action, such as reporting the incident to the police.

[0777] 4. Maintenance Notice

[0778] Maintenance schedule management

[0779] The server manages the regular maintenance schedule for each camera in a database and has reminder logic.

[0780] Sending notifications

[0781] Check when the server needs maintenance and send a notification to users.

[0782] User Response

[0783] Users receive a notification, check the maintenance schedule using a dedicated app or web interface, and contact the person in charge to arrange a schedule.

[0784] 5. Data analysis and security consulting

[0785] Data collection

[0786] The server periodically backs up past video data and stores it in a database for analysis.

[0787] Data analysis

[0788] The server uses AI and data analysis tools to analyze the collected video data and extract abnormal behavior patterns and security vulnerabilities.

[0789] Reporting

[0790] The server automatically creates a security assessment report based on the analysis results, and in some cases adds additional comments from security experts.

[0791] User Feedback

[0792] The server sends the completed security report to the user, who receives the report and implements the suggested remediation measures.

[0793] Specific examples

[0794] For example, let's say a user installs a camera at the entrance of their home. The camera captures video in front of the entrance and sends it to a server. The server uses an AI model to analyze the video and detect suspicious activity. If a suspicious activity is detected, the server immediately sends an alert to the user. The user receives the notification, checks the video on their smartphone, and takes action, such as notifying the police, depending on the situation. The server also regularly notifies the user monthly about system maintenance, and the user contacts the person in charge to carry out the maintenance. The server then analyzes the data it collects, identifies trends such as suspicious activity patterns, and security specialists create a report based on the analysis results, proposing specific improvement measures. The user can then strengthen their security measures based on this report.

[0795] The above is an embodiment of the present invention, which makes it possible to effectively utilize the old communication infrastructure and provide a wide-area, highly accurate security system.

[0796] The processing flow will be explained below.

[0797] Camera installation and initial setup

[0798] Step 1: Camera installation

[0799] The user physically installs security cameras into the old communications infrastructure.

[0800] Step 2: Turn on the camera and connect to the network

[0801] The user powers on the camera and it connects to the network.

[0802] Step 3: Obtaining and registering an IP address

[0803] The server obtains the camera's IP address and recognizes it within the network.

[0804] Step 4: MAC Address Authentication

[0805] The server authenticates the camera's MAC address and registers it in its internal database.

[0806] Step 5: Initial Setup

[0807] Users access a dedicated app or web interface to set the camera name and monitoring area.

[0808] Step 6: Apply the settings

[0809] The server reflects the setting information received from the user in the camera.

[0810] Real-time video monitoring

[0811] Step 1: Capture the footage

[0812] The camera captures real-time images of the surveillance area.

[0813] Step 2: Sending video data

[0814] The video data captured by the camera is compressed, encrypted, and sent to the server.

[0815] Step 3: Receiving video data

[0816] The server receives the video data sent from the camera.

[0817] Step 4: Save

[0818] The video data received by the server is temporarily stored in a buffer.

[0819] Step 5: Permanent storage

[0820] The server backs up the video data in the temporary buffer to a database.

[0821] Step 6: User monitoring

[0822] Users access the system from their device (smartphone or PC) via a dedicated app or web interface and view the footage in real time.

[0823] Anomaly detection

[0824] Step 1: Analyzing the footage

[0825] The video data received by the server is input into an AI model to recognize movement patterns and objects.

[0826] Step 2: Anomaly detection

[0827] The server uses an AI model to detect anomalies.

[0828] Step 3: Alert Generation

[0829] If the server detects an abnormality, it generates alert data.

[0830] Step 4: Sending notifications

[0831] Send server-generated alerts to users via push notifications.

[0832] Step 5: User interaction

[0833] Users receive a notification and can view detailed footage in a dedicated app or web interface, and if necessary, call the police.

[0834] Maintenance Notification

[0835] Step 1: Schedule Management

[0836] The server stores the regular maintenance schedule for each camera in a management database.

[0837] Step 2: Trigger the reminder logic

[0838] The server will trigger reminder logic during maintenance periods.

[0839] Step 3: Sending notifications

[0840] Check when the server needs maintenance and send a notification to users.

[0841] Step 4: User response

[0842] Users will receive a notification and can check the maintenance schedule via a dedicated app or web interface.

[0843] Step 5: Scheduling

[0844] The user contacts the maintenance staff and schedules the maintenance.

[0845] Data analysis and security consulting

[0846] Step 1: Data collection

[0847] The server periodically backs up past video data and stores it in a database for analysis.

[0848] Step 2: Data analysis

[0849] The server uses AI and data analysis tools to analyze the collected video data and extract abnormal behavior patterns and security vulnerabilities.

[0850] Step 3: Reporting

[0851] The server automatically creates a security assessment report based on the analysis results.

[0852] Step 4: Staff review

[0853] Security professionals will review the report and provide additional comments and suggestions as needed.

[0854] Step 5: Send your feedback

[0855] The server sends the completed security report to the user.

[0856] Step 6: User execution

[0857] The user receives the report and implements the suggested improvements.

[0858] Example 1

[0859] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0860] Conventional security systems require the construction of new infrastructure, which is expensive, making it difficult to cover a wide area. Real-time anomaly detection and maintenance management are also inadequate. This leads to issues such as delays in rapid response and difficulty in long-term system operation.

[0861] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0862] In this invention, the server includes a monitoring device attached to the old communication infrastructure, a central processing unit that receives video data transmitted from the monitoring device, artificial intelligence means in the central processing unit that analyzes the video data, notification means that notifies the user when an abnormality is detected, a terminal for the user to check the video data, setting means that performs initial configuration of the monitoring device through the terminal, means that compress and encrypt the video data and transmit it, means that temporarily store the video data and back it up in a database for permanent storage, means that manage a periodic maintenance schedule, means that send reminders based on the maintenance schedule, means that periodically analyze the video data and create a security evaluation report, and means that feed back the security evaluation report to the user. This makes it possible to effectively utilize the old communication infrastructure to cover a wide area, and to enable real-time abnormality detection, prompt notification, and maintenance management.

[0863] "Old communications infrastructure" refers to communications technologies and equipment that were used in the past and are no longer in use but can be reused.

[0864] "Monitoring equipment" refers to equipment installed to monitor a specific area, and has the function of capturing video data and transmitting it to a central processing unit.

[0865] "Video data" refers to the data that can be stored and transmitted in digital format from images captured by surveillance equipment.

[0866] "Central processing unit" refers to a computer device that receives and analyzes video data transmitted from a monitoring device.

[0867] "Artificial intelligence means" refers to artificial intelligence (AI) technology used to analyze received video data and detect anomalies and recognize behavioral patterns.

[0868] "Notification means" refers to technology or devices that immediately notify the user when an abnormality is detected.

[0869] "Terminal" refers to the device used by the user to view video data and change system settings. Examples include smartphones and personal computers.

[0870] "Setting means" refers to a system that has the function of setting the initial settings of the monitoring device and various parameters through a terminal.

[0871] "Compression and encryption methods" refers to technologies that reduce the data size so that video data can be transmitted efficiently and encrypt it to maintain security.

[0872] "Temporary storage means" refers to a technology that has the function of temporarily storing video data in a buffer after receiving it.

[0873] A "permanent storage database" refers to a database system for safely storing important video data for a long period of time.

[0874] "Means for managing maintenance schedules" refers to a system that has the function of managing regular maintenance of monitoring devices and systems in a planned manner and encouraging maintenance to be carried out.

[0875] "Means for sending reminders" refers to technology that has a notification function to notify users of upcoming maintenance or other important events.

[0876] A "security assessment report" is a report that evaluates the security status of a system based on collected and analyzed data and proposes improvement measures.

[0877] "Feedback means" refers to a function that provides the created security evaluation report to the user and provides information for the user to take further measures.

[0878] The present invention provides a security system that covers a wide area by reusing old communication infrastructure. The system includes a monitoring device, a central processing unit, an artificial intelligence means, a notification means, a terminal, and various means related thereto.

[0879] Camera installation and initial setup

[0880] Camera installation

[0881] The user installs an all-weather surveillance device (security camera) near the old communication infrastructure (e.g., old PHS antenna). Specifically, the user fixes the camera using screws and brackets and connects a power cable. This allows the camera to be used in external environments.

[0882] Network Connection

[0883] The server obtains the camera's IP address via the DHCP server and configures the camera's network settings. The server also authenticates the camera's MAC address and registers it in its internal database. During this process, command line tools can be used to verify the network connection if necessary.

[0884] Initial Setup

[0885] The user accesses the camera's settings page using a dedicated app or web interface and enters initial settings such as the camera name and monitoring area. The server confirms these settings and applies them to the camera. The server then generates a configuration file in JSON or XML format and sends it to the camera to complete the setup.

[0886] Real-time video monitoring

[0887] Video capture and transmission

[0888] The camera captures video of the monitored area in real time, compresses the video data in H.264 format, encrypts it with AES, and sends it to the server.

[0889] Receiving and storing video

[0890] The server receives the video data sent from the camera, stores it in a temporary buffer, and also backs it up in a database for permanent storage. MySQL or PostgreSQL is used as the database management system.

[0891] User monitoring

[0892] Users access the system via a dedicated app or web interface on their smartphone or PC and view the video in real time. The video is displayed on the device using WebRTC and streaming technology.

[0893] Anomaly detection

[0894] Video analysis

[0895] The server analyzes the received video data using an AI model (e.g., YOLOv4, OpenCV), recognizing movement patterns and objects to detect abnormalities.

[0896] Sending alerts

[0897] If the server detects an abnormality, it immediately notifies the user via push notification or email using Firebase Cloud Messaging (FCM) or Twilio.

[0898] User Support

[0899] The user receives a notification and can view detailed video footage via a dedicated app or web interface. If an abnormality is detected, the user can take action, such as reporting the incident to the police.

[0900] Maintenance Notification

[0901] Maintenance schedule management

[0902] The server manages the regular maintenance schedule for each camera in a database and has reminder logic.

[0903] Sending notifications

[0904] Check when the server needs maintenance and send a notification to users.

[0905] User Response

[0906] Users receive a notification, check the maintenance schedule using a dedicated app or web interface, and contact the person in charge to arrange a schedule.

[0907] Data analysis and security consulting

[0908] Data collection

[0909] The server periodically backs up past video data and stores it in a database for analysis.

[0910] Data analysis

[0911] The server analyzes the collected video data using AI and data analysis tools (e.g., TensorFlow, PyTorch) to extract abnormal behavior patterns and security vulnerabilities.

[0912] Reporting

[0913] The server automatically creates a security assessment report based on the analysis results, and security specialists add comments as needed.

[0914] User Feedback

[0915] The server sends the completed security report to the user, who receives the report and implements the suggested remediation measures.

[0916] Specific examples

[0917] For example, let's say a user installs a camera at the entrance of their home. This camera constantly captures video of the area in front of the entrance and sends it to a server. The server then uses an AI model to analyze the video in real time and detect any suspicious activity. If a suspicious person is detected, the server immediately sends a push notification to the user's smartphone or PC, allowing the user to review the detailed video and then report the incident to the police.

[0918] The server also periodically manages camera and system maintenance schedules and sends timely reminders to users. Furthermore, the server analyzes the collected data, creates reports suggesting security improvements, and provides feedback to users, allowing users to continuously strengthen their system security.

[0919] An example prompt might be, "How do I configure an AI model to receive video data captured by a camera and detect abnormal behavior patterns?"

[0920] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0921] Step 1: Camera installation

[0922] The user physically installs an all-weather surveillance device (security camera) near the old communications infrastructure (e.g., old PHS antenna). The camera is fixed in place using screws and brackets, and a power cable is connected. After installation, the camera starts up by turning on the power. The inputs required are the camera's installation position and power source, and the output is a properly installed camera.

[0923] Step 2: Network Connection

[0924] The server obtains the camera's IP address via the DHCP server and configures the network settings. It also authenticates the camera's MAC address and registers it in its internal database. It checks the network connection using command line tools (e.g., ipconfig, ifconfig). The input is the camera's MAC address and network information, and the authenticated camera's IP address is obtained as output.

[0925] Step 3: Initial Setup

[0926] The user accesses the camera's settings page using a dedicated app or web interface and sets the camera's name (e.g., front door camera) and monitoring area. The server checks these settings and reflects them on the camera. A configuration file in JSON or XML format is generated and sent to the camera. The input is the configuration information entered by the user, and the output is the initial settings reflected on the camera.

[0927] Step 4: Record and send footage

[0928] The camera captures real-time video of the monitored area. The video data is compressed in H.264 format, encrypted with AES, and sent to the server. The input is real-time video of the monitored area, and the output is compressed and encrypted video data.

[0929] Step 5: Receive and save the footage

[0930] The server receives the video data sent from the camera and stores it in a temporary buffer. At the same time, it also creates a backup in a database for permanent storage. MySQL or PostgreSQL is used as the database management system. The input is compressed and encrypted video data, and the output is the temporarily stored and backed up video data.

[0931] Step 6: User monitoring

[0932] Users access the system on their smartphones or PCs through a dedicated app or web interface and view the video in real time. WebRTC and streaming technology are used to display the video on the device. The input is video data retrieved from the database, and the output is real-time video displayed on the user's device.

[0933] Step 7: Analyzing the footage

[0934] The server uses an AI model (e.g., YOLOv4, OpenCV) to analyze the received video data, recognizing movement patterns and objects to detect anomalies. The input is the received video data, and the output is anomaly detection information obtained as the analysis result.

[0935] Step 8: Sending an alert

[0936] If the server detects an anomaly, it immediately sends a notification to the user. Notifications are sent in the form of push notifications or emails using Firebase Cloud Messaging (FCM) or Twilio. The input is the anomaly detection information, and the output is a notification to the user.

[0937] Step 9: User interaction

[0938] The user receives a notification and checks the detailed video on a dedicated app or web interface. If an abnormality is detected, the user can take action, such as reporting the incident to the police. The input is the notification and detailed video data, and the output is the appropriate user response.

[0939] Step 10: Maintenance Schedule Management

[0940] The server manages the periodic maintenance schedule of each camera in a database and has reminder logic. The input is the camera's operational data and maintenance schedule, and the output is the managed maintenance schedule.

[0941] Step 11: Sending notifications

[0942] The server checks when maintenance is required and sends a notification to the user. The input is the maintenance schedule, and the output is the maintenance notification to the user.

[0943] Step 12: User Response

[0944] The user receives the notification, checks the maintenance schedule using a dedicated app or web interface, and contacts the person in charge to adjust the schedule. The input is the maintenance notification and schedule adjustment information, and the output is the adjusted maintenance schedule.

[0945] Step 13: Data collection

[0946] The server periodically backs up past video data and stores it in an analytical database. The input is past video data, and the output is the backup data.

[0947] Step 14: Data analysis

[0948] The server analyzes the collected video data using AI and data analysis tools (e.g., TensorFlow, PyTorch). It extracts abnormal behavior patterns and security vulnerabilities. The input is the backup data, and the output is the analysis results.

[0949] Step 15: Reporting

[0950] The server automatically creates a security assessment report based on the analysis results, and security experts add comments as needed. The input is the analysis results, and the created security assessment report is obtained as the output.

[0951] Step 16: User Feedback

[0952] The server sends the completed security assessment report to the user, who receives the report and implements the suggested remediation measures. The input is the security assessment report, and the output is the user's implementation of the security remediation measures.

[0953] (Application example 1)

[0954] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0955] In wide-area security systems that reuse old communication infrastructure, there is a need to provide new methods for users to remotely check camera footage in real time and quickly detect and respond to abnormalities. Another challenge is to centralize security management and reduce the burden on users by providing periodic maintenance notifications and security reports based on analysis results.

[0956] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0957] In this invention, the server includes a monitoring device attached to the old communication infrastructure, a central processing unit that receives video data transmitted from the monitoring device, artificial intelligence means in the central processing unit that analyzes the video data, notification means that notifies the user when an abnormality is detected, a terminal for the user to check the video data, a terminal having a user interface for changing the settings of the monitoring device, means for the central processing unit to send periodic maintenance notifications, and means for the central processing unit to generate security reports based on the analysis results. This allows the user to check the video in real time from a remote location, quickly detect and respond to abnormalities, and receive periodic maintenance notifications and security reports based on the analysis results.

[0958] "Old communications infrastructure" refers to the communications equipment and infrastructure that was previously used, including those that have now been replaced primarily by new technologies and equipment.

[0959] A "monitoring device" is a device such as a camera or sensor that has the function of acquiring images and data of a specific area and transmitting them to a central processing unit.

[0960] "Central processing unit" refers to a computer system that analyzes received video data and performs processes such as detecting abnormalities, saving data, and notifying users.

[0961] "Artificial intelligence means" refers to technology that uses algorithms such as machine learning and deep learning to analyze video data and recognize movement patterns and objects to detect abnormalities.

[0962] "Notification means" has the function of sending an alert to the user when an abnormality is detected, and includes push notifications and email notifications.

[0963] A "terminal" is a device such as a smartphone or PC used by a user, and is used to check video data from a surveillance device in real time and change settings.

[0964] "User interface" refers to an interface that allows a user to access a monitoring device or a central processing unit to change settings, check maintenance, and so on.

[0965] "Maintenance Notification" means a reminder sent to a user when scheduled maintenance of a monitoring device or system is required.

[0966] A "security report" is a document generated based on the results of video data analysis, and provides information including the security status and improvement measures.

[0967] In this invention, a specific method for realizing a wide-area security system that utilizes the old communication infrastructure will be described.

[0968] 1. System Configuration

[0969] The system is configured using the following hardware and software:

[0970] Hardware

[0971] Old communication infrastructure: Old PHS antennas, etc.

[0972] Surveillance equipment: Weatherproof security cameras.

[0973] User device: Smartphone or PC.

[0974] software

[0975] Central processing unit: Runs as a server and includes a web framework using Flask and a real-time image processing system using OpenCV.

[0976] Artificial intelligence means: A video data analysis system that utilizes machine learning and deep learning models.

[0977] Notification methods: Push notifications and email notification systems.

[0978] 2. Program Processing

[0979] The central processing unit (server) receives the video data transmitted from the monitoring device and analyzes it in real time. Specifically, the server includes the following means:

[0980] 1. Acquiring video data

[0981] The monitoring device (camera) captures images of the designated monitoring area in real time and transmits the image data to a central processing unit, which receives the image data and begins analyzing it.

[0982] 2. Anomaly detection

[0983] Video data is analyzed using artificial intelligence tools such as machine learning and deep learning to detect anomalies based on movement patterns and object recognition. For example, if suspicious movements or specific objects are detected, they will be flagged as an anomaly.

[0984] 3. Sending notifications

[0985] When an abnormality is detected, the central processing unit uses the notification means to send a push notification or email notification to the user, allowing the user to quickly confirm the abnormality and take the necessary action.

[0986] 4. User Interface

[0987] Users can access a dedicated user interface via a device (smartphone or PC) to check video data in real time, and can also change the settings of the monitoring device and set maintenance schedules.

[0988] 5. Generate security reports

[0989] The central processing unit periodically backs up the collected video data and analyzes it using AI. A security report based on the analysis results is provided to the user, suggesting improvement measures.

[0990] 3. Specific Examples

[0991] For example, if a user installs a surveillance camera at the entrance of their home, they can remotely check the situation at the entrance in real time via their smartphone. If the camera detects a suspicious person, a push notification is sent immediately, allowing the user to confirm the abnormality. In addition, regular maintenance notifications and detailed security reports are provided, strengthening security measures.

[0992] Prompt Sentence Examples

[0993] Write a Python program that analyzes video data from a home security camera in real time to detect suspicious individuals. The program should have a web interface using Flask, video analysis functionality using OpenCV, and the ability to send a push notification when an anomaly is detected.

[0994] This completes the description of the embodiment of the invention. This system makes it possible to realize wide-area, highly accurate security management that effectively utilizes the old communication infrastructure.

[0995] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0996] Step 1:

[0997] The server receives the video data transmitted from the monitoring device.

[0998] Specific behavior:

[0999] The monitoring device (camera) captures real-time video of the designated monitoring area and sends it as an RTSP stream to the server. The server receives the video data using a web framework such as Flask and stores it in a buffer frame by frame.

[1000] Input: Real-time video data as an RTSP stream.

[1001] Output: Video frame data waiting to be analyzed.

[1002] Step 2:

[1003] The server analyzes the received video data.

[1004] Specific behavior:

[1005] Video data is analyzed in real time using OpenCV and generative AI models, allowing the server to recognize motion patterns and objects in the video frames and determine whether anomalies have occurred.

[1006] Input: Buffered video frame data.

[1007] Output: Anomaly detection result (flagged as normal or abnormal).

[1008] Step 3:

[1009] If the server detects an abnormality, it will notify the user.

[1010] Specific behavior:

[1011] If the anomaly detection result indicates an abnormal state, the server will use notification means to send a push notification or email to the user's device, allowing the user to immediately check the situation.

[1012] Input: Anomaly detection result (anomaly status flag).

[1013] Output: Push notification or email notification.

[1014] Step 4:

[1015] The user checks the video data on a smartphone or PC.

[1016] Specific behavior:

[1017] Users can access real-time video data stored on the server through a dedicated app or web interface, allowing them to directly check the current situation and identify any abnormalities.

[1018] Input: Real-time video data stored on the server.

[1019] Output: Real-time video displayed on the user's device.

[1020] Step 5:

[1021] The server sends periodic maintenance notifications.

[1022] Specific behavior:

[1023] The server manages the maintenance schedule for the monitoring device and sends reminders to the user when maintenance is required.

[1024] Input: Maintenance schedule data.

[1025] Output: Reminder notification.

[1026] Step 6:

[1027] The server generates a security report based on the analysis results.

[1028] Specific behavior:

[1029] The server analyzes the collected video data to identify abnormal behavior patterns and security vulnerabilities, and automatically generates a security report that is sent to the user.

[1030] Input: Analyzed video data and anomaly detection results.

[1031] Output: Security report.

[1032] Step 7:

[1033] The user implements countermeasures based on the security report.

[1034] Specific behavior:

[1035] The user reviews the security report and implements the suggested improvements, changing the settings of monitoring devices and strengthening physical security as necessary.

[1036] Input: Security report.

[1037] Output: Results of the implementation of the improvement measures.

[1038] The above are the specific processing steps in this invention.

[1039] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1040] The present invention provides a system that reuses old communication infrastructure and combines a wide-area security system with an emotion engine that recognizes user emotions. Specific embodiments of this system are described below.

[1041] 1. Camera installation and initial settings

[1042] Camera installation

[1043] The user physically installs a security camera near the old communication infrastructure (old PHS antenna, etc.). The camera is weatherproof and designed to withstand the external environment.

[1044] Network Connection

[1045] The server obtains the camera's IP address and configures the network settings. The server also authenticates the camera's MAC address and registers it in its internal database.

[1046] Initial Setup

[1047] The user accesses the camera's settings page through a dedicated app or web interface. The user enters initial settings such as the camera name (e.g., front door camera) and monitoring area, and the server confirms these settings and applies them to the camera.

[1048] 2. Real-time video monitoring

[1049] Video capture and transmission

[1050] The camera captures video of the monitored area in real time, compresses and encrypts the video data, and sends it to the server.

[1051] Receiving and storing video

[1052] The server receives the video data sent from the camera, stores it in a temporary buffer, and also backs it up in a database for permanent storage.

[1053] User monitoring

[1054] Users access the system via a dedicated app or web interface on their smartphone or PC and view footage in real time.

[1055] 3. Anomaly Detection

[1056] Video analysis

[1057] The video data received by the server is input into an AI model to recognize movement patterns and objects.

[1058] Anomaly detection

[1059] The server uses an AI model to detect anomalies.

[1060] Alert Generation

[1061] If the server detects an abnormality, it generates alert data.

[1062] Sending notifications

[1063] Send server-generated alerts to users via push notifications.

[1064] User Support

[1065] Users receive a notification and can view detailed footage in a dedicated app or web interface, and if necessary, call the police.

[1066] 4. Maintenance Notice

[1067] Maintenance schedule management

[1068] The server manages the regular maintenance schedule for each camera in a database and has reminder logic.

[1069] Sending notifications

[1070] Check when the server needs maintenance and send a notification to users.

[1071] User Response

[1072] Users will receive notifications and can check the maintenance schedule via a dedicated app or web interface.

[1073] Schedule adjustment

[1074] The user contacts the maintenance staff and schedules the maintenance.

[1075] 5. Data analysis and security consulting

[1076] Data collection

[1077] The server periodically backs up past video data and stores it in a database for analysis.

[1078] Data analysis

[1079] The server uses AI and data analysis tools to analyze the collected video data and extract abnormal behavior patterns and security vulnerabilities.

[1080] Reporting

[1081] The server automatically creates a security assessment report based on the analysis results.

[1082] Staff Reviews

[1083] Security professionals will review the report and provide additional comments and suggestions as needed.

[1084] Send Feedback

[1085] The server sends the completed security report to the user.

[1086] User execution

[1087] The user receives the report and implements the suggested improvements.

[1088] 6. Adding an Emotion Engine

[1089] Collecting Emotional Data

[1090] The device's built-in camera and microphone analyze the user's facial expressions and tone of voice to collect emotional data.

[1091] emotion recognition

[1092] The server analyzes the collected data through the emotion engine and recognizes the user's emotional state.

[1093] Feedback of sentiment analysis results

[1094] The server provides feedback to the device based on the user's emotional state. For example, if the user is feeling anxious, it displays a comforting or encouraging message.

[1095] Alert Tuning

[1096] The server adjusts the frequency and content of alerts based on the user's emotional state: for example, if the user exhibits high anxiety levels, alerts are sent more quickly than usual.

[1097] Specific examples

[1098] For example, a user installs a camera at the entrance of their home, which captures footage of the area in front of the entrance and sends it to a server. The server then uses an AI model to analyze the footage and detect suspicious activity. If a suspicious person is detected, the server immediately sends an alert to the user. The user receives a notification, checks the footage on their smartphone, and, if necessary, calls the police.

[1099] Furthermore, if the user's emotion engine detects stress, the system will send a notification encouraging the user to relax. For example, if the user shows high anxiety levels, the system will provide safety advice in addition to camera footage, allowing the user to feel psychologically at ease and take appropriate action.

[1100] The above is an embodiment of the present invention, which makes it possible to effectively utilize the old communication infrastructure, provide a wide-area, highly accurate security system, and also enable flexible responses that take user emotions into consideration.

[1101] The processing flow will be explained below.

[1102] 1. Camera installation and initial settings

[1103] Step 1: Camera installation

[1104] The user physically installs security cameras into the old communications infrastructure.

[1105] Step 2: Power on the camera

[1106] The user supplies power to the camera and starts the system.

[1107] Step 3: Network Connection

[1108] The server obtains the camera's IP address and checks the network connection.

[1109] Step 4: MAC address registration

[1110] The server registers the camera's MAC address in its internal database and performs authentication.

[1111] Step 5: Initial Setup

[1112] The user opens a dedicated app or web interface and enters settings such as the camera name and monitoring area.

[1113] Step 6: Apply the settings

[1114] The server reflects the setting information received from the user in the camera.

[1115] 2. Real-time video monitoring

[1116] Step 1: Capture the footage

[1117] The camera captures real-time images of the surveillance area.

[1118] Step 2: Sending video data

[1119] The video data captured by the camera is compressed, encrypted, and sent to the server.

[1120] Step 3: Receiving video data

[1121] The server receives the video data sent from the camera.

[1122] Step 4: Save

[1123] The video data received by the server is temporarily stored in a buffer.

[1124] Step 5: Permanent storage

[1125] The server backs up the video data in the temporary buffer to a database.

[1126] Step 6: User monitoring

[1127] Users access the system from their device (smartphone or PC) via a dedicated app or web interface and view the footage in real time.

[1128] 3. Anomaly Detection

[1129] Step 1: Analyzing the footage

[1130] The video data received by the server is input into an AI model to recognize movement patterns and objects.

[1131] Step 2: Anomaly detection

[1132] The server uses an AI model to detect anomalies.

[1133] Step 3: Alert Generation

[1134] If the server detects an abnormality, it generates alert data.

[1135] Step 4: Sending notifications

[1136] Send server-generated alerts to users via push notifications.

[1137] Step 5: User interaction

[1138] Users receive a notification and can view detailed footage in a dedicated app or web interface, and if necessary, call the police.

[1139] 4. Maintenance Notice

[1140] Step 1: Schedule Management

[1141] The server manages the regular maintenance schedule for each camera.

[1142] Step 2: Trigger the reminder logic

[1143] The server will trigger reminder logic during maintenance periods.

[1144] Step 3: Sending notifications

[1145] Check when the server needs maintenance and send a notification to users.

[1146] Step 4: User response

[1147] Users will receive notifications and can check maintenance schedules via a dedicated app or web interface.

[1148] Step 5: Scheduling

[1149] The user contacts the maintenance staff and schedules the maintenance.

[1150] 5. Data analysis and security consulting

[1151] Step 1: Data collection

[1152] The server periodically backs up past video data and stores it in a database for analysis.

[1153] Step 2: Data analysis

[1154] The server uses AI and data analysis tools to analyze the collected video data and extract abnormal behavior patterns and security vulnerabilities.

[1155] Step 3: Reporting

[1156] The server automatically creates a security assessment report based on the analysis results.

[1157] Step 4: Staff review

[1158] Security professionals will review the report and provide additional comments and suggestions as needed.

[1159] Step 5: Send your feedback

[1160] The server sends the completed security report to the user.

[1161] Step 6: User execution

[1162] The user receives the report and implements the suggested improvements.

[1163] 6. Adding an Emotion Engine

[1164] Step 1: Collecting emotion data

[1165] The device's built-in camera and microphone analyze the user's facial expressions and tone of voice to collect emotional data.

[1166] Step 2: Emotion Recognition

[1167] The server analyzes the collected data through the emotion engine and recognizes the user's emotional state.

[1168] Step 3: Feedback of sentiment analysis results

[1169] The server provides feedback to the device based on the user's emotional state. For example, if the user is feeling anxious, it displays a comforting or encouraging message.

[1170] Step 4: Adjust your alerts

[1171] The server adjusts the frequency and content of alerts based on the user's emotional state: for example, if the user exhibits high anxiety levels, alerts are sent more quickly than usual.

[1172] Specific examples

[1173] For example, a user installs a camera at the entrance of their home, which captures footage of the area in front of the entrance and sends it to a server. The server then uses an AI model to analyze the footage and detect suspicious activity. If a suspicious person is detected, the server immediately sends an alert to the user. The user receives a notification, checks the footage on their smartphone, and, if necessary, calls the police.

[1174] Furthermore, if the user's emotion engine detects stress, the system will send a notification encouraging the user to relax. For example, if the user shows high anxiety levels, the system will provide safety advice in addition to camera footage, allowing the user to feel psychologically at ease and take appropriate action.

[1175] The above is an embodiment of the present invention, which makes it possible to effectively utilize the old communication infrastructure, provide a wide-area, highly accurate security system, and also enable flexible responses that take user emotions into consideration.

[1176] Example 2

[1177] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1178] Conventional monitoring systems face the challenges of making it difficult to reuse old communication infrastructure, and the complex installation and network connection required to cover a wide area. Furthermore, while there is a demand for improved accuracy in anomaly detection and flexible responses based on the user's emotional state, current systems have difficulty meeting these demands. Furthermore, managing regular maintenance and effectively notifying users is also an issue.

[1179] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1180] In this invention, the server includes a monitoring device attached to the old communication infrastructure, a central processing unit that receives video data transmitted from the monitoring device, and artificial intelligence (AI) means in the central processing unit that analyzes the video data. This enables high-precision monitoring that covers a wide area by reusing the old communication infrastructure. The server also includes AI means that analyzes the video data, a notification means that notifies the user when an abnormality is detected, a terminal on which the user can view the video data, a means for the central processing unit to manage the periodic maintenance schedule of the monitoring device and send reminders to the user, and an emotion recognition means that analyzes the user's emotional state and adjusts the frequency and content of alerts when an abnormality is detected. This enables flexible responses based on the user's emotional state and enables efficient periodic maintenance management.

[1181] "Old communications infrastructure" refers to communications equipment and facilities that were used in the past but are no longer in use.

[1182] "Surveillance equipment" means equipment installed to monitor a specific area, including cameras and sensors.

[1183] "Central Processing Unit" means a computer system or server that analyzes and processes data received from monitoring devices.

[1184] "Artificial intelligence means" refers to technologies that use machine learning models and algorithms to analyze data and perform anomaly detection and object recognition.

[1185] "Notification means" refers to a means for sending an alert or notification to the user when an abnormality is detected, and includes push notifications and emails.

[1186] A "terminal" is a device that allows a user to view video data and operate a surveillance device, and includes smartphones and PCs.

[1187] "Maintenance Schedule Management" refers to the function of planning and managing regular maintenance of monitoring devices and sending reminders.

[1188] "Emotion recognition means" refers to technology that analyzes a user's facial expressions and tone of voice to recognize their emotional state.

[1189] "Motion pattern" refers to the pattern of movement of people or objects detected within the monitored area.

[1190] "Object recognition" refers to the technology of detecting and identifying specific objects from video data.

[1191] The present invention provides a system that reuses old communication infrastructure and combines a wide-coverage security system with an emotion engine that recognizes user emotions. Detailed embodiments of this system are described below.

[1192] Camera installation and initial setup

[1193] Camera installation

[1194] A user installs an all-weather security camera near an old communication infrastructure (for example, an old PHS antenna). The camera is configured as an IP camera and is designed to withstand external environments.

[1195] Network Connection

[1196] The server scans and identifies the IP addresses of IP cameras connected to the network, then authenticates the camera's MAC address using an authentication server and registers the camera information in an internal database.

[1197] Initial Setup

[1198] Users access the camera settings page via a dedicated app or web interface on their smartphone or PC. For initial setup, the user enters the camera name (e.g., front door camera) and monitoring area, and the server confirms the settings and updates them on the camera.

[1199] Real-time video monitoring

[1200] Video capture and transmission

[1201] The cameras continuously capture images of the designated surveillance area, compress the video data into Motion JPEG format in real time, and then encrypt the video data using the SSL / TLS protocol before sending it to the server.

[1202] Receiving and storing video

[1203] The server receives the video data sent from the camera using the SSL / TLS protocol and temporarily stores it in buffer memory. At the same time, the video data is permanently stored in a database for backup purposes.

[1204] User monitoring

[1205] Users can access the server via a dedicated app or web interface on their smartphone or PC to view the streaming video in real time. Users can also use zoom and recording / playback functions as needed.

[1206] Anomaly detection

[1207] Video analysis

[1208] The server uses Python's OpenCV library to analyze the received video data frame by frame, and an AI model (e.g., YOLO or SSD) detects motion patterns and objects.

[1209] Anomaly detection

[1210] The server uses an anomaly detection AI model using TensorFlow or PyTorch to detect suspicious behavior or objects, such as sudden movements of a suspicious person or abnormal vehicle behavior.

[1211] Alert Generation

[1212] When the server detects an abnormality, it generates alert data, which includes a timestamp, location, and details of the abnormality.

[1213] Sending notifications

[1214] Server-generated alerts are sent to users via push notifications, using Firebase Cloud Messaging (FCM) or Apple Push Notification Service (APNS).

[1215] User Support

[1216] Users can check the notification they receive on their smartphone, view detailed footage through a dedicated app or web interface, and, if necessary, report the incident to the police.

[1217] Maintenance Notification

[1218] Maintenance schedule management

[1219] The server monitors the operating status and operating time of each camera, and manages regular maintenance schedules in a database. It has a reminder function and checks them regularly.

[1220] Sending notifications

[1221] The server checks when maintenance is required and sends reminder notifications to users.

[1222] User Response

[1223] Users will receive notifications and can view recommended maintenance dates via a dedicated app or web interface.

[1224] Schedule adjustment

[1225] The user contacts the maintenance staff and schedules the maintenance.

[1226] Data analysis and security consulting

[1227] Data collection

[1228] The server regularly backs up past video data and anomaly detection data and stores it in a database for analysis.

[1229] Data analysis

[1230] The server uses Python to analyze large amounts of video data using AI models and data analysis tools (e.g., scikit-learn, pandas) to extract abnormal behavior patterns and security vulnerabilities.

[1231] Reporting

[1232] The server automatically creates a security assessment report based on the analysis results, which includes an overview of detected anomalies and recommended remediation measures.

[1233] Staff Reviews

[1234] Security professionals review the automatically generated reports and provide additional comments and specific suggestions as needed.

[1235] Send Feedback

[1236] The server generates a completed security report in PDF format and sends it to the user.

[1237] User execution

[1238] The user reviews the report and implements the suggested improvements.

[1239] Adding an Emotion Engine

[1240] Collecting Emotional Data

[1241] The device's built-in camera and microphone collect real-time facial expression data and tone of voice, using facial recognition and voice analysis APIs.

[1242] emotion recognition

[1243] The server analyzes the collected data using an emotion engine. An emotion recognition algorithm is used to identify the user's current emotional state (e.g., joy, anxiety, anger, etc.).

[1244] Feedback of sentiment analysis results

[1245] Based on the analysis results of the emotion engine, the server provides feedback such as messages to the user on the device. For example, if the user is feeling anxious, a message such as "Take a deep breath to relax" will be displayed.

[1246] Alert Tuning

[1247] The server adjusts the frequency and content of alerts depending on the user's emotional state: if the user shows high anxiety levels, alerts are sent more quickly than usual.

[1248] Specific examples

[1249] For example, a user installs a camera at the entrance of their home, which captures footage of the area in front of the entrance and sends it to a server. The server then uses an AI model to analyze the footage and detect suspicious activity. If a suspicious person is detected, the server immediately sends an alert to the user. The user receives a notification, checks the footage on their smartphone, and, if necessary, calls the police.

[1250] Furthermore, if the user's emotion engine detects stress, the system will send a notification encouraging the user to relax. For example, if the user shows high anxiety levels, the system will provide safety advice in addition to camera footage, allowing the user to feel psychologically at ease and take appropriate action.

[1251] Prompt Sentence Examples

[1252] "The entrance camera has detected suspicious activity. Please review the footage immediately."

[1253] "Our systems have detected high levels of anxiety. Please take a deep breath to calm yourself and review our latest safety information."

[1254] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1255] Step 1: Camera installation and initial setup

[1256] Camera installation

[1257] The user physically installs a weatherproof IP camera near the old communication infrastructure (for example, an old PHS antenna), thus physically installing the surveillance device.

[1258] Input: IP camera, old communication infrastructure

[1259] Output: Physically installed monitoring equipment

[1260] Network Connection

[1261] The server scans and identifies the IP addresses of cameras connected to the network, then authenticates the MAC addresses of the cameras and registers the camera information in its internal database.

[1262] Input: Camera IP address, MAC address

[1263] Output: Authenticated cameras, camera information registered in the internal database

[1264] Initial Setup

[1265] The user accesses the dedicated app or web interface using a smartphone or PC. They log in to the camera setting page and enter the camera name (e.g., front door camera) and monitoring area. The server checks these settings and applies them to the camera.

[1266] Input: Camera name, monitoring area information

[1267] Output: Camera with settings reflected

[1268] Step 2: Real-time video monitoring

[1269] Video capture and transmission

[1270] The cameras continuously capture video of the configured surveillance area, compress the video into Motion JPEG format, and then send it to the server using the SSL / TLS protocol.

[1271] Input: Video of the monitored area

[1272] Output: Compressed video data, data encrypted with SSL / TLS protocol

[1273] Receiving and storing video

[1274] The server receives the video data sent from the camera using the SSL / TLS protocol, stores it in a temporary buffer memory, and also backs it up in a database for permanent storage.

[1275] Input: Encrypted video data

[1276] Output: Data stored in temporary buffer, data stored in database

[1277] User monitoring

[1278] Users can access the server via a dedicated app or web interface on their smartphone or PC, check the video in real time, and also use zoom and recording / playback functions.

[1279] Input: Streaming video

[1280] Output: Real-time video, user-controlled zoom and recording data

[1281] Step 3: Anomaly detection

[1282] Video analysis

[1283] The server uses Python's OpenCV library to analyze the received video data frame by frame, and an AI model (e.g., YOLO or SSD) detects motion patterns and objects.

[1284] Input: Received video data

[1285] Output: Object detection results, movement patterns

[1286] Anomaly detection

[1287] The server uses an anomaly detection AI model using TensorFlow or PyTorch to detect suspicious behavior or objects.

[1288] Input: Object detection results, movement patterns

[1289] Output: Anomaly detection results

[1290] Alert Generation

[1291] When the server detects an abnormality, it generates alert data, which includes a timestamp, location, and details of the abnormality.

[1292] Input: Anomaly detection result

[1293] Output: Generated alert data

[1294] Sending notifications

[1295] Server-generated alerts are sent to users via push notifications, using Firebase Cloud Messaging (FCM) or Apple Push Notification Service (APNS).

[1296] Input: Alert data

[1297] Output: Notification sent to the user

[1298] User Support

[1299] The user receives a notification on their smartphone, checks the video in detail using a dedicated app or web interface, and, if necessary, reports the incident to the police.

[1300] Input: Received notification, detailed video data

[1301] Output: User confirmation result, possibly police report

[1302] Step 4: Maintenance Notification

[1303] Maintenance schedule management

[1304] The server monitors the operating status and operating time of each camera, and manages regular maintenance schedules in a database. It has a reminder function and performs regular checks.

[1305] Input: Operation data of each camera

[1306] Output: Managed maintenance schedule

[1307] Sending notifications

[1308] The server checks when maintenance is required and sends reminder notifications to users.

[1309] Input: Managed Maintenance Schedule

[1310] Output: Notification sent to the user

[1311] User Response

[1312] Users will receive notifications and can view recommended maintenance dates via a dedicated app or web interface.

[1313] Input: Reminder

[1314] Output: Maintenance dates confirmed by the user

[1315] Schedule adjustment

[1316] The user contacts the maintenance staff and schedules the maintenance.

[1317] Input: User request

[1318] Output: Adjusted maintenance schedule

[1319] Step 5: Data analysis and security consulting

[1320] Data collection

[1321] The server regularly backs up past video data and anomaly detection data and stores it in a database for analysis.

[1322] Input: Past video data, anomaly detection data

[1323] Output: Backed up data, saved database

[1324] Data analysis

[1325] The server uses Python to analyze large amounts of video data using AI models and data analysis tools (e.g., scikit-learn, pandas) to extract abnormal behavior patterns and security vulnerabilities.

[1326] Input: Saved database

[1327] Output: Analyzed data, anomalous patterns, security vulnerabilities

[1328] Reporting

[1329] The server automatically creates a security assessment report based on the analysis results, which includes an overview of detected anomalies and recommended remediation measures.

[1330] Input: Analyzed data, anomalous patterns, security vulnerabilities

[1331] Output: Security Assessment Report

[1332] Staff Reviews

[1333] Security professionals review the automatically generated reports and provide additional comments and specific suggestions as needed.

[1334] Input: Security Assessment Report

[1335] Output: Reviewed report

[1336] Send Feedback

[1337] The server generates a completed security report in PDF format and sends it to the user.

[1338] Input:Reviewed report

[1339] Output: PDF report sent to the user

[1340] User execution

[1341] The user reviews the report and implements the suggested improvements.

[1342] Input: PDF report

[1343] Output: Implemented remedial measures

[1344] Step 6: Adding an Emotion Engine

[1345] Collecting Emotional Data

[1346] The device's built-in camera and microphone collect real-time facial expression data and tone of voice, using facial recognition and voice analysis APIs.

[1347] Input: User's facial expression data, tone of voice

[1348] Output: Collected emotion data

[1349] emotion recognition

[1350] The server analyzes the collected data using an emotion engine. An emotion recognition algorithm is used to identify the user's current emotional state (e.g., joy, anxiety, anger, etc.).

[1351] Input: Collected emotion data

[1352] Output: Identified emotional state

[1353] Feedback of sentiment analysis results

[1354] Based on the analysis results of the emotion engine, the server provides feedback such as messages to the user on the device. For example, if the user is feeling anxious, a message such as "Take a deep breath to relax" will be displayed.

[1355] Input: Identified emotional state

[1356] Output: The feedback message provided to the user

[1357] Alert Tuning

[1358] The server adjusts the frequency and content of alerts depending on the user's emotional state: if the user shows high anxiety levels, alerts are sent more quickly than usual.

[1359] Input: Identified emotional state

[1360] Output: Adjusted alert notification

[1361] (Application example 2)

[1362] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1363] In recent years, security systems using surveillance cameras have been introduced in many places, but these systems have difficulty simultaneously ensuring the safety and psychological security of all users. Furthermore, because there is no established method for effectively utilizing existing communication infrastructure, high costs and operational effort have become problems. Furthermore, few systems can provide feedback based on the user's emotional state, limiting the user's response in emergencies. To solve these issues, it is necessary to provide flexible responses that take user emotions into account while performing high-precision monitoring over a wide area.

[1364] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for managing monitoring devices attached to the old communication infrastructure, artificial intelligence means for receiving and analyzing video data transmitted from the monitoring devices, and emotion engine means for recognizing the user's emotions and providing feedback based on them. This enables high-precision monitoring over a wide area and flexible feedback according to the user's emotional state while making effective use of the old communication infrastructure.

[1365] "Old communications infrastructure" refers to physical infrastructure such as communications equipment and antennas that were used in the past, and are currently unused but can be reused.

[1366] "Surveillance equipment" means devices such as cameras and sensors that are installed to monitor a designated area.

[1367] A "central processing unit" is a computer system that receives, analyzes, and manages data sent from the monitoring device.

[1368] "Artificial intelligence means" refers to machine learning models and algorithms used to analyze received video data and recognize movement patterns and objects.

[1369] "Notification means" refers to a system or device that sends an alert or notification to the user when an abnormality is detected.

[1370] A "terminal" is a device such as a smartphone or PC that a user uses to check video data and notifications.

[1371] An "emotion engine" refers to software or algorithms that analyze a user's facial expressions, voice, etc. to recognize their emotional state.

[1372] A "means for providing feedback" is a system or application that sends messages or notifications based on the user's emotional state.

[1373] The present invention combines an emotion engine with a highly accurate security system that covers a wide area by reusing old communication infrastructure. Specific embodiments of the present invention are described below.

[1374] 1. Camera installation and initial settings

[1375] Camera installation

[1376] Users install weatherproof security cameras near old communication infrastructure (old PHS antennas, etc.) The cameras are designed to withstand the external environment.

[1377] Network Connection

[1378] The central processing unit (server) acquires the camera's IP address and configures the network settings. The server also authenticates the camera's MAC address and registers it in its internal database.

[1379] Initial Setup

[1380] Users access the camera's settings page through a dedicated application or web interface. They enter initial settings such as the camera's name (e.g., front door camera) and monitoring area, and the server updates the camera with these settings.

[1381] 2. Real-time video monitoring

[1382] Video capture and transmission

[1383] The camera captures video of the designated monitoring area in real time, compresses and encrypts the video data, and sends it to the server.

[1384] Receiving and storing video

[1385] The server receives the video data sent from the camera, stores it in a temporary buffer, and also backs it up in a database for permanent storage.

[1386] User monitoring

[1387] Users access the system via a dedicated application or web interface on their smartphone or PC and view footage in real time.

[1388] 3. Anomaly Detection and Emotion Recognition

[1389] Video analysis

[1390] The server inputs the received video data into a generative AI model to recognize movement patterns and objects.

[1391] Anomaly detection

[1392] The server uses an AI model to detect anomalies.

[1393] Collecting Emotional Data

[1394] The device's built-in camera and microphone analyze the user's facial expressions and tone of voice to collect emotional data.

[1395] emotion recognition

[1396] The server analyzes the collected data through an emotion engine and recognizes the user's emotional state.

[1397] Providing Feedback

[1398] The server then provides feedback to the device based on the user's emotional state. For example, if the user is feeling anxious, it displays a comforting or encouraging message.

[1399] 4. Notifications and User Response

[1400] Alert Generation

[1401] The server generates alert data when an abnormality is detected.

[1402] Sending notifications

[1403] The server then sends the generated alerts to the user via push notifications, and adjusts the frequency and content of the alerts based on the user's emotional state. For example, if the user shows a high level of anxiety, the alerts will be sent more quickly than usual.

[1404] User Support

[1405] Users receive a notification and can review the video in detail in a dedicated app or web interface, and if necessary, call the police.

[1406] 5. Maintenance notifications and data analysis

[1407] Maintenance schedule management

[1408] The server manages the regular maintenance schedule for each camera in a database and is equipped with reminder logic.

[1409] Sending notifications

[1410] The server checks when maintenance is required and sends a notification to the user.

[1411] User Response

[1412] Users will receive notifications and can check maintenance dates via a dedicated application or web interface.

[1413] Data collection and analysis

[1414] The server periodically backs up past video data and analyzes it using AI and data analysis tools to identify abnormal behavior patterns and security vulnerabilities.

[1415] Reporting

[1416] The server automatically creates a security assessment report based on the analysis results and sends it to the user.

[1417] Specific examples

[1418] For example, a user installs a camera at the entrance of their home, which captures footage of the area in front of the entrance and sends it to a server. The server then uses a generative AI model to analyze the footage and detect suspicious activity. If a suspicious activity is detected, the server immediately sends an alert to the user. The user receives the notification, checks the footage on their smartphone, and, if necessary, calls the police. If the user's emotion engine detects stress in the user, the system sends a notification encouraging the user to relax. For example, if the user shows a high level of anxiety, the system will provide safety advice in addition to the camera footage. This provides the user with psychological peace of mind and allows them to take appropriate action.

[1419] Prompt Sentence Examples

[1420] A suspicious person is standing in front of the house.

[1421] Addresses high anxiety levels in users.

[1422] The user is prompted to relax and given instructions on how to report the incident to the police.

[1423] The above is a specific embodiment for carrying out the present invention, which makes it possible to effectively utilize the old communication infrastructure, provide a wide-area, highly accurate security system, and also enable flexible responses that take user emotions into consideration.

[1424] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1425] Step 1: Camera Settings

[1426] Users install weatherproof security cameras near the old communication infrastructure, then enter the camera name (for example, front door camera) and monitoring area through a dedicated application or web interface, and the server updates the camera with this information.

[1427] Input: Camera IP address, monitoring area settings

[1428] Output: The camera setting information is registered on the server.

[1429] Specific operation: The server obtains the IP address of the camera, configures the network settings, and then reflects the camera name and monitoring area settings on the camera.

[1430] Step 2: Real-time video monitoring

[1431] The cameras capture video of the designated surveillance area in real time, compressing and encrypting the video data before sending it to the server. The server temporarily stores the received video data in a buffer and also backs it up in a database for permanent storage. Users can view the video in real time on their smartphones or PCs using a dedicated application or web interface.

[1432] Input: Real-time video data

[1433] Output: Video data stored on the server, real-time video confirmation by the user

[1434] How it works: The camera captures video, compresses and encrypts it, and sends it to the server. The server receives the video data, stores it in a temporary buffer, and also stores it in a database for permanent storage. The user can view the video in real time via a dedicated application or web interface.

[1435] Step 3: Video analysis and anomaly detection

[1436] The server inputs the received video data into a generative AI model to recognize movement patterns and objects. If an abnormality is detected based on the analysis, the server generates alert data.

[1437] Input: Received video data

[1438] Output: Alert data

[1439] Specific operation: The server inputs the video data into the AI ​​model, analyzes movement patterns and object recognition, and generates alert data indicating the abnormality if an abnormality is detected.

[1440] Step 4: Collect and recognize emotion data

[1441] The device's built-in camera and microphone analyze the user's facial expressions and tone of voice to collect emotional data. The server then analyzes the collected data through an emotion engine to recognize the user's emotional state.

[1442] Input: User's facial expression and voice data

[1443] Output: User's emotional state

[1444] Specific operation: The system collects the user's emotions in real time using a camera and microphone, and analyzes them using the server's emotion engine. The system recognizes the user's emotional state based on the analysis results.

[1445] Step 5: Provide emotional feedback

[1446] The server then provides feedback to the device based on the user's emotional state. For example, if the user is feeling anxious, it displays a comforting or encouraging message.

[1447] Input: User's emotional state

[1448] Output: Feedback message

[1449] Specific operation: The server generates feedback (e.g., a message to ease anxiety) according to the user's emotional state and sends it to the device.

[1450] Step 6: Send notifications and respond to users

[1451] The server sends a push notification to the user using the generated alert data and feedback based on the user's emotional state. The user receives the notification and can view detailed footage in a dedicated application or web interface. If necessary, the user can report the incident to the police.

[1452] Input: Alert data, feedback based on emotional state

[1453] Output: Notify user, notify police

[1454] Specific operation: After receiving a notification from the server, the user checks the detailed video using a dedicated application or web interface and, if necessary, reports the incident to the police.

[1455] Step 7: Maintenance notifications and data analysis

[1456] The server sets a regular maintenance schedule for each camera and notifies the user of the required date and time. It also periodically backs up past video data and analyzes it using AI and data analysis tools to identify abnormal behavior patterns and security vulnerabilities. Based on the analysis results, it sends a security assessment report to the user.

[1457] Input: Maintenance schedule, past video data

[1458] Output: Maintenance notification, security assessment report

[1459] Specific operations: The server manages maintenance schedules, sends notifications to users, analyzes regularly backed-up video data, and generates and sends security assessment reports to users.

[1460] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1461] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1462] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1463] [Third embodiment]

[1464] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1465] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1466] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1467] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1468] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1469] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1470] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1471] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1472] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1473] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1474] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1475] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1476] The present invention provides a security system that covers a wide area by reusing the old communication infrastructure. Specific embodiments of the present invention are described below.

[1477] 1. Camera installation and initial settings

[1478] Camera installation

[1479] The user physically installs a security camera near the old communication infrastructure (old PHS antenna, etc.). The camera is weatherproof and designed to withstand the external environment.

[1480] Network Connection

[1481] The server obtains the camera's IP address and configures the network settings. The server also authenticates the camera's MAC address and registers it in its internal database.

[1482] Initial Setup

[1483] The user accesses the camera's settings page through a dedicated app or web interface. The user enters initial settings such as the camera name (e.g., front door camera) and monitoring area, and the server confirms these settings and applies them to the camera.

[1484] 2. Real-time video monitoring

[1485] Video capture and transmission

[1486] The camera captures video of the monitored area in real time, compresses and encrypts the video data, and sends it to the server.

[1487] Receiving and storing video

[1488] The server receives the video data sent from the camera, stores it in a temporary buffer, and also backs it up in a database for permanent storage.

[1489] User monitoring

[1490] Users access the system via a dedicated app or web interface on their smartphone or PC and view footage in real time.

[1491] 3. Anomaly Detection

[1492] Video analysis

[1493] The server uses an AI model to analyze the received video data, recognize movement patterns and objects, and detect abnormalities.

[1494] Sending alerts

[1495] Any abnormalities detected by the server will be immediately notified to the user via push notification or email.

[1496] User Support

[1497] The user receives a notification and can view detailed video footage via a dedicated app or web interface. If an abnormality is detected, the user can take action, such as reporting the incident to the police.

[1498] 4. Maintenance Notice

[1499] Maintenance schedule management

[1500] The server manages the regular maintenance schedule for each camera in a database and has reminder logic.

[1501] Sending notifications

[1502] Check when the server needs maintenance and send a notification to users.

[1503] User Response

[1504] Users receive a notification, check the maintenance schedule using a dedicated app or web interface, and contact the person in charge to arrange a schedule.

[1505] 5. Data analysis and security consulting

[1506] Data collection

[1507] The server periodically backs up past video data and stores it in a database for analysis.

[1508] Data analysis

[1509] The server uses AI and data analysis tools to analyze the collected video data and extract abnormal behavior patterns and security vulnerabilities.

[1510] Reporting

[1511] The server automatically creates a security assessment report based on the analysis results, and in some cases adds additional comments from security experts.

[1512] User Feedback

[1513] The server sends the completed security report to the user, who receives the report and implements the suggested remediation measures.

[1514] Specific examples

[1515] For example, let's say a user installs a camera at the entrance of their home. The camera captures video in front of the entrance and sends it to a server. The server uses an AI model to analyze the video and detect suspicious activity. If a suspicious activity is detected, the server immediately sends an alert to the user. The user receives the notification, checks the video on their smartphone, and takes action, such as notifying the police, depending on the situation. The server also regularly notifies the user monthly about system maintenance, and the user contacts the person in charge to carry out the maintenance. The server then analyzes the data it collects, identifies trends such as suspicious activity patterns, and security specialists create a report based on the analysis results, proposing specific improvement measures. The user can then strengthen their security measures based on this report.

[1516] The above is an embodiment of the present invention, which makes it possible to effectively utilize the old communication infrastructure and provide a wide-area, highly accurate security system.

[1517] The processing flow will be explained below.

[1518] Camera installation and initial setup

[1519] Step 1: Camera installation

[1520] The user physically installs security cameras into the old communications infrastructure.

[1521] Step 2: Turn on the camera and connect to the network

[1522] The user powers on the camera and it connects to the network.

[1523] Step 3: Obtaining and registering an IP address

[1524] The server obtains the camera's IP address and recognizes it within the network.

[1525] Step 4: MAC Address Authentication

[1526] The server authenticates the camera's MAC address and registers it in its internal database.

[1527] Step 5: Initial Setup

[1528] Users access a dedicated app or web interface to set the camera name and monitoring area.

[1529] Step 6: Apply the settings

[1530] The server reflects the setting information received from the user in the camera.

[1531] Real-time video monitoring

[1532] Step 1: Capture the footage

[1533] The camera captures real-time images of the surveillance area.

[1534] Step 2: Sending video data

[1535] The video data captured by the camera is compressed, encrypted, and sent to the server.

[1536] Step 3: Receiving video data

[1537] The server receives the video data sent from the camera.

[1538] Step 4: Save

[1539] The video data received by the server is temporarily stored in a buffer.

[1540] Step 5: Permanent storage

[1541] The server backs up the video data in the temporary buffer to a database.

[1542] Step 6: User monitoring

[1543] Users access the system from their device (smartphone or PC) via a dedicated app or web interface and view the footage in real time.

[1544] Anomaly detection

[1545] Step 1: Analyzing the footage

[1546] The video data received by the server is input into an AI model to recognize movement patterns and objects.

[1547] Step 2: Anomaly detection

[1548] The server uses an AI model to detect anomalies.

[1549] Step 3: Alert Generation

[1550] If the server detects an abnormality, it generates alert data.

[1551] Step 4: Sending notifications

[1552] Send server-generated alerts to users via push notifications.

[1553] Step 5: User interaction

[1554] Users receive a notification and can view detailed footage in a dedicated app or web interface, and if necessary, call the police.

[1555] Maintenance Notification

[1556] Step 1: Schedule Management

[1557] The server stores the regular maintenance schedule for each camera in a management database.

[1558] Step 2: Trigger the reminder logic

[1559] The server will trigger reminder logic during maintenance periods.

[1560] Step 3: Sending notifications

[1561] Check when the server needs maintenance and send a notification to users.

[1562] Step 4: User response

[1563] Users will receive a notification and can check the maintenance schedule via a dedicated app or web interface.

[1564] Step 5: Scheduling

[1565] The user contacts the maintenance staff and schedules the maintenance.

[1566] Data analysis and security consulting

[1567] Step 1: Data collection

[1568] The server periodically backs up past video data and stores it in a database for analysis.

[1569] Step 2: Data analysis

[1570] The server uses AI and data analysis tools to analyze the collected video data and extract abnormal behavior patterns and security vulnerabilities.

[1571] Step 3: Reporting

[1572] The server automatically creates a security assessment report based on the analysis results.

[1573] Step 4: Staff review

[1574] Security professionals will review the report and provide additional comments and suggestions as needed.

[1575] Step 5: Send your feedback

[1576] The server sends the completed security report to the user.

[1577] Step 6: User execution

[1578] The user receives the report and implements the suggested improvements.

[1579] Example 1

[1580] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1581] Conventional security systems require the construction of new infrastructure, which is expensive, making it difficult to cover a wide area. Real-time anomaly detection and maintenance management are also inadequate. This leads to issues such as delays in rapid response and difficulty in long-term system operation.

[1582] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1583] In this invention, the server includes a monitoring device attached to the old communication infrastructure, a central processing unit that receives video data transmitted from the monitoring device, artificial intelligence means in the central processing unit that analyzes the video data, notification means that notifies the user when an abnormality is detected, a terminal for the user to check the video data, setting means that performs initial configuration of the monitoring device through the terminal, means that compress and encrypt the video data and transmit it, means that temporarily store the video data and back it up in a database for permanent storage, means that manage a periodic maintenance schedule, means that send reminders based on the maintenance schedule, means that periodically analyze the video data and create a security evaluation report, and means that feed back the security evaluation report to the user. This makes it possible to effectively utilize the old communication infrastructure to cover a wide area, and to enable real-time abnormality detection, prompt notification, and maintenance management.

[1584] "Old communications infrastructure" refers to communications technologies and equipment that were used in the past and are no longer in use but can be reused.

[1585] "Monitoring equipment" refers to equipment installed to monitor a specific area, and has the function of capturing video data and transmitting it to a central processing unit.

[1586] "Video data" refers to the data that can be stored and transmitted in digital format from images captured by surveillance equipment.

[1587] "Central processing unit" refers to a computer device that receives and analyzes video data transmitted from a monitoring device.

[1588] "Artificial intelligence means" refers to artificial intelligence (AI) technology used to analyze received video data and detect anomalies and recognize behavioral patterns.

[1589] "Notification means" refers to technology or devices that immediately notify the user when an abnormality is detected.

[1590] "Terminal" refers to the device used by the user to view video data and change system settings. Examples include smartphones and personal computers.

[1591] "Setting means" refers to a system that has the function of setting the initial settings of the monitoring device and various parameters through a terminal.

[1592] "Compression and encryption methods" refers to technologies that reduce the data size so that video data can be transmitted efficiently and encrypt it to maintain security.

[1593] "Temporary storage means" refers to a technology that has the function of temporarily storing video data in a buffer after receiving it.

[1594] A "permanent storage database" refers to a database system for safely storing important video data for a long period of time.

[1595] "Means for managing maintenance schedules" refers to a system that has the function of managing regular maintenance of monitoring devices and systems in a planned manner and encouraging maintenance to be carried out.

[1596] "Means for sending reminders" refers to technology that has a notification function to notify users of upcoming maintenance or other important events.

[1597] A "security assessment report" is a report that evaluates the security status of a system based on collected and analyzed data and proposes improvement measures.

[1598] "Feedback means" refers to a function that provides the created security evaluation report to the user and provides information for the user to take further measures.

[1599] The present invention provides a security system that covers a wide area by reusing old communication infrastructure. The system includes a monitoring device, a central processing unit, an artificial intelligence means, a notification means, a terminal, and various means related thereto.

[1600] Camera installation and initial setup

[1601] Camera installation

[1602] The user installs an all-weather surveillance device (security camera) near the old communication infrastructure (e.g., old PHS antenna). Specifically, the user fixes the camera using screws and brackets and connects a power cable. This allows the camera to be used in external environments.

[1603] Network Connection

[1604] The server obtains the camera's IP address via the DHCP server and configures the camera's network settings. The server also authenticates the camera's MAC address and registers it in its internal database. During this process, command line tools can be used to verify the network connection if necessary.

[1605] Initial Setup

[1606] The user accesses the camera's settings page using a dedicated app or web interface and enters initial settings such as the camera name and monitoring area. The server confirms these settings and applies them to the camera. The server then generates a configuration file in JSON or XML format and sends it to the camera to complete the setup.

[1607] Real-time video monitoring

[1608] Video capture and transmission

[1609] The camera captures video of the monitored area in real time, compresses the video data in H.264 format, encrypts it with AES, and sends it to the server.

[1610] Receiving and storing video

[1611] The server receives the video data sent from the camera, stores it in a temporary buffer, and also backs it up in a database for permanent storage. MySQL or PostgreSQL is used as the database management system.

[1612] User monitoring

[1613] Users access the system via a dedicated app or web interface on their smartphone or PC and view the video in real time. The video is displayed on the device using WebRTC and streaming technology.

[1614] Anomaly detection

[1615] Video analysis

[1616] The server analyzes the received video data using an AI model (e.g., YOLOv4, OpenCV), recognizing movement patterns and objects to detect abnormalities.

[1617] Sending alerts

[1618] If the server detects an abnormality, it immediately notifies the user via push notification or email using Firebase Cloud Messaging (FCM) or Twilio.

[1619] User Support

[1620] The user receives a notification and can view detailed video footage via a dedicated app or web interface. If an abnormality is detected, the user can take action, such as reporting the incident to the police.

[1621] Maintenance Notification

[1622] Maintenance schedule management

[1623] The server manages the regular maintenance schedule for each camera in a database and has reminder logic.

[1624] Sending notifications

[1625] Check when the server needs maintenance and send a notification to users.

[1626] User Response

[1627] Users receive a notification, check the maintenance schedule using a dedicated app or web interface, and contact the person in charge to arrange a schedule.

[1628] Data analysis and security consulting

[1629] Data collection

[1630] The server periodically backs up past video data and stores it in a database for analysis.

[1631] Data analysis

[1632] The server analyzes the collected video data using AI and data analysis tools (e.g., TensorFlow, PyTorch) to extract abnormal behavior patterns and security vulnerabilities.

[1633] Reporting

[1634] The server automatically creates a security assessment report based on the analysis results, and security specialists add comments as needed.

[1635] User Feedback

[1636] The server sends the completed security report to the user, who receives the report and implements the suggested remediation measures.

[1637] Specific examples

[1638] For example, let's say a user installs a camera at the entrance of their home. This camera constantly captures video of the area in front of the entrance and sends it to a server. The server then uses an AI model to analyze the video in real time and detect any suspicious activity. If a suspicious person is detected, the server immediately sends a push notification to the user's smartphone or PC, allowing the user to review the detailed video and then report the incident to the police.

[1639] The server also periodically manages camera and system maintenance schedules and sends timely reminders to users. Furthermore, the server analyzes the collected data, creates reports suggesting security improvements, and provides feedback to users, allowing users to continuously strengthen their system security.

[1640] An example prompt might be, "How do I configure an AI model to receive video data captured by a camera and detect abnormal behavior patterns?"

[1641] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1642] Step 1: Camera installation

[1643] The user physically installs an all-weather surveillance device (security camera) near the old communications infrastructure (e.g., old PHS antenna). The camera is fixed in place using screws and brackets, and a power cable is connected. After installation, the camera starts up by turning on the power. The inputs required are the camera's installation position and power source, and the output is a properly installed camera.

[1644] Step 2: Network Connection

[1645] The server obtains the camera's IP address via the DHCP server and configures the network settings. It also authenticates the camera's MAC address and registers it in its internal database. It checks the network connection using command line tools (e.g., ipconfig, ifconfig). The input is the camera's MAC address and network information, and the authenticated camera's IP address is obtained as output.

[1646] Step 3: Initial Setup

[1647] The user accesses the camera's settings page using a dedicated app or web interface and sets the camera's name (e.g., front door camera) and monitoring area. The server checks these settings and reflects them on the camera. A configuration file in JSON or XML format is generated and sent to the camera. The input is the configuration information entered by the user, and the output is the initial settings reflected on the camera.

[1648] Step 4: Record and send footage

[1649] The camera captures real-time video of the monitored area. The video data is compressed in H.264 format, encrypted with AES, and sent to the server. The input is real-time video of the monitored area, and the output is compressed and encrypted video data.

[1650] Step 5: Receive and save the footage

[1651] The server receives the video data sent from the camera and stores it in a temporary buffer. At the same time, it also creates a backup in a database for permanent storage. MySQL or PostgreSQL is used as the database management system. The input is compressed and encrypted video data, and the output is the temporarily stored and backed up video data.

[1652] Step 6: User monitoring

[1653] Users access the system on their smartphones or PCs through a dedicated app or web interface and view the video in real time. WebRTC and streaming technology are used to display the video on the device. The input is video data retrieved from the database, and the output is real-time video displayed on the user's device.

[1654] Step 7: Analyzing the footage

[1655] The server uses an AI model (e.g., YOLOv4, OpenCV) to analyze the received video data, recognizing movement patterns and objects to detect anomalies. The input is the received video data, and the output is anomaly detection information obtained as the analysis result.

[1656] Step 8: Sending an alert

[1657] If the server detects an anomaly, it immediately sends a notification to the user. Notifications are sent in the form of push notifications or emails using Firebase Cloud Messaging (FCM) or Twilio. The input is the anomaly detection information, and the output is a notification to the user.

[1658] Step 9: User interaction

[1659] The user receives a notification and checks the detailed video on a dedicated app or web interface. If an abnormality is detected, the user can take action, such as reporting the incident to the police. The input is the notification and detailed video data, and the output is the appropriate user response.

[1660] Step 10: Maintenance Schedule Management

[1661] The server manages the periodic maintenance schedule of each camera in a database and has reminder logic. The input is the camera's operational data and maintenance schedule, and the output is the managed maintenance schedule.

[1662] Step 11: Sending notifications

[1663] The server checks when maintenance is required and sends a notification to the user. The input is the maintenance schedule, and the output is the maintenance notification to the user.

[1664] Step 12: User Response

[1665] The user receives the notification, checks the maintenance schedule using a dedicated app or web interface, and contacts the person in charge to adjust the schedule. The input is the maintenance notification and schedule adjustment information, and the output is the adjusted maintenance schedule.

[1666] Step 13: Data collection

[1667] The server periodically backs up past video data and stores it in an analytical database. The input is past video data, and the output is the backup data.

[1668] Step 14: Data analysis

[1669] The server analyzes the collected video data using AI and data analysis tools (e.g., TensorFlow, PyTorch). It extracts abnormal behavior patterns and security vulnerabilities. The input is the backup data, and the output is the analysis results.

[1670] Step 15: Reporting

[1671] The server automatically creates a security assessment report based on the analysis results, and security experts add comments as needed. The input is the analysis results, and the created security assessment report is obtained as the output.

[1672] Step 16: User Feedback

[1673] The server sends the completed security assessment report to the user, who receives the report and implements the suggested remediation measures. The input is the security assessment report, and the output is the user's implementation of the security remediation measures.

[1674] (Application example 1)

[1675] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1676] In wide-area security systems that reuse old communication infrastructure, there is a need to provide new methods for users to remotely check camera footage in real time and quickly detect and respond to abnormalities. Another challenge is to centralize security management and reduce the burden on users by providing periodic maintenance notifications and security reports based on analysis results.

[1677] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1678] In this invention, the server includes a monitoring device attached to the old communication infrastructure, a central processing unit that receives video data transmitted from the monitoring device, artificial intelligence means in the central processing unit that analyzes the video data, notification means that notifies the user when an abnormality is detected, a terminal for the user to check the video data, a terminal having a user interface for changing the settings of the monitoring device, means for the central processing unit to send periodic maintenance notifications, and means for the central processing unit to generate security reports based on the analysis results. This allows the user to check the video in real time from a remote location, quickly detect and respond to abnormalities, and receive periodic maintenance notifications and security reports based on the analysis results.

[1679] "Old communications infrastructure" refers to the communications equipment and infrastructure that was previously used, including those that have now been replaced primarily by new technologies and equipment.

[1680] A "monitoring device" is a device such as a camera or sensor that has the function of acquiring images and data of a specific area and transmitting them to a central processing unit.

[1681] "Central processing unit" refers to a computer system that analyzes received video data and performs processes such as detecting abnormalities, saving data, and notifying users.

[1682] "Artificial intelligence means" refers to technology that uses algorithms such as machine learning and deep learning to analyze video data and recognize movement patterns and objects to detect abnormalities.

[1683] "Notification means" has the function of sending an alert to the user when an abnormality is detected, and includes push notifications and email notifications.

[1684] A "terminal" is a device such as a smartphone or PC used by a user, and is used to check video data from a surveillance device in real time and change settings.

[1685] "User interface" refers to an interface that allows a user to access a monitoring device or a central processing unit to change settings, check maintenance, and so on.

[1686] "Maintenance Notification" means a reminder sent to a user when scheduled maintenance of a monitoring device or system is required.

[1687] A "security report" is a document generated based on the results of video data analysis, and provides information including the security status and improvement measures.

[1688] In this invention, a specific method for realizing a wide-area security system that utilizes the old communication infrastructure will be described.

[1689] 1. System Configuration

[1690] The system is configured using the following hardware and software:

[1691] Hardware

[1692] Old communication infrastructure: Old PHS antennas, etc.

[1693] Surveillance equipment: Weatherproof security cameras.

[1694] User device: Smartphone or PC.

[1695] software

[1696] Central processing unit: Runs as a server and includes a web framework using Flask and a real-time image processing system using OpenCV.

[1697] Artificial intelligence means: A video data analysis system that utilizes machine learning and deep learning models.

[1698] Notification methods: Push notifications and email notification systems.

[1699] 2. Program Processing

[1700] The central processing unit (server) receives the video data transmitted from the monitoring device and analyzes it in real time. Specifically, the server includes the following means:

[1701] 1. Acquiring video data

[1702] The monitoring device (camera) captures images of the designated monitoring area in real time and transmits the image data to a central processing unit, which receives the image data and begins analyzing it.

[1703] 2. Anomaly detection

[1704] Video data is analyzed using artificial intelligence tools such as machine learning and deep learning to detect anomalies based on movement patterns and object recognition. For example, if suspicious movements or specific objects are detected, they will be flagged as an anomaly.

[1705] 3. Sending notifications

[1706] When an abnormality is detected, the central processing unit uses the notification means to send a push notification or email notification to the user, allowing the user to quickly confirm the abnormality and take the necessary action.

[1707] 4. User Interface

[1708] Users can access a dedicated user interface via a device (smartphone or PC) to check video data in real time, and can also change the settings of the monitoring device and set maintenance schedules.

[1709] 5. Generate security reports

[1710] The central processing unit periodically backs up the collected video data and analyzes it using AI. A security report based on the analysis results is provided to the user, suggesting improvement measures.

[1711] 3. Specific Examples

[1712] For example, if a user installs a surveillance camera at the entrance of their home, they can remotely check the situation at the entrance in real time via their smartphone. If the camera detects a suspicious person, a push notification is sent immediately, allowing the user to confirm the abnormality. In addition, regular maintenance notifications and detailed security reports are provided, strengthening security measures.

[1713] Prompt Sentence Examples

[1714] Write a Python program that analyzes video data from a home security camera in real time to detect suspicious individuals. The program should have a web interface using Flask, video analysis functionality using OpenCV, and the ability to send a push notification when an anomaly is detected.

[1715] This completes the description of the embodiment of the invention. This system makes it possible to realize wide-area, highly accurate security management that effectively utilizes the old communication infrastructure.

[1716] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1717] Step 1:

[1718] The server receives the video data transmitted from the monitoring device.

[1719] Specific behavior:

[1720] The monitoring device (camera) captures real-time video of the designated monitoring area and sends it as an RTSP stream to the server. The server receives the video data using a web framework such as Flask and stores it in a buffer frame by frame.

[1721] Input: Real-time video data as an RTSP stream.

[1722] Output: Video frame data waiting to be analyzed.

[1723] Step 2:

[1724] The server analyzes the received video data.

[1725] Specific behavior:

[1726] Video data is analyzed in real time using OpenCV and generative AI models, allowing the server to recognize motion patterns and objects in the video frames and determine whether anomalies have occurred.

[1727] Input: Buffered video frame data.

[1728] Output: Anomaly detection result (flagged as normal or abnormal).

[1729] Step 3:

[1730] If the server detects an abnormality, it will notify the user.

[1731] Specific behavior:

[1732] If the anomaly detection result indicates an abnormal state, the server will use notification means to send a push notification or email to the user's device, allowing the user to immediately check the situation.

[1733] Input: Anomaly detection result (anomaly status flag).

[1734] Output: Push notification or email notification.

[1735] Step 4:

[1736] The user checks the video data on a smartphone or PC.

[1737] Specific behavior:

[1738] Users can access real-time video data stored on the server through a dedicated app or web interface, allowing them to directly check the current situation and identify any abnormalities.

[1739] Input: Real-time video data stored on the server.

[1740] Output: Real-time video displayed on the user's device.

[1741] Step 5:

[1742] The server sends periodic maintenance notifications.

[1743] Specific behavior:

[1744] The server manages the maintenance schedule for the monitoring device and sends reminders to the user when maintenance is required.

[1745] Input: Maintenance schedule data.

[1746] Output: Reminder notification.

[1747] Step 6:

[1748] The server generates a security report based on the analysis results.

[1749] Specific behavior:

[1750] The server analyzes the collected video data to identify abnormal behavior patterns and security vulnerabilities, and automatically generates a security report that is sent to the user.

[1751] Input: Analyzed video data and anomaly detection results.

[1752] Output: Security report.

[1753] Step 7:

[1754] The user implements countermeasures based on the security report.

[1755] Specific behavior:

[1756] The user reviews the security report and implements the suggested improvements, changing the settings of monitoring devices and strengthening physical security as necessary.

[1757] Input: Security report.

[1758] Output: Results of the implementation of the improvement measures.

[1759] The above are the specific processing steps in this invention.

[1760] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1761] The present invention provides a system that reuses old communication infrastructure and combines a wide-area security system with an emotion engine that recognizes user emotions. Specific embodiments of this system are described below.

[1762] 1. Camera installation and initial settings

[1763] Camera installation

[1764] The user physically installs a security camera near the old communication infrastructure (old PHS antenna, etc.). The camera is weatherproof and designed to withstand the external environment.

[1765] Network Connection

[1766] The server obtains the camera's IP address and configures the network settings. The server also authenticates the camera's MAC address and registers it in its internal database.

[1767] Initial Setup

[1768] The user accesses the camera's settings page through a dedicated app or web interface. The user enters initial settings such as the camera name (e.g., front door camera) and monitoring area, and the server confirms these settings and applies them to the camera.

[1769] 2. Real-time video monitoring

[1770] Video capture and transmission

[1771] The camera captures video of the monitored area in real time, compresses and encrypts the video data, and sends it to the server.

[1772] Receiving and storing video

[1773] The server receives the video data sent from the camera, stores it in a temporary buffer, and also backs it up in a database for permanent storage.

[1774] User monitoring

[1775] Users access the system via a dedicated app or web interface on their smartphone or PC and view footage in real time.

[1776] 3. Anomaly Detection

[1777] Video analysis

[1778] The video data received by the server is input into an AI model to recognize movement patterns and objects.

[1779] Anomaly detection

[1780] The server uses an AI model to detect anomalies.

[1781] Alert Generation

[1782] If the server detects an abnormality, it generates alert data.

[1783] Sending notifications

[1784] Send server-generated alerts to users via push notifications.

[1785] User Support

[1786] Users receive a notification and can view detailed footage in a dedicated app or web interface, and if necessary, call the police.

[1787] 4. Maintenance Notice

[1788] Maintenance schedule management

[1789] The server manages the regular maintenance schedule for each camera in a database and has reminder logic.

[1790] Sending notifications

[1791] Check when the server needs maintenance and send a notification to users.

[1792] User Response

[1793] Users will receive notifications and can check the maintenance schedule via a dedicated app or web interface.

[1794] Schedule adjustment

[1795] The user contacts the maintenance staff and schedules the maintenance.

[1796] 5. Data analysis and security consulting

[1797] Data collection

[1798] The server periodically backs up past video data and stores it in a database for analysis.

[1799] Data analysis

[1800] The server uses AI and data analysis tools to analyze the collected video data and extract abnormal behavior patterns and security vulnerabilities.

[1801] Reporting

[1802] The server automatically creates a security assessment report based on the analysis results.

[1803] Staff Reviews

[1804] Security professionals will review the report and provide additional comments and suggestions as needed.

[1805] Send Feedback

[1806] The server sends the completed security report to the user.

[1807] User execution

[1808] The user receives the report and implements the suggested improvements.

[1809] 6. Adding an Emotion Engine

[1810] Collecting Emotional Data

[1811] The device's built-in camera and microphone analyze the user's facial expressions and tone of voice to collect emotional data.

[1812] emotion recognition

[1813] The server analyzes the collected data through the emotion engine and recognizes the user's emotional state.

[1814] Feedback of sentiment analysis results

[1815] The server provides feedback to the device based on the user's emotional state. For example, if the user is feeling anxious, it displays a comforting or encouraging message.

[1816] Alert Tuning

[1817] The server adjusts the frequency and content of alerts based on the user's emotional state: for example, if the user exhibits high anxiety levels, alerts are sent more quickly than usual.

[1818] Specific examples

[1819] For example, a user installs a camera at the entrance of their home, which captures footage of the area in front of the entrance and sends it to a server. The server then uses an AI model to analyze the footage and detect suspicious activity. If a suspicious person is detected, the server immediately sends an alert to the user. The user receives a notification, checks the footage on their smartphone, and, if necessary, calls the police.

[1820] Furthermore, if the user's emotion engine detects stress, the system will send a notification encouraging the user to relax. For example, if the user shows high anxiety levels, the system will provide safety advice in addition to camera footage, allowing the user to feel psychologically at ease and take appropriate action.

[1821] The above is an embodiment of the present invention, which makes it possible to effectively utilize the old communication infrastructure, provide a wide-area, highly accurate security system, and also enable flexible responses that take user emotions into consideration.

[1822] The processing flow will be explained below.

[1823] 1. Camera installation and initial settings

[1824] Step 1: Camera installation

[1825] The user physically installs security cameras into the old communications infrastructure.

[1826] Step 2: Power on the camera

[1827] The user supplies power to the camera and starts the system.

[1828] Step 3: Network Connection

[1829] The server obtains the camera's IP address and checks the network connection.

[1830] Step 4: MAC address registration

[1831] The server registers the camera's MAC address in its internal database and performs authentication.

[1832] Step 5: Initial Setup

[1833] The user opens a dedicated app or web interface and enters settings such as the camera name and monitoring area.

[1834] Step 6: Apply the settings

[1835] The server reflects the setting information received from the user in the camera.

[1836] 2. Real-time video monitoring

[1837] Step 1: Capture the footage

[1838] The camera captures real-time images of the surveillance area.

[1839] Step 2: Sending video data

[1840] The video data captured by the camera is compressed, encrypted, and sent to the server.

[1841] Step 3: Receiving video data

[1842] The server receives the video data sent from the camera.

[1843] Step 4: Save

[1844] The video data received by the server is temporarily stored in a buffer.

[1845] Step 5: Permanent storage

[1846] The server backs up the video data in the temporary buffer to a database.

[1847] Step 6: User monitoring

[1848] Users access the system from their device (smartphone or PC) via a dedicated app or web interface and view the footage in real time.

[1849] 3. Anomaly Detection

[1850] Step 1: Analyzing the footage

[1851] The video data received by the server is input into an AI model to recognize movement patterns and objects.

[1852] Step 2: Anomaly detection

[1853] The server uses an AI model to detect anomalies.

[1854] Step 3: Alert Generation

[1855] If the server detects an abnormality, it generates alert data.

[1856] Step 4: Sending notifications

[1857] Send server-generated alerts to users via push notifications.

[1858] Step 5: User interaction

[1859] Users receive a notification and can view detailed footage in a dedicated app or web interface, and if necessary, call the police.

[1860] 4. Maintenance Notice

[1861] Step 1: Schedule Management

[1862] The server manages the regular maintenance schedule for each camera.

[1863] Step 2: Trigger the reminder logic

[1864] The server will trigger reminder logic during maintenance periods.

[1865] Step 3: Sending notifications

[1866] Check when the server needs maintenance and send a notification to users.

[1867] Step 4: User response

[1868] Users will receive notifications and can check maintenance schedules via a dedicated app or web interface.

[1869] Step 5: Scheduling

[1870] The user contacts the maintenance staff and schedules the maintenance.

[1871] 5. Data analysis and security consulting

[1872] Step 1: Data collection

[1873] The server periodically backs up past video data and stores it in a database for analysis.

[1874] Step 2: Data analysis

[1875] The server uses AI and data analysis tools to analyze the collected video data and extract abnormal behavior patterns and security vulnerabilities.

[1876] Step 3: Reporting

[1877] The server automatically creates a security assessment report based on the analysis results.

[1878] Step 4: Staff review

[1879] Security professionals will review the report and provide additional comments and suggestions as needed.

[1880] Step 5: Send your feedback

[1881] The server sends the completed security report to the user.

[1882] Step 6: User execution

[1883] The user receives the report and implements the suggested improvements.

[1884] 6. Adding an Emotion Engine

[1885] Step 1: Collecting emotion data

[1886] The device's built-in camera and microphone analyze the user's facial expressions and tone of voice to collect emotional data.

[1887] Step 2: Emotion Recognition

[1888] The server analyzes the collected data through the emotion engine and recognizes the user's emotional state.

[1889] Step 3: Feedback of sentiment analysis results

[1890] The server provides feedback to the device based on the user's emotional state. For example, if the user is feeling anxious, it displays a comforting or encouraging message.

[1891] Step 4: Adjust your alerts

[1892] The server adjusts the frequency and content of alerts based on the user's emotional state: for example, if the user exhibits high anxiety levels, alerts are sent more quickly than usual.

[1893] Specific examples

[1894] For example, a user installs a camera at the entrance of their home, which captures footage of the area in front of the entrance and sends it to a server. The server then uses an AI model to analyze the footage and detect suspicious activity. If a suspicious person is detected, the server immediately sends an alert to the user. The user receives a notification, checks the footage on their smartphone, and, if necessary, calls the police.

[1895] Furthermore, if the user's emotion engine detects stress, the system will send a notification encouraging the user to relax. For example, if the user shows high anxiety levels, the system will provide safety advice in addition to camera footage, allowing the user to feel psychologically at ease and take appropriate action.

[1896] The above is an embodiment of the present invention, which makes it possible to effectively utilize the old communication infrastructure, provide a wide-area, highly accurate security system, and also enable flexible responses that take user emotions into consideration.

[1897] Example 2

[1898] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1899] Conventional monitoring systems face the challenges of making it difficult to reuse old communication infrastructure, and the complex installation and network connection required to cover a wide area. Furthermore, while there is a demand for improved accuracy in anomaly detection and flexible responses based on the user's emotional state, current systems have difficulty meeting these demands. Furthermore, managing regular maintenance and effectively notifying users is also an issue.

[1900] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1901] In this invention, the server includes a monitoring device attached to the old communication infrastructure, a central processing unit that receives video data transmitted from the monitoring device, and artificial intelligence (AI) means in the central processing unit that analyzes the video data. This enables high-precision monitoring that covers a wide area by reusing the old communication infrastructure. The server also includes AI means that analyzes the video data, a notification means that notifies the user when an abnormality is detected, a terminal on which the user can view the video data, a means for the central processing unit to manage the periodic maintenance schedule of the monitoring device and send reminders to the user, and an emotion recognition means that analyzes the user's emotional state and adjusts the frequency and content of alerts when an abnormality is detected. This enables flexible responses based on the user's emotional state and enables efficient periodic maintenance management.

[1902] "Old communications infrastructure" refers to communications equipment and facilities that were used in the past but are no longer in use.

[1903] "Surveillance equipment" means equipment installed to monitor a specific area, including cameras and sensors.

[1904] "Central Processing Unit" means a computer system or server that analyzes and processes data received from monitoring devices.

[1905] "Artificial intelligence means" refers to technologies that use machine learning models and algorithms to analyze data and perform anomaly detection and object recognition.

[1906] "Notification means" refers to a means for sending an alert or notification to the user when an abnormality is detected, and includes push notifications and emails.

[1907] A "terminal" is a device that allows a user to view video data and operate a surveillance device, and includes smartphones and PCs.

[1908] "Maintenance Schedule Management" refers to the function of planning and managing regular maintenance of monitoring devices and sending reminders.

[1909] "Emotion recognition means" refers to technology that analyzes a user's facial expressions and tone of voice to recognize their emotional state.

[1910] "Motion pattern" refers to the pattern of movement of people or objects detected within the monitored area.

[1911] "Object recognition" refers to the technology of detecting and identifying specific objects from video data.

[1912] The present invention provides a system that reuses old communication infrastructure and combines a wide-coverage security system with an emotion engine that recognizes user emotions. Detailed embodiments of this system are described below.

[1913] Camera installation and initial setup

[1914] Camera installation

[1915] A user installs an all-weather security camera near an old communication infrastructure (for example, an old PHS antenna). The camera is configured as an IP camera and is designed to withstand external environments.

[1916] Network Connection

[1917] The server scans and identifies the IP addresses of IP cameras connected to the network, then authenticates the camera's MAC address using an authentication server and registers the camera information in an internal database.

[1918] Initial Setup

[1919] Users access the camera settings page via a dedicated app or web interface on their smartphone or PC. For initial setup, the user enters the camera name (e.g., front door camera) and monitoring area, and the server confirms the settings and updates them on the camera.

[1920] Real-time video monitoring

[1921] Video capture and transmission

[1922] The cameras continuously capture images of the designated surveillance area, compress the video data into Motion JPEG format in real time, and then encrypt the video data using the SSL / TLS protocol before sending it to the server.

[1923] Receiving and storing video

[1924] The server receives the video data sent from the camera using the SSL / TLS protocol and temporarily stores it in buffer memory. At the same time, the video data is permanently stored in a database for backup purposes.

[1925] User monitoring

[1926] Users can access the server via a dedicated app or web interface on their smartphone or PC to view the streaming video in real time. Users can also use zoom and recording / playback functions as needed.

[1927] Anomaly detection

[1928] Video analysis

[1929] The server uses Python's OpenCV library to analyze the received video data frame by frame, and an AI model (e.g., YOLO or SSD) detects motion patterns and objects.

[1930] Anomaly detection

[1931] The server uses an anomaly detection AI model using TensorFlow or PyTorch to detect suspicious behavior or objects, such as sudden movements of a suspicious person or abnormal vehicle behavior.

[1932] Alert Generation

[1933] When the server detects an abnormality, it generates alert data, which includes a timestamp, location, and details of the abnormality.

[1934] Sending notifications

[1935] Server-generated alerts are sent to users via push notifications, using Firebase Cloud Messaging (FCM) or Apple Push Notification Service (APNS).

[1936] User Support

[1937] Users can check the notification they receive on their smartphone, view detailed footage through a dedicated app or web interface, and, if necessary, report the incident to the police.

[1938] Maintenance Notification

[1939] Maintenance schedule management

[1940] The server monitors the operating status and operating time of each camera, and manages regular maintenance schedules in a database. It has a reminder function and checks them regularly.

[1941] Sending notifications

[1942] The server checks when maintenance is required and sends reminder notifications to users.

[1943] User Response

[1944] Users will receive notifications and can view recommended maintenance dates via a dedicated app or web interface.

[1945] Schedule adjustment

[1946] The user contacts the maintenance staff and schedules the maintenance.

[1947] Data analysis and security consulting

[1948] Data collection

[1949] The server regularly backs up past video data and anomaly detection data and stores it in a database for analysis.

[1950] Data analysis

[1951] The server uses Python to analyze large amounts of video data using AI models and data analysis tools (e.g., scikit-learn, pandas) to extract abnormal behavior patterns and security vulnerabilities.

[1952] Reporting

[1953] The server automatically creates a security assessment report based on the analysis results, which includes an overview of detected anomalies and recommended remediation measures.

[1954] Staff Reviews

[1955] Security professionals review the automatically generated reports and provide additional comments and specific suggestions as needed.

[1956] Send Feedback

[1957] The server generates a completed security report in PDF format and sends it to the user.

[1958] User execution

[1959] The user reviews the report and implements the suggested improvements.

[1960] Adding an Emotion Engine

[1961] Collecting Emotional Data

[1962] The device's built-in camera and microphone collect real-time facial expression data and tone of voice, using facial recognition and voice analysis APIs.

[1963] emotion recognition

[1964] The server analyzes the collected data using an emotion engine. An emotion recognition algorithm is used to identify the user's current emotional state (e.g., joy, anxiety, anger, etc.).

[1965] Feedback of sentiment analysis results

[1966] Based on the analysis results of the emotion engine, the server provides feedback such as messages to the user on the device. For example, if the user is feeling anxious, a message such as "Take a deep breath to relax" will be displayed.

[1967] Alert Tuning

[1968] The server adjusts the frequency and content of alerts depending on the user's emotional state: if the user shows high anxiety levels, alerts are sent more quickly than usual.

[1969] Specific examples

[1970] For example, a user installs a camera at the entrance of their home, which captures footage of the area in front of the entrance and sends it to a server. The server then uses an AI model to analyze the footage and detect suspicious activity. If a suspicious person is detected, the server immediately sends an alert to the user. The user receives a notification, checks the footage on their smartphone, and, if necessary, calls the police.

[1971] Furthermore, if the user's emotion engine detects stress, the system will send a notification encouraging the user to relax. For example, if the user shows high anxiety levels, the system will provide safety advice in addition to camera footage, allowing the user to feel psychologically at ease and take appropriate action.

[1972] Prompt Sentence Examples

[1973] "The entrance camera has detected suspicious activity. Please review the footage immediately."

[1974] "Our systems have detected high levels of anxiety. Please take a deep breath to calm yourself and review our latest safety information."

[1975] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1976] Step 1: Camera installation and initial setup

[1977] Camera installation

[1978] The user physically installs a weatherproof IP camera near the old communication infrastructure (for example, an old PHS antenna), thus physically installing the surveillance device.

[1979] Input: IP camera, old communication infrastructure

[1980] Output: Physically installed monitoring equipment

[1981] Network Connection

[1982] The server scans and identifies the IP addresses of cameras connected to the network, then authenticates the MAC addresses of the cameras and registers the camera information in its internal database.

[1983] Input: Camera IP address, MAC address

[1984] Output: Authenticated cameras, camera information registered in the internal database

[1985] Initial Setup

[1986] The user accesses the dedicated app or web interface using a smartphone or PC. They log in to the camera setting page and enter the camera name (e.g., front door camera) and monitoring area. The server checks these settings and applies them to the camera.

[1987] Input: Camera name, monitoring area information

[1988] Output: Camera with settings reflected

[1989] Step 2: Real-time video monitoring

[1990] Video capture and transmission

[1991] The cameras continuously capture video of the configured surveillance area, compress the video into Motion JPEG format, and then send it to the server using the SSL / TLS protocol.

[1992] Input: Video of the monitored area

[1993] Output: Compressed video data, data encrypted with SSL / TLS protocol

[1994] Receiving and storing video

[1995] The server receives the video data sent from the camera using the SSL / TLS protocol, stores it in a temporary buffer memory, and also backs it up in a database for permanent storage.

[1996] Input: Encrypted video data

[1997] Output: Data stored in temporary buffer, data stored in database

[1998] User monitoring

[1999] Users can access the server via a dedicated app or web interface on their smartphone or PC, check the video in real time, and also use zoom and recording / playback functions.

[2000] Input: Streaming video

[2001] Output: Real-time video, user-controlled zoom and recording data

[2002] Step 3: Anomaly detection

[2003] Video analysis

[2004] The server uses Python's OpenCV library to analyze the received video data frame by frame, and an AI model (e.g., YOLO or SSD) detects motion patterns and objects.

[2005] Input: Received video data

[2006] Output: Object detection results, movement patterns

[2007] Anomaly detection

[2008] The server uses an anomaly detection AI model using TensorFlow or PyTorch to detect suspicious behavior or objects.

[2009] Input: Object detection results, movement patterns

[2010] Output: Anomaly detection results

[2011] Alert Generation

[2012] When the server detects an abnormality, it generates alert data, which includes a timestamp, location, and details of the abnormality.

[2013] Input: Anomaly detection result

[2014] Output: Generated alert data

[2015] Sending notifications

[2016] Server-generated alerts are sent to users via push notifications, using Firebase Cloud Messaging (FCM) or Apple Push Notification Service (APNS).

[2017] Input: Alert data

[2018] Output: Notification sent to the user

[2019] User Support

[2020] The user receives a notification on their smartphone, checks the video in detail using a dedicated app or web interface, and, if necessary, reports the incident to the police.

[2021] Input: Received notification, detailed video data

[2022] Output: User confirmation result, possibly police report

[2023] Step 4: Maintenance Notification

[2024] Maintenance schedule management

[2025] The server monitors the operating status and operating time of each camera, and manages regular maintenance schedules in a database. It has a reminder function and performs regular checks.

[2026] Input: Operation data of each camera

[2027] Output: Managed maintenance schedule

[2028] Sending notifications

[2029] The server checks when maintenance is required and sends reminder notifications to users.

[2030] Input: Managed Maintenance Schedule

[2031] Output: Notification sent to the user

[2032] User Response

[2033] Users will receive notifications and can view recommended maintenance dates via a dedicated app or web interface.

[2034] Input: Reminder

[2035] Output: Maintenance dates confirmed by the user

[2036] Schedule adjustment

[2037] The user contacts the maintenance staff and schedules the maintenance.

[2038] Input: User request

[2039] Output: Adjusted maintenance schedule

[2040] Step 5: Data analysis and security consulting

[2041] Data collection

[2042] The server regularly backs up past video data and anomaly detection data and stores it in a database for analysis.

[2043] Input: Past video data, anomaly detection data

[2044] Output: Backed up data, saved database

[2045] Data analysis

[2046] The server uses Python to analyze large amounts of video data using AI models and data analysis tools (e.g., scikit-learn, pandas) to extract abnormal behavior patterns and security vulnerabilities.

[2047] Input: Saved database

[2048] Output: Analyzed data, anomalous patterns, security vulnerabilities

[2049] Reporting

[2050] The server automatically creates a security assessment report based on the analysis results, which includes an overview of detected anomalies and recommended remediation measures.

[2051] Input: Analyzed data, anomalous patterns, security vulnerabilities

[2052] Output: Security Assessment Report

[2053] Staff Reviews

[2054] Security professionals review the automatically generated reports and provide additional comments and specific suggestions as needed.

[2055] Input: Security Assessment Report

[2056] Output: Reviewed report

[2057] Send Feedback

[2058] The server generates a completed security report in PDF format and sends it to the user.

[2059] Input:Reviewed report

[2060] Output: PDF report sent to the user

[2061] User execution

[2062] The user reviews the report and implements the suggested improvements.

[2063] Input: PDF report

[2064] Output: Implemented remedial measures

[2065] Step 6: Adding an Emotion Engine

[2066] Collecting Emotional Data

[2067] The device's built-in camera and microphone collect real-time facial expression data and tone of voice, using facial recognition and voice analysis APIs.

[2068] Input: User's facial expression data, tone of voice

[2069] Output: Collected emotion data

[2070] emotion recognition

[2071] The server analyzes the collected data using an emotion engine. An emotion recognition algorithm is used to identify the user's current emotional state (e.g., joy, anxiety, anger, etc.).

[2072] Input: Collected emotion data

[2073] Output: Identified emotional state

[2074] Feedback of sentiment analysis results

[2075] Based on the analysis results of the emotion engine, the server provides feedback such as messages to the user on the device. For example, if the user is feeling anxious, a message such as "Take a deep breath to relax" will be displayed.

[2076] Input: Identified emotional state

[2077] Output: The feedback message provided to the user

[2078] Alert Tuning

[2079] The server adjusts the frequency and content of alerts depending on the user's emotional state: if the user shows high anxiety levels, alerts are sent more quickly than usual.

[2080] Input: Identified emotional state

[2081] Output: Adjusted alert notification

[2082] (Application example 2)

[2083] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[2084] In recent years, security systems using surveillance cameras have been introduced in many places, but these systems have difficulty simultaneously ensuring the safety and psychological security of all users. Furthermore, because there is no established method for effectively utilizing existing communication infrastructure, high costs and operational effort have become problems. Furthermore, few systems can provide feedback based on the user's emotional state, limiting the user's response in emergencies. To solve these issues, it is necessary to provide flexible responses that take user emotions into account while performing high-precision monitoring over a wide area.

[2085] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for managing monitoring devices attached to the old communication infrastructure, artificial intelligence means for receiving and analyzing video data transmitted from the monitoring devices, and emotion engine means for recognizing the user's emotions and providing feedback based on them. This enables high-precision monitoring over a wide area and flexible feedback according to the user's emotional state while making effective use of the old communication infrastructure.

[2086] "Old communications infrastructure" refers to physical infrastructure such as communications equipment and antennas that were used in the past, and are currently unused but can be reused.

[2087] "Surveillance equipment" means devices such as cameras and sensors that are installed to monitor a designated area.

[2088] A "central processing unit" is a computer system that receives, analyzes, and manages data sent from the monitoring device.

[2089] "Artificial intelligence means" refers to machine learning models and algorithms used to analyze received video data and recognize movement patterns and objects.

[2090] "Notification means" refers to a system or device that sends an alert or notification to the user when an abnormality is detected.

[2091] A "terminal" is a device such as a smartphone or PC that a user uses to check video data and notifications.

[2092] An "emotion engine" refers to software or algorithms that analyze a user's facial expressions, voice, etc. to recognize their emotional state.

[2093] A "means for providing feedback" is a system or application that sends messages or notifications based on the user's emotional state.

[2094] The present invention combines an emotion engine with a highly accurate security system that covers a wide area by reusing old communication infrastructure. Specific embodiments of the present invention are described below.

[2095] 1. Camera installation and initial settings

[2096] Camera installation

[2097] Users install weatherproof security cameras near old communication infrastructure (old PHS antennas, etc.) The cameras are designed to withstand the external environment.

[2098] Network Connection

[2099] The central processing unit (server) acquires the camera's IP address and configures the network settings. The server also authenticates the camera's MAC address and registers it in its internal database.

[2100] Initial Setup

[2101] Users access the camera's settings page through a dedicated application or web interface. They enter initial settings such as the camera's name (e.g., front door camera) and monitoring area, and the server updates the camera with these settings.

[2102] 2. Real-time video monitoring

[2103] Video capture and transmission

[2104] The camera captures video of the designated monitoring area in real time, compresses and encrypts the video data, and sends it to the server.

[2105] Receiving and storing video

[2106] The server receives the video data sent from the camera, stores it in a temporary buffer, and also backs it up in a database for permanent storage.

[2107] User monitoring

[2108] Users access the system via a dedicated application or web interface on their smartphone or PC and view footage in real time.

[2109] 3. Anomaly Detection and Emotion Recognition

[2110] Video analysis

[2111] The server inputs the received video data into a generative AI model to recognize movement patterns and objects.

[2112] Anomaly detection

[2113] The server uses an AI model to detect anomalies.

[2114] Collecting Emotional Data

[2115] The device's built-in camera and microphone analyze the user's facial expressions and tone of voice to collect emotional data.

[2116] emotion recognition

[2117] The server analyzes the collected data through an emotion engine and recognizes the user's emotional state.

[2118] Providing Feedback

[2119] The server then provides feedback to the device based on the user's emotional state. For example, if the user is feeling anxious, it displays a comforting or encouraging message.

[2120] 4. Notifications and User Response

[2121] Alert Generation

[2122] The server generates alert data when an abnormality is detected.

[2123] Sending notifications

[2124] The server then sends the generated alerts to the user via push notifications, and adjusts the frequency and content of the alerts based on the user's emotional state. For example, if the user shows a high level of anxiety, the alerts will be sent more quickly than usual.

[2125] User Support

[2126] Users receive a notification and can review the video in detail in a dedicated app or web interface, and if necessary, call the police.

[2127] 5. Maintenance notifications and data analysis

[2128] Maintenance schedule management

[2129] The server manages the regular maintenance schedule for each camera in a database and is equipped with reminder logic.

[2130] Sending notifications

[2131] The server checks when maintenance is required and sends a notification to the user.

[2132] User Response

[2133] Users will receive notifications and can check maintenance dates via a dedicated application or web interface.

[2134] Data collection and analysis

[2135] The server periodically backs up past video data and analyzes it using AI and data analysis tools to identify abnormal behavior patterns and security vulnerabilities.

[2136] Reporting

[2137] The server automatically creates a security assessment report based on the analysis results and sends it to the user.

[2138] Specific examples

[2139] For example, a user installs a camera at the entrance of their home, which captures footage of the area in front of the entrance and sends it to a server. The server then uses a generative AI model to analyze the footage and detect suspicious activity. If a suspicious activity is detected, the server immediately sends an alert to the user. The user receives the notification, checks the footage on their smartphone, and, if necessary, calls the police. If the user's emotion engine detects stress in the user, the system sends a notification encouraging the user to relax. For example, if the user shows a high level of anxiety, the system will provide safety advice in addition to the camera footage. This provides the user with psychological peace of mind and allows them to take appropriate action.

[2140] Prompt Sentence Examples

[2141] A suspicious person is standing in front of the house.

[2142] Addresses high anxiety levels in users.

[2143] The user is prompted to relax and given instructions on how to report the incident to the police.

[2144] The above is a specific embodiment for carrying out the present invention, which makes it possible to effectively utilize the old communication infrastructure, provide a wide-area, highly accurate security system, and also enable flexible responses that take user emotions into consideration.

[2145] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2146] Step 1: Camera Settings

[2147] Users install weatherproof security cameras near the old communication infrastructure, then enter the camera name (for example, front door camera) and monitoring area through a dedicated application or web interface, and the server updates the camera with this information.

[2148] Input: Camera IP address, monitoring area settings

[2149] Output: The camera setting information is registered on the server.

[2150] Specific operation: The server obtains the IP address of the camera, configures the network settings, and then reflects the camera name and monitoring area settings on the camera.

[2151] Step 2: Real-time video monitoring

[2152] The cameras capture video of the designated surveillance area in real time, compressing and encrypting the video data before sending it to the server. The server temporarily stores the received video data in a buffer and also backs it up in a database for permanent storage. Users can view the video in real time on their smartphones or PCs using a dedicated application or web interface.

[2153] Input: Real-time video data

[2154] Output: Video data stored on the server, real-time video confirmation by the user

[2155] How it works: The camera captures video, compresses and encrypts it, and sends it to the server. The server receives the video data, stores it in a temporary buffer, and also stores it in a database for permanent storage. The user can view the video in real time via a dedicated application or web interface.

[2156] Step 3: Video analysis and anomaly detection

[2157] The server inputs the received video data into a generative AI model to recognize movement patterns and objects. If an abnormality is detected based on the analysis, the server generates alert data.

[2158] Input: Received video data

[2159] Output: Alert data

[2160] Specific operation: The server inputs the video data into the AI ​​model, analyzes movement patterns and object recognition, and generates alert data indicating the abnormality if an abnormality is detected.

[2161] Step 4: Collect and recognize emotion data

[2162] The device's built-in camera and microphone analyze the user's facial expressions and tone of voice to collect emotional data. The server then analyzes the collected data through an emotion engine to recognize the user's emotional state.

[2163] Input: User's facial expression and voice data

[2164] Output: User's emotional state

[2165] Specific operation: The system collects the user's emotions in real time using a camera and microphone, and analyzes them using the server's emotion engine. The system recognizes the user's emotional state based on the analysis results.

[2166] Step 5: Provide emotional feedback

[2167] The server then provides feedback to the device based on the user's emotional state. For example, if the user is feeling anxious, it displays a comforting or encouraging message.

[2168] Input: User's emotional state

[2169] Output: Feedback message

[2170] Specific operation: The server generates feedback (e.g., a message to ease anxiety) according to the user's emotional state and sends it to the device.

[2171] Step 6: Send notifications and respond to users

[2172] The server sends a push notification to the user using the generated alert data and feedback based on the user's emotional state. The user receives the notification and can view detailed footage in a dedicated application or web interface. If necessary, the user can report the incident to the police.

[2173] Input: Alert data, feedback based on emotional state

[2174] Output: Notify user, notify police

[2175] Specific operation: After receiving a notification from the server, the user checks the detailed video using a dedicated application or web interface and, if necessary, reports the incident to the police.

[2176] Step 7: Maintenance notifications and data analysis

[2177] The server sets a regular maintenance schedule for each camera and notifies the user of the required date and time. It also periodically backs up past video data and analyzes it using AI and data analysis tools to identify abnormal behavior patterns and security vulnerabilities. Based on the analysis results, it sends a security assessment report to the user.

[2178] Input: Maintenance schedule, past video data

[2179] Output: Maintenance notification, security assessment report

[2180] Specific operations: The server manages maintenance schedules, sends notifications to users, analyzes regularly backed-up video data, and generates and sends security assessment reports to users.

[2181] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[2182] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2183] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[2184] [Fourth embodiment]

[2185] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[2186] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[2187] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[2188] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[2189] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[2190] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[2191] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[2192] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[2193] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[2194] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[2195] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[2196] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[2197] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2198] The present invention provides a security system that covers a wide area by reusing the old communication infrastructure. Specific embodiments of the present invention are described below.

[2199] 1. Camera installation and initial settings

[2200] Camera installation

[2201] The user physically installs a security camera near the old communication infrastructure (old PHS antenna, etc.). The camera is weatherproof and designed to withstand the external environment.

[2202] Network Connection

[2203] The server obtains the camera's IP address and configures the network settings. The server also authenticates the camera's MAC address and registers it in its internal database.

[2204] Initial Setup

[2205] The user accesses the camera's settings page through a dedicated app or web interface. The user enters initial settings such as the camera name (e.g., front door camera) and monitoring area, and the server confirms these settings and applies them to the camera.

[2206] 2. Real-time video monitoring

[2207] Video capture and transmission

[2208] The camera captures video of the monitored area in real time, compresses and encrypts the video data, and sends it to the server.

[2209] Receiving and storing video

[2210] The server receives the video data sent from the camera, stores it in a temporary buffer, and also backs it up in a database for permanent storage.

[2211] User monitoring

[2212] Users access the system via a dedicated app or web interface on their smartphone or PC and view footage in real time.

[2213] 3. Anomaly Detection

[2214] Video analysis

[2215] The server uses an AI model to analyze the received video data, recognize movement patterns and objects, and detect abnormalities.

[2216] Sending alerts

[2217] Any abnormalities detected by the server will be immediately notified to the user via push notification or email.

[2218] User Support

[2219] The user receives a notification and can view detailed video footage via a dedicated app or web interface. If an abnormality is detected, the user can take action, such as reporting the incident to the police.

[2220] 4. Maintenance Notice

[2221] Maintenance schedule management

[2222] The server manages the regular maintenance schedule for each camera in a database and has reminder logic.

[2223] Sending notifications

[2224] Check when the server needs maintenance and send a notification to users.

[2225] User Response

[2226] Users receive a notification, check the maintenance schedule using a dedicated app or web interface, and contact the person in charge to arrange a schedule.

[2227] 5. Data analysis and security consulting

[2228] Data collection

[2229] The server periodically backs up past video data and stores it in a database for analysis.

[2230] Data analysis

[2231] The server uses AI and data analysis tools to analyze the collected video data and extract abnormal behavior patterns and security vulnerabilities.

[2232] Reporting

[2233] The server automatically creates a security assessment report based on the analysis results, and in some cases adds additional comments from security experts.

[2234] User Feedback

[2235] The server sends the completed security report to the user, who receives the report and implements the suggested remediation measures.

[2236] Specific examples

[2237] For example, let's say a user installs a camera at the entrance of their home. The camera captures video in front of the entrance and sends it to a server. The server uses an AI model to analyze the video and detect suspicious activity. If a suspicious activity is detected, the server immediately sends an alert to the user. The user receives the notification, checks the video on their smartphone, and takes action, such as notifying the police, depending on the situation. The server also regularly notifies the user monthly about system maintenance, and the user contacts the person in charge to carry out the maintenance. The server then analyzes the data it collects, identifies trends such as suspicious activity patterns, and security specialists create a report based on the analysis results, proposing specific improvement measures. The user can then strengthen their security measures based on this report.

[2238] The above is an embodiment of the present invention, which makes it possible to effectively utilize the old communication infrastructure and provide a wide-area, highly accurate security system.

[2239] The processing flow will be explained below.

[2240] Camera installation and initial setup

[2241] Step 1: Camera installation

[2242] The user physically installs security cameras into the old communications infrastructure.

[2243] Step 2: Turn on the camera and connect to the network

[2244] The user powers on the camera and it connects to the network.

[2245] Step 3: Obtaining and registering an IP address

[2246] The server obtains the camera's IP address and recognizes it within the network.

[2247] Step 4: MAC Address Authentication

[2248] The server authenticates the camera's MAC address and registers it in its internal database.

[2249] Step 5: Initial Setup

[2250] Users access a dedicated app or web interface to set the camera name and monitoring area.

[2251] Step 6: Apply the settings

[2252] The server reflects the setting information received from the user in the camera.

[2253] Real-time video monitoring

[2254] Step 1: Capture the footage

[2255] The camera captures real-time images of the surveillance area.

[2256] Step 2: Sending video data

[2257] The video data captured by the camera is compressed, encrypted, and sent to the server.

[2258] Step 3: Receiving video data

[2259] The server receives the video data sent from the camera.

[2260] Step 4: Save

[2261] The video data received by the server is temporarily stored in a buffer.

[2262] Step 5: Permanent storage

[2263] The server backs up the video data in the temporary buffer to a database.

[2264] Step 6: User monitoring

[2265] Users access the system from their device (smartphone or PC) via a dedicated app or web interface and view the footage in real time.

[2266] Anomaly detection

[2267] Step 1: Analyzing the footage

[2268] The video data received by the server is input into an AI model to recognize movement patterns and objects.

[2269] Step 2: Anomaly detection

[2270] The server uses an AI model to detect anomalies.

[2271] Step 3: Alert Generation

[2272] If the server detects an abnormality, it generates alert data.

[2273] Step 4: Sending notifications

[2274] Send server-generated alerts to users via push notifications.

[2275] Step 5: User interaction

[2276] Users receive a notification and can view detailed footage in a dedicated app or web interface, and if necessary, call the police.

[2277] Maintenance Notification

[2278] Step 1: Schedule Management

[2279] The server stores the regular maintenance schedule for each camera in a management database.

[2280] Step 2: Trigger the reminder logic

[2281] The server will trigger reminder logic during maintenance periods.

[2282] Step 3: Sending notifications

[2283] Check when the server needs maintenance and send a notification to users.

[2284] Step 4: User response

[2285] Users will receive a notification and can check the maintenance schedule via a dedicated app or web interface.

[2286] Step 5: Scheduling

[2287] The user contacts the maintenance staff and schedules the maintenance.

[2288] Data analysis and security consulting

[2289] Step 1: Data collection

[2290] The server periodically backs up past video data and stores it in a database for analysis.

[2291] Step 2: Data analysis

[2292] The server uses AI and data analysis tools to analyze the collected video data and extract abnormal behavior patterns and security vulnerabilities.

[2293] Step 3: Reporting

[2294] The server automatically creates a security assessment report based on the analysis results.

[2295] Step 4: Staff review

[2296] Security professionals will review the report and provide additional comments and suggestions as needed.

[2297] Step 5: Send your feedback

[2298] The server sends the completed security report to the user.

[2299] Step 6: User execution

[2300] The user receives the report and implements the suggested improvements.

[2301] Example 1

[2302] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2303] Conventional security systems require the construction of new infrastructure, which is expensive, making it difficult to cover a wide area. Real-time anomaly detection and maintenance management are also inadequate. This leads to issues such as delays in rapid response and difficulty in long-term system operation.

[2304] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[2305] In this invention, the server includes a monitoring device attached to the old communication infrastructure, a central processing unit that receives video data transmitted from the monitoring device, artificial intelligence means in the central processing unit that analyzes the video data, notification means that notifies the user when an abnormality is detected, a terminal for the user to check the video data, setting means that performs initial configuration of the monitoring device through the terminal, means that compress and encrypt the video data and transmit it, means that temporarily store the video data and back it up in a database for permanent storage, means that manage a periodic maintenance schedule, means that send reminders based on the maintenance schedule, means that periodically analyze the video data and create a security evaluation report, and means that feed back the security evaluation report to the user. This makes it possible to effectively utilize the old communication infrastructure to cover a wide area, and to enable real-time abnormality detection, prompt notification, and maintenance management.

[2306] "Old communications infrastructure" refers to communications technologies and equipment that were used in the past and are no longer in use but can be reused.

[2307] "Monitoring equipment" refers to equipment installed to monitor a specific area, and has the function of capturing video data and transmitting it to a central processing unit.

[2308] "Video data" refers to the data that can be stored and transmitted in digital format from images captured by surveillance equipment.

[2309] "Central processing unit" refers to a computer device that receives and analyzes video data transmitted from a monitoring device.

[2310] "Artificial intelligence means" refers to artificial intelligence (AI) technology used to analyze received video data and detect anomalies and recognize behavioral patterns.

[2311] "Notification means" refers to technology or devices that immediately notify the user when an abnormality is detected.

[2312] "Terminal" refers to the device used by the user to view video data and change system settings. Examples include smartphones and personal computers.

[2313] "Setting means" refers to a system that has the function of setting the initial settings of the monitoring device and various parameters through a terminal.

[2314] "Compression and encryption methods" refers to technologies that reduce the data size so that video data can be transmitted efficiently and encrypt it to maintain security.

[2315] "Temporary storage means" refers to a technology that has the function of temporarily storing video data in a buffer after receiving it.

[2316] A "permanent storage database" refers to a database system for safely storing important video data for a long period of time.

[2317] "Means for managing maintenance schedules" refers to a system that has the function of managing regular maintenance of monitoring devices and systems in a planned manner and encouraging maintenance to be carried out.

[2318] "Means for sending reminders" refers to technology that has a notification function to notify users of upcoming maintenance or other important events.

[2319] A "security assessment report" is a report that evaluates the security status of a system based on collected and analyzed data and proposes improvement measures.

[2320] "Feedback means" refers to a function that provides the created security evaluation report to the user and provides information for the user to take further measures.

[2321] The present invention provides a security system that covers a wide area by reusing old communication infrastructure. The system includes a monitoring device, a central processing unit, an artificial intelligence means, a notification means, a terminal, and various means related thereto.

[2322] Camera installation and initial setup

[2323] Camera installation

[2324] The user installs an all-weather surveillance device (security camera) near the old communication infrastructure (e.g., old PHS antenna). Specifically, the user fixes the camera using screws and brackets and connects a power cable. This allows the camera to be used in external environments.

[2325] Network Connection

[2326] The server obtains the camera's IP address via the DHCP server and configures the camera's network settings. The server also authenticates the camera's MAC address and registers it in its internal database. During this process, command line tools can be used to verify the network connection if necessary.

[2327] Initial Setup

[2328] The user accesses the camera's settings page using a dedicated app or web interface and enters initial settings such as the camera name and monitoring area. The server confirms these settings and applies them to the camera. The server then generates a configuration file in JSON or XML format and sends it to the camera to complete the setup.

[2329] Real-time video monitoring

[2330] Video capture and transmission

[2331] The camera captures video of the monitored area in real time, compresses the video data in H.264 format, encrypts it with AES, and sends it to the server.

[2332] Receiving and storing video

[2333] The server receives the video data sent from the camera, stores it in a temporary buffer, and also backs it up in a database for permanent storage. MySQL or PostgreSQL is used as the database management system.

[2334] User monitoring

[2335] Users access the system via a dedicated app or web interface on their smartphone or PC and view the video in real time. The video is displayed on the device using WebRTC and streaming technology.

[2336] Anomaly detection

[2337] Video analysis

[2338] The server analyzes the received video data using an AI model (e.g., YOLOv4, OpenCV), recognizing movement patterns and objects to detect abnormalities.

[2339] Sending alerts

[2340] If the server detects an abnormality, it immediately notifies the user via push notification or email using Firebase Cloud Messaging (FCM) or Twilio.

[2341] User Support

[2342] The user receives a notification and can view detailed video footage via a dedicated app or web interface. If an abnormality is detected, the user can take action, such as reporting the incident to the police.

[2343] Maintenance Notification

[2344] Maintenance schedule management

[2345] The server manages the regular maintenance schedule for each camera in a database and has reminder logic.

[2346] Sending notifications

[2347] Check when the server needs maintenance and send a notification to users.

[2348] User Response

[2349] Users receive a notification, check the maintenance schedule using a dedicated app or web interface, and contact the person in charge to arrange a schedule.

[2350] Data analysis and security consulting

[2351] Data collection

[2352] The server periodically backs up past video data and stores it in a database for analysis.

[2353] Data analysis

[2354] The server analyzes the collected video data using AI and data analysis tools (e.g., TensorFlow, PyTorch) to extract abnormal behavior patterns and security vulnerabilities.

[2355] Reporting

[2356] The server automatically creates a security assessment report based on the analysis results, and security specialists add comments as needed.

[2357] User Feedback

[2358] The server sends the completed security report to the user, who receives the report and implements the suggested remediation measures.

[2359] Specific examples

[2360] For example, let's say a user installs a camera at the entrance of their home. This camera constantly captures video of the area in front of the entrance and sends it to a server. The server then uses an AI model to analyze the video in real time and detect any suspicious activity. If a suspicious person is detected, the server immediately sends a push notification to the user's smartphone or PC, allowing the user to review the detailed video and then report the incident to the police.

[2361] The server also periodically manages camera and system maintenance schedules and sends timely reminders to users. Furthermore, the server analyzes the collected data, creates reports suggesting security improvements, and provides feedback to users, allowing users to continuously strengthen their system security.

[2362] An example prompt might be, "How do I configure an AI model to receive video data captured by a camera and detect abnormal behavior patterns?"

[2363] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2364] Step 1: Camera installation

[2365] The user physically installs an all-weather surveillance device (security camera) near the old communications infrastructure (e.g., old PHS antenna). The camera is fixed in place using screws and brackets, and a power cable is connected. After installation, the camera starts up by turning on the power. The inputs required are the camera's installation position and power source, and the output is a properly installed camera.

[2366] Step 2: Network Connection

[2367] The server obtains the camera's IP address via the DHCP server and configures the network settings. It also authenticates the camera's MAC address and registers it in its internal database. It checks the network connection using command line tools (e.g., ipconfig, ifconfig). The input is the camera's MAC address and network information, and the authenticated camera's IP address is obtained as output.

[2368] Step 3: Initial Setup

[2369] The user accesses the camera's settings page using a dedicated app or web interface and sets the camera's name (e.g., front door camera) and monitoring area. The server checks these settings and reflects them on the camera. A configuration file in JSON or XML format is generated and sent to the camera. The input is the configuration information entered by the user, and the output is the initial settings reflected on the camera.

[2370] Step 4: Record and send footage

[2371] The camera captures real-time video of the monitored area. The video data is compressed in H.264 format, encrypted with AES, and sent to the server. The input is real-time video of the monitored area, and the output is compressed and encrypted video data.

[2372] Step 5: Receive and save the footage

[2373] The server receives the video data sent from the camera and stores it in a temporary buffer. At the same time, it also creates a backup in a database for permanent storage. MySQL or PostgreSQL is used as the database management system. The input is compressed and encrypted video data, and the output is the temporarily stored and backed up video data.

[2374] Step 6: User monitoring

[2375] Users access the system on their smartphones or PCs through a dedicated app or web interface and view the video in real time. WebRTC and streaming technology are used to display the video on the device. The input is video data retrieved from the database, and the output is real-time video displayed on the user's device.

[2376] Step 7: Analyzing the footage

[2377] The server uses an AI model (e.g., YOLOv4, OpenCV) to analyze the received video data, recognizing movement patterns and objects to detect anomalies. The input is the received video data, and the output is anomaly detection information obtained as the analysis result.

[2378] Step 8: Sending an alert

[2379] If the server detects an anomaly, it immediately sends a notification to the user. Notifications are sent in the form of push notifications or emails using Firebase Cloud Messaging (FCM) or Twilio. The input is the anomaly detection information, and the output is a notification to the user.

[2380] Step 9: User interaction

[2381] The user receives a notification and checks the detailed video on a dedicated app or web interface. If an abnormality is detected, the user can take action, such as reporting the incident to the police. The input is the notification and detailed video data, and the output is the appropriate user response.

[2382] Step 10: Maintenance Schedule Management

[2383] The server manages the periodic maintenance schedule of each camera in a database and has reminder logic. The input is the camera's operational data and maintenance schedule, and the output is the managed maintenance schedule.

[2384] Step 11: Sending notifications

[2385] The server checks when maintenance is required and sends a notification to the user. The input is the maintenance schedule, and the output is the maintenance notification to the user.

[2386] Step 12: User Response

[2387] The user receives the notification, checks the maintenance schedule using a dedicated app or web interface, and contacts the person in charge to adjust the schedule. The input is the maintenance notification and schedule adjustment information, and the output is the adjusted maintenance schedule.

[2388] Step 13: Data collection

[2389] The server periodically backs up past video data and stores it in an analytical database. The input is past video data, and the output is the backup data.

[2390] Step 14: Data analysis

[2391] The server analyzes the collected video data using AI and data analysis tools (e.g., TensorFlow, PyTorch). It extracts abnormal behavior patterns and security vulnerabilities. The input is the backup data, and the output is the analysis results.

[2392] Step 15: Reporting

[2393] The server automatically creates a security assessment report based on the analysis results, and security experts add comments as needed. The input is the analysis results, and the created security assessment report is obtained as the output.

[2394] Step 16: User Feedback

[2395] The server sends the completed security assessment report to the user, who receives the report and implements the suggested remediation measures. The input is the security assessment report, and the output is the user's implementation of the security remediation measures.

[2396] (Application example 1)

[2397] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2398] In wide-area security systems that reuse old communication infrastructure, there is a need to provide new methods for users to remotely check camera footage in real time and quickly detect and respond to abnormalities. Another challenge is to centralize security management and reduce the burden on users by providing periodic maintenance notifications and security reports based on analysis results.

[2399] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[2400] In this invention, the server includes a monitoring device attached to the old communication infrastructure, a central processing unit that receives video data transmitted from the monitoring device, artificial intelligence means in the central processing unit that analyzes the video data, notification means that notifies the user when an abnormality is detected, a terminal for the user to check the video data, a terminal having a user interface for changing the settings of the monitoring device, means for the central processing unit to send periodic maintenance notifications, and means for the central processing unit to generate security reports based on the analysis results. This allows the user to check the video in real time from a remote location, quickly detect and respond to abnormalities, and receive periodic maintenance notifications and security reports based on the analysis results.

[2401] "Old communications infrastructure" refers to the communications equipment and infrastructure that was previously used, including those that have now been replaced primarily by new technologies and equipment.

[2402] A "monitoring device" is a device such as a camera or sensor that has the function of acquiring images and data of a specific area and transmitting them to a central processing unit.

[2403] "Central processing unit" refers to a computer system that analyzes received video data and performs processes such as detecting abnormalities, saving data, and notifying users.

[2404] "Artificial intelligence means" refers to technology that uses algorithms such as machine learning and deep learning to analyze video data and recognize movement patterns and objects to detect abnormalities.

[2405] "Notification means" has the function of sending an alert to the user when an abnormality is detected, and includes push notifications and email notifications.

[2406] A "terminal" is a device such as a smartphone or PC used by a user, and is used to check video data from a surveillance device in real time and change settings.

[2407] "User interface" refers to an interface that allows a user to access a monitoring device or a central processing unit to change settings, check maintenance, and so on.

[2408] "Maintenance Notification" means a reminder sent to a user when scheduled maintenance of a monitoring device or system is required.

[2409] A "security report" is a document generated based on the results of video data analysis, and provides information including the security status and improvement measures.

[2410] In this invention, a specific method for realizing a wide-area security system that utilizes the old communication infrastructure will be described.

[2411] 1. System Configuration

[2412] The system is configured using the following hardware and software:

[2413] Hardware

[2414] Old communication infrastructure: Old PHS antennas, etc.

[2415] Surveillance equipment: Weatherproof security cameras.

[2416] User device: Smartphone or PC.

[2417] software

[2418] Central processing unit: Runs as a server and includes a web framework using Flask and a real-time image processing system using OpenCV.

[2419] Artificial intelligence means: A video data analysis system that utilizes machine learning and deep learning models.

[2420] Notification methods: Push notifications and email notification systems.

[2421] 2. Program Processing

[2422] The central processing unit (server) receives the video data transmitted from the monitoring device and analyzes it in real time. Specifically, the server includes the following means:

[2423] 1. Acquiring video data

[2424] The monitoring device (camera) captures images of the designated monitoring area in real time and transmits the image data to a central processing unit, which receives the image data and begins analyzing it.

[2425] 2. Anomaly detection

[2426] Video data is analyzed using artificial intelligence tools such as machine learning and deep learning to detect anomalies based on movement patterns and object recognition. For example, if suspicious movements or specific objects are detected, they will be flagged as an anomaly.

[2427] 3. Sending notifications

[2428] When an abnormality is detected, the central processing unit uses the notification means to send a push notification or email notification to the user, allowing the user to quickly confirm the abnormality and take the necessary action.

[2429] 4. User Interface

[2430] Users can access a dedicated user interface via a device (smartphone or PC) to check video data in real time, and can also change the settings of the monitoring device and set maintenance schedules.

[2431] 5. Generate security reports

[2432] The central processing unit periodically backs up the collected video data and analyzes it using AI. A security report based on the analysis results is provided to the user, suggesting improvement measures.

[2433] 3. Specific Examples

[2434] For example, if a user installs a surveillance camera at the entrance of their home, they can remotely check the situation at the entrance in real time via their smartphone. If the camera detects a suspicious person, a push notification is sent immediately, allowing the user to confirm the abnormality. In addition, regular maintenance notifications and detailed security reports are provided, strengthening security measures.

[2435] Prompt Sentence Examples

[2436] Write a Python program that analyzes video data from a home security camera in real time to detect suspicious individuals. The program should have a web interface using Flask, video analysis functionality using OpenCV, and the ability to send a push notification when an anomaly is detected.

[2437] This completes the description of the embodiment of the invention. This system makes it possible to realize wide-area, highly accurate security management that effectively utilizes the old communication infrastructure.

[2438] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2439] Step 1:

[2440] The server receives the video data transmitted from the monitoring device.

[2441] Specific behavior:

[2442] The monitoring device (camera) captures real-time video of the designated monitoring area and sends it as an RTSP stream to the server. The server receives the video data using a web framework such as Flask and stores it in a buffer frame by frame.

[2443] Input: Real-time video data as an RTSP stream.

[2444] Output: Video frame data waiting to be analyzed.

[2445] Step 2:

[2446] The server analyzes the received video data.

[2447] Specific behavior:

[2448] Video data is analyzed in real time using OpenCV and generative AI mod...

Claims

1. Monitoring equipment installed on old communications infrastructure, a central processing unit that receives video data transmitted from the monitoring device; an artificial intelligence means in the central processing unit for analyzing the video data; a notification means for notifying the user when an abnormality is detected; a terminal for the user to check the video data; A system including:

2. 2. The system according to claim 1, wherein the artificial intelligence means performs analysis using movement patterns and object recognition when detecting the abnormality.

3. 2. The system of claim 1, wherein the user can change settings of the monitoring device through the terminal.

4. 2. The system of claim 1, wherein said central processing unit further comprises maintenance means for managing a periodic maintenance schedule and notifying said user.

Images