System

Abstract

A system is provided.SOLUTION: To prevent off-line planned mass theft and on-line resale, and to enhance security measures including safety education, the system includes means for optimizing staff and artificial intelligence collaboration, means for real-time access to security assistants via smart phones and devices, means for providing patrol information and suggesting optimal patrol paths and frequencies, means for collaborating with online platforms to detect and report fraudulent transactions, and means for automatically removing lists of items reported as stolen.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] Modern stores and online platforms face dual security threats: large-scale theft and subsequent unauthorized resale. Providing effective, integrated security measures to address this challenge is challenging. In particular, systems are needed to detect and prevent in-store theft early and prevent the resale of stolen goods online. Conventional security systems lack integrated measures against both offline and online threats, and the procedures for detecting and reporting fraudulent transactions and removing items are complex and time-consuming. Furthermore, they lack the efficiency of security patrols and real-time support for staff. Therefore, addressing these issues and building an effective, comprehensive security system is a key challenge. [Means for solving the problem]

[0005] This invention provides a system that optimizes collaboration between staff and artificial intelligence to minimize security risks both offline and online. Specifically, it provides staff with real-time access to a security assistant via smartphone or other device to obtain necessary information and assistance. It also includes a means for providing security guards with real-time patrol information and suggesting optimal patrol routes and frequency. Furthermore, it incorporates a means for linking with online platforms to automatically delete listings of reported stolen goods and detect and report fraudulent transactions. This strengthens security both offline and online, resolving existing issues. This system implements comprehensive security measures, including providing security policies, responding to queries, linking with databases, and acquiring information from surveillance cameras and sensors.

[0006] "Offline premeditated mass theft" refers to the planned theft of large quantities of merchandise or items of value from a physical store or facility.

[0007] "Online reselling" refers to the use of the Internet to sell stolen goods or other merchandise to third parties.

[0008] A "security assistant" is an artificial intelligence system that provides real-time security information and advice to staff and security guards.

[0009] "Smartphones and devices" refers to portable electronic devices that can connect to the Internet, such as mobile phones and tablets.

[0010] "Patrol information" refers to information about time periods and areas that security guards need when patrolling stores and facilities.

[0011] "Patrol routes and frequency" refers to the way and number of times security guards patrol a particular area to maintain security.

[0012] An "online platform" refers to a website or application operated on the Internet where goods and services are bought and sold.

[0013] "Unfair trade" refers to the buying and selling of goods or services that is conducted in an unlawful or fraudulent manner.

[0014] "Automatic deletion" refers to the removal of something without human intervention if the system determines that certain conditions are met.

[0015] "Database" refers to a collection of data and a source of information containing information about security policies and indicators of theft.

[0016] "Surveillance cameras and sensors" refer to devices that monitor and record the situation within a store or facility and detect abnormalities. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] The present invention is a system that optimizes collaboration between staff and artificial intelligence (AI) to prevent offline planned large-scale theft and online resale, and to strengthen security measures including safety training. Specific embodiments for implementing the present invention are described below.

[0039] 1. Real-time information provision

[0040] First, store staff and security guards access the Security Assistant via their smartphones or other devices. The Security Assistant resides on a central server, accepts queries sent by users, and provides appropriate security policies and training content in response. This allows users to quickly obtain the latest security information.

[0041] Examples:

[0042] A user asks: "Can you tell me which areas have had the most thefts recently?"

[0043] The server replies: "There has been an increase in thefts in the electronics section recently. Please be especially wary of people using large bags."

[0044] 2. Improving the efficiency of security patrols

[0045] Next, to improve the efficiency of patrols, the server provides real-time patrol information. Information obtained from surveillance cameras and sensors is analyzed, and the optimal patrol route and frequency is notified to the security guard's device. This allows the security guard to patrol effectively and minimize the risk of theft.

[0046] Examples:

[0047] The server calculates: "The area with the highest risk of theft in the next 30 minutes is the electronics section."

[0048] The server announces: "Next, please make your rounds in the food section in 20 minutes."

[0049] 3. Collaboration with online platforms

[0050] In addition, the server will work with online platforms to detect and quickly respond to fraudulent online transactions. If a fraudulent transaction is discovered, the information will be automatically reported to the police and the online platform. Any items reported as stolen will be automatically removed from the relevant platform.

[0051] Examples:

[0052] The server detects: "Fraudulent seller ID 12345 has listed a stolen laptop."

[0053] The server informs: "We have contacted the online platform and asked them to remove this item immediately."

[0054] summary

[0055] The security system of this invention comprehensively prevents offline and online theft through collaboration between staff and AI. Security measures are strengthened through real-time information provision, efficient security patrols, and collaboration with online platforms. By explaining the system's implementation with concrete examples, users can easily understand and put it into practice.

[0056] The processing flow will be explained below.

[0057] Real-time information provision

[0058] Step 1:

[0059] User

[0060] Access the security assistant from your smartphone or dedicated device.

[0061] Enter the question, "What are some signs of recent theft?"

[0062] Step 2:

[0063] server

[0064] Analyze the questions received from the user.

[0065] Based on the question, the database is searched for information on appropriate security policies and indicators of theft.

[0066] Step 3:

[0067] server

[0068] Based on the search results, answers are generated in a form that is easy for the user to understand.

[0069] Create an answer like, "Recently, there has been an increase in suspicious activity, especially in the electronics section. Please be especially careful of people with unusual behavior, especially those carrying large bags."

[0070] Step 4:

[0071] server

[0072] The generated answer is sent to the user's device.

[0073] Step 5:

[0074] User

[0075] Check the answers displayed on your device and take the necessary action.

[0076] Improving the efficiency of security patrols

[0077] Step 1:

[0078] server

[0079] Collect data obtained from surveillance cameras and sensors currently installed in stores.

[0080] Also refer to historical data on patrols.

[0081] Step 2:

[0082] server

[0083] The collected data is analyzed to identify high-risk areas for the next patrol.

[0084] Step 3:

[0085] server

[0086] Calculate the optimal route and frequency.

[0087] Develop a plan that states, "The area with the highest risk of theft over the next 30 minutes is the electronics section."

[0088] Step 4:

[0089] server

[0090] The patrol plan is sent to the security guard's device.

[0091] Step 5:

[0092] User

[0093] Check the patrol plan displayed on the device and carry out the patrol according to the instructions.

[0094] Collaboration with online platforms

[0095] Step 1:

[0096] server

[0097] Obtain product listings from online platforms and scan them.

[0098] Check against a list of stolen items to detect fraudulent listings.

[0099] Step 2:

[0100] server

[0101] It automatically generates information about detected fraudulent listings and prepares the data for reporting to the police and online platforms.

[0102] Step 3:

[0103] server

[0104] Create a report stating "Fake seller ID 12345 has listed a stolen laptop" and notify the relevant platform.

[0105] Step 4:

[0106] server

[0107] Update relevant stolen goods lists and automatically reflect them on the online platform.

[0108] Step 5:

[0109] Terminal (online platform)

[0110] We will promptly remove any fraudulent listings based on the removal request we receive.

[0111] Step 6:

[0112] server

[0113] Notify tracking users that the fraudulent listing has been removed.

[0114] Step 7:

[0115] User

[0116] We will review the notification and take further action as necessary.

[0117] Example 1

[0118] 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."

[0119] With the current increase in planned large-scale thefts offline and online resale, it is difficult to prevent these crimes using traditional security measures. Furthermore, while a swift and effective response from staff and security guards is required, limited information and resources may not be enough. There is also a lack of means to quickly detect fraudulent transactions online and take appropriate action. A new system is needed to solve these issues and improve the overall level of security.

[0120] 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.

[0121] In this invention, the server includes: means for a user to access the security assistant via a smartphone or terminal; means for the server to receive and analyze queries sent from the user; means for the server to search a database for and provide appropriate security information; means for the server to acquire and analyze data in real time from surveillance cameras and sensors; means for the server to identify high-risk areas and calculate optimal patrol routes; means for the server to notify security guard devices of the analysis results; means for the server to monitor transactions on online platforms and detect fraudulent transactions; and means for the server to report detected fraudulent transactions and remove products from related platforms. This enables quick access to the security assistant, provision of optimal security information, efficient patrols, and immediate detection and response to fraudulent transactions.

[0122] "User" refers to a person who accesses the Security Assistant and submits a query.

[0123] "Smartphone or device" refers to the electronic device used to access the security assistant.

[0124] "Security assistant" refers to a program that provides appropriate security information based on a user's query.

[0125] "Server" refers to the computer system that runs the Security Assistant and receives queries from users, analyzes them, retrieves information from a database, and provides the information.

[0126] "Query" refers to a question or request that a user sends to the Security Assistant.

[0127] "Analysis" refers to the processing of data by the server to understand the content of the query it receives and determine the appropriate response.

[0128] "Appropriate security information" refers to specific security measures and advice provided in response to a user's query.

[0129] "Database" refers to an information repository where security information and policies are stored.

[0130] "Surveillance camera" refers to a device that monitors a physical location and provides video data.

[0131] "Sensor" refers to a device that detects and provides data about a monitored environment.

[0132] "Acquiring data in real time" refers to instantly sending information provided by surveillance cameras and sensors to a server.

[0133] "High-risk areas" are locations where the likelihood of theft or fraud is expected to be high.

[0134] "Optimal patrol route" refers to a route planned to ensure the most effective patrol.

[0135] "Security guard" refers to a person who monitors and patrols security inside and outside a store.

[0136] "Online platform" refers to a website or application that conducts the trading of goods and services over the Internet.

[0137] "Transaction" refers to the act of buying and selling goods and services.

[0138] "Illicit trafficking" refers to the buying and selling of stolen or illegal goods.

[0139] "Relevant Platform" refers to the online platform on which the fraudulent transaction took place.

[0140] "Product removal" refers to the removal of a product that has been the subject of fraudulent trading on an online platform.

[0141] "Push Notification" refers to a real-time notification sent from a server to a user's device.

[0142] The present invention is a system designed to prevent offline planned large-scale theft and online resale, and to strengthen security measures, including safety training. This system allows users to access a security assistant in real time via their smartphones or terminals, and a server analyzes and provides a wide range of information. The following describes in detail specific embodiments of the invention.

[0143] User Device and Security Assistant Access Method

[0144] Users can access the Security Assistant via a dedicated application or browser on their smartphone or device (e.g., iOS or Android device), allowing them to quickly obtain appropriate security information.

[0145] Query reception and analysis system

[0146] When a user submits a query to the security assistant, the server receives the query, analyzes it using a natural language processing engine (e.g., NLTK or SpaCy), and searches a database for appropriate security information based on the query's content.

[0147] Providing security information

[0148] After the query is analyzed, the server retrieves relevant security information from a database (e.g., MySQL or PostgreSQL) and provides it to the user's device. This process allows users to receive the latest security information in real time.

[0149] Acquiring data from surveillance cameras and sensors

[0150] The server receives real-time information from the surveillance cameras and sensors installed in the store. Video data from the surveillance cameras is collected via the RTSP server, as well as data from the sensors (e.g., motion detection, volume level).

[0151] Data analysis and route calculation

[0152] The server analyzes the collected data using machine learning algorithms (e.g., TensorFlow or PyTorch) to identify areas with a high risk of theft, and then calculates the optimal patrol route using algorithms such as Dijkstra's algorithm.

[0153] Notifying security guards

[0154] The calculated patrol route information is sent to the security guard's smartphone or tablet via a push notification service (e.g., Firebase Cloud Messaging). Security guards who receive the notification can patrol the designated area efficiently.

[0155] Collaboration with online platforms

[0156] The server monitors transaction data from online platforms in real time via APIs and other means. If fraudulent transactions are detected, the server uses an anomaly detection algorithm to identify them. The server then reports the detected fraudulent transactions to the relevant platforms via APIs, and the relevant items are automatically deleted.

[0157] Examples of prompt statements

[0158] Below are some examples of prompt sentences that the security assistant can use to provide the user with appropriate information.

[0159] Prompt: "There have been reports of a high number of thefts in the electronics section of a store. Please generate a message to warn users."

[0160] In this way, the system can provide appropriate information in response to user queries and respond quickly and effectively to dynamically changing security risks.

[0161] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0162] Step 1: User accesses Security Assistant

[0163] Input: Smartphone or device

[0164] Output: Connection to Security Assistant

[0165] How it works: The user opens a browser or dedicated application on their smartphone or device, accesses a specific URL, and then enters their authentication information on the login screen that appears to access the security assistant.

[0166] Step 2: The server receives and parses the query

[0167] Input: User query

[0168] Output: Analysis results

[0169] What happens: When a user enters and submits a query, the server receives the HTTP request. The server uses a natural language processing engine (e.g., NLTK or SpaCy) to analyze the query. This analysis identifies the information or request the query is asking for.

[0170] Step 3: The server provides the appropriate security information

[0171] Input: Query parsing results

[0172] Output: Security information

[0173] Specific operation: Based on the analysis results, the server searches a database (e.g., MySQL or PostgreSQL) that stores security information. If relevant information is found, it retrieves the data and generates a message to respond to the user. This message is sent to the user's device and displayed in the browser or application.

[0174] Step 4: The server collects data from the surveillance cameras and sensors.

[0175] Input: Surveillance camera and sensor data

[0176] Output: Real-time environmental data

[0177] Specific operation: The server collects real-time data from the surveillance cameras and sensors installed in the store. Video data from the surveillance cameras is transmitted via the RTSP server, and data from the sensors (e.g., motion detection, volume level) is collected periodically or in real time.

[0178] Step 5: The server analyzes the data and calculates the optimal route.

[0179] Input: Real-time environmental data

[0180] Output: Optimal patrol routes and identification of high-risk areas

[0181] How it works: The server analyzes the acquired data using machine learning algorithms (e.g., TensorFlow or PyTorch) to identify areas with a high risk of theft. It then uses algorithms such as Dijkstra's algorithm to calculate patrol routes for security guards to patrol efficiently.

[0182] Step 6: The server notifies the security guard's device of the analysis results

[0183] Input: Information on optimal patrol routes and high-risk areas

[0184] Output: Notification to the guard's device

[0185] Specific operation: The server generates a notification for the guard based on the calculation results and sends it to the guard's smartphone or tablet using a push notification service (e.g., Firebase Cloud Messaging). The notification indicates the area and time that the guard should next patrol.

[0186] Step 7: The server monitors the online platform transactions.

[0187] Input: Transaction data from online platform

[0188] Output: Monitoring report

[0189] Specific operation: The server uses API to obtain transaction data from the online platform and monitors it in real time. The monitored data is periodically sent to the server and stored as needed.

[0190] Step 8: The server detects fraudulent transactions

[0191] Input: Transaction data

[0192] Output: Fraudulent transaction detection results

[0193] Specific operation: The server analyzes the acquired transaction data using an anomaly detection algorithm to identify fraudulent transactions. Detected fraudulent transactions are listed and the necessary information is extracted.

[0194] Step 9: Report any fraudulent transactions detected by the server and remove the items from the relevant platforms.

[0195] Input: Fraudulent transaction detection results

[0196] Output: Report and delete product

[0197] Specific operation: When the server detects a fraudulent transaction, it will contact the relevant platform via API and initiate the process of delisting the relevant product. If necessary, it will also report the matter to the police or authorized authorities. As a result, the product related to the fraudulent transaction will be promptly removed from the platform.

[0198] (Application example 1)

[0199] 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."

[0200] Traditional security systems lack sufficient coordination between staff and artificial intelligence, resulting in insufficient countermeasures against planned large-scale offline theft and online resale. Real-time patrol and security information is often delayed, leading to inefficient patrol planning. Furthermore, it is difficult to quickly detect and respond to fraudulent online transactions, and there is a lack of functionality to automatically delete listings of products reported as stolen, resulting in insufficient security measures.

[0201] 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.

[0202] In this invention, the server includes a means for allowing staff and security guards to access the security assistant via smartphones or devices to obtain security information in real time, a means for providing real-time patrol information and suggesting optimal patrol routes and frequencies, a means for analyzing information obtained from surveillance cameras and sensors to detect and immediately notify fraudulent online transactions, a means for obtaining security information about specific areas using a generative AI model, and a means for immediately responding when a fraudulent transaction is detected and automatically deleting the list of items reported as stolen. This optimizes collaboration between staff and artificial intelligence, effectively preventing theft both offline and online, and significantly strengthening security measures.

[0203] "Staff" refers to people who are responsible for operating and managing security systems, such as employees and security guards who work in physical stores.

[0204] "Artificial intelligence" refers to computer systems that mimic human intelligence and have capabilities such as learning, reasoning, and recognition.

[0205] A "smartphone" is a mobile phone that can access the Internet and run applications.

[0206] A "device" is an electronic device that can input, output, and process information, such as a smartphone or tablet.

[0207] The "Security Assistant" is a system that staff can access in real time and that provides security and patrol information.

[0208] "Patrol information" is information about security patrols within the store, including instructions on the optimal patrol route and frequency.

[0209] An "online platform" is a website or application that allows trading of goods and services over the Internet.

[0210] "Illicit trade" is the trading of goods or services that is not legal, such as the resale of stolen goods or fraud.

[0211] A "generative AI model" is an artificial intelligence model that learns patterns and features from data and automatically generates security information.

[0212] "Notification propagation processing" is the process of immediately notifying relevant systems and personnel when a fraudulent transaction is detected.

[0213] "Monitoring camera data" refers to video and image data acquired from monitoring cameras within a store.

[0214] This invention relates to a security system for preventing offline and online theft and strengthening safety training. The system aims to optimize collaboration between staff and artificial intelligence (AI) and provide real-time security information via smartphones and other devices.

[0215] 1. System Configuration

[0216] Hardware

[0217] The system primarily uses the following hardware:

[0218] Smartphones: Mobile devices used by staff and security guards.

[0219] Central Server: A computer system that provides security assistance and data analysis.

[0220] Surveillance camera: A camera that records video data within the store.

[0221] Sensors: Devices that collect movement and environmental data within the store.

[0222] software

[0223] The system uses the following software:

[0224] Security Assistant: An application that allows staff to obtain real-time security information.

[0225] Generative AI model: An AI system for generating security information about a specific area.

[0226] Database: Data storage to respond to queries to staff and provide appropriate security policies.

[0227] Online Transaction Monitoring System: A system that detects and immediately notifies you of fraudulent online transactions.

[0228] 2. Processing Flow

[0229] Real-time information provision

[0230] When staff or security guards access the security assistant via their smartphones, the server uses a generative AI model to provide the latest theft information and points of caution for each area.

[0231] Example: A staff member requests, "Can you tell me which areas have seen a lot of theft recently?" The server responds, "We've seen an increase in thefts in the electronics section recently. Please be especially wary of people using large bags."

[0232] Improving the efficiency of patrols

[0233] The server analyzes data obtained from surveillance cameras and sensors in real time and notifies the smartphone of the optimal patrol route and frequency.

[0234] Example: A server announces, "The area with the highest theft risk in the next 30 minutes is the electronics section," followed by instructions to "patrol the food section in 20 minutes."

[0235] Collaboration with online platforms

[0236] The server works with online platforms to detect fraudulent online transactions, immediately notifying relevant parties if a fraudulent transaction is discovered, and automatically removing listings of products reported as stolen.

[0237] Example: If the server detects that "Fraudulent seller ID 12345 has listed a stolen laptop," it will immediately notify the online platform that it has contacted and requested that the item be removed immediately.

[0238] 3. Use of generative AI models

[0239] Generative AI models learn patterns and features from data and automatically generate security information for specific areas.

[0240] Example prompt sentence:

[0241] "What security information has been released in the electronics section recently?"

[0242] "What areas are most at risk of theft?"

[0243] "Please tell me what kind of products the fraudulent seller ID 12345 is selling."

[0244] In this way, the present invention optimizes collaboration between staff and AI, making it possible to provide effective security measures in real time.

[0245] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0246] Step 1:

[0247] A user accesses the security assistant app using a smartphone. As input, the user sends a request such as, "Tell me in which areas thefts have been occurring frequently recently." The device then sends this request to the security server.

[0248] Step 2:

[0249] The security server analyzes the request and identifies the area for which information is being requested. The server uses a generative AI model to generate up-to-date security information for that area. It uses historical and current data related to the area as input and generates up-to-date security information as output.

[0250] Step 3:

[0251] The server sends the generated security information to the user's device, which then displays the received information to the user. Specifically, the device displays a message such as, "There has been an increase in thefts in the electronics section recently. Please be especially careful of people using large bags."

[0252] Step 4:

[0253] The server collects real-time data from surveillance cameras and sensors. As input, it receives video and movement data from the cameras and sensors, analyzes them internally, and generates important data for patrol planning as output.

[0254] Step 5:

[0255] The server analyzes the collected data to create a patrol plan. Based on this analysis, it calculates the optimal patrol route and frequency, and generates a patrol plan as output. For example, it may say, "The area with the highest risk of theft in the next 30 minutes is the electronics section."

[0256] Step 6:

[0257] The server then sends the generated patrol plan to the guard's smartphone. The device receives this information and notifies the guard of the optimal patrol route and frequency. For example, it displays instructions such as, "Next, patrol the food section in 20 minutes."

[0258] Step 7:

[0259] The server monitors the online platform to detect fraudulent transactions. As input, it periodically checks online transaction data and searches for fraudulent transaction patterns. As output, it generates detailed information about any fraudulent transactions that are found.

[0260] Step 8:

[0261] When a fraudulent transaction is detected, the server performs a notification propagation process to promptly respond. For example, it generates a warning such as "Fraudulent seller ID 12345 has listed a laptop that is listed as stolen goods" and sends a notification to the relevant online platform and relevant parties. Furthermore, the item listed as stolen goods is automatically removed from the online platform.

[0262] 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.

[0263] The present invention relates to a system that optimizes collaboration between staff and artificial intelligence (AI) to prevent offline and online security threats and strengthen security measures, including safety training. In particular, the present invention incorporates an emotion engine to recognize the user's emotional state and improve the efficiency and effectiveness of the entire system. Specific embodiments for implementing the present invention are described below.

[0264] 1. Real-time information provision

[0265] First, staff and security guards access the Security Assistant via their smartphones or dedicated devices. The Security Assistant resides on a central server, accepts queries sent by users, and provides appropriate security policies and training content. It also uses an emotion engine to recognize the user's current emotional state and detect stress levels, allowing the user to receive the most effective support.

[0266] Examples:

[0267] A user asks: "Can you tell me which areas have had the most thefts recently?"

[0268] The server analyzes the user's emotional state through an emotion engine and detects that the user is in a high stress state.

[0269] The server replies: "There has been an increase in thefts in the electronics section recently. Be especially wary of people using large bags. You seem stressed, so please keep an eye out for any suspicious activity."

[0270] 2. Improving the efficiency of security patrols

[0271] The server then provides real-time patrol information, analyzing data from surveillance cameras and sensors to calculate optimal patrol routes and frequencies, and notifying the guards' devices. Using an emotion engine, the server provides patrol instructions based on the guards' emotional state.

[0272] Examples:

[0273] The server analyzes the data and comes up with a plan: "The area with the highest risk of theft over the next 30 minutes is the electronics section."

[0274] The server analyzes the emotional state of the security guard through an emotion engine, and if stress is high, provides instructions such as "Take a short break before patrolling."

[0275] 3. Collaboration with online platforms

[0276] Furthermore, the server works with online platforms to detect and quickly respond to fraudulent online transactions. When a fraudulent transaction is discovered, the information is automatically reported to the police and the online platform. In addition, items reported as stolen are automatically removed from the relevant platform. An emotion engine provides appropriate notifications and follow-ups based on the user's emotional state.

[0277] Examples:

[0278] The server scans online platforms to detect fraudulent listings.

[0279] The server reports: "Fraudulent seller ID 12345 has listed a stolen laptop."

[0280] The server analyzes the user's emotional state through the emotion engine and sends a notification to provide reassurance: "The fraudulent listing removal process has been completed. Do you need further assistance?"

[0281] summary

[0282] The security system of the present invention effectively strengthens offline and online security measures by integrating an emotion engine. By incorporating emotion recognition technology, security measures can be further strengthened through real-time information provision, efficient security patrols, and integration with online platforms. By explaining the system embodiment through concrete examples, users can easily understand and put it into practice.

[0283] The processing flow will be explained below.

[0284] Real-time information provision

[0285] Step 1:

[0286] User

[0287] Access the security assistant from your smartphone or dedicated device.

[0288] Enter the question, "What are some signs of recent theft?"

[0289] Step 2:

[0290] server

[0291] Analyze the questions received from the user.

[0292] Based on the question, the database is searched for information on appropriate security policies and indicators of theft.

[0293] Step 3:

[0294] server

[0295] Based on the search results, answers are generated in a form that is easy for the user to understand.

[0296] Step 4:

[0297] server

[0298] Analyze the user's current emotional state via an emotion engine.

[0299] Adjust the tone and content of your responses depending on the user's emotional state.

[0300] Step 5:

[0301] server

[0302] Generates the answer, "Recently, there has been an increase in suspicious activity, especially in the electronics section. Please be especially wary of people carrying large bags and exhibiting unusual behavior. This person appears to be under a lot of stress, so please keep a close eye on them to see if there are any particularly suspicious people around."

[0303] Step 6:

[0304] server

[0305] The generated answer is sent to the user's device.

[0306] Step 7:

[0307] User

[0308] Check the answers displayed on your device and take the necessary action.

[0309] Improving the efficiency of security patrols

[0310] Step 1:

[0311] server

[0312] Collect data obtained from surveillance cameras and sensors currently installed in stores.

[0313] Also refer to historical data on patrols.

[0314] Step 2:

[0315] server

[0316] The collected data is analyzed to identify high-risk areas for the next patrol.

[0317] Step 3:

[0318] server

[0319] Calculate the optimal route and frequency.

[0320] Develop a plan that states, "The area with the highest risk of theft over the next 30 minutes is the electronics section."

[0321] Step 4:

[0322] server

[0323] Analyzing the emotional state of security guards through an emotion engine.

[0324] Adjust patrol instructions according to emotional state.

[0325] Step 5:

[0326] server

[0327] Provide instructions such as, "Take a short break and then make your rounds."

[0328] Step 6:

[0329] server

[0330] The patrol plan is sent to the security guard's device.

[0331] Step 7:

[0332] User

[0333] Check the patrol plan displayed on the device and carry out the patrol according to the instructions.

[0334] Collaboration with online platforms

[0335] Step 1:

[0336] server

[0337] Obtain product listings from online platforms and scan them.

[0338] Check against a list of stolen items to detect fraudulent listings.

[0339] Step 2:

[0340] server

[0341] Automatically generate information about detected fraudulent listings and prepare the data for reporting to the police and online platforms.

[0342] Step 3:

[0343] server

[0344] Create a report stating "Fake seller ID 12345 has listed a stolen laptop" and notify the relevant platform.

[0345] Step 4:

[0346] server

[0347] Update relevant stolen goods lists and automatically reflect them on the online platform.

[0348] Step 5:

[0349] Terminal (online platform)

[0350] We will promptly remove any fraudulent listings based on the removal request we receive.

[0351] Step 6:

[0352] server

[0353] Notify tracking users that the fraudulent listing has been removed.

[0354] Step 7:

[0355] server

[0356] It analyzes the user's emotional state through an emotion engine and sends notifications to alleviate anxiety.

[0357] Step 8:

[0358] User

[0359] We will review the notification and take further action as necessary.

[0360] Example 2

[0361] 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."

[0362] Today's security threats extend not only offline but also online, and simultaneous countermeasures are necessary. In particular, planned large-scale thefts and the resulting online resale activities cause significant damage to businesses and individuals. Furthermore, frontline staff and security guards often experience high levels of stress, and appropriate support tailored to their emotional state is required, but existing systems are unable to adequately address these challenges.

[0363] 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.

[0364] In this invention, the server includes means for optimizing collaboration between staff and artificial intelligence, means for providing real-time access to the security assistant via a smartphone or device, means for providing patrol information and suggesting optimal patrol routes and frequencies, means for linking with an online platform to detect and report fraudulent transactions, means for automatically deleting listings of items reported as stolen, and means for recognizing a user's emotional state using an emotion engine and providing appropriate support. This makes it possible to strengthen offline and online security measures in an integrated manner and provide appropriate support according to the emotional state of staff and security guards.

[0365] "Offline and online security threats" refers to physical intrusions and thefts that do not occur via the Internet, as well as cyber attacks and fraudulent transactions that occur via the Internet.

[0366] "Staff" refers to employees and guards engaged in security assistant and patrol duties.

[0367] "Artificial intelligence" refers to programs and systems that use technologies such as machine learning and natural language processing to imitate human intellectual tasks.

[0368] "Smartphones and devices" refers to portable electronic devices that can connect to the Internet and run applications.

[0369] "Security Assistant" refers to a software program or service that assists with security measures.

[0370] "Real-time access" refers to the sending and receiving of information occurring immediately.

[0371] "Patrol information" refers to information necessary for security guards to patrol, such as information regarding patrol routes and areas requiring caution.

[0372] "Optimal patrol routes and frequencies" refers to efficient patrol routes and their frequency planned to minimize security risks.

[0373] "Online platform" refers to an internet service for the purpose of e-commerce and communication.

[0374] "Unfair trading" refers to the buying, selling, or exchanging of fraudulently obtained goods or services.

[0375] "List of Items Reported as Stolen" means a list of items officially reported as stolen.

[0376] "Emotion engine" refers to an algorithm or program for analyzing a user's emotional state.

[0377] "User's emotional state" refers to the user's stress level or emotional state.

[0378] "Means for providing appropriate support" refers to a method for instantly providing necessary assistance or support based on the user's emotional state.

[0379] The present invention relates to a system that optimizes collaboration between staff and artificial intelligence to prevent offline and online security threats and strengthen security measures, including safety training. In particular, the present invention incorporates an emotion engine to recognize the user's emotional state and improve the efficiency and effectiveness of the entire system. Specific embodiments for implementing the present invention are described below.

[0380] 1. Real-time information provision

[0381] First, staff and security guards access the Security Assistant via their smartphones or dedicated devices. The Security Assistant resides on a central server, accepts queries sent by users, and provides appropriate security policies and training content. It also uses an emotion engine to recognize the user's current emotional state and detect stress levels, allowing the user to receive the most effective support.

[0382] Specific examples

[0383] A user asks: "Can you tell me which areas have had the most thefts recently?"

[0384] The server analyzes the user's emotional state through an emotion engine and detects that the user is in a high stress state.

[0385] The server replies: "There has been an increase in thefts in the electronics section recently. Be especially wary of people using large bags. You seem stressed, so please keep an eye out for any suspicious activity."

[0386] 2. Improving the efficiency of security patrols

[0387] The server then provides real-time patrol information, analyzing data from surveillance cameras and sensors to calculate optimal patrol routes and frequencies, and notifying the guards' devices. Using an emotion engine, the server provides patrol instructions based on the guards' emotional state.

[0388] Specific examples

[0389] The server analyzes the data and comes up with a plan: "The area with the highest risk of theft over the next 30 minutes is the electronics section."

[0390] The server analyzes the emotional state of the security guard through an emotion engine, and if stress is high, provides instructions such as "Take a short break before patrolling."

[0391] 3. Collaboration with online platforms

[0392] Furthermore, the server works with online platforms to detect and quickly respond to fraudulent online transactions. When a fraudulent transaction is discovered, the information is automatically reported to the police and the online platform. In addition, items reported as stolen are automatically removed from the relevant platform. An emotion engine provides appropriate notifications and follow-ups based on the user's emotional state.

[0393] Specific examples

[0394] The server scans online platforms to detect fraudulent listings.

[0395] The server reports: "Fraudulent seller ID 12345 has listed a stolen laptop."

[0396] The server analyzes the user's emotional state through the emotion engine and sends a notification to provide reassurance: "The fraudulent listing removal process has been completed. Do you need further assistance?"

[0397] Prompt Sentence Examples

[0398] User-supplied question: "What areas have seen a lot of thefts recently?"

[0399] Server prompt: "User wants to know where the latest thefts have been occurring. Please generate an answer that will alleviate anxiety and stress."

[0400] summary

[0401] This system integrates an emotion engine to enhance offline and online security measures. By providing real-time information, streamlining security patrols, and connecting with online platforms, emotion recognition technology further strengthens security measures. The system's implementation is explained through concrete examples, making it easy for users to understand and put into practice.

[0402] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0403] 1. Real-time information provision

[0404] Step 1:

[0405] User accesses Security Assistant

[0406] Input: A user launches the Security Assistant app using a smartphone or dedicated device.

[0407] What happens: A user enters a query in the app and presses submit.

[0408] Output: The query entered by the user is sent to the server.

[0409] Step 2:

[0410] The server accepts the query

[0411] Input: The query sent from the user device.

[0412] How it works: The server receives the query, parses it in text format, and searches the corresponding database.

[0413] Output: Parsed query data and associated security information.

[0414] Step 3:

[0415] The server uses an emotion engine to analyze the user's emotional state.

[0416] Input: Parsed query data.

[0417] Operation: The server launches the emotion engine to extract emotion patterns from the user's input text.

[0418] Output: User's emotional state data (e.g., how stressed they are).

[0419] Step 4:

[0420] The server provides appropriate security policies and training content.

[0421] Input: User emotional state data and associated security information.

[0422] How it works: The server extracts the latest security information from a database and generates an answer for the user.

[0423] Output: Specific security information and advice to provide to the user (e.g., "Recent hotspots for thefts").

[0424] 2. Improving the efficiency of security patrols

[0425] Step 1:

[0426] The server collects data from surveillance cameras and sensors.

[0427] Input: Real-time data from surveillance cameras and sensors.

[0428] How it works: The server collects the images and data captured by each surveillance camera and sensor and stores them in a central database.

[0429] Output: Collected monitoring data.

[0430] Step 2:

[0431] The server analyzes the data

[0432] Input: Collected monitoring data.

[0433] How it works: The server uses machine learning algorithms to analyze the data and detect anomalies.

[0434] Output: Data about high-risk areas (e.g. areas with high risk of theft).

[0435] Step 3:

[0436] The server notifies the guard's device of the optimal patrol route.

[0437] Input: Data about high-risk areas.

[0438] Operation: The server calculates the optimal patrol route and timing and sends it to the security guard's device.

[0439] Output: Patrol instructions for guards (e.g., "The next patrol area is the electronics section, head there now.").

[0440] Step 4:

[0441] The server uses an emotion engine to analyze the emotional state of the guard.

[0442] Input: Historical data and real-time feedback from security guards.

[0443] How it works: The server uses the emotion engine to analyze the stress level of the security guards.

[0444] Output: Instructions to the guard based on their emotional state (e.g., "Please take a short break before patrolling.").

[0445] 3. Collaboration with online platforms

[0446] Step 1:

[0447] The server scans the transaction data of the online platform.

[0448] Input: Transaction data from online platforms.

[0449] How it works: The server accesses the database through the API and periodically scans the retrieved data.

[0450] Output: Scanned transaction data.

[0451] Step 2:

[0452] The server detects fraudulent transactions

[0453] Input: Scanned transaction data.

[0454] How it works: The server checks against an existing list of stolen items to detect suspicious transactions.

[0455] Output: Detection results for fraudulent transactions (e.g., "Fraudulent seller ID 12345 listed a stolen laptop.").

[0456] Step 3:

[0457] The server automatically reports the incident to the police and the platform.

[0458] Input: Detection results for fraudulent transactions.

[0459] How it works: The server uses an automated reporting function to notify the police or platform administrators.

[0460] Output: Report message (e.g., "Stolen goods have been listed").

[0461] Step 4:

[0462] The server uses an emotion engine to send notifications based on the user's emotional state.

[0463] Input: Report result and user emotional state data.

[0464] How it works: The server uses the emotion engine to generate notifications to increase the user's sense of security.

[0465] Output: Notification message based on emotional state (e.g., "Your fraudulent listing has been removed. Do you need further assistance?").

[0466] (Application example 2)

[0467] 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."

[0468] The modern security environment faces increasing security threats both offline and online. Planned large-scale thefts and online resale of stolen goods are particularly problematic. Furthermore, security staff may operate under high stress, reducing their efficiency and effectiveness. In these situations, a system is needed to provide more advanced security measures and instructions while taking into account the emotional state of staff.

[0469] The identification processing 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 optimizing collaboration between staff and artificial intelligence, means for providing real-time access to the security assistant via a smartphone or device, means for providing patrol information and suggesting optimal patrol routes and frequencies, means for connecting with an online platform to detect and report fraudulent transactions, means for automatically deleting listings of items reported as stolen, means for recognizing a user's emotional state and detecting stress levels using an emotion engine, and means for providing appropriate support and instructions according to the user's emotional state. This effectively prevents offline and online security threats and improves the efficiency and effectiveness of staff.

[0470] "Measures to optimize collaboration between staff and artificial intelligence" refers to methods for enabling staff and artificial intelligence to work together effectively to strengthen security measures.

[0471] "Real-time access to a security assistant via smartphone or device" refers to a method that allows users to use their smartphone or other device to get instant security-related information and assistance.

[0472] "Means for providing patrol information and suggesting optimal patrol routes and frequencies" refers to a system that analyzes surveillance areas and data to suggest optimal patrol routes and frequencies to security guards.

[0473] "Means to work with online platforms to detect and report fraudulent transactions" refers to a system that works with online marketplaces and trading platforms to detect and report fraudulent activities and fraudulent transactions.

[0474] "Means for automatically removing listings of items reported as stolen" refers to a system that automatically removes items identified as stolen from the relevant online platform.

[0475] "Means for recognizing a user's emotional state and detecting stress levels using an emotion engine" refers to a system that uses emotion engine technology to detect a user's psychological state and stress level.

[0476] "Means for providing appropriate support and instructions according to the user's emotional state" refers to a system that takes into account the user's emotional state and provides optimal support and instructions accordingly.

[0477] The present invention provides a system for preventing offline and online security threats and improving staff efficiency and effectiveness, particularly by utilizing an emotion engine to recognize the user's emotional state and provide appropriate support and instructions to enhance security measures.

[0478] System Program

[0479] The server runs a program with the following functions: First, users (staff or security guards) can access the security assistant using a smartphone or head-mounted display. The system receives queries and emotional states from users in real time and uses an emotion engine to analyze them. The emotion engine detects the user's psychological state and stress level and provides optimal security policies and training content accordingly.

[0480] Hardware and software usage

[0481] The system uses the following hardware and software:

[0482] Hardware:

[0483] Smartphone

[0484] Head-mounted displays (e.g., Google Glass, Microsoft HoloLens)

[0485] software:

[0486] Emotion engine library (emotion_recognition)

[0487] Security policy provision library (security_ai)

[0488] The server receives information input by the user and uses an emotion engine to analyze the user's emotional state and detect their stress level. For example, if a user sends a query such as "Please tell me which areas have seen a lot of thefts recently," the server will analyze the user's emotions using the emotion engine and return an appropriate response if the user's stress level is high.

[0489] Furthermore, the server calculates patrol routes and frequency based on data from surveillance cameras and sensors, and notifies the user's device of this information. For example, it can provide information such as "The area with the highest risk of theft in the next 30 minutes is the electronics section," and depending on the user's stress level, it can instruct the user to "take a short break before patrolling."

[0490] Examples and prompts

[0491] As a concrete example, consider the case where a security guard on night patrol checks for new information. When the user inputs, "Have you had any problems in the electronics section recently?", the server uses an emotion engine to analyze the user's stress level. If the result shows that the stress level is high, the server displays a message saying, "You seem to be under a lot of stress. Please take a short break," followed by security information such as, "There has been an increase in thefts in the electronics section recently. Please be especially careful of people carrying large bags."

[0492] An example of a prompt sentence to input to the generative AI model is as follows:

[0493] Generate a notification message if "User stress level is high":

[0494] "Take a break and relax. Stress levels are high right now."

[0495] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0496] Step 1:

[0497] A user (staff or security guard) launches the security assistant application using a smartphone or head-mounted display.

[0498] Specific behavior:

[0499] The user operates the device to start the application.

[0500] The terminal sends the user's login information to the server for authentication.

[0501] Input: User login information

[0502] Output: Authentication success or failure response

[0503] Step 2:

[0504] The server receives queries and emotional states from users in real time.

[0505] Specific behavior:

[0506] The user enters a question or report through the device.

[0507] The terminal sends the input data to the server.

[0508] Input: User questions, reports, and speech data

[0509] Output: Input data transmitted to the server

[0510] Step 3:

[0511] The server uses an emotion engine to analyze the user's emotional state and detect stress levels.

[0512] Specific behavior:

[0513] The server calls the emotion engine (emotion_recognition library) and analyzes the input data.

[0514] Assess your emotional state and stress levels.

[0515] Input: User questions, reports, and speech data

[0516] Output: Emotional state and stress level data

[0517] Step 4:

[0518] The server provides optimal security policies and training content according to the user's emotional state.

[0519] Specific behavior:

[0520] The server retrieves the appropriate security policies and training content from a database.

[0521] If stress levels are high, it generates additional instructions including how to respond and alerts.

[0522] Input: Emotional state and stress level data

[0523] Output: Appropriate security policies, training content, and additional instruction data

[0524] Step 5:

[0525] The server calculates the optimal patrol route and frequency based on data from surveillance cameras and sensors, and notifies the user's device.

[0526] Specific behavior:

[0527] The server analyzes the real-time data received from the monitoring devices.

[0528] The optimal route is calculated and notified to the user's terminal.

[0529] Input: Data from surveillance cameras and sensors

[0530] Output: Information on optimal route and frequency

[0531] Step 6:

[0532] The server interacts with online platforms to detect and report fraudulent transactions.

[0533] Specific behavior:

[0534] A server retrieves and analyzes transaction data from the online platform.

[0535] If a fraudulent transaction is detected, it will be automatically reported and the item will be removed from the stolen goods list.

[0536] Input: Transaction data from online platforms

[0537] Output: Fraud detection and reporting data

[0538] Step 7:

[0539] The server sends the entire processing result to the user's device and provides appropriate support and instructions.

[0540] Specific behavior:

[0541] The server generates a message according to the emotional state and the necessary security information.

[0542] The generated information is sent to the user's terminal and displayed.

[0543] Input: Security information and support instruction data

[0544] Output: Messages and information that appear on the user's device

[0545] 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.

[0546] 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.

[0547] 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.

[0548] [Second embodiment]

[0549] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0550] 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.

[0551] 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).

[0552] 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.

[0553] 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.

[0554] 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).

[0555] 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.

[0556] 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.

[0557] 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.

[0558] 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.

[0559] 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.

[0560] 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."

[0561] The present invention is a system that optimizes collaboration between staff and artificial intelligence (AI) to prevent offline planned large-scale theft and online resale, and to strengthen security measures including safety training. Specific embodiments for implementing the present invention are described below.

[0562] 1. Real-time information provision

[0563] First, store staff and security guards access the Security Assistant via their smartphones or other devices. The Security Assistant resides on a central server, accepts queries sent by users, and provides appropriate security policies and training content in response. This allows users to quickly obtain the latest security information.

[0564] Examples:

[0565] A user asks: "Can you tell me which areas have had the most thefts recently?"

[0566] The server replies: "There has been an increase in thefts in the electronics section recently. Please be especially wary of people using large bags."

[0567] 2. Improving the efficiency of security patrols

[0568] Next, to improve the efficiency of patrols, the server provides real-time patrol information. Information obtained from surveillance cameras and sensors is analyzed, and the optimal patrol route and frequency is notified to the security guard's device. This allows the security guard to patrol effectively and minimize the risk of theft.

[0569] Examples:

[0570] The server calculates: "The area with the highest risk of theft in the next 30 minutes is the electronics section."

[0571] The server announces: "Next, please make your rounds in the food section in 20 minutes."

[0572] 3. Collaboration with online platforms

[0573] In addition, the server will work with online platforms to detect and quickly respond to fraudulent online transactions. If a fraudulent transaction is discovered, the information will be automatically reported to the police and the online platform. Any items reported as stolen will be automatically removed from the relevant platform.

[0574] Examples:

[0575] The server detects: "Fraudulent seller ID 12345 has listed a stolen laptop."

[0576] The server informs: "We have contacted the online platform and asked them to remove this item immediately."

[0577] summary

[0578] The security system of this invention comprehensively prevents offline and online theft through collaboration between staff and AI. Security measures are strengthened through real-time information provision, efficient security patrols, and collaboration with online platforms. By explaining the system's implementation with concrete examples, users can easily understand and put it into practice.

[0579] The processing flow will be explained below.

[0580] Real-time information provision

[0581] Step 1:

[0582] User

[0583] Access the security assistant from your smartphone or dedicated device.

[0584] Enter the question, "What are some signs of recent theft?"

[0585] Step 2:

[0586] server

[0587] Analyze the questions received from the user.

[0588] Based on the question, the database is searched for information on appropriate security policies and indicators of theft.

[0589] Step 3:

[0590] server

[0591] Based on the search results, answers are generated in a form that is easy for the user to understand.

[0592] Create an answer like, "Recently, there has been an increase in suspicious activity, especially in the electronics section. Please be especially careful of people with unusual behavior, especially those carrying large bags."

[0593] Step 4:

[0594] server

[0595] The generated answer is sent to the user's device.

[0596] Step 5:

[0597] User

[0598] Check the answers displayed on your device and take the necessary action.

[0599] Improving the efficiency of security patrols

[0600] Step 1:

[0601] server

[0602] Collect data obtained from surveillance cameras and sensors currently installed in stores.

[0603] Also refer to historical data on patrols.

[0604] Step 2:

[0605] server

[0606] The collected data is analyzed to identify high-risk areas for the next patrol.

[0607] Step 3:

[0608] server

[0609] Calculate the optimal route and frequency.

[0610] Develop a plan that states, "The area with the highest risk of theft over the next 30 minutes is the electronics section."

[0611] Step 4:

[0612] server

[0613] The patrol plan is sent to the security guard's device.

[0614] Step 5:

[0615] User

[0616] Check the patrol plan displayed on the device and carry out the patrol according to the instructions.

[0617] Collaboration with online platforms

[0618] Step 1:

[0619] server

[0620] Obtain product listings from online platforms and scan them.

[0621] Check against a list of stolen items to detect fraudulent listings.

[0622] Step 2:

[0623] server

[0624] It automatically generates information about detected fraudulent listings and prepares the data for reporting to the police and online platforms.

[0625] Step 3:

[0626] server

[0627] Create a report stating "Fake seller ID 12345 has listed a stolen laptop" and notify the relevant platform.

[0628] Step 4:

[0629] server

[0630] Update relevant stolen goods lists and automatically reflect them on the online platform.

[0631] Step 5:

[0632] Terminal (online platform)

[0633] We will promptly remove any fraudulent listings based on the removal request we receive.

[0634] Step 6:

[0635] server

[0636] Notify tracking users that the fraudulent listing has been removed.

[0637] Step 7:

[0638] User

[0639] We will review the notification and take further action as necessary.

[0640] Example 1

[0641] 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."

[0642] With the current increase in planned large-scale thefts offline and online resale, it is difficult to prevent these crimes using traditional security measures. Furthermore, while a swift and effective response from staff and security guards is required, limited information and resources may not be enough. There is also a lack of means to quickly detect fraudulent transactions online and take appropriate action. A new system is needed to solve these issues and improve the overall level of security.

[0643] 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.

[0644] In this invention, the server includes: means for a user to access the security assistant via a smartphone or terminal; means for the server to receive and analyze queries sent from the user; means for the server to search a database for and provide appropriate security information; means for the server to acquire and analyze data in real time from surveillance cameras and sensors; means for the server to identify high-risk areas and calculate optimal patrol routes; means for the server to notify security guard devices of the analysis results; means for the server to monitor transactions on online platforms and detect fraudulent transactions; and means for the server to report detected fraudulent transactions and remove products from related platforms. This enables quick access to the security assistant, provision of optimal security information, efficient patrols, and immediate detection and response to fraudulent transactions.

[0645] "User" refers to a person who accesses the Security Assistant and submits a query.

[0646] "Smartphone or device" refers to the electronic device used to access the security assistant.

[0647] "Security assistant" refers to a program that provides appropriate security information based on a user's query.

[0648] "Server" refers to the computer system that runs the Security Assistant and receives queries from users, analyzes them, retrieves information from a database, and provides the information.

[0649] "Query" refers to a question or request that a user sends to the Security Assistant.

[0650] "Analysis" refers to the processing of data by the server to understand the content of the query it receives and determine the appropriate response.

[0651] "Appropriate security information" refers to specific security measures and advice provided in response to a user's query.

[0652] "Database" refers to an information repository where security information and policies are stored.

[0653] "Surveillance camera" refers to a device that monitors a physical location and provides video data.

[0654] "Sensor" refers to a device that detects and provides data about a monitored environment.

[0655] "Acquiring data in real time" refers to instantly sending information provided by surveillance cameras and sensors to a server.

[0656] "High-risk areas" are locations where the likelihood of theft or fraud is expected to be high.

[0657] "Optimal patrol route" refers to a route planned to ensure the most effective patrol.

[0658] "Security guard" refers to a person who monitors and patrols security inside and outside a store.

[0659] "Online platform" refers to a website or application that conducts the trading of goods and services over the Internet.

[0660] "Transaction" refers to the act of buying and selling goods and services.

[0661] "Illicit trafficking" refers to the buying and selling of stolen or illegal goods.

[0662] "Relevant Platform" refers to the online platform on which the fraudulent transaction took place.

[0663] "Product removal" refers to the removal of a product that has been the subject of fraudulent trading on an online platform.

[0664] "Push Notification" refers to a real-time notification sent from a server to a user's device.

[0665] The present invention is a system designed to prevent offline planned large-scale theft and online resale, and to strengthen security measures, including safety training. This system allows users to access a security assistant in real time via their smartphones or terminals, and a server analyzes and provides a wide range of information. The following describes in detail specific embodiments of the invention.

[0666] User Device and Security Assistant Access Method

[0667] Users can access the Security Assistant via a dedicated application or browser on their smartphone or device (e.g., iOS or Android device), allowing them to quickly obtain appropriate security information.

[0668] Query reception and analysis system

[0669] When a user submits a query to the security assistant, the server receives the query, analyzes it using a natural language processing engine (e.g., NLTK or SpaCy), and searches a database for appropriate security information based on the query's content.

[0670] Providing security information

[0671] After the query is analyzed, the server retrieves relevant security information from a database (e.g., MySQL or PostgreSQL) and provides it to the user's device. This process allows users to receive the latest security information in real time.

[0672] Acquiring data from surveillance cameras and sensors

[0673] The server receives real-time information from the surveillance cameras and sensors installed in the store. Video data from the surveillance cameras is collected via the RTSP server, as well as data from the sensors (e.g., motion detection, volume level).

[0674] Data analysis and route calculation

[0675] The server analyzes the collected data using machine learning algorithms (e.g., TensorFlow or PyTorch) to identify areas with a high risk of theft, and then calculates the optimal patrol route using algorithms such as Dijkstra's algorithm.

[0676] Notifying security guards

[0677] The calculated patrol route information is sent to the security guard's smartphone or tablet via a push notification service (e.g., Firebase Cloud Messaging). Security guards who receive the notification can patrol the designated area efficiently.

[0678] Collaboration with online platforms

[0679] The server monitors transaction data from online platforms in real time via APIs and other means. If fraudulent transactions are detected, the server uses an anomaly detection algorithm to identify them. The server then reports the detected fraudulent transactions to the relevant platforms via APIs, and the relevant items are automatically deleted.

[0680] Examples of prompt statements

[0681] Below are some examples of prompt sentences that the security assistant can use to provide the user with appropriate information.

[0682] Prompt: "There have been reports of a high number of thefts in the electronics section of a store. Please generate a message to warn users."

[0683] In this way, the system can provide appropriate information in response to user queries and respond quickly and effectively to dynamically changing security risks.

[0684] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0685] Step 1: User accesses Security Assistant

[0686] Input: Smartphone or device

[0687] Output: Connection to Security Assistant

[0688] How it works: The user opens a browser or dedicated application on their smartphone or device, accesses a specific URL, and then enters their authentication information on the login screen that appears to access the security assistant.

[0689] Step 2: The server receives and parses the query

[0690] Input: User query

[0691] Output: Analysis results

[0692] What happens: When a user enters and submits a query, the server receives the HTTP request. The server uses a natural language processing engine (e.g., NLTK or SpaCy) to analyze the query. This analysis identifies the information or request the query is asking for.

[0693] Step 3: The server provides the appropriate security information

[0694] Input: Query parsing results

[0695] Output: Security information

[0696] Specific operation: Based on the analysis results, the server searches a database (e.g., MySQL or PostgreSQL) that stores security information. If relevant information is found, it retrieves the data and generates a message to respond to the user. This message is sent to the user's device and displayed in the browser or application.

[0697] Step 4: The server collects data from the surveillance cameras and sensors.

[0698] Input: Surveillance camera and sensor data

[0699] Output: Real-time environmental data

[0700] Specific operation: The server collects real-time data from the surveillance cameras and sensors installed in the store. Video data from the surveillance cameras is transmitted via the RTSP server, and data from the sensors (e.g., motion detection, volume level) is collected periodically or in real time.

[0701] Step 5: The server analyzes the data and calculates the optimal route.

[0702] Input: Real-time environmental data

[0703] Output: Optimal patrol routes and identification of high-risk areas

[0704] How it works: The server analyzes the acquired data using machine learning algorithms (e.g., TensorFlow or PyTorch) to identify areas with a high risk of theft. It then uses algorithms such as Dijkstra's algorithm to calculate patrol routes for security guards to patrol efficiently.

[0705] Step 6: The server notifies the security guard's device of the analysis results

[0706] Input: Information on optimal patrol routes and high-risk areas

[0707] Output: Notification to the guard's device

[0708] Specific operation: The server generates a notification for the guard based on the calculation results and sends it to the guard's smartphone or tablet using a push notification service (e.g., Firebase Cloud Messaging). The notification indicates the area and time that the guard should next patrol.

[0709] Step 7: The server monitors the online platform transactions.

[0710] Input: Transaction data from online platform

[0711] Output: Monitoring report

[0712] Specific operation: The server uses API to obtain transaction data from the online platform and monitors it in real time. The monitored data is periodically sent to the server and stored as needed.

[0713] Step 8: The server detects fraudulent transactions

[0714] Input: Transaction data

[0715] Output: Fraudulent transaction detection results

[0716] Specific operation: The server analyzes the acquired transaction data using an anomaly detection algorithm to identify fraudulent transactions. Detected fraudulent transactions are listed and the necessary information is extracted.

[0717] Step 9: Report any fraudulent transactions detected by the server and remove the items from the relevant platforms.

[0718] Input: Fraudulent transaction detection results

[0719] Output: Report and delete product

[0720] Specific operation: When the server detects a fraudulent transaction, it will contact the relevant platform via API and initiate the process of delisting the relevant product. If necessary, it will also report the matter to the police or authorized authorities. As a result, the product related to the fraudulent transaction will be promptly removed from the platform.

[0721] (Application example 1)

[0722] 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."

[0723] Traditional security systems lack sufficient coordination between staff and artificial intelligence, resulting in insufficient countermeasures against planned large-scale offline theft and online resale. Real-time patrol and security information is often delayed, leading to inefficient patrol planning. Furthermore, it is difficult to quickly detect and respond to fraudulent online transactions, and there is a lack of functionality to automatically delete listings of products reported as stolen, resulting in insufficient security measures.

[0724] 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.

[0725] In this invention, the server includes a means for allowing staff and security guards to access the security assistant via smartphones or devices to obtain security information in real time, a means for providing real-time patrol information and suggesting optimal patrol routes and frequencies, a means for analyzing information obtained from surveillance cameras and sensors to detect and immediately notify fraudulent online transactions, a means for obtaining security information about specific areas using a generative AI model, and a means for immediately responding when a fraudulent transaction is detected and automatically deleting the list of items reported as stolen. This optimizes collaboration between staff and artificial intelligence, effectively preventing theft both offline and online, and significantly strengthening security measures.

[0726] "Staff" refers to people who are responsible for operating and managing security systems, such as employees and security guards who work in physical stores.

[0727] "Artificial intelligence" refers to computer systems that mimic human intelligence and have capabilities such as learning, reasoning, and recognition.

[0728] A "smartphone" is a mobile phone that can access the Internet and run applications.

[0729] A "device" is an electronic device that can input, output, and process information, such as a smartphone or tablet.

[0730] The "Security Assistant" is a system that staff can access in real time and that provides security and patrol information.

[0731] "Patrol information" is information about security patrols within the store, including instructions on the optimal patrol route and frequency.

[0732] An "online platform" is a website or application that allows trading of goods and services over the Internet.

[0733] "Illicit trade" is the trading of goods or services that is not legal, such as the resale of stolen goods or fraud.

[0734] A "generative AI model" is an artificial intelligence model that learns patterns and features from data and automatically generates security information.

[0735] "Notification propagation processing" is the process of immediately notifying relevant systems and personnel when a fraudulent transaction is detected.

[0736] "Monitoring camera data" refers to video and image data acquired from monitoring cameras within a store.

[0737] This invention relates to a security system for preventing offline and online theft and strengthening safety training. The system aims to optimize collaboration between staff and artificial intelligence (AI) and provide real-time security information via smartphones and other devices.

[0738] 1. System Configuration

[0739] Hardware

[0740] The system primarily uses the following hardware:

[0741] Smartphones: Mobile devices used by staff and security guards.

[0742] Central Server: A computer system that provides security assistance and data analysis.

[0743] Surveillance camera: A camera that records video data within the store.

[0744] Sensors: Devices that collect movement and environmental data within the store.

[0745] software

[0746] The system uses the following software:

[0747] Security Assistant: An application that allows staff to obtain real-time security information.

[0748] Generative AI model: An AI system for generating security information about a specific area.

[0749] Database: Data storage to respond to queries to staff and provide appropriate security policies.

[0750] Online Transaction Monitoring System: A system that detects and immediately notifies you of fraudulent online transactions.

[0751] 2. Processing Flow

[0752] Real-time information provision

[0753] When staff or security guards access the security assistant via their smartphones, the server uses a generative AI model to provide the latest theft information and points of caution for each area.

[0754] Example: A staff member requests, "Can you tell me which areas have seen a lot of theft recently?" The server responds, "We've seen an increase in thefts in the electronics section recently. Please be especially wary of people using large bags."

[0755] Improving the efficiency of patrols

[0756] The server analyzes data obtained from surveillance cameras and sensors in real time and notifies the smartphone of the optimal patrol route and frequency.

[0757] Example: A server announces, "The area with the highest theft risk in the next 30 minutes is the electronics section," followed by instructions to "patrol the food section in 20 minutes."

[0758] Collaboration with online platforms

[0759] The server works with online platforms to detect fraudulent online transactions, immediately notifying relevant parties if a fraudulent transaction is discovered, and automatically removing listings of products reported as stolen.

[0760] Example: If the server detects that "Fraudulent seller ID 12345 has listed a stolen laptop," it will immediately notify the online platform that it has contacted and requested that the item be removed immediately.

[0761] 3. Use of generative AI models

[0762] Generative AI models learn patterns and features from data and automatically generate security information for specific areas.

[0763] Example prompt sentence:

[0764] "What security information has been released in the electronics section recently?"

[0765] "What areas are most at risk of theft?"

[0766] "Please tell me what kind of products the fraudulent seller ID 12345 is selling."

[0767] In this way, the present invention optimizes collaboration between staff and AI, making it possible to provide effective security measures in real time.

[0768] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0769] Step 1:

[0770] A user accesses the security assistant app using a smartphone. As input, the user sends a request such as, "Tell me in which areas thefts have been occurring frequently recently." The device then sends this request to the security server.

[0771] Step 2:

[0772] The security server analyzes the request and identifies the area for which information is being requested. The server uses a generative AI model to generate up-to-date security information for that area. It uses historical and current data related to the area as input and generates up-to-date security information as output.

[0773] Step 3:

[0774] The server sends the generated security information to the user's device, which then displays the received information to the user. Specifically, the device displays a message such as, "There has been an increase in thefts in the electronics section recently. Please be especially careful of people using large bags."

[0775] Step 4:

[0776] The server collects real-time data from surveillance cameras and sensors. As input, it receives video and movement data from the cameras and sensors, analyzes them internally, and generates important data for patrol planning as output.

[0777] Step 5:

[0778] The server analyzes the collected data to create a patrol plan. Based on this analysis, it calculates the optimal patrol route and frequency, and generates a patrol plan as output. For example, it may say, "The area with the highest risk of theft in the next 30 minutes is the electronics section."

[0779] Step 6:

[0780] The server then sends the generated patrol plan to the guard's smartphone. The device receives this information and notifies the guard of the optimal patrol route and frequency. For example, it displays instructions such as, "Next, patrol the food section in 20 minutes."

[0781] Step 7:

[0782] The server monitors the online platform to detect fraudulent transactions. As input, it periodically checks online transaction data and searches for fraudulent transaction patterns. As output, it generates detailed information about any fraudulent transactions that are found.

[0783] Step 8:

[0784] When a fraudulent transaction is detected, the server performs a notification propagation process to promptly respond. For example, it generates a warning such as "Fraudulent seller ID 12345 has listed a laptop that is listed as stolen goods" and sends a notification to the relevant online platform and relevant parties. Furthermore, the item listed as stolen goods is automatically removed from the online platform.

[0785] 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.

[0786] The present invention relates to a system that optimizes collaboration between staff and artificial intelligence (AI) to prevent offline and online security threats and strengthen security measures, including safety training. In particular, the present invention incorporates an emotion engine to recognize the user's emotional state and improve the efficiency and effectiveness of the entire system. Specific embodiments for implementing the present invention are described below.

[0787] 1. Real-time information provision

[0788] First, staff and security guards access the Security Assistant via their smartphones or dedicated devices. The Security Assistant resides on a central server, accepts queries sent by users, and provides appropriate security policies and training content. It also uses an emotion engine to recognize the user's current emotional state and detect stress levels, allowing the user to receive the most effective support.

[0789] Examples:

[0790] A user asks: "Can you tell me which areas have had the most thefts recently?"

[0791] The server analyzes the user's emotional state through an emotion engine and detects that the user is in a high stress state.

[0792] The server replies: "There has been an increase in thefts in the electronics section recently. Be especially wary of people using large bags. You seem stressed, so please keep an eye out for any suspicious activity."

[0793] 2. Improving the efficiency of security patrols

[0794] The server then provides real-time patrol information, analyzing data from surveillance cameras and sensors to calculate optimal patrol routes and frequencies, and notifying the guards' devices. Using an emotion engine, the server provides patrol instructions based on the guards' emotional state.

[0795] Examples:

[0796] The server analyzes the data and comes up with a plan: "The area with the highest risk of theft over the next 30 minutes is the electronics section."

[0797] The server analyzes the emotional state of the security guard through an emotion engine, and if stress is high, provides instructions such as "Take a short break before patrolling."

[0798] 3. Collaboration with online platforms

[0799] Furthermore, the server works with online platforms to detect and quickly respond to fraudulent online transactions. When a fraudulent transaction is discovered, the information is automatically reported to the police and the online platform. In addition, items reported as stolen are automatically removed from the relevant platform. An emotion engine provides appropriate notifications and follow-ups based on the user's emotional state.

[0800] Examples:

[0801] The server scans online platforms to detect fraudulent listings.

[0802] The server reports: "Fraudulent seller ID 12345 has listed a stolen laptop."

[0803] The server analyzes the user's emotional state through the emotion engine and sends a notification to provide reassurance: "The fraudulent listing removal process has been completed. Do you need further assistance?"

[0804] summary

[0805] The security system of the present invention effectively strengthens offline and online security measures by integrating an emotion engine. By incorporating emotion recognition technology, security measures can be further strengthened through real-time information provision, efficient security patrols, and integration with online platforms. By explaining the system embodiment through concrete examples, users can easily understand and put it into practice.

[0806] The processing flow will be explained below.

[0807] Real-time information provision

[0808] Step 1:

[0809] User

[0810] Access the security assistant from your smartphone or dedicated device.

[0811] Enter the question, "What are some signs of recent theft?"

[0812] Step 2:

[0813] server

[0814] Analyze the questions received from the user.

[0815] Based on the question, the database is searched for information on appropriate security policies and indicators of theft.

[0816] Step 3:

[0817] server

[0818] Based on the search results, answers are generated in a form that is easy for the user to understand.

[0819] Step 4:

[0820] server

[0821] Analyze the user's current emotional state via an emotion engine.

[0822] Adjust the tone and content of your responses depending on the user's emotional state.

[0823] Step 5:

[0824] server

[0825] Generates the answer, "Recently, there has been an increase in suspicious activity, especially in the electronics section. Please be especially wary of people carrying large bags and exhibiting unusual behavior. This person appears to be under a lot of stress, so please keep a close eye on them to see if there are any particularly suspicious people around."

[0826] Step 6:

[0827] server

[0828] The generated answer is sent to the user's device.

[0829] Step 7:

[0830] User

[0831] Check the answers displayed on your device and take the necessary action.

[0832] Improving the efficiency of security patrols

[0833] Step 1:

[0834] server

[0835] Collect data obtained from surveillance cameras and sensors currently installed in stores.

[0836] Also refer to historical data on patrols.

[0837] Step 2:

[0838] server

[0839] The collected data is analyzed to identify high-risk areas for the next patrol.

[0840] Step 3:

[0841] server

[0842] Calculate the optimal route and frequency.

[0843] Develop a plan that states, "The area with the highest risk of theft over the next 30 minutes is the electronics section."

[0844] Step 4:

[0845] server

[0846] Analyzing the emotional state of security guards through an emotion engine.

[0847] Adjust patrol instructions according to emotional state.

[0848] Step 5:

[0849] server

[0850] Provide instructions such as, "Take a short break and then make your rounds."

[0851] Step 6:

[0852] server

[0853] The patrol plan is sent to the security guard's device.

[0854] Step 7:

[0855] User

[0856] Check the patrol plan displayed on the device and carry out the patrol according to the instructions.

[0857] Collaboration with online platforms

[0858] Step 1:

[0859] server

[0860] Obtain product listings from online platforms and scan them.

[0861] Check against a list of stolen items to detect fraudulent listings.

[0862] Step 2:

[0863] server

[0864] Automatically generate information about detected fraudulent listings and prepare the data for reporting to the police and online platforms.

[0865] Step 3:

[0866] server

[0867] Create a report stating "Fake seller ID 12345 has listed a stolen laptop" and notify the relevant platform.

[0868] Step 4:

[0869] server

[0870] Update relevant stolen goods lists and automatically reflect them on the online platform.

[0871] Step 5:

[0872] Terminal (online platform)

[0873] We will promptly remove any fraudulent listings based on the removal request we receive.

[0874] Step 6:

[0875] server

[0876] Notify tracking users that the fraudulent listing has been removed.

[0877] Step 7:

[0878] server

[0879] It analyzes the user's emotional state through an emotion engine and sends notifications to alleviate anxiety.

[0880] Step 8:

[0881] User

[0882] We will review the notification and take further action as necessary.

[0883] Example 2

[0884] 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."

[0885] Today's security threats extend not only offline but also online, and simultaneous countermeasures are necessary. In particular, planned large-scale thefts and the resulting online resale activities cause significant damage to businesses and individuals. Furthermore, frontline staff and security guards often experience high levels of stress, and appropriate support tailored to their emotional state is required, but existing systems are unable to adequately address these challenges.

[0886] 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.

[0887] In this invention, the server includes means for optimizing collaboration between staff and artificial intelligence, means for providing real-time access to the security assistant via a smartphone or device, means for providing patrol information and suggesting optimal patrol routes and frequencies, means for linking with an online platform to detect and report fraudulent transactions, means for automatically deleting listings of items reported as stolen, and means for recognizing a user's emotional state using an emotion engine and providing appropriate support. This makes it possible to strengthen offline and online security measures in an integrated manner and provide appropriate support according to the emotional state of staff and security guards.

[0888] "Offline and online security threats" refers to physical intrusions and thefts that do not occur via the Internet, as well as cyber attacks and fraudulent transactions that occur via the Internet.

[0889] "Staff" refers to employees and guards engaged in security assistant and patrol duties.

[0890] "Artificial intelligence" refers to programs and systems that use technologies such as machine learning and natural language processing to imitate human intellectual tasks.

[0891] "Smartphones and devices" refers to portable electronic devices that can connect to the Internet and run applications.

[0892] "Security Assistant" refers to a software program or service that assists with security measures.

[0893] "Real-time access" refers to the sending and receiving of information occurring immediately.

[0894] "Patrol information" refers to information necessary for security guards to patrol, such as information regarding patrol routes and areas requiring caution.

[0895] "Optimal patrol routes and frequencies" refers to efficient patrol routes and their frequency planned to minimize security risks.

[0896] "Online platform" refers to an internet service for the purpose of e-commerce and communication.

[0897] "Unfair trading" refers to the buying, selling, or exchanging of fraudulently obtained goods or services.

[0898] "List of Items Reported as Stolen" means a list of items officially reported as stolen.

[0899] "Emotion engine" refers to an algorithm or program for analyzing a user's emotional state.

[0900] "User's emotional state" refers to the user's stress level or emotional state.

[0901] "Means for providing appropriate support" refers to a method for instantly providing necessary assistance or support based on the user's emotional state.

[0902] The present invention relates to a system that optimizes collaboration between staff and artificial intelligence to prevent offline and online security threats and strengthen security measures, including safety training. In particular, the present invention incorporates an emotion engine to recognize the user's emotional state and improve the efficiency and effectiveness of the entire system. Specific embodiments for implementing the present invention are described below.

[0903] 1. Real-time information provision

[0904] First, staff and security guards access the Security Assistant via their smartphones or dedicated devices. The Security Assistant resides on a central server, accepts queries sent by users, and provides appropriate security policies and training content. It also uses an emotion engine to recognize the user's current emotional state and detect stress levels, allowing the user to receive the most effective support.

[0905] Specific examples

[0906] A user asks: "Can you tell me which areas have had the most thefts recently?"

[0907] The server analyzes the user's emotional state through an emotion engine and detects that the user is in a high stress state.

[0908] The server replies: "There has been an increase in thefts in the electronics section recently. Be especially wary of people using large bags. You seem stressed, so please keep an eye out for any suspicious activity."

[0909] 2. Improving the efficiency of security patrols

[0910] The server then provides real-time patrol information, analyzing data from surveillance cameras and sensors to calculate optimal patrol routes and frequencies, and notifying the guards' devices. Using an emotion engine, the server provides patrol instructions based on the guards' emotional state.

[0911] Specific examples

[0912] The server analyzes the data and comes up with a plan: "The area with the highest risk of theft over the next 30 minutes is the electronics section."

[0913] The server analyzes the emotional state of the security guard through an emotion engine, and if stress is high, provides instructions such as "Take a short break before patrolling."

[0914] 3. Collaboration with online platforms

[0915] Furthermore, the server works with online platforms to detect and quickly respond to fraudulent online transactions. When a fraudulent transaction is discovered, the information is automatically reported to the police and the online platform. In addition, items reported as stolen are automatically removed from the relevant platform. An emotion engine provides appropriate notifications and follow-ups based on the user's emotional state.

[0916] Specific examples

[0917] The server scans online platforms to detect fraudulent listings.

[0918] The server reports: "Fraudulent seller ID 12345 has listed a stolen laptop."

[0919] The server analyzes the user's emotional state through the emotion engine and sends a notification to provide reassurance: "The fraudulent listing removal process has been completed. Do you need further assistance?"

[0920] Prompt Sentence Examples

[0921] User-supplied question: "What areas have seen a lot of thefts recently?"

[0922] Server prompt: "User wants to know where the latest thefts have been occurring. Please generate an answer that will alleviate anxiety and stress."

[0923] summary

[0924] This system integrates an emotion engine to enhance offline and online security measures. By providing real-time information, streamlining security patrols, and connecting with online platforms, emotion recognition technology further strengthens security measures. The system's implementation is explained through concrete examples, making it easy for users to understand and put into practice.

[0925] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0926] 1. Real-time information provision

[0927] Step 1:

[0928] User accesses Security Assistant

[0929] Input: A user launches the Security Assistant app using a smartphone or dedicated device.

[0930] What happens: A user enters a query in the app and presses submit.

[0931] Output: The query entered by the user is sent to the server.

[0932] Step 2:

[0933] The server accepts the query

[0934] Input: The query sent from the user device.

[0935] How it works: The server receives the query, parses it in text format, and searches the corresponding database.

[0936] Output: Parsed query data and associated security information.

[0937] Step 3:

[0938] The server uses an emotion engine to analyze the user's emotional state.

[0939] Input: Parsed query data.

[0940] Operation: The server launches the emotion engine to extract emotion patterns from the user's input text.

[0941] Output: User's emotional state data (e.g., how stressed they are).

[0942] Step 4:

[0943] The server provides appropriate security policies and training content.

[0944] Input: User emotional state data and associated security information.

[0945] How it works: The server extracts the latest security information from a database and generates an answer for the user.

[0946] Output: Specific security information and advice to provide to the user (e.g., "Recent hotspots for thefts").

[0947] 2. Improving the efficiency of security patrols

[0948] Step 1:

[0949] The server collects data from surveillance cameras and sensors.

[0950] Input: Real-time data from surveillance cameras and sensors.

[0951] How it works: The server collects the images and data captured by each surveillance camera and sensor and stores them in a central database.

[0952] Output: Collected monitoring data.

[0953] Step 2:

[0954] The server analyzes the data

[0955] Input: Collected monitoring data.

[0956] How it works: The server uses machine learning algorithms to analyze the data and detect anomalies.

[0957] Output: Data about high-risk areas (e.g. areas with high risk of theft).

[0958] Step 3:

[0959] The server notifies the guard's device of the optimal patrol route.

[0960] Input: Data about high-risk areas.

[0961] Operation: The server calculates the optimal patrol route and timing and sends it to the security guard's device.

[0962] Output: Patrol instructions for guards (e.g., "The next patrol area is the electronics section, head there now.").

[0963] Step 4:

[0964] The server uses an emotion engine to analyze the emotional state of the guard.

[0965] Input: Historical data and real-time feedback from security guards.

[0966] How it works: The server uses the emotion engine to analyze the stress level of the security guards.

[0967] Output: Instructions to the guard based on their emotional state (e.g., "Please take a short break before patrolling.").

[0968] 3. Collaboration with online platforms

[0969] Step 1:

[0970] The server scans the transaction data of the online platform.

[0971] Input: Transaction data from online platforms.

[0972] How it works: The server accesses the database through the API and periodically scans the retrieved data.

[0973] Output: Scanned transaction data.

[0974] Step 2:

[0975] The server detects fraudulent transactions

[0976] Input: Scanned transaction data.

[0977] How it works: The server checks against an existing list of stolen items to detect suspicious transactions.

[0978] Output: Detection results for fraudulent transactions (e.g., "Fraudulent seller ID 12345 listed a stolen laptop.").

[0979] Step 3:

[0980] The server automatically reports the incident to the police and the platform.

[0981] Input: Detection results for fraudulent transactions.

[0982] How it works: The server uses an automated reporting function to notify the police or platform administrators.

[0983] Output: Report message (e.g., "Stolen goods have been listed").

[0984] Step 4:

[0985] The server uses an emotion engine to send notifications based on the user's emotional state.

[0986] Input: Report result and user emotional state data.

[0987] How it works: The server uses the emotion engine to generate notifications to increase the user's sense of security.

[0988] Output: Notification message based on emotional state (e.g., "Your fraudulent listing has been removed. Do you need further assistance?").

[0989] (Application example 2)

[0990] 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."

[0991] The modern security environment faces increasing security threats both offline and online. Planned large-scale thefts and online resale of stolen goods are particularly problematic. Furthermore, security staff may operate under high stress, reducing their efficiency and effectiveness. In these situations, a system is needed to provide more advanced security measures and instructions while taking into account the emotional state of staff.

[0992] The identification processing 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 optimizing collaboration between staff and artificial intelligence, means for providing real-time access to the security assistant via a smartphone or device, means for providing patrol information and suggesting optimal patrol routes and frequencies, means for connecting with an online platform to detect and report fraudulent transactions, means for automatically deleting listings of items reported as stolen, means for recognizing a user's emotional state and detecting stress levels using an emotion engine, and means for providing appropriate support and instructions according to the user's emotional state. This effectively prevents offline and online security threats and improves the efficiency and effectiveness of staff.

[0993] "Measures to optimize collaboration between staff and artificial intelligence" refers to methods for enabling staff and artificial intelligence to work together effectively to strengthen security measures.

[0994] "Real-time access to a security assistant via smartphone or device" refers to a method that allows users to use their smartphone or other device to get instant security-related information and assistance.

[0995] "Means for providing patrol information and suggesting optimal patrol routes and frequencies" refers to a system that analyzes surveillance areas and data to suggest optimal patrol routes and frequencies to security guards.

[0996] "Means to work with online platforms to detect and report fraudulent transactions" refers to a system that works with online marketplaces and trading platforms to detect and report fraudulent activities and fraudulent transactions.

[0997] "Means for automatically removing listings of items reported as stolen" refers to a system that automatically removes items identified as stolen from the relevant online platform.

[0998] "Means for recognizing a user's emotional state and detecting stress levels using an emotion engine" refers to a system that uses emotion engine technology to detect a user's psychological state and stress level.

[0999] "Means for providing appropriate support and instructions according to the user's emotional state" refers to a system that takes into account the user's emotional state and provides optimal support and instructions accordingly.

[1000] The present invention provides a system for preventing offline and online security threats and improving staff efficiency and effectiveness, particularly by utilizing an emotion engine to recognize the user's emotional state and provide appropriate support and instructions to enhance security measures.

[1001] System Program

[1002] The server runs a program with the following functions: First, users (staff or security guards) can access the security assistant using a smartphone or head-mounted display. The system receives queries and emotional states from users in real time and uses an emotion engine to analyze them. The emotion engine detects the user's psychological state and stress level and provides optimal security policies and training content accordingly.

[1003] Hardware and software usage

[1004] The system uses the following hardware and software:

[1005] Hardware:

[1006] Smartphone

[1007] Head-mounted displays (e.g., Google Glass, Microsoft HoloLens)

[1008] software:

[1009] Emotion engine library (emotion_recognition)

[1010] Security policy provision library (security_ai)

[1011] The server receives information input by the user and uses an emotion engine to analyze the user's emotional state and detect their stress level. For example, if a user sends a query such as "Please tell me which areas have seen a lot of thefts recently," the server will analyze the user's emotions using the emotion engine and return an appropriate response if the user's stress level is high.

[1012] Furthermore, the server calculates patrol routes and frequency based on data from surveillance cameras and sensors, and notifies the user's device of this information. For example, it can provide information such as "The area with the highest risk of theft in the next 30 minutes is the electronics section," and depending on the user's stress level, it can instruct the user to "take a short break before patrolling."

[1013] Examples and prompts

[1014] As a concrete example, consider the case where a security guard on night patrol checks for new information. When the user inputs, "Have you had any problems in the electronics section recently?", the server uses an emotion engine to analyze the user's stress level. If the result shows that the stress level is high, the server displays a message saying, "You seem to be under a lot of stress. Please take a short break," followed by security information such as, "There has been an increase in thefts in the electronics section recently. Please be especially careful of people carrying large bags."

[1015] An example of a prompt sentence to input to the generative AI model is as follows:

[1016] Generate a notification message if "User stress level is high":

[1017] "Take a break and relax. Stress levels are high right now."

[1018] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1019] Step 1:

[1020] A user (staff or security guard) launches the security assistant application using a smartphone or head-mounted display.

[1021] Specific behavior:

[1022] The user operates the device to start the application.

[1023] The terminal sends the user's login information to the server for authentication.

[1024] Input: User login information

[1025] Output: Authentication success or failure response

[1026] Step 2:

[1027] The server receives queries and emotional states from users in real time.

[1028] Specific behavior:

[1029] The user enters a question or report through the device.

[1030] The terminal sends the input data to the server.

[1031] Input: User questions, reports, and speech data

[1032] Output: Input data transmitted to the server

[1033] Step 3:

[1034] The server uses an emotion engine to analyze the user's emotional state and detect stress levels.

[1035] Specific behavior:

[1036] The server calls the emotion engine (emotion_recognition library) and analyzes the input data.

[1037] Assess your emotional state and stress levels.

[1038] Input: User questions, reports, and speech data

[1039] Output: Emotional state and stress level data

[1040] Step 4:

[1041] The server provides optimal security policies and training content according to the user's emotional state.

[1042] Specific behavior:

[1043] The server retrieves the appropriate security policies and training content from a database.

[1044] If stress levels are high, it generates additional instructions including how to respond and alerts.

[1045] Input: Emotional state and stress level data

[1046] Output: Appropriate security policies, training content, and additional instruction data

[1047] Step 5:

[1048] The server calculates the optimal patrol route and frequency based on data from surveillance cameras and sensors, and notifies the user's device.

[1049] Specific behavior:

[1050] The server analyzes the real-time data received from the monitoring devices.

[1051] The optimal route is calculated and notified to the user's terminal.

[1052] Input: Data from surveillance cameras and sensors

[1053] Output: Information on optimal route and frequency

[1054] Step 6:

[1055] The server interacts with online platforms to detect and report fraudulent transactions.

[1056] Specific behavior:

[1057] A server retrieves and analyzes transaction data from the online platform.

[1058] If a fraudulent transaction is detected, it will be automatically reported and the item will be removed from the stolen goods list.

[1059] Input: Transaction data from online platforms

[1060] Output: Fraud detection and reporting data

[1061] Step 7:

[1062] The server sends the entire processing result to the user's device and provides appropriate support and instructions.

[1063] Specific behavior:

[1064] The server generates a message according to the emotional state and the necessary security information.

[1065] The generated information is sent to the user's terminal and displayed.

[1066] Input: Security information and support instruction data

[1067] Output: Messages and information that appear on the user's device

[1068] 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.

[1069] 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.

[1070] 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.

[1071] [Third embodiment]

[1072] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1073] 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.

[1074] 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).

[1075] 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.

[1076] 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.

[1077] 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).

[1078] 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.

[1079] 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.

[1080] 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.

[1081] 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.

[1082] 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.

[1083] 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."

[1084] The present invention is a system that optimizes collaboration between staff and artificial intelligence (AI) to prevent offline planned large-scale theft and online resale, and to strengthen security measures including safety training. Specific embodiments for implementing the present invention are described below.

[1085] 1. Real-time information provision

[1086] First, store staff and security guards access the Security Assistant via their smartphones or other devices. The Security Assistant resides on a central server, accepts queries sent by users, and provides appropriate security policies and training content in response. This allows users to quickly obtain the latest security information.

[1087] Examples:

[1088] A user asks: "Can you tell me which areas have had the most thefts recently?"

[1089] The server replies: "There has been an increase in thefts in the electronics section recently. Please be especially wary of people using large bags."

[1090] 2. Improving the efficiency of security patrols

[1091] Next, to improve the efficiency of patrols, the server provides real-time patrol information. Information obtained from surveillance cameras and sensors is analyzed, and the optimal patrol route and frequency is notified to the security guard's device. This allows the security guard to patrol effectively and minimize the risk of theft.

[1092] Examples:

[1093] The server calculates: "The area with the highest risk of theft in the next 30 minutes is the electronics section."

[1094] The server announces: "Next, please make your rounds in the food section in 20 minutes."

[1095] 3. Collaboration with online platforms

[1096] In addition, the server will work with online platforms to detect and quickly respond to fraudulent online transactions. If a fraudulent transaction is discovered, the information will be automatically reported to the police and the online platform. Any items reported as stolen will be automatically removed from the relevant platform.

[1097] Examples:

[1098] The server detects: "Fraudulent seller ID 12345 has listed a stolen laptop."

[1099] The server informs: "We have contacted the online platform and asked them to remove this item immediately."

[1100] summary

[1101] The security system of this invention comprehensively prevents offline and online theft through collaboration between staff and AI. Security measures are strengthened through real-time information provision, efficient security patrols, and collaboration with online platforms. By explaining the system's implementation with concrete examples, users can easily understand and put it into practice.

[1102] The processing flow will be explained below.

[1103] Real-time information provision

[1104] Step 1:

[1105] User

[1106] Access the security assistant from your smartphone or dedicated device.

[1107] Enter the question, "What are some signs of recent theft?"

[1108] Step 2:

[1109] server

[1110] Analyze the questions received from the user.

[1111] Based on the question, the database is searched for information on appropriate security policies and indicators of theft.

[1112] Step 3:

[1113] server

[1114] Based on the search results, answers are generated in a form that is easy for the user to understand.

[1115] Create an answer like, "Recently, there has been an increase in suspicious activity, especially in the electronics section. Please be especially careful of people with unusual behavior, especially those carrying large bags."

[1116] Step 4:

[1117] server

[1118] The generated answer is sent to the user's device.

[1119] Step 5:

[1120] User

[1121] Check the answers displayed on your device and take the necessary action.

[1122] Improving the efficiency of security patrols

[1123] Step 1:

[1124] server

[1125] Collect data obtained from surveillance cameras and sensors currently installed in stores.

[1126] Also refer to historical data on patrols.

[1127] Step 2:

[1128] server

[1129] The collected data is analyzed to identify high-risk areas for the next patrol.

[1130] Step 3:

[1131] server

[1132] Calculate the optimal route and frequency.

[1133] Develop a plan that states, "The area with the highest risk of theft over the next 30 minutes is the electronics section."

[1134] Step 4:

[1135] server

[1136] The patrol plan is sent to the security guard's device.

[1137] Step 5:

[1138] User

[1139] Check the patrol plan displayed on the device and carry out the patrol according to the instructions.

[1140] Collaboration with online platforms

[1141] Step 1:

[1142] server

[1143] Obtain product listings from online platforms and scan them.

[1144] Check against a list of stolen items to detect fraudulent listings.

[1145] Step 2:

[1146] server

[1147] It automatically generates information about detected fraudulent listings and prepares the data for reporting to the police and online platforms.

[1148] Step 3:

[1149] server

[1150] Create a report stating "Fake seller ID 12345 has listed a stolen laptop" and notify the relevant platform.

[1151] Step 4:

[1152] server

[1153] Update relevant stolen goods lists and automatically reflect them on the online platform.

[1154] Step 5:

[1155] Terminal (online platform)

[1156] We will promptly remove any fraudulent listings based on the removal request we receive.

[1157] Step 6:

[1158] server

[1159] Notify tracking users that the fraudulent listing has been removed.

[1160] Step 7:

[1161] User

[1162] We will review the notification and take further action as necessary.

[1163] Example 1

[1164] 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."

[1165] With the current increase in planned large-scale thefts offline and online resale, it is difficult to prevent these crimes using traditional security measures. Furthermore, while a swift and effective response from staff and security guards is required, limited information and resources may not be enough. There is also a lack of means to quickly detect fraudulent transactions online and take appropriate action. A new system is needed to solve these issues and improve the overall level of security.

[1166] 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.

[1167] In this invention, the server includes: means for a user to access the security assistant via a smartphone or terminal; means for the server to receive and analyze queries sent from the user; means for the server to search a database for and provide appropriate security information; means for the server to acquire and analyze data in real time from surveillance cameras and sensors; means for the server to identify high-risk areas and calculate optimal patrol routes; means for the server to notify security guard devices of the analysis results; means for the server to monitor transactions on online platforms and detect fraudulent transactions; and means for the server to report detected fraudulent transactions and remove products from related platforms. This enables quick access to the security assistant, provision of optimal security information, efficient patrols, and immediate detection and response to fraudulent transactions.

[1168] "User" refers to a person who accesses the Security Assistant and submits a query.

[1169] "Smartphone or device" refers to the electronic device used to access the security assistant.

[1170] "Security assistant" refers to a program that provides appropriate security information based on a user's query.

[1171] "Server" refers to the computer system that runs the Security Assistant and receives queries from users, analyzes them, retrieves information from a database, and provides the information.

[1172] "Query" refers to a question or request that a user sends to the Security Assistant.

[1173] "Analysis" refers to the processing of data by the server to understand the content of the query it receives and determine the appropriate response.

[1174] "Appropriate security information" refers to specific security measures and advice provided in response to a user's query.

[1175] "Database" refers to an information repository where security information and policies are stored.

[1176] "Surveillance camera" refers to a device that monitors a physical location and provides video data.

[1177] "Sensor" refers to a device that detects and provides data about a monitored environment.

[1178] "Acquiring data in real time" refers to instantly sending information provided by surveillance cameras and sensors to a server.

[1179] "High-risk areas" are locations where the likelihood of theft or fraud is expected to be high.

[1180] "Optimal patrol route" refers to a route planned to ensure the most effective patrol.

[1181] "Security guard" refers to a person who monitors and patrols security inside and outside a store.

[1182] "Online platform" refers to a website or application that conducts the trading of goods and services over the Internet.

[1183] "Transaction" refers to the act of buying and selling goods and services.

[1184] "Illicit trafficking" refers to the buying and selling of stolen or illegal goods.

[1185] "Relevant Platform" refers to the online platform on which the fraudulent transaction took place.

[1186] "Product removal" refers to the removal of a product that has been the subject of fraudulent trading on an online platform.

[1187] "Push Notification" refers to a real-time notification sent from a server to a user's device.

[1188] The present invention is a system designed to prevent offline planned large-scale theft and online resale, and to strengthen security measures, including safety training. This system allows users to access a security assistant in real time via their smartphones or terminals, and a server analyzes and provides a wide range of information. The following describes in detail specific embodiments of the invention.

[1189] User Device and Security Assistant Access Method

[1190] Users can access the Security Assistant via a dedicated application or browser on their smartphone or device (e.g., iOS or Android device), allowing them to quickly obtain appropriate security information.

[1191] Query reception and analysis system

[1192] When a user submits a query to the security assistant, the server receives the query, analyzes it using a natural language processing engine (e.g., NLTK or SpaCy), and searches a database for appropriate security information based on the query's content.

[1193] Providing security information

[1194] After the query is analyzed, the server retrieves relevant security information from a database (e.g., MySQL or PostgreSQL) and provides it to the user's device. This process allows users to receive the latest security information in real time.

[1195] Acquiring data from surveillance cameras and sensors

[1196] The server receives real-time information from the surveillance cameras and sensors installed in the store. Video data from the surveillance cameras is collected via the RTSP server, as well as data from the sensors (e.g., motion detection, volume level).

[1197] Data analysis and route calculation

[1198] The server analyzes the collected data using machine learning algorithms (e.g., TensorFlow or PyTorch) to identify areas with a high risk of theft, and then calculates the optimal patrol route using algorithms such as Dijkstra's algorithm.

[1199] Notifying security guards

[1200] The calculated patrol route information is sent to the security guard's smartphone or tablet via a push notification service (e.g., Firebase Cloud Messaging). Security guards who receive the notification can patrol the designated area efficiently.

[1201] Collaboration with online platforms

[1202] The server monitors transaction data from online platforms in real time via APIs and other means. If fraudulent transactions are detected, the server uses an anomaly detection algorithm to identify them. The server then reports the detected fraudulent transactions to the relevant platforms via APIs, and the relevant items are automatically deleted.

[1203] Examples of prompt statements

[1204] Below are some examples of prompt sentences that the security assistant can use to provide the user with appropriate information.

[1205] Prompt: "There have been reports of a high number of thefts in the electronics section of a store. Please generate a message to warn users."

[1206] In this way, the system can provide appropriate information in response to user queries and respond quickly and effectively to dynamically changing security risks.

[1207] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1208] Step 1: User accesses Security Assistant

[1209] Input: Smartphone or device

[1210] Output: Connection to Security Assistant

[1211] How it works: The user opens a browser or dedicated application on their smartphone or device, accesses a specific URL, and then enters their authentication information on the login screen that appears to access the security assistant.

[1212] Step 2: The server receives and parses the query

[1213] Input: User query

[1214] Output: Analysis results

[1215] What happens: When a user enters and submits a query, the server receives the HTTP request. The server uses a natural language processing engine (e.g., NLTK or SpaCy) to analyze the query. This analysis identifies the information or request the query is asking for.

[1216] Step 3: The server provides the appropriate security information

[1217] Input: Query parsing results

[1218] Output: Security information

[1219] Specific operation: Based on the analysis results, the server searches a database (e.g., MySQL or PostgreSQL) that stores security information. If relevant information is found, it retrieves the data and generates a message to respond to the user. This message is sent to the user's device and displayed in the browser or application.

[1220] Step 4: The server collects data from the surveillance cameras and sensors.

[1221] Input: Surveillance camera and sensor data

[1222] Output: Real-time environmental data

[1223] Specific operation: The server collects real-time data from the surveillance cameras and sensors installed in the store. Video data from the surveillance cameras is transmitted via the RTSP server, and data from the sensors (e.g., motion detection, volume level) is collected periodically or in real time.

[1224] Step 5: The server analyzes the data and calculates the optimal route.

[1225] Input: Real-time environmental data

[1226] Output: Optimal patrol routes and identification of high-risk areas

[1227] How it works: The server analyzes the acquired data using machine learning algorithms (e.g., TensorFlow or PyTorch) to identify areas with a high risk of theft. It then uses algorithms such as Dijkstra's algorithm to calculate patrol routes for security guards to patrol efficiently.

[1228] Step 6: The server notifies the security guard's device of the analysis results

[1229] Input: Information on optimal patrol routes and high-risk areas

[1230] Output: Notification to the guard's device

[1231] Specific operation: The server generates a notification for the guard based on the calculation results and sends it to the guard's smartphone or tablet using a push notification service (e.g., Firebase Cloud Messaging). The notification indicates the area and time that the guard should next patrol.

[1232] Step 7: The server monitors the online platform transactions.

[1233] Input: Transaction data from online platform

[1234] Output: Monitoring report

[1235] Specific operation: The server uses API to obtain transaction data from the online platform and monitors it in real time. The monitored data is periodically sent to the server and stored as needed.

[1236] Step 8: The server detects fraudulent transactions

[1237] Input: Transaction data

[1238] Output: Fraudulent transaction detection results

[1239] Specific operation: The server analyzes the acquired transaction data using an anomaly detection algorithm to identify fraudulent transactions. Detected fraudulent transactions are listed and the necessary information is extracted.

[1240] Step 9: Report any fraudulent transactions detected by the server and remove the items from the relevant platforms.

[1241] Input: Fraudulent transaction detection results

[1242] Output: Report and delete product

[1243] Specific operation: When the server detects a fraudulent transaction, it will contact the relevant platform via API and initiate the process of delisting the relevant product. If necessary, it will also report the matter to the police or authorized authorities. As a result, the product related to the fraudulent transaction will be promptly removed from the platform.

[1244] (Application example 1)

[1245] 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."

[1246] Traditional security systems lack sufficient coordination between staff and artificial intelligence, resulting in insufficient countermeasures against planned large-scale offline theft and online resale. Real-time patrol and security information is often delayed, leading to inefficient patrol planning. Furthermore, it is difficult to quickly detect and respond to fraudulent online transactions, and there is a lack of functionality to automatically delete listings of products reported as stolen, resulting in insufficient security measures.

[1247] 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.

[1248] In this invention, the server includes a means for allowing staff and security guards to access the security assistant via smartphones or devices to obtain security information in real time, a means for providing real-time patrol information and suggesting optimal patrol routes and frequencies, a means for analyzing information obtained from surveillance cameras and sensors to detect and immediately notify fraudulent online transactions, a means for obtaining security information about specific areas using a generative AI model, and a means for immediately responding when a fraudulent transaction is detected and automatically deleting the list of items reported as stolen. This optimizes collaboration between staff and artificial intelligence, effectively preventing theft both offline and online, and significantly strengthening security measures.

[1249] "Staff" refers to people who are responsible for operating and managing security systems, such as employees and security guards who work in physical stores.

[1250] "Artificial intelligence" refers to computer systems that mimic human intelligence and have capabilities such as learning, reasoning, and recognition.

[1251] A "smartphone" is a mobile phone that can access the Internet and run applications.

[1252] A "device" is an electronic device that can input, output, and process information, such as a smartphone or tablet.

[1253] The "Security Assistant" is a system that staff can access in real time and that provides security and patrol information.

[1254] "Patrol information" is information about security patrols within the store, including instructions on the optimal patrol route and frequency.

[1255] An "online platform" is a website or application that allows trading of goods and services over the Internet.

[1256] "Illicit trade" is the trading of goods or services that is not legal, such as the resale of stolen goods or fraud.

[1257] A "generative AI model" is an artificial intelligence model that learns patterns and features from data and automatically generates security information.

[1258] "Notification propagation processing" is the process of immediately notifying relevant systems and personnel when a fraudulent transaction is detected.

[1259] "Monitoring camera data" refers to video and image data acquired from monitoring cameras within a store.

[1260] This invention relates to a security system for preventing offline and online theft and strengthening safety training. The system aims to optimize collaboration between staff and artificial intelligence (AI) and provide real-time security information via smartphones and other devices.

[1261] 1. System Configuration

[1262] Hardware

[1263] The system primarily uses the following hardware:

[1264] Smartphones: Mobile devices used by staff and security guards.

[1265] Central Server: A computer system that provides security assistance and data analysis.

[1266] Surveillance camera: A camera that records video data within the store.

[1267] Sensors: Devices that collect movement and environmental data within the store.

[1268] software

[1269] The system uses the following software:

[1270] Security Assistant: An application that allows staff to obtain real-time security information.

[1271] Generative AI model: An AI system for generating security information about a specific area.

[1272] Database: Data storage to respond to queries to staff and provide appropriate security policies.

[1273] Online Transaction Monitoring System: A system that detects and immediately notifies you of fraudulent online transactions.

[1274] 2. Processing Flow

[1275] Real-time information provision

[1276] When staff or security guards access the security assistant via their smartphones, the server uses a generative AI model to provide the latest theft information and points of caution for each area.

[1277] Example: A staff member requests, "Can you tell me which areas have seen a lot of theft recently?" The server responds, "We've seen an increase in thefts in the electronics section recently. Please be especially wary of people using large bags."

[1278] Improving the efficiency of patrols

[1279] The server analyzes data obtained from surveillance cameras and sensors in real time and notifies the smartphone of the optimal patrol route and frequency.

[1280] Example: A server announces, "The area with the highest theft risk in the next 30 minutes is the electronics section," followed by instructions to "patrol the food section in 20 minutes."

[1281] Collaboration with online platforms

[1282] The server works with online platforms to detect fraudulent online transactions, immediately notifying relevant parties if a fraudulent transaction is discovered, and automatically removing listings of products reported as stolen.

[1283] Example: If the server detects that "Fraudulent seller ID 12345 has listed a stolen laptop," it will immediately notify the online platform that it has contacted and requested that the item be removed immediately.

[1284] 3. Use of generative AI models

[1285] Generative AI models learn patterns and features from data and automatically generate security information for specific areas.

[1286] Example prompt sentence:

[1287] "What security information has been released in the electronics section recently?"

[1288] "What areas are most at risk of theft?"

[1289] "Please tell me what kind of products the fraudulent seller ID 12345 is selling."

[1290] In this way, the present invention optimizes collaboration between staff and AI, making it possible to provide effective security measures in real time.

[1291] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1292] Step 1:

[1293] A user accesses the security assistant app using a smartphone. As input, the user sends a request such as, "Tell me in which areas thefts have been occurring frequently recently." The device then sends this request to the security server.

[1294] Step 2:

[1295] The security server analyzes the request and identifies the area for which information is being requested. The server uses a generative AI model to generate up-to-date security information for that area. It uses historical and current data related to the area as input and generates up-to-date security information as output.

[1296] Step 3:

[1297] The server sends the generated security information to the user's device, which then displays the received information to the user. Specifically, the device displays a message such as, "There has been an increase in thefts in the electronics section recently. Please be especially careful of people using large bags."

[1298] Step 4:

[1299] The server collects real-time data from surveillance cameras and sensors. As input, it receives video and movement data from the cameras and sensors, analyzes them internally, and generates important data for patrol planning as output.

[1300] Step 5:

[1301] The server analyzes the collected data to create a patrol plan. Based on this analysis, it calculates the optimal patrol route and frequency, and generates a patrol plan as output. For example, it may say, "The area with the highest risk of theft in the next 30 minutes is the electronics section."

[1302] Step 6:

[1303] The server then sends the generated patrol plan to the guard's smartphone. The device receives this information and notifies the guard of the optimal patrol route and frequency. For example, it displays instructions such as, "Next, patrol the food section in 20 minutes."

[1304] Step 7:

[1305] The server monitors the online platform to detect fraudulent transactions. As input, it periodically checks online transaction data and searches for fraudulent transaction patterns. As output, it generates detailed information about any fraudulent transactions that are found.

[1306] Step 8:

[1307] When a fraudulent transaction is detected, the server performs a notification propagation process to promptly respond. For example, it generates a warning such as "Fraudulent seller ID 12345 has listed a laptop that is listed as stolen goods" and sends a notification to the relevant online platform and relevant parties. Furthermore, the item listed as stolen goods is automatically removed from the online platform.

[1308] 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.

[1309] The present invention relates to a system that optimizes collaboration between staff and artificial intelligence (AI) to prevent offline and online security threats and strengthen security measures, including safety training. In particular, the present invention incorporates an emotion engine to recognize the user's emotional state and improve the efficiency and effectiveness of the entire system. Specific embodiments for implementing the present invention are described below.

[1310] 1. Real-time information provision

[1311] First, staff and security guards access the Security Assistant via their smartphones or dedicated devices. The Security Assistant resides on a central server, accepts queries sent by users, and provides appropriate security policies and training content. It also uses an emotion engine to recognize the user's current emotional state and detect stress levels, allowing the user to receive the most effective support.

[1312] Examples:

[1313] A user asks: "Can you tell me which areas have had the most thefts recently?"

[1314] The server analyzes the user's emotional state through an emotion engine and detects that the user is in a high stress state.

[1315] The server replies: "There has been an increase in thefts in the electronics section recently. Be especially wary of people using large bags. You seem stressed, so please keep an eye out for any suspicious activity."

[1316] 2. Improving the efficiency of security patrols

[1317] The server then provides real-time patrol information, analyzing data from surveillance cameras and sensors to calculate optimal patrol routes and frequencies, and notifying the guards' devices. Using an emotion engine, the server provides patrol instructions based on the guards' emotional state.

[1318] Examples:

[1319] The server analyzes the data and comes up with a plan: "The area with the highest risk of theft over the next 30 minutes is the electronics section."

[1320] The server analyzes the emotional state of the security guard through an emotion engine, and if stress is high, provides instructions such as "Take a short break before patrolling."

[1321] 3. Collaboration with online platforms

[1322] Furthermore, the server works with online platforms to detect and quickly respond to fraudulent online transactions. When a fraudulent transaction is discovered, the information is automatically reported to the police and the online platform. In addition, items reported as stolen are automatically removed from the relevant platform. An emotion engine provides appropriate notifications and follow-ups based on the user's emotional state.

[1323] Examples:

[1324] The server scans online platforms to detect fraudulent listings.

[1325] The server reports: "Fraudulent seller ID 12345 has listed a stolen laptop."

[1326] The server analyzes the user's emotional state through the emotion engine and sends a notification to provide reassurance: "The fraudulent listing removal process has been completed. Do you need further assistance?"

[1327] summary

[1328] The security system of the present invention effectively strengthens offline and online security measures by integrating an emotion engine. By incorporating emotion recognition technology, security measures can be further strengthened through real-time information provision, efficient security patrols, and integration with online platforms. By explaining the system embodiment through concrete examples, users can easily understand and put it into practice.

[1329] The processing flow will be explained below.

[1330] Real-time information provision

[1331] Step 1:

[1332] User

[1333] Access the security assistant from your smartphone or dedicated device.

[1334] Enter the question, "What are some signs of recent theft?"

[1335] Step 2:

[1336] server

[1337] Analyze the questions received from the user.

[1338] Based on the question, the database is searched for information on appropriate security policies and indicators of theft.

[1339] Step 3:

[1340] server

[1341] Based on the search results, answers are generated in a form that is easy for the user to understand.

[1342] Step 4:

[1343] server

[1344] Analyze the user's current emotional state via an emotion engine.

[1345] Adjust the tone and content of your responses depending on the user's emotional state.

[1346] Step 5:

[1347] server

[1348] Generates the answer, "Recently, there has been an increase in suspicious activity, especially in the electronics section. Please be especially wary of people carrying large bags and exhibiting unusual behavior. This person appears to be under a lot of stress, so please keep a close eye on them to see if there are any particularly suspicious people around."

[1349] Step 6:

[1350] server

[1351] The generated answer is sent to the user's device.

[1352] Step 7:

[1353] User

[1354] Check the answers displayed on your device and take the necessary action.

[1355] Improving the efficiency of security patrols

[1356] Step 1:

[1357] server

[1358] Collect data obtained from surveillance cameras and sensors currently installed in stores.

[1359] Also refer to historical data on patrols.

[1360] Step 2:

[1361] server

[1362] The collected data is analyzed to identify high-risk areas for the next patrol.

[1363] Step 3:

[1364] server

[1365] Calculate the optimal route and frequency.

[1366] Develop a plan that states, "The area with the highest risk of theft over the next 30 minutes is the electronics section."

[1367] Step 4:

[1368] server

[1369] Analyzing the emotional state of security guards through an emotion engine.

[1370] Adjust patrol instructions according to emotional state.

[1371] Step 5:

[1372] server

[1373] Provide instructions such as, "Take a short break and then make your rounds."

[1374] Step 6:

[1375] server

[1376] The patrol plan is sent to the security guard's device.

[1377] Step 7:

[1378] User

[1379] Check the patrol plan displayed on the device and carry out the patrol according to the instructions.

[1380] Collaboration with online platforms

[1381] Step 1:

[1382] server

[1383] Obtain product listings from online platforms and scan them.

[1384] Check against a list of stolen items to detect fraudulent listings.

[1385] Step 2:

[1386] server

[1387] Automatically generate information about detected fraudulent listings and prepare the data for reporting to the police and online platforms.

[1388] Step 3:

[1389] server

[1390] Create a report stating "Fake seller ID 12345 has listed a stolen laptop" and notify the relevant platform.

[1391] Step 4:

[1392] server

[1393] Update relevant stolen goods lists and automatically reflect them on the online platform.

[1394] Step 5:

[1395] Terminal (online platform)

[1396] We will promptly remove any fraudulent listings based on the removal request we receive.

[1397] Step 6:

[1398] server

[1399] Notify tracking users that the fraudulent listing has been removed.

[1400] Step 7:

[1401] server

[1402] It analyzes the user's emotional state through an emotion engine and sends notifications to alleviate anxiety.

[1403] Step 8:

[1404] User

[1405] We will review the notification and take further action as necessary.

[1406] Example 2

[1407] 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."

[1408] Today's security threats extend not only offline but also online, and simultaneous countermeasures are necessary. In particular, planned large-scale thefts and the resulting online resale activities cause significant damage to businesses and individuals. Furthermore, frontline staff and security guards often experience high levels of stress, and appropriate support tailored to their emotional state is required, but existing systems are unable to adequately address these challenges.

[1409] 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.

[1410] In this invention, the server includes means for optimizing collaboration between staff and artificial intelligence, means for providing real-time access to the security assistant via a smartphone or device, means for providing patrol information and suggesting optimal patrol routes and frequencies, means for linking with an online platform to detect and report fraudulent transactions, means for automatically deleting listings of items reported as stolen, and means for recognizing a user's emotional state using an emotion engine and providing appropriate support. This makes it possible to strengthen offline and online security measures in an integrated manner and provide appropriate support according to the emotional state of staff and security guards.

[1411] "Offline and online security threats" refers to physical intrusions and thefts that do not occur via the Internet, as well as cyber attacks and fraudulent transactions that occur via the Internet.

[1412] "Staff" refers to employees and guards engaged in security assistant and patrol duties.

[1413] "Artificial intelligence" refers to programs and systems that use technologies such as machine learning and natural language processing to imitate human intellectual tasks.

[1414] "Smartphones and devices" refers to portable electronic devices that can connect to the Internet and run applications.

[1415] "Security Assistant" refers to a software program or service that assists with security measures.

[1416] "Real-time access" refers to the sending and receiving of information occurring immediately.

[1417] "Patrol information" refers to information necessary for security guards to patrol, such as information regarding patrol routes and areas requiring caution.

[1418] "Optimal patrol routes and frequencies" refers to efficient patrol routes and their frequency planned to minimize security risks.

[1419] "Online platform" refers to an internet service for the purpose of e-commerce and communication.

[1420] "Unfair trading" refers to the buying, selling, or exchanging of fraudulently obtained goods or services.

[1421] "List of Items Reported as Stolen" means a list of items officially reported as stolen.

[1422] "Emotion engine" refers to an algorithm or program for analyzing a user's emotional state.

[1423] "User's emotional state" refers to the user's stress level or emotional state.

[1424] "Means for providing appropriate support" refers to a method for instantly providing necessary assistance or support based on the user's emotional state.

[1425] The present invention relates to a system that optimizes collaboration between staff and artificial intelligence to prevent offline and online security threats and strengthen security measures, including safety training. In particular, the present invention incorporates an emotion engine to recognize the user's emotional state and improve the efficiency and effectiveness of the entire system. Specific embodiments for implementing the present invention are described below.

[1426] 1. Real-time information provision

[1427] First, staff and security guards access the Security Assistant via their smartphones or dedicated devices. The Security Assistant resides on a central server, accepts queries sent by users, and provides appropriate security policies and training content. It also uses an emotion engine to recognize the user's current emotional state and detect stress levels, allowing the user to receive the most effective support.

[1428] Specific examples

[1429] A user asks: "Can you tell me which areas have had the most thefts recently?"

[1430] The server analyzes the user's emotional state through an emotion engine and detects that the user is in a high stress state.

[1431] The server replies: "There has been an increase in thefts in the electronics section recently. Be especially wary of people using large bags. You seem stressed, so please keep an eye out for any suspicious activity."

[1432] 2. Improving the efficiency of security patrols

[1433] The server then provides real-time patrol information, analyzing data from surveillance cameras and sensors to calculate optimal patrol routes and frequencies, and notifying the guards' devices. Using an emotion engine, the server provides patrol instructions based on the guards' emotional state.

[1434] Specific examples

[1435] The server analyzes the data and comes up with a plan: "The area with the highest risk of theft over the next 30 minutes is the electronics section."

[1436] The server analyzes the emotional state of the security guard through an emotion engine, and if stress is high, provides instructions such as "Take a short break before patrolling."

[1437] 3. Collaboration with online platforms

[1438] Furthermore, the server works with online platforms to detect and quickly respond to fraudulent online transactions. When a fraudulent transaction is discovered, the information is automatically reported to the police and the online platform. In addition, items reported as stolen are automatically removed from the relevant platform. An emotion engine provides appropriate notifications and follow-ups based on the user's emotional state.

[1439] Specific examples

[1440] The server scans online platforms to detect fraudulent listings.

[1441] The server reports: "Fraudulent seller ID 12345 has listed a stolen laptop."

[1442] The server analyzes the user's emotional state through the emotion engine and sends a notification to provide reassurance: "The fraudulent listing removal process has been completed. Do you need further assistance?"

[1443] Prompt Sentence Examples

[1444] User-supplied question: "What areas have seen a lot of thefts recently?"

[1445] Server prompt: "User wants to know where the latest thefts have been occurring. Please generate an answer that will alleviate anxiety and stress."

[1446] summary

[1447] This system integrates an emotion engine to enhance offline and online security measures. By providing real-time information, streamlining security patrols, and connecting with online platforms, emotion recognition technology further strengthens security measures. The system's implementation is explained through concrete examples, making it easy for users to understand and put into practice.

[1448] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1449] 1. Real-time information provision

[1450] Step 1:

[1451] User accesses Security Assistant

[1452] Input: A user launches the Security Assistant app using a smartphone or dedicated device.

[1453] What happens: A user enters a query in the app and presses submit.

[1454] Output: The query entered by the user is sent to the server.

[1455] Step 2:

[1456] The server accepts the query

[1457] Input: The query sent from the user device.

[1458] How it works: The server receives the query, parses it in text format, and searches the corresponding database.

[1459] Output: Parsed query data and associated security information.

[1460] Step 3:

[1461] The server uses an emotion engine to analyze the user's emotional state.

[1462] Input: Parsed query data.

[1463] Operation: The server launches the emotion engine to extract emotion patterns from the user's input text.

[1464] Output: User's emotional state data (e.g., how stressed they are).

[1465] Step 4:

[1466] The server provides appropriate security policies and training content.

[1467] Input: User emotional state data and associated security information.

[1468] How it works: The server extracts the latest security information from a database and generates an answer for the user.

[1469] Output: Specific security information and advice to provide to the user (e.g., "Recent hotspots for thefts").

[1470] 2. Improving the efficiency of security patrols

[1471] Step 1:

[1472] The server collects data from surveillance cameras and sensors.

[1473] Input: Real-time data from surveillance cameras and sensors.

[1474] How it works: The server collects the images and data captured by each surveillance camera and sensor and stores them in a central database.

[1475] Output: Collected monitoring data.

[1476] Step 2:

[1477] The server analyzes the data

[1478] Input: Collected monitoring data.

[1479] How it works: The server uses machine learning algorithms to analyze the data and detect anomalies.

[1480] Output: Data about high-risk areas (e.g. areas with high risk of theft).

[1481] Step 3:

[1482] The server notifies the guard's device of the optimal patrol route.

[1483] Input: Data about high-risk areas.

[1484] Operation: The server calculates the optimal patrol route and timing and sends it to the security guard's device.

[1485] Output: Patrol instructions for guards (e.g., "The next patrol area is the electronics section, head there now.").

[1486] Step 4:

[1487] The server uses an emotion engine to analyze the emotional state of the guard.

[1488] Input: Historical data and real-time feedback from security guards.

[1489] How it works: The server uses the emotion engine to analyze the stress level of the security guards.

[1490] Output: Instructions to the guard based on their emotional state (e.g., "Please take a short break before patrolling.").

[1491] 3. Collaboration with online platforms

[1492] Step 1:

[1493] The server scans the transaction data of the online platform.

[1494] Input: Transaction data from online platforms.

[1495] How it works: The server accesses the database through the API and periodically scans the retrieved data.

[1496] Output: Scanned transaction data.

[1497] Step 2:

[1498] The server detects fraudulent transactions

[1499] Input: Scanned transaction data.

[1500] How it works: The server checks against an existing list of stolen items to detect suspicious transactions.

[1501] Output: Detection results for fraudulent transactions (e.g., "Fraudulent seller ID 12345 listed a stolen laptop.").

[1502] Step 3:

[1503] The server automatically reports the incident to the police and the platform.

[1504] Input: Detection results for fraudulent transactions.

[1505] How it works: The server uses an automated reporting function to notify the police or platform administrators.

[1506] Output: Report message (e.g., "Stolen goods have been listed").

[1507] Step 4:

[1508] The server uses an emotion engine to send notifications based on the user's emotional state.

[1509] Input: Report result and user emotional state data.

[1510] How it works: The server uses the emotion engine to generate notifications to increase the user's sense of security.

[1511] Output: Notification message based on emotional state (e.g., "Your fraudulent listing has been removed. Do you need further assistance?").

[1512] (Application example 2)

[1513] 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."

[1514] The modern security environment faces increasing security threats both offline and online. Planned large-scale thefts and online resale of stolen goods are particularly problematic. Furthermore, security staff may operate under high stress, reducing their efficiency and effectiveness. In these situations, a system is needed to provide more advanced security measures and instructions while taking into account the emotional state of staff.

[1515] The identification processing 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 optimizing collaboration between staff and artificial intelligence, means for providing real-time access to the security assistant via a smartphone or device, means for providing patrol information and suggesting optimal patrol routes and frequencies, means for connecting with an online platform to detect and report fraudulent transactions, means for automatically deleting listings of items reported as stolen, means for recognizing a user's emotional state and detecting stress levels using an emotion engine, and means for providing appropriate support and instructions according to the user's emotional state. This effectively prevents offline and online security threats and improves the efficiency and effectiveness of staff.

[1516] "Measures to optimize collaboration between staff and artificial intelligence" refers to methods for enabling staff and artificial intelligence to work together effectively to strengthen security measures.

[1517] "Real-time access to a security assistant via smartphone or device" refers to a method that allows users to use their smartphone or other device to get instant security-related information and assistance.

[1518] "Means for providing patrol information and suggesting optimal patrol routes and frequencies" refers to a system that analyzes surveillance areas and data to suggest optimal patrol routes and frequencies to security guards.

[1519] "Means to work with online platforms to detect and report fraudulent transactions" refers to a system that works with online marketplaces and trading platforms to detect and report fraudulent activities and fraudulent transactions.

[1520] "Means for automatically removing listings of items reported as stolen" refers to a system that automatically removes items identified as stolen from the relevant online platform.

[1521] "Means for recognizing a user's emotional state and detecting stress levels using an emotion engine" refers to a system that uses emotion engine technology to detect a user's psychological state and stress level.

[1522] "Means for providing appropriate support and instructions according to the user's emotional state" refers to a system that takes into account the user's emotional state and provides optimal support and instructions accordingly.

[1523] The present invention provides a system for preventing offline and online security threats and improving staff efficiency and effectiveness, particularly by utilizing an emotion engine to recognize the user's emotional state and provide appropriate support and instructions to enhance security measures.

[1524] System Program

[1525] The server runs a program with the following functions: First, users (staff or security guards) can access the security assistant using a smartphone or head-mounted display. The system receives queries and emotional states from users in real time and uses an emotion engine to analyze them. The emotion engine detects the user's psychological state and stress level and provides optimal security policies and training content accordingly.

[1526] Hardware and software usage

[1527] The system uses the following hardware and software:

[1528] Hardware:

[1529] Smartphone

[1530] Head-mounted displays (e.g., Google Glass, Microsoft HoloLens)

[1531] software:

[1532] Emotion engine library (emotion_recognition)

[1533] Security policy provision library (security_ai)

[1534] The server receives information input by the user and uses an emotion engine to analyze the user's emotional state and detect their stress level. For example, if a user sends a query such as "Please tell me which areas have seen a lot of thefts recently," the server will analyze the user's emotions using the emotion engine and return an appropriate response if the user's stress level is high.

[1535] Furthermore, the server calculates patrol routes and frequency based on data from surveillance cameras and sensors, and notifies the user's device of this information. For example, it can provide information such as "The area with the highest risk of theft in the next 30 minutes is the electronics section," and depending on the user's stress level, it can instruct the user to "take a short break before patrolling."

[1536] Examples and prompts

[1537] As a concrete example, consider the case where a security guard on night patrol checks for new information. When the user inputs, "Have you had any problems in the electronics section recently?", the server uses an emotion engine to analyze the user's stress level. If the result shows that the stress level is high, the server displays a message saying, "You seem to be under a lot of stress. Please take a short break," followed by security information such as, "There has been an increase in thefts in the electronics section recently. Please be especially careful of people carrying large bags."

[1538] An example of a prompt sentence to input to the generative AI model is as follows:

[1539] Generate a notification message if "User stress level is high":

[1540] "Take a break and relax. Stress levels are high right now."

[1541] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1542] Step 1:

[1543] A user (staff or security guard) launches the security assistant application using a smartphone or head-mounted display.

[1544] Specific behavior:

[1545] The user operates the device to start the application.

[1546] The terminal sends the user's login information to the server for authentication.

[1547] Input: User login information

[1548] Output: Authentication success or failure response

[1549] Step 2:

[1550] The server receives queries and emotional states from users in real time.

[1551] Specific behavior:

[1552] The user enters a question or report through the device.

[1553] The terminal sends the input data to the server.

[1554] Input: User questions, reports, and speech data

[1555] Output: Input data transmitted to the server

[1556] Step 3:

[1557] The server uses an emotion engine to analyze the user's emotional state and detect stress levels.

[1558] Specific behavior:

[1559] The server calls the emotion engine (emotion_recognition library) and analyzes the input data.

[1560] Assess your emotional state and stress levels.

[1561] Input: User questions, reports, and speech data

[1562] Output: Emotional state and stress level data

[1563] Step 4:

[1564] The server provides optimal security policies and training content according to the user's emotional state.

[1565] Specific behavior:

[1566] The server retrieves the appropriate security policies and training content from a database.

[1567] If stress levels are high, it generates additional instructions including how to respond and alerts.

[1568] Input: Emotional state and stress level data

[1569] Output: Appropriate security policies, training content, and additional instruction data

[1570] Step 5:

[1571] The server calculates the optimal patrol route and frequency based on data from surveillance cameras and sensors, and notifies the user's device.

[1572] Specific behavior:

[1573] The server analyzes the real-time data received from the monitoring devices.

[1574] The optimal route is calculated and notified to the user's terminal.

[1575] Input: Data from surveillance cameras and sensors

[1576] Output: Information on optimal route and frequency

[1577] Step 6:

[1578] The server interacts with online platforms to detect and report fraudulent transactions.

[1579] Specific behavior:

[1580] A server retrieves and analyzes transaction data from the online platform.

[1581] If a fraudulent transaction is detected, it will be automatically reported and the item will be removed from the stolen goods list.

[1582] Input: Transaction data from online platforms

[1583] Output: Fraud detection and reporting data

[1584] Step 7:

[1585] The server sends the entire processing result to the user's device and provides appropriate support and instructions.

[1586] Specific behavior:

[1587] The server generates a message according to the emotional state and the necessary security information.

[1588] The generated information is sent to the user's terminal and displayed.

[1589] Input: Security information and support instruction data

[1590] Output: Messages and information that appear on the user's device

[1591] 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.

[1592] 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.

[1593] 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.

[1594] [Fourth embodiment]

[1595] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1596] 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.

[1597] 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).

[1598] 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.

[1599] 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.

[1600] 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).

[1601] 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.

[1602] 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.

[1603] 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.

[1604] 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.

[1605] 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.

[1606] 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.

[1607] 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."

[1608] The present invention is a system that optimizes collaboration between staff and artificial intelligence (AI) to prevent offline planned large-scale theft and online resale, and to strengthen security measures including safety training. Specific embodiments for implementing the present invention are described below.

[1609] 1. Real-time information provision

[1610] First, store staff and security guards access the Security Assistant via their smartphones or other devices. The Security Assistant resides on a central server, accepts queries sent by users, and provides appropriate security policies and training content in response. This allows users to quickly obtain the latest security information.

[1611] Examples:

[1612] A user asks: "Can you tell me which areas have had the most thefts recently?"

[1613] The server replies: "There has been an increase in thefts in the electronics section recently. Please be especially wary of people using large bags."

[1614] 2. Improving the efficiency of security patrols

[1615] Next, to improve the efficiency of patrols, the server provides real-time patrol information. Information obtained from surveillance cameras and sensors is analyzed, and the optimal patrol route and frequency is notified to the security guard's device. This allows the security guard to patrol effectively and minimize the risk of theft.

[1616] Examples:

[1617] The server calculates: "The area with the highest risk of theft in the next 30 minutes is the electronics section."

[1618] The server announces: "Next, please make your rounds in the food section in 20 minutes."

[1619] 3. Collaboration with online platforms

[1620] In addition, the server will work with online platforms to detect and quickly respond to fraudulent online transactions. If a fraudulent transaction is discovered, the information will be automatically reported to the police and the online platform. Any items reported as stolen will be automatically removed from the relevant platform.

[1621] Examples:

[1622] The server detects: "Fraudulent seller ID 12345 has listed a stolen laptop."

[1623] The server informs: "We have contacted the online platform and asked them to remove this item immediately."

[1624] summary

[1625] The security system of this invention comprehensively prevents offline and online theft through collaboration between staff and AI. Security measures are strengthened through real-time information provision, efficient security patrols, and collaboration with online platforms. By explaining the system's implementation with concrete examples, users can easily understand and put it into practice.

[1626] The processing flow will be explained below.

[1627] Real-time information provision

[1628] Step 1:

[1629] User

[1630] Access the security assistant from your smartphone or dedicated device.

[1631] Enter the question, "What are some signs of recent theft?"

[1632] Step 2:

[1633] server

[1634] Analyze the questions received from the user.

[1635] Based on the question, the database is searched for information on appropriate security policies and indicators of theft.

[1636] Step 3:

[1637] server

[1638] Based on the search results, answers are generated in a form that is easy for the user to understand.

[1639] Create an answer like, "Recently, there has been an increase in suspicious activity, especially in the electronics section. Please be especially careful of people with unusual behavior, especially those carrying large bags."

[1640] Step 4:

[1641] server

[1642] The generated answer is sent to the user's device.

[1643] Step 5:

[1644] User

[1645] Check the answers displayed on your device and take the necessary action.

[1646] Improving the efficiency of security patrols

[1647] Step 1:

[1648] server

[1649] Collect data obtained from surveillance cameras and sensors currently installed in stores.

[1650] Also refer to historical data on patrols.

[1651] Step 2:

[1652] server

[1653] The collected data is analyzed to identify high-risk areas for the next patrol.

[1654] Step 3:

[1655] server

[1656] Calculate the optimal route and frequency.

[1657] Develop a plan that states, "The area with the highest risk of theft over the next 30 minutes is the electronics section."

[1658] Step 4:

[1659] server

[1660] The patrol plan is sent to the security guard's device.

[1661] Step 5:

[1662] User

[1663] Check the patrol plan displayed on the device and carry out the patrol according to the instructions.

[1664] Collaboration with online platforms

[1665] Step 1:

[1666] server

[1667] Obtain product listings from online platforms and scan them.

[1668] Check against a list of stolen items to detect fraudulent listings.

[1669] Step 2:

[1670] server

[1671] It automatically generates information about detected fraudulent listings and prepares the data for reporting to the police and online platforms.

[1672] Step 3:

[1673] server

[1674] Create a report stating "Fake seller ID 12345 has listed a stolen laptop" and notify the relevant platform.

[1675] Step 4:

[1676] server

[1677] Update relevant stolen goods lists and automatically reflect them on the online platform.

[1678] Step 5:

[1679] Terminal (online platform)

[1680] We will promptly remove any fraudulent listings based on the removal request we receive.

[1681] Step 6:

[1682] server

[1683] Notify tracking users that the fraudulent listing has been removed.

[1684] Step 7:

[1685] User

[1686] We will review the notification and take further action as necessary.

[1687] Example 1

[1688] 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."

[1689] With the current increase in planned large-scale thefts offline and online resale, it is difficult to prevent these crimes using traditional security measures. Furthermore, while a swift and effective response from staff and security guards is required, limited information and resources may not be enough. There is also a lack of means to quickly detect fraudulent transactions online and take appropriate action. A new system is needed to solve these issues and improve the overall level of security.

[1690] 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.

[1691] In this invention, the server includes: means for a user to access the security assistant via a smartphone or terminal; means for the server to receive and analyze queries sent from the user; means for the server to search a database for and provide appropriate security information; means for the server to acquire and analyze data in real time from surveillance cameras and sensors; means for the server to identify high-risk areas and calculate optimal patrol routes; means for the server to notify security guard devices of the analysis results; means for the server to monitor transactions on online platforms and detect fraudulent transactions; and means for the server to report detected fraudulent transactions and remove products from related platforms. This enables quick access to the security assistant, provision of optimal security information, efficient patrols, and immediate detection and response to fraudulent transactions.

[1692] "User" refers to a person who accesses the Security Assistant and submits a query.

[1693] "Smartphone or device" refers to the electronic device used to access the security assistant.

[1694] "Security assistant" refers to a program that provides appropriate security information based on a user's query.

[1695] "Server" refers to the computer system that runs the Security Assistant and receives queries from users, analyzes them, retrieves information from a database, and provides the information.

[1696] "Query" refers to a question or request that a user sends to the Security Assistant.

[1697] "Analysis" refers to the processing of data by the server to understand the content of the query it receives and determine the appropriate response.

[1698] "Appropriate security information" refers to specific security measures and advice provided in response to a user's query.

[1699] "Database" refers to an information repository where security information and policies are stored.

[1700] "Surveillance camera" refers to a device that monitors a physical location and provides video data.

[1701] "Sensor" refers to a device that detects and provides data about a monitored environment.

[1702] "Acquiring data in real time" refers to instantly sending information provided by surveillance cameras and sensors to a server.

[1703] "High-risk areas" are locations where the likelihood of theft or fraud is expected to be high.

[1704] "Optimal patrol route" refers to a route planned to ensure the most effective patrol.

[1705] "Security guard" refers to a person who monitors and patrols security inside and outside a store.

[1706] "Online platform" refers to a website or application that conducts the trading of goods and services over the Internet.

[1707] "Transaction" refers to the act of buying and selling goods and services.

[1708] "Illicit trafficking" refers to the buying and selling of stolen or illegal goods.

[1709] "Relevant Platform" refers to the online platform on which the fraudulent transaction took place.

[1710] "Product removal" refers to the removal of a product that has been the subject of fraudulent trading on an online platform.

[1711] "Push Notification" refers to a real-time notification sent from a server to a user's device.

[1712] The present invention is a system designed to prevent offline planned large-scale theft and online resale, and to strengthen security measures, including safety training. This system allows users to access a security assistant in real time via their smartphones or terminals, and a server analyzes and provides a wide range of information. The following describes in detail specific embodiments of the invention.

[1713] User Device and Security Assistant Access Method

[1714] Users can access the Security Assistant via a dedicated application or browser on their smartphone or device (e.g., iOS or Android device), allowing them to quickly obtain appropriate security information.

[1715] Query reception and analysis system

[1716] When a user submits a query to the security assistant, the server receives the query, analyzes it using a natural language processing engine (e.g., NLTK or SpaCy), and searches a database for appropriate security information based on the query's content.

[1717] Providing security information

[1718] After the query is analyzed, the server retrieves relevant security information from a database (e.g., MySQL or PostgreSQL) and provides it to the user's device. This process allows users to receive the latest security information in real time.

[1719] Acquiring data from surveillance cameras and sensors

[1720] The server receives real-time information from the surveillance cameras and sensors installed in the store. Video data from the surveillance cameras is collected via the RTSP server, as well as data from the sensors (e.g., motion detection, volume level).

[1721] Data analysis and route calculation

[1722] The server analyzes the collected data using machine learning algorithms (e.g., TensorFlow or PyTorch) to identify areas with a high risk of theft, and then calculates the optimal patrol route using algorithms such as Dijkstra's algorithm.

[1723] Notifying security guards

[1724] The calculated patrol route information is sent to the security guard's smartphone or tablet via a push notification service (e.g., Firebase Cloud Messaging). Security guards who receive the notification can patrol the designated area efficiently.

[1725] Collaboration with online platforms

[1726] The server monitors transaction data from online platforms in real time via APIs and other means. If fraudulent transactions are detected, the server uses an anomaly detection algorithm to identify them. The server then reports the detected fraudulent transactions to the relevant platforms via APIs, and the relevant items are automatically deleted.

[1727] Examples of prompt statements

[1728] Below are some examples of prompt sentences that the security assistant can use to provide the user with appropriate information.

[1729] Prompt: "There have been reports of a high number of thefts in the electronics section of a store. Please generate a message to warn users."

[1730] In this way, the system can provide appropriate information in response to user queries and respond quickly and effectively to dynamically changing security risks.

[1731] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1732] Step 1: User accesses Security Assistant

[1733] Input: Smartphone or device

[1734] Output: Connection to Security Assistant

[1735] How it works: The user opens a browser or dedicated application on their smartphone or device, accesses a specific URL, and then enters their authentication information on the login screen that appears to access the security assistant.

[1736] Step 2: The server receives and parses the query

[1737] Input: User query

[1738] Output: Analysis results

[1739] What happens: When a user enters and submits a query, the server receives the HTTP request. The server uses a natural language processing engine (e.g., NLTK or SpaCy) to analyze the query. This analysis identifies the information or request the query is asking for.

[1740] Step 3: The server provides the appropriate security information

[1741] Input: Query parsing results

[1742] Output: Security information

[1743] Specific operation: Based on the analysis results, the server searches a database (e.g., MySQL or PostgreSQL) that stores security information. If relevant information is found, it retrieves the data and generates a message to respond to the user. This message is sent to the user's device and displayed in the browser or application.

[1744] Step 4: The server collects data from the surveillance cameras and sensors.

[1745] Input: Surveillance camera and sensor data

[1746] Output: Real-time environmental data

[1747] Specific operation: The server collects real-time data from the surveillance cameras and sensors installed in the store. Video data from the surveillance cameras is transmitted via the RTSP server, and data from the sensors (e.g., motion detection, volume level) is collected periodically or in real time.

[1748] Step 5: The server analyzes the data and calculates the optimal route.

[1749] Input: Real-time environmental data

[1750] Output: Optimal patrol routes and identification of high-risk areas

[1751] How it works: The server analyzes the acquired data using machine learning algorithms (e.g., TensorFlow or PyTorch) to identify areas with a high risk of theft. It then uses algorithms such as Dijkstra's algorithm to calculate patrol routes for security guards to patrol efficiently.

[1752] Step 6: The server notifies the security guard's device of the analysis results

[1753] Input: Information on optimal patrol routes and high-risk areas

[1754] Output: Notification to the guard's device

[1755] Specific operation: The server generates a notification for the guard based on the calculation results and sends it to the guard's smartphone or tablet using a push notification service (e.g., Firebase Cloud Messaging). The notification indicates the area and time that the guard should next patrol.

[1756] Step 7: The server monitors the online platform transactions.

[1757] Input: Transaction data from online platform

[1758] Output: Monitoring report

[1759] Specific operation: The server uses API to obtain transaction data from the online platform and monitors it in real time. The monitored data is periodically sent to the server and stored as needed.

[1760] Step 8: The server detects fraudulent transactions

[1761] Input: Transaction data

[1762] Output: Fraudulent transaction detection results

[1763] Specific operation: The server analyzes the acquired transaction data using an anomaly detection algorithm to identify fraudulent transactions. Detected fraudulent transactions are listed and the necessary information is extracted.

[1764] Step 9: Report any fraudulent transactions detected by the server and remove the items from the relevant platforms.

[1765] Input: Fraudulent transaction detection results

[1766] Output: Report and delete product

[1767] Specific operation: When the server detects a fraudulent transaction, it will contact the relevant platform via API and initiate the process of delisting the relevant product. If necessary, it will also report the matter to the police or authorized authorities. As a result, the product related to the fraudulent transaction will be promptly removed from the platform.

[1768] (Application example 1)

[1769] 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."

[1770] Traditional security systems lack sufficient coordination between staff and artificial intelligence, resulting in insufficient countermeasures against planned large-scale offline theft and online resale. Real-time patrol and security information is often delayed, leading to inefficient patrol planning. Furthermore, it is difficult to quickly detect and respond to fraudulent online transactions, and there is a lack of functionality to automatically delete listings of products reported as stolen, resulting in insufficient security measures.

[1771] 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.

[1772] In this invention, the server includes a means for allowing staff and security guards to access the security assistant via smartphones or devices to obtain security information in real time, a means for providing real-time patrol information and suggesting optimal patrol routes and frequencies, a means for analyzing information obtained from surveillance cameras and sensors to detect and immediately notify fraudulent online transactions, a means for obtaining security information about specific areas using a generative AI model, and a means for immediately responding when a fraudulent transaction is detected and automatically deleting the list of items reported as stolen. This optimizes collaboration between staff and artificial intelligence, effectively preventing theft both offline and online, and significantly strengthening security measures.

[1773] "Staff" refers to people who are responsible for operating and managing security systems, such as employees and security guards who work in physical stores.

[1774] "Artificial intelligence" refers to computer systems that mimic human intelligence and have capabilities such as learning, reasoning, and recognition.

[1775] A "smartphone" is a mobile phone that can access the Internet and run applications.

[1776] A "device" is an electronic device that can input, output, and process information, such as a smartphone or tablet.

[1777] The "Security Assistant" is a system that staff can access in real time and that provides security and patrol information.

[1778] "Patrol information" is information about security patrols within the store, including instructions on the optimal patrol route and frequency.

[1779] An "online platform" is a website or application that allows trading of goods and services over the Internet.

[1780] "Illicit trade" is the trading of goods or services that is not legal, such as the resale of stolen goods or fraud.

[1781] A "generative AI model" is an artificial intelligence model that learns patterns and features from data and automatically generates security information.

[1782] "Notification propagation processing" is the process of immediately notifying relevant systems and personnel when a fraudulent transaction is detected.

[1783] "Monitoring camera data" refers to video and image data acquired from monitoring cameras within a store.

[1784] This invention relates to a security system for preventing offline and online theft and strengthening safety training. The system aims to optimize collaboration between staff and artificial intelligence (AI) and provide real-time security information via smartphones and other devices.

[1785] 1. System Configuration

[1786] Hardware

[1787] The system primarily uses the following hardware:

[1788] Smartphones: Mobile devices used by staff and security guards.

[1789] Central Server: A computer system that provides security assistance and data analysis.

[1790] Surveillance camera: A camera that records video data within the store.

[1791] Sensors: Devices that collect movement and environmental data within the store.

[1792] software

[1793] The system uses the following software:

[1794] Security Assistant: An application that allows staff to obtain real-time security information.

[1795] Generative AI model: An AI system for generating security information about a specific area.

[1796] Database: Data storage to respond to queries to staff and provide appropriate security policies.

[1797] Online Transaction Monitoring System: A system that detects and immediately notifies you of fraudulent online transactions.

[1798] 2. Processing Flow

[1799] Real-time information provision

[1800] When staff or security guards access the security assistant via their smartphones, the server uses a generative AI model to provide the latest theft information and points of caution for each area.

[1801] Example: A staff member requests, "Can you tell me which areas have seen a lot of theft recently?" The server responds, "We've seen an increase in thefts in the electronics section recently. Please be especially wary of people using large bags."

[1802] Improving the efficiency of patrols

[1803] The server analyzes data obtained from surveillance cameras and sensors in real time and notifies the smartphone of the optimal patrol route and frequency.

[1804] Example: A server announces, "The area with the highest theft risk in the next 30 minutes is the electronics section," followed by instructions to "patrol the food section in 20 minutes."

[1805] Collaboration with online platforms

[1806] The server works with online platforms to detect fraudulent online transactions, immediately notifying relevant parties if a fraudulent transaction is discovered, and automatically removing listings of products reported as stolen.

[1807] Example: If the server detects that "Fraudulent seller ID 12345 has listed a stolen laptop," it will immediately notify the online platform that it has contacted and requested that the item be removed immediately.

[1808] 3. Use of generative AI models

[1809] Generative AI models learn patterns and features from data and automatically generate security information for specific areas.

[1810] Example prompt sentence:

[1811] "What security information has been released in the electronics section recently?"

[1812] "What areas are most at risk of theft?"

[1813] "Please tell me what kind of products the fraudulent seller ID 12345 is selling."

[1814] In this way, the present invention optimizes collaboration between staff and AI, making it possible to provide effective security measures in real time.

[1815] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1816] Step 1:

[1817] A user accesses the security assistant app using a smartphone. As input, the user sends a request such as, "Tell me in which areas thefts have been occurring frequently recently." The device then sends this request to the security server.

[1818] Step 2:

[1819] The security server analyzes the request and identifies the area for which information is being requested. The server uses a generative AI model to generate up-to-date security information for that area. It uses historical and current data related to the area as input and generates up-to-date security information as output.

[1820] Step 3:

[1821] The server sends the generated security information to the user's device, which then displays the received information to the user. Specifically, the device displays a message such as, "There has been an increase in thefts in the electronics section recently. Please be especially careful of people using large bags."

[1822] Step 4:

[1823] The server collects real-time data from surveillance cameras and sensors. As input, it receives video and movement data from the cameras and sensors, analyzes them internally, and generates important data for patrol planning as output.

[1824] Step 5:

[1825] The server analyzes the collected data to create a patrol plan. Based on this analysis, it calculates the optimal patrol route and frequency, and generates a patrol plan as output. For example, it may say, "The area with the highest risk of theft in the next 30 minutes is the electronics section."

[1826] Step 6:

[1827] The server then sends the generated patrol plan to the guard's smartphone. The device receives this information and notifies the guard of the optimal patrol route and frequency. For example, it displays instructions such as, "Next, patrol the food section in 20 minutes."

[1828] Step 7:

[1829] The server monitors the online platform to detect fraudulent transactions. As input, it periodically checks online transaction data and searches for fraudulent transaction patterns. As output, it generates detailed information about any fraudulent transactions that are found.

[1830] Step 8:

[1831] When a fraudulent transaction is detected, the server performs a notification propagation process to promptly respond. For example, it generates a warning such as "Fraudulent seller ID 12345 has listed a laptop that is listed as stolen goods" and sends a notification to the relevant online platform and relevant parties. Furthermore, the item listed as stolen goods is automatically removed from the online platform.

[1832] 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.

[1833] The present invention relates to a system that optimizes collaboration between staff and artificial intelligence (AI) to prevent offline and online security threats and strengthen security measures, including safety training. In particular, the present invention incorporates an emotion engine to recognize the user's emotional state and improve the efficiency and effectiveness of the entire system. Specific embodiments for implementing the present invention are described below.

[1834] 1. Real-time information provision

[1835] First, staff and security guards access the Security Assistant via their smartphones or dedicated devices. The Security Assistant resides on a central server, accepts queries sent by users, and provides appropriate security policies and training content. It also uses an emotion engine to recognize the user's current emotional state and detect stress levels, allowing the user to receive the most effective support.

[1836] Examples:

[1837] A user asks: "Can you tell me which areas have had the most thefts recently?"

[1838] The server analyzes the user's emotional state through an emotion engine and detects that the user is in a high stress state.

[1839] The server replies: "There has been an increase in thefts in the electronics section recently. Be especially wary of people using large bags. You seem stressed, so please keep an eye out for any suspicious activity."

[1840] 2. Improving the efficiency of security patrols

[1841] The server then provides real-time patrol information, analyzing data from surveillance cameras and sensors to calculate optimal patrol routes and frequencies, and notifying the guards' devices. Using an emotion engine, the server provides patrol instructions based on the guards' emotional state.

[1842] Examples:

[1843] The server analyzes the data and comes up with a plan: "The area with the highest risk of theft over the next 30 minutes is the electronics section."

[1844] The server analyzes the emotional state of the security guard through an emotion engine, and if stress is high, provides instructions such as "Take a short break before patrolling."

[1845] 3. Collaboration with online platforms

[1846] Furthermore, the server works with online platforms to detect and quickly respond to fraudulent online transactions. When a fraudulent transaction is discovered, the information is automatically reported to the police and the online platform. In addition, items reported as stolen are automatically removed from the relevant platform. An emotion engine provides appropriate notifications and follow-ups based on the user's emotional state.

[1847] Examples:

[1848] The server scans online platforms to detect fraudulent listings.

[1849] The server reports: "Fraudulent seller ID 12345 has listed a stolen laptop."

[1850] The server analyzes the user's emotional state through the emotion engine and sends a notification to provide reassurance: "The fraudulent listing removal process has been completed. Do you need further assistance?"

[1851] summary

[1852] The security system of the present invention effectively strengthens offline and online security measures by integrating an emotion engine. By incorporating emotion recognition technology, security measures can be further strengthened through real-time information provision, efficient security patrols, and integration with online platforms. By explaining the system embodiment through concrete examples, users can easily understand and put it into practice.

[1853] The processing flow will be explained below.

[1854] Real-time information provision

[1855] Step 1:

[1856] User

[1857] Access the security assistant from your smartphone or dedicated device.

[1858] Enter the question, "What are some signs of recent theft?"

[1859] Step 2:

[1860] server

[1861] Analyze the questions received from the user.

[1862] Based on the question, the database is searched for information on appropriate security policies and indicators of theft.

[1863] Step 3:

[1864] server

[1865] Based on the search results, answers are generated in a form that is easy for the user to understand.

[1866] Step 4:

[1867] server

[1868] Analyze the user's current emotional state via an emotion engine.

[1869] Adjust the tone and content of your responses depending on the user's emotional state.

[1870] Step 5:

[1871] server

[1872] Generates the answer, "Recently, there has been an increase in suspicious activity, especially in the electronics section. Please be especially wary of people carrying large bags and exhibiting unusual behavior. This person appears to be under a lot of stress, so please keep a close eye on them to see if there are any particularly suspicious people around."

[1873] Step 6:

[1874] server

[1875] The generated answer is sent to the user's device.

[1876] Step 7:

[1877] User

[1878] Check the answers displayed on your device and take the necessary action.

[1879] Improving the efficiency of security patrols

[1880] Step 1:

[1881] server

[1882] Collect data obtained from surveillance cameras and sensors currently installed in stores.

[1883] Also refer to historical data on patrols.

[1884] Step 2:

[1885] server

[1886] The collected data is analyzed to identify high-risk areas for the next patrol.

[1887] Step 3:

[1888] server

[1889] Calculate the optimal route and frequency.

[1890] Develop a plan that states, "The area with the highest risk of theft over the next 30 minutes is the electronics section."

[1891] Step 4:

[1892] server

[1893] Analyzing the emotional state of security guards through an emotion engine.

[1894] Adjust patrol instructions according to emotional state.

[1895] Step 5:

[1896] server

[1897] Provide instructions such as, "Take a short break and then make your rounds."

[1898] Step 6:

[1899] server

[1900] The patrol plan is sent to the security guard's device.

[1901] Step 7:

[1902] User

[1903] Check the patrol plan displayed on the device and carry out the patrol according to the instructions.

[1904] Collaboration with online platforms

[1905] Step 1:

[1906] server

[1907] Obtain product listings from online platforms and scan them.

[1908] Check against a list of stolen items to detect fraudulent listings.

[1909] Step 2:

[1910] server

[1911] Automatically generate information about detected fraudulent listings and prepare the data for reporting to the police and online platforms.

[1912] Step 3:

[1913] server

[1914] Create a report stating "Fake seller ID 12345 has listed a stolen laptop" and notify the relevant platform.

[1915] Step 4:

[1916] server

[1917] Update relevant stolen goods lists and automatically reflect them on the online platform.

[1918] Step 5:

[1919] Terminal (online platform)

[1920] We will promptly remove any fraudulent listings based on the removal request we receive.

[1921] Step 6:

[1922] server

[1923] Notify tracking users that the fraudulent listing has been removed.

[1924] Step 7:

[1925] server

[1926] It analyzes the user's emotional state through an emotion engine and sends notifications to alleviate anxiety.

[1927] Step 8:

[1928] User

[1929] We will review the notification and take further action as necessary.

[1930] Example 2

[1931] 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 robot 414 will be referred to as a "terminal."

[1932] Today's security threats extend not only offline but also online, and simultaneous countermeasures are necessary. In particular, planned large-scale thefts and the resulting online resale activities cause significant damage to businesses and individuals. Furthermore, frontline staff and security guards often experience high levels of stress, and appropriate support tailored to their emotional state is required, but existing systems are unable to adequately address these challenges.

[1933] 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.

[1934] In this invention, the server includes means for optimizing collaboration between staff and artificial intelligence, means for providing real-time access to the security assistant via a smartphone or device, means for providing patrol information and suggesting optimal patrol routes and frequencies, means for linking with an online platform to detect and report fraudulent transactions, means for automatically deleting listings of items reported as stolen, and means for recognizing a user's emotional state using an emotion engine and providing appropriate support. This makes it possible to strengthen offline and online security measures in an integrated manner and provide appropriate support according to the emotional state of staff and security guards.

[1935] "Offline and online security threats" refers to physical intrusions and thefts that do not occur via the Internet, as well as cyber attacks and fraudulent transactions that occur via the Internet.

[1936] "Staff" refers to employees and guards engaged in security assistant and patrol duties.

[1937] "Artificial intelligence" refers to programs and systems that use technologies such as machine learning and natural language processing to imitate human intellectual tasks.

[1938] "Smartphones and devices" refers to portable electronic devices that can connect to the Internet and run applications.

[1939] "Security Assistant" refers to a software program or service that assists with security measures.

[1940] "Real-time access" refers to the sending and receiving of information occurring immediately.

[1941] "Patrol information" refers to information necessary for security guards to patrol, such as information regarding patrol routes and areas requiring caution.

[1942] "Optimal patrol routes and frequencies" refers to efficient patrol routes and their frequency planned to minimize security risks.

[1943] "Online platform" refers to an internet service for the purpose of e-commerce and communication.

[1944] "Unfair trading" refers to the buying, selling, or exchanging of fraudulently obtained goods or services.

[1945] "List of Items Reported as Stolen" means a list of items officially reported as stolen.

[1946] "Emotion engine" refers to an algorithm or program for analyzing a user's emotional state.

[1947] "User's emotional state" refers to the user's stress level or emotional state.

[1948] "Means for providing appropriate support" refers to a method for instantly providing necessary assistance or support based on the user's emotional state.

[1949] The present invention relates to a system that optimizes collaboration between staff and artificial intelligence to prevent offline and online security threats and strengthen security measures, including safety training. In particular, the present invention incorporates an emotion engine to recognize the user's emotional state and improve the efficiency and effectiveness of the entire system. Specific embodiments for implementing the present invention are described below.

[1950] 1. Real-time information provision

[1951] First, staff and security guards access the Security Assistant via their smartphones or dedicated devices. The Security Assistant resides on a central server, accepts queries sent by users, and provides appropriate security policies and training content. It also uses an emotion engine to recognize the user's current emotional state and detect stress levels, allowing the user to receive the most effective support.

[1952] Specific examples

[1953] A user asks: "Can you tell me which areas have had the most thefts recently?"

[1954] The server analyzes the user's emotional state through an emotion engine and detects that the user is in a high stress state.

[1955] The server replies: "There has been an increase in thefts in the electronics section recently. Be especially wary of people using large bags. You seem stressed, so please keep an eye out for any suspicious activity."

[1956] 2. Improving the efficiency of security patrols

[1957] The server then provides real-time patrol information, analyzing data from surveillance cameras and sensors to calculate optimal patrol routes and frequencies, and notifying the guards' devices. Using an emotion engine, the server provides patrol instructions based on the guards' emotional state.

[1958] Specific examples

[1959] The server analyzes the data and comes up with a plan: "The area with the highest risk of theft over the next 30 minutes is the electronics section."

[1960] The server analyzes the emotional state of the security guard through an emotion engine, and if stress is high, provides instructions such as "Take a short break before patrolling."

[1961] 3. Collaboration with online platforms

[1962] Furthermore, the server works with online platforms to detect and quickly respond to fraudulent online transactions. When a fraudulent transaction is discovered, the information is automatically reported to the police and the online platform. In addition, items reported as stolen are automatically removed from the relevant platform. An emotion engine provides appropriate notifications and follow-ups based on the user's emotional state.

[1963] Specific examples

[1964] The server scans online platforms to detect fraudulent listings.

[1965] The server reports: "Fraudulent seller ID 12345 has listed a stolen laptop."

[1966] The server analyzes the user's emotional state through the emotion engine and sends a notification to provide reassurance: "The fraudulent listing removal process has been completed. Do you need further assistance?"

[1967] Prompt Sentence Examples

[1968] User-supplied question: "What areas have seen a lot of thefts recently?"

[1969] Server prompt: "User wants to know where the latest thefts have been occurring. Please generate an answer that will alleviate anxiety and stress."

[1970] summary

[1971] This system integrates an emotion engine to enhance offline and online security measures. By providing real-time information, streamlining security patrols, and connecting with online platforms, emotion recognition technology further strengthens security measures. The system's implementation is explained through concrete examples, making it easy for users to understand and put into practice.

[1972] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1973] 1. Real-time information provision

[1974] Step 1:

[1975] User accesses Security Assistant

[1976] Input: A user launches the Security Assistant app using a smartphone or dedicated device.

[1977] What happens: A user enters a query in the app and presses submit.

[1978] Output: The query entered by the user is sent to the server.

[1979] Step 2:

[1980] The server accepts the query

[1981] Input: The query sent from the user device.

[1982] How it works: The server receives the query, parses it in text format, and searches the corresponding database.

[1983] Output: Parsed query data and associated security information.

[1984] Step 3:

[1985] The server uses an emotion engine to analyze the user's emotional state.

[1986] Input: Parsed query data.

[1987] Operation: The server launches the emotion engine to extract emotion patterns from the user's input text.

[1988] Output: User's emotional state data (e.g., how stressed they are).

[1989] Step 4:

[1990] The server provides appropriate security policies and training content.

[1991] Input: User emotional state data and associated security information.

[1992] How it works: The server extracts the latest security information from a database and generates an answer for the user.

[1993] Output: Specific security information and advice to provide to the user (e.g., "Recent hotspots for thefts").

[1994] 2. Improving the efficiency of security patrols

[1995] Step 1:

[1996] The server collects data from surveillance cameras and sensors.

[1997] Input: Real-time data from surveillance cameras and sensors.

[1998] How it works: The server collects the images and data captured by each surveillance camera and sensor and stores them in a central database.

[1999] Output: Collected monitoring data.

[2000] Step 2:

[2001] The server analyzes the data

[2002] Input: Collected monitoring data.

[2003] How it works: The server uses machine learning algorithms to analyze the data and detect anomalies.

[2004] Output: Data about high-risk areas (e.g. areas with high risk of theft).

[2005] Step 3:

[2006] The server notifies the guard's device of the optimal patrol route.

[2007] Input: Data about high-risk areas.

[2008] Operation: The server calculates the optimal patrol route and timing and sends it to the security guard's device.

[2009] Output: Patrol instructions for guards (e.g., "The next patrol area is the electronics section, head there now.").

[2010] Step 4:

[2011] The server uses an emotion engine to analyze the emotional state of the guard.

[2012] Input: Historical data and real-time feedback from security guards.

[2013] How it works: The server uses the emotion engine to analyze the stress level of the security guards.

[2014] Output: Instructions to the guard based on their emotional state (e.g., "Please take a short break before patrolling.").

[2015] 3. Collaboration with online platforms

[2016] Step 1:

[2017] The server scans the transaction data of the online platform.

[2018] Input: Transaction data from online platforms.

[2019] How it works: The server accesses the database through the API and periodically scans the retrieved data.

[2020] Output: Scanned transaction data.

[2021] Step 2:

[2022] The server detects fraudulent transactions

[2023] Input: Scanned transaction data.

[2024] How it works: The server checks against an existing list of stolen items to detect suspicious transactions.

[2025] Output: Detection results for fraudulent transactions (e.g., "Fraudulent seller ID 12345 listed a stolen laptop.").

[2026] Step 3:

[2027] The server automatically reports the incident to the police and the platform.

[2028] Input: Detection results for fraudulent transactions.

[2029] How it works: The server uses an automated reporting function to notify the police or platform administrators.

[2030] Output: Report message (e.g., "Stolen goods have been listed").

[2031] Step 4:

[2032] The server uses an emotion engine to send notifications based on the user's emotional state.

[2033] Input: Report result and user emotional state data.

[2034] How it works: The server uses the emotion engine to generate notifications to increase the user's sense of security.

[2035] Output: Notification message based on emotional state (e.g., "Your fraudulent listing has been removed. Do you need further assistance?").

[2036] (Application example 2)

[2037] 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 robot 414 will be referred to as a "terminal."

[2038] The modern security environment faces increasing security threats both offline and online. Planned large-scale thefts and online resale of stolen goods are particularly problematic. Furthermore, security staff may operate under high stress, reducing their efficiency and effectiveness. In these situations, a system is needed to provide more advanced security measures and instructions while taking into account the emotional state of staff.

[2039] The identification processing 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 optimizing collaboration between staff and artificial intelligence, means for providing real-time access to the security assistant via a smartphone or device, means for providing patrol information and suggesting optimal patrol routes and frequencies, means for connecting with an online platform to detect and report fraudulent transactions, means for automatically deleting listings of items reported as stolen, means for recognizing a user's emotional state and detecting stress levels using an emotion engine, and means for providing appropriate support and instructions according to the user's emotional state. This effectively prevents offline and online security threats and improves the efficiency and effectiveness of staff.

[2040] "Measures to optimize collaboration between staff and artificial intelligence" refers to methods for enabling staff and artificial intelligence to work together effectively to strengthen security measures.

[2041] "Real-time access to a security assistant via smartphone or device" refers to a method that allows users to use their smartphone or other device to get instant security-related information and assistance.

[2042] "Means for providing patrol information and suggesting optimal patrol routes and frequencies" refers to a system that analyzes surveillance areas and data to suggest optimal patrol routes and frequencies to security guards.

[2043] "Means to work with online platforms to detect and report fraudulent transactions" refers to a system that works with online marketplaces and trading platforms to detect and report fraudulent activities and fraudulent transactions.

[2044] "Means for automatically removing listings of items reported as stolen" refers to a system that automatically removes items identified as stolen from the relevant online platform.

[2045] "Means for recognizing a user's emotional state and detecting stress levels using an emotion engine" refers to a system that uses emotion engine technology to detect a user's psychological state and stress level.

[2046] "Means for providing appropriate support and instructions according to the user's emotional state" refers to a system that takes into account the user's emotional state and provides optimal support and instructions accordingly.

[2047] The present invention provides a system for preventing offline and online security threats and improving staff efficiency and effectiveness, particularly by utilizing an emotion engine to recognize the user's emotional state and provide appropriate support and instructions to enhance security measures.

[2048] System Program

[2049] The server runs a program with the following functions: First, users (staff or security guards) can access the security assistant using a smartphone or head-mounted display. The system receives queries and emotional states from users in real time and uses an emotion engine to analyze them. The emotion engine detects the user's psychological state and stress level and provides optimal security policies and training content accordingly.

[2050] Hardware and software usage

[2051] The system uses the following hardware and software:

[2052] Hardware:

[2053] Smartphone

[2054] Head-mounted displays (e.g., Google Glass, Microsoft HoloLens)

[2055] software:

[2056] Emotion engine library (emotion_recognition)

[2057] Security policy provision library (security_ai)

[2058] The server receives information input by the user and uses an emotion engine to analyze the user's emotional state and detect their stress level. For example, if a user sends a query such as "Please tell me which areas have seen a lot of thefts recently," the server will analyze the user's emotions using the emotion engine and return an appropriate response if the user's stress level is high.

[2059] Furthermore, the server calculates patrol routes and frequency based on data from surveillance cameras and sensors, and notifies the user's device of this information. For example, it can provide information such as "The area with the highest risk of theft in the next 30 minutes is the electronics section," and depending on the user's stress level, it can instruct the user to "take a short break before patrolling."

[2060] Examples and prompts

[2061] As a concrete example, consider the case where a security guard on night patrol checks for new information. When the user inputs, "Have you had any problems in the electronics section recently?", the server uses an emotion engine to analyze the user's stress level. If the result shows that the stress level is high, the server displays a message saying, "You seem to be under a lot of stress. Please take a short break," followed by security information such as, "There has been an increase in thefts in the electronics section recently. Please be especially careful of people carrying large bags."

[2062] An example of a prompt sentence to input to the generative AI model is as follows:

[2063] Generate a notification message if "User stress level is high":

[2064] "Take a break and relax. Stress levels are high right now."

[2065] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2066] Step 1:

[2067] A user (staff or security guard) launches the security assistant application using a smartphone or head-mounted display.

[2068] Specific behavior:

[2069] The user operates the device to start the application.

[2070] The terminal sends the user's login information to the server for authentication.

[2071] Input: User login information

[2072] Output: Authentication success or failure response

[2073] Step 2:

[2074] The server receives queries and emotional states from users in real time.

[2075] Specific behavior:

[2076] The user enters a question or report through the device.

[2077] The terminal sends the input data to the server.

[2078] Input: User questions, reports, and speech data

[2079] Output: Input data transmitted to the server

[2080] Step 3:

[2081] The server uses an emotion engine to analyze the user's emotional state and detect stress levels.

[2082] Specific behavior:

[2083] The server calls the emotion engine (emotion_recognition library) and analyzes the input data.

[2084] Assess your emotional state and stress levels.

[2085] Input: User questions, reports, and speech data

[2086] Output: Emotional state and stress level data

[2087] Step 4:

[2088] The server provides optimal security policies and training content according to the user's emotional state.

[2089] Specific behavior:

[2090] The server retrieves the appropriate security policies and training content from a database.

[2091] If stress levels are high, it generates additional instructions including how to respond and alerts.

[2092] Input: Emotional state and stress level data

[2093] Output: Appropriate security policies, training content, and additional instruction data

[2094] Step 5:

[2095] The server calculates the optimal patrol route and frequency based on data from surveillance cameras and sensors, and notifies the user's device.

[2096] Specific behavior:

[2097] The server analyzes the real-time data received from the monitoring devices.

[2098] The optimal route is calculated and notified to the user's terminal.

[2099] Input: Data from surveillance cameras and sensors

[2100] Output: Information on optimal route and frequency

[2101] Step 6:

[2102] The server interacts with online platforms to detect and report fraudulent transactions.

[2103] Specific behavior:

[2104] A server retrieves and analyzes transaction data from the online platform.

[2105] If a fraudulent transaction is detected, it will be automatically reported and the item will be removed from the stolen goods list.

[2106] Input: Transaction data from online platforms

[2107] Output: Fraud detection and reporting data

[2108] Step 7:

[2109] The server sends the entire processing result to the user's device and provides appropriate support and instructions.

[2110] Specific behavior:

[2111] The server generates a message according to the emotional state and the necessary security information.

[2112] The generated information is sent to the user's terminal and displayed.

[2113] Input: Security information and support instruction data

[2114] Output: Messages and information that appear on the user's device

[2115] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2116] 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.

[2117] 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 robot 414.

[2118] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2119] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2120] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2121] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2122] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2123] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2124] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2125] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2126] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2127] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2128] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[2129] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2130] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2131] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2132] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2133] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2134] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2135] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2136] The following is further disclosed regarding the above embodiment.

[2137] (Claim 1)

[2138] To prevent planned large-scale theft offline and resale online, and to strengthen security measures, including safety training,

[2139] Measures to optimize collaboration between staff and artificial intelligence,

[2140] Real-time access to a security assistant via smartphone or device;

[2141] A means of providing patrol information and suggesting optimal patrol routes and frequencies;

[2142] Measures to work with online platforms to detect and report fraudulent transactions;

[2143] A means to automatically remove listings of items reported as stolen; and

[2144] A system including:

[2145] (Claim 2)

[2146] 10. The system of claim 1, including a database for responding to queries from staff and security personnel and providing appropriate security policies.

[2147] (Claim 3)

[2148] 10. The system of claim 1, further comprising means for creating a patrol plan and notifying a security guard based on information obtained from the surveillance cameras and sensors.

[2149] "Example 1"

[2150] (Claim 1)

[2151] A means for users to access the security assistant via their smartphone or device;

[2152] a means for the server to receive and analyze queries sent by users;

[2153] a means by which the server retrieves and provides appropriate security information from a database;

[2154] A means for the server to collect and analyze data from surveillance cameras and sensors in real time,

[2155] A means for the server to identify high-risk areas and calculate optimal patrol routes;

[2156] A means for the server to notify the security guard's device of the analysis results;

[2157] a means for the server to monitor transactions on the online platform and detect fraudulent transactions;

[2158] A means for the server to report detected fraudulent transactions and remove the products from the relevant platform;

[2159] A system including:

[2160] (Claim 2)

[2161] 10. The system of claim 1, including a database for responding to queries from staff and security personnel and providing appropriate security policies.

[2162] (Claim 3)

[2163] 10. The system of claim 1, further comprising means for creating a patrol plan and notifying a security guard based on information obtained from the surveillance cameras and sensors.

[2164] "Application Example 1"

[2165] (Claim 1)

[2166] To prevent planned large-scale theft offline and resale online, and to strengthen security measures, including safety training,

[2167] Measures to optimize collaboration between staff and artificial intelligence,

[2168] Real-time access to a security assistant via smartphone or device;

[2169] A means of providing real-time patrol information and suggesting optimal patrol routes and frequencies;

[2170] Measures to work with online platforms to detect and report fraudulent transactions;

[2171] A means to automatically remove listings of items reported as stolen; and

[2172] A means for staff and security guards to use generative AI models to obtain security information about specific areas;

[2173] a means for analyzing real-time surveillance camera data to efficiently plan patrols;

[2174] A means for immediately notifying and processing fraudulent online transactions when they are detected;

[2175] A system including:

[2176] (Claim 2)

[2177] 10. The system of claim 1, including a database for responding to queries from staff and security personnel and providing appropriate security policies.

[2178] (Claim 3)

[2179] 2. The system according to claim 1, further comprising means for creating a patrol plan and notifying security guards based on information obtained from the surveillance cameras and sensors.

[2180] "Example 2: Combining Emotion Engines"

[2181] (Claim 1)

[2182] To prevent offline and online security threats and strengthen security measures, including safety training,

[2183] Measures to optimize collaboration between staff and artificial intelligence,

[2184] Real-time access to a security assistant via smartphone or device;

[2185] A means of providing patrol information and suggesting optimal patrol routes and frequencies;

[2186] Measures to work with online platforms to detect and report fraudulent transactions;

[2187] A means to automatically remove listings of items reported as stolen; and

[2188] a means for recognizing the emotional state of the user using an emotion engine and providing appropriate support;

[2189] A system including:

[2190] (Claim 2)

[2191] 10. The system of claim 1, including a database for responding to queries from staff and security personnel and providing appropriate security policies.

[2192] (Claim 3)

[2193] 10. The system of claim 1, further comprising means for creating a patrol plan and notifying a security guard based on information obtained from the surveillance cameras and sensors.

[2194] "Application example 2 when combining emotion engines"

[2195] (Claim 1)

[2196] To prevent planned large-scale theft offline and resale online, and to strengthen security measures, including safety training,

[2197] Measures to optimize collaboration between staff and artificial intelligence,

[2198] Real-time access to a security assistant via smartphone or device;

[2199] A means of providing patrol information and suggesting optimal patrol routes and frequencies;

[2200] Measures to work with online platforms to detect and report fraudulent transactions;

[2201] A means to automatically remove listings of items reported as stolen; and

[2202] means for recognizing a user's emotional state using an emotion engine to detect stress levels;

[2203] A system including means for providing appropriate support and instruction depending on the user's emotional state.

[2204] (Claim 2)

[2205] 10. The system of claim 1, including a database for responding to queries from staff and security personnel and providing appropriate security policies.

[2206] (Claim 3)

[2207] 10. The system of claim 1, further comprising means for creating a patrol plan and notifying a security guard based on information obtained from the surveillance cameras and sensors. [Explanation of symbols]

[2208] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. To prevent planned large-scale theft offline and resale online, and to strengthen security measures, including safety training, Measures to optimize collaboration between staff and artificial intelligence, Real-time access to a security assistant via smartphone or device; A means of providing patrol information and suggesting optimal patrol routes and frequencies; Measures to work with online platforms to detect and report fraudulent transactions; A means to automatically remove listings of items reported as stolen; and A system including:

2. 10. The system of claim 1, further comprising a database for responding to queries from staff and security personnel and providing appropriate security policies.

3. The system according to claim 1, further comprising means for creating a patrol plan and notifying a security guard based on information obtained from the surveillance camera and sensors.

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