system

Abstract

Provide a system. 【Solution means】 In an information processing device, means for monitoring information including a specific instruction word and collecting this information; means for transmitting the preprocessed information to an arithmetic unit via a communication network; means for receiving the transmitted information and analyzing the risk by a calculation model; means for generating a warning signal based on the analysis result and notifying the information processing device; In an information processing device, means for displaying the received warning signal through a usage interface; means for automatically detecting links in the communication received by the user and evaluating the safety; means for displaying a warning regarding a link for which the risk has been determined; A system including the above.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern times, the risk of crimes on SNS and Internet browsers, especially among the younger generation, is increasing. In such an environment, the risk of being involved in illegal acts such as part-time jobs in the dark exists daily. However, many users do not have sufficient IT literacy to judge the reliability of information, and thus there is a problem that it is difficult to defend against crimes. Therefore, there is a need for a system that can quickly and effectively detect dangerous information on the Internet and prevent it in advance, especially targeting young people.

Means for Solving the Problems

[0005] This invention provides a system that monitors data on social media and the web from an information terminal, automatically collects information containing specific keywords, and analyzes user activity in real time. Preprocessed data is securely transmitted to a server, where the server determines the risk level using natural language processing and machine learning algorithms. Based on this analysis, information deemed to be high-risk is notified to the information terminal, prompting the user to take appropriate action. Furthermore, the server stores the risk information in a database and shares it with other organizations to provide broader and more integrated preventative measures.

[0006] An "information terminal" is a device, such as a computer or smartphone, that a user directly operates and which can collect data and receive warnings.

[0007] A "keyword" is a word or phrase used to identify specific information and functions as a trigger in detecting dangerous information.

[0008] "Data" refers to the content of text and messages obtained from social media and browsers on the internet, and is the information that is the subject of analysis.

[0009] A "server" is a computer system that provides computing resources to receive and analyze data over a network, and processes data analysis and generates warnings.

[0010] An "AI model" is an algorithm or statistical model designed to analyze patterns and characteristics of data, and it is a method of determining risk using natural language processing and machine learning techniques.

[0011] A "warning signal" is an alert that is sent to an information terminal based on the analysis results, and it is information intended to inform the user of fraud or danger.

[0012] A "user interface" is a screen display or control panel on an information terminal that enables interaction between the user and the computer, and is used to present warnings and other information.

[0013] A "database" is a collection of data that stores information in an organized structure, enabling efficient searching and management, and is used for storing and managing shared information about potential dangers.

[0014] "Natural language processing" is a computer technology aimed at understanding, generating, and responding to human language, and is a technique used in text analysis.

[0015] A "machine learning algorithm" is a computational method for recognizing patterns from data and making predictions or classifications, and is one of the main constituent technologies of AI models. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] Shows an emotion map where multiple emotions are mapped. [Figure 10] Shows an emotion map where multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Modes for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the language used in the following description will be explained.

[0019] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0020] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] The system of the present invention operates in a network environment including information terminals, servers, and databases. The information terminal used by the user collects data in real time via SNS or web browsers and filters it using specific keywords as triggers. The data obtained through this filtering process is converted to a standard data format through preprocessing and transmitted to the server using a secure protocol.

[0038] The server performs analysis using an AI model based on the received data. This AI model is built using natural language processing and machine learning algorithms to determine the risks within the data. Based on the analysis results, a warning signal is generated for information deemed to be high risk and sent to the information terminal. The warning is accompanied by an appropriate message and information on a contact point to enable the user to take immediate action.

[0039] On the user's information terminal, an alert message is displayed through the user interface based on the received warning signal. This allows the user to recognize potential dangers and take necessary actions. Through this series of processes, the system helps prevent crimes committed by users while using the internet.

[0040] As a concrete example, when a user sees a post on social media containing text such as "easy high-paying job" or "secret part-time job," the information is filtered and analyzed by AI. If it is determined to be high-risk, a warning message such as "This job posting is dangerous" will be displayed on the device, and a link to access more specific precautions and additional information will be provided. Through this process, users can effectively avoid potential online dangers.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The device monitors the user's activity on social media and in their browser, and detects content containing specific keywords. The detected data is filtered and then recorded for collection.

[0044] Step 2:

[0045] The device preprocesses the data collected through filtering and converts it into a data format (e.g., JSON) that can be analyzed by an AI model. The converted data is then sent to the server via a secure connection.

[0046] Step 3:

[0047] The server places the received data into an analysis queue and runs the AI ​​model. The AI ​​model uses natural language processing and machine learning algorithms to analyze patterns in the data and determine the potential for danger.

[0048] Step 4:

[0049] The server calculates a risk score based on the analysis results and generates a warning signal if the threshold is exceeded. The warning includes specific risk information, as well as information on potential countermeasures and contact points for consultation.

[0050] Step 5:

[0051] The terminal receives a warning signal from the server. Based on this warning signal, the terminal's user interface displays an alert to the user and, if necessary, provides additional safety information and links.

[0052] Step 6:

[0053] Users can check alerts displayed on their devices and, if necessary, decide on their next course of action based on the information provided. This promotes awareness of crime risks and preventative actions.

[0054] (Example 1)

[0055] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0056] In modern internet use, users have access to a wide variety of information, but it is especially important to prevent exposure to dangerous information found on social media and websites. However, it is extremely difficult for users to manually monitor all information and assess its risks, thus increasing the need for systems that efficiently and automatically identify and warn about dangerous information.

[0057] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0058] In this invention, the server includes means for monitoring and acquiring information containing specified words or phrases, means for transmitting the pre-processed information to a processing unit via a communication network, and means for evaluating the risk using a generative model. This makes it possible to automatically identify dangerous information from information accessed by users on the internet and issue warnings immediately.

[0059] An "information processing device" is a device used by a user that has the ability to monitor and acquire information containing specified words or phrases.

[0060] "Specified terms" refer to keywords or phrases used to identify specific information as a trigger.

[0061] "Communication network" refers to the infrastructure of the internet and other networks used to send and receive information.

[0062] A "processing device" refers to a computer server or a component that forms part of a system used to analyze received information and perform risk assessments.

[0063] "Preprocessing" refers to the initial data processing process to convert raw information data into a format suitable for analysis.

[0064] "Generative models" refer to AI technologies that use natural language processing and machine learning algorithms to analyze data and assess risk.

[0065] "User interface" refers to the interface through which a user receives warnings and information from a system via an information processing device.

[0066] "Storage device" refers to a database or file system used to store information within a system.

[0067] To implement this invention, it is necessary to construct a network environment that includes an information processing device, a communication network, a processing device, and a storage device. The information processing device refers to a device such as a smartphone or personal computer that a user uses on a daily basis, and has the function of monitoring and acquiring information containing specific designated words or phrases. This processing includes data collection functions via web browsers and social networking services (SNS) applications.

[0068] The information processing device preprocesses the data into a parseable format and transmits this information to the processing device via a communication network. The HTTPS protocol is used for the communication network to ensure security.

[0069] The processing unit operates a generative model to evaluate the risk level of the received information. This generative model is an AI system that combines natural language processing technology and machine learning algorithms. Specifically, the processing unit utilizes Python's TENSORFLOW® and NLTK for analysis. Information deemed highly risky through this analysis is notified to the information processing unit as a warning signal via the user interface.

[0070] Users receive warnings from the information processing device at the user interface, allowing them to recognize potential dangers and take necessary actions. For example, if a user sees posts on social media such as "easy high-paying jobs" or "secret part-time jobs," the information may be immediately filtered, and a generative model may determine that it is high-risk. In that case, a warning such as "This job posting is dangerous" will be displayed on the device. This process allows users to appropriately avoid online risks.

[0071] A concrete example of a prompt message to input into a generative AI model would be something like, "Assess the risk of this message and determine if it poses a danger." This allows the generative model to efficiently assess the danger of the information and issue appropriate warnings.

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] Users collect information through social media and websites using information processing devices. This collected data is stored on the device as raw data. At this stage, it is not determined whether or not it contains specific designated words or phrases.

[0075] Step 2:

[0076] The terminal filters the entire collected raw data using pre-configured specified phrases (e.g., "easy high-paying job," "secret part-time job") as triggers. The filtering process is performed using regular expressions or keyword matching algorithms, and data matching the triggers is extracted. The output at this point is the filtered data.

[0077] Step 3:

[0078] The terminal preprocesses the filtered data into a standard JSON format and sends that formatted data to the server using the secure HTTPS protocol. The output resulting from the preprocessing is structured formatted data.

[0079] Step 4:

[0080] The server receives the formatted data and inputs it into the generative AI model. The generative AI model analyzes the data using natural language processing and performs a risk assessment. In this analysis process, the generative AI model calculates a risk score from the text content and evaluates the danger of the information based on that score. The risk assessment result is obtained as output.

[0081] Step 5:

[0082] Based on the risk assessment results, the server generates a warning signal for information deemed to be high-risk. This warning includes a specific message to immediately alert the user. The output at this step is a warning signal.

[0083] Step 6:

[0084] The terminal receives a warning signal from the server and displays an alert message to the user through the user interface. Based on this signal, the user reviews the detailed warning and recognizes the potential danger. The displayed message may also include links or instructions prompting further action. This output is the warning display to the user.

[0085] (Application Example 1)

[0086] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0087] In today's information and communication environment, the threat of phishing scams and malware through links in emails and messages received by users is increasing. There is a need to provide effective methods to ensure users can use information safely against such online dangers. However, there are limited appropriate technical means to immediately detect these threats and warn users about the dangers. Therefore, the challenge is to provide new measures to enable secure information communication.

[0088] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0089] In this invention, the server includes means for automatically detecting links in communications received by the user and evaluating their security, means for generating and notifying warnings regarding links deemed dangerous based on the analysis results, and means for displaying the warning signals through a user interface. This allows the user to check the security of links in real time and prevent information leaks and attacks on devices caused by malicious links.

[0090] An "information processing device" is a general term for electronic devices that have the functions of collecting, analyzing, and displaying data.

[0091] "Communication network" refers to the entire network infrastructure used for sending and receiving information.

[0092] A "processing unit" is a part of a server or computer that processes data and performs calculations.

[0093] A "computational model" refers to an algorithm used to analyze data and support decision-making based on the results.

[0094] A "user interface" refers to the visual and operational points of contact that users use when operating digital devices.

[0095] "Storage device" refers to all digital storage media that can store information and retrieve it as needed.

[0096] An "organization" refers to a group or entity with a specific purpose, and is the entity that shares information.

[0097] The system for realizing this invention operates in a network environment including an information processing device, a computing device, and a communication network. The information processing device has a function to monitor the content of communications received by the user in real time and to automatically detect links. The detected links are pre-processed for security evaluation and transmitted to the computing device via the communication network.

[0098] The computing unit analyzes the risk of a link using a computational model. This model is built using natural language processing and machine learning algorithms to determine whether a link may be a phishing or malware link. If the link is deemed unsafe, the computing unit generates a warning signal and notifies the information processing unit again via the communication network.

[0099] The information processing device displays the received warning signal to the user through a user interface. This process allows the user to immediately take precautions against dangerous links, thereby preventing information leaks and attacks on devices.

[0100] In terms of specific hardware, smart devices (e.g., Android® and iOS devices) are used as information processing units, and servers are used as computing units. On the software side, TensorFlow or PyTorch are used for natural language processing, and Swift (for iOS) or Java® (for Android) are used for the user interface.

[0101] As a concrete example, the prompt "Analyze links in new emails and display warning messages for potentially dangerous links" is used to implement a process that displays a warning to the user about links that pose a risk of phishing scams. In this way, users can achieve enhanced security in the communications they receive on a daily basis.

[0102] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0103] Step 1:

[0104] The device monitors emails and messages received by the user in real time. It uses the text data received by the user as input and automatically extracts link URLs from it. The output is a list of the extracted URLs.

[0105] Step 2:

[0106] The terminal preprocesses the extracted URLs. It receives a list of URLs as input and converts the data into a standard format. This makes it easier to send to the computing device over the communication network. The output is a list of preprocessed URLs.

[0107] Step 3:

[0108] The server receives a list of pre-processed URLs sent from terminals via the communication network. The input is a list of URLs, and a computational model is used to evaluate the security of each URL based on this list. Natural language processing and machine learning algorithms are applied to the data processing to determine the risk. The output is the security evaluation result for each URL.

[0109] Step 4:

[0110] The server generates a warning signal for URLs deemed high-risk based on the analysis results from a computational model. The input is the safety assessment result, and the output is the warning signal. This warning signal is transmitted to the terminal via the communication network.

[0111] Step 5:

[0112] The terminal receives a warning signal sent from the server. The input is the warning signal, which is displayed to the user as a warning message through the user interface. The output is the displayed warning message.

[0113] Step 6:

[0114] Users are alerted to warning messages and cautioned against dangerous links. This allows them to take action to ensure their online safety by avoiding clicking on links if necessary. The input is the warning message, and the output is the appropriate action taken by the user.

[0115] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0116] This invention comprises a system incorporating an information terminal, a server, a database, and an emotion engine. The information terminal monitors the user's activity on social media and browsers and collects data based on specific keywords in real time. In addition, it is equipped with an emotion engine that extracts emotion information from the user's actions and interactions. The collected data and emotion information undergo preprocessing and are transmitted to the server via the network.

[0117] The server analyzes the received data using an AI model and determines the level of risk using natural language processing and machine learning algorithms. It also dynamically adjusts the content and presentation method of warning signals by taking into account the user's emotional information analyzed by an emotion engine. Warning signals are generated in a way that matches the user's emotional state and are sent to the information terminal.

[0118] The information terminal receives warning signals transmitted from the server and displays alerts through the user interface based on these signals. Users are presented with messages and options appropriate to their situation, making it easier for them to choose the optimal course of action.

[0119] As a concrete example, consider a scenario where a user finds phrases like "high pay" or "special job offer" on social media. The information terminal collects this data and uses an emotion engine to analyze whether the user is feeling anxious or suspicious. The server generates a warning, taking into account both the text's potential dangers and the sentiment analysis. For example, if the user is wary, more detailed information and specific countermeasures are presented. In this way, the system can respond flexibly, taking into account the user's psychological state, and protect them from potential online dangers.

[0120] The following describes the processing flow.

[0121] Step 1:

[0122] The device monitors the user's activities on social media and in their browser in real time, automatically detecting posts and messages containing specific keywords. In addition, an emotion engine uses user interactions such as facial expressions and operation speed to infer the user's emotional state.

[0123] Step 2:

[0124] The terminal combines the data collected through filters with estimated sentiment information, preprocesses it, and converts it into an integrated data format. This data is batch-processed to minimize latency and sent to the server over the network.

[0125] Step 3:

[0126] The server adds the received data to the analysis queue and activates the AI ​​model. The AI ​​model uses natural language processing techniques to scrutinize the data and identify potential dangers within the text. Simultaneously, it analyzes emotional data to assess the user's psychological state.

[0127] Step 4:

[0128] The server combines the risk score and sentiment analysis results to adjust the content of the warning signal. For example, if the user already feels a strong sense of risk, a warning will be generated that includes specific countermeasures and important information.

[0129] Step 5:

[0130] The device receives appropriately customized warning signals and immediately notifies the user. The device's user interface presents this to the user as a visual or audio message, providing guidance for appropriate action.

[0131] Step 6:

[0132] The user reviews the information provided by the device and decides on specific actions to take in response to the risks they face. The device also continuously collects data on the user's subsequent actions and prepares to return it to the system as feedback for improvement.

[0133] (Example 2)

[0134] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0135] In modern society, the information users encounter online is vast and diverse, and can sometimes contain potentially dangerous content. Traditional methods have been insufficient in detecting the dangers of this information and providing warnings tailored to users' emotional states. Therefore, there is a need for a system that allows users to quickly and accurately identify potential online risks.

[0136] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0137] In this invention, the server includes a function for extracting user emotional information, means for generating emotional information, means for analyzing the information using a generation AI model to determine potential dangers, and means for generating an alarm signal based on the analysis results and notifying an information processing device. This enables an automatic and dynamic response to potentially dangerous information, taking into account the user's psychological state.

[0138] An "information processing device" is a network device used by a user, and is a means of monitoring the user's online behavior and collecting and processing specific data.

[0139] "User" refers to the human entity that operates the information processing device, and is the entity that plays the role of both the source of information and the recipient of its intended purpose within the system.

[0140] "Linguistic expressions" refer to specific keywords or phrases contained in information on a network, and are data elements that trigger risk assessments.

[0141] "Emotional information" refers to information representing the emotional state of a user, extracted from their behavior and interactions acquired through an information processing device.

[0142] A "communication network" is a network infrastructure that enables the transmission and reception of data between an information processing device and an analysis device.

[0143] An "analysis device" is a server system that processes received information using an AI model to analyze potential risks.

[0144] A "generative AI model" is a program or system architecture that utilizes natural language processing and machine learning algorithms to derive useful insights from data.

[0145] A "warning signal" is a warning message generated by an analysis device and notified to an information processing device; it is information that serves as an indicator to inform users of potential risks.

[0146] A "command provision device" is a user interface incorporated into an information processing device, designed to effectively display alarm signals to the user.

[0147] An "information set" is a storage system for saving information and analysis results obtained by an analysis device, and includes a database that enables information sharing with other components.

[0148] This invention is a system implemented using an information processing device, an analysis device, and related software and hardware. The information processing device uses smartphones and computers as devices to monitor user behavior and collect information including specific linguistic expressions. These devices collect data and generate sentiment information in real time via software such as web browsers and social networking applications.

[0149] When a user uses social media, the information processing device identifies posts containing specific keywords and extracts the user's emotional information using an emotion engine. The emotion engine analyzes psychological indicators derived from the user's activity history and posting content.

[0150] The analysis device receives data collected via a communication network and performs analysis using a generative AI model. During this process, it utilizes software libraries such as TensorFlow and PyTorch to perform assessments of potential risks using natural language processing and machine learning. From the analysis results, an alarm signal is generated that takes into account the user's psychological state.

[0151] In information processing systems, received alarm signals are displayed on the user interface. A specific example of this display is a message such as, "This information may contain risks. Please review the details." This alert is communicated to the user via display or audio output, prompting appropriate action.

[0152] Examples of prompt messages are as follows:

[0153] "We want to build a system that detects specific keywords on social media and adjusts warnings based on the user's emotional state. Please explain how this system works."

[0154] As a result, users are protected from potential dangers they may encounter online, and the system is able to respond flexibly and dynamically, taking into account the user's psychological state.

[0155] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0156] Step 1:

[0157] The device monitors user behavior. Specifically, the device analyzes information accessed by the user on social media and browsers, and collects input text data containing specific keywords. This input data includes the user's browsing history and posted content. The collected data is filtered and preprocessed into a format suitable for analysis. The output of the preprocessing is text data formatted for analysis.

[0158] Step 2:

[0159] The device generates user emotion information. The device utilizes an emotion engine to generate emotion data based on user actions and keywords in text. The input for this process consists of the user's emotional responses and action history. The emotion engine analyzes the user's input data using natural language processing techniques and outputs data indicating the emotional state. This output represents the user's current emotional state using numerical values ​​and categories.

[0160] Step 3:

[0161] The terminal transmits collected text data and sentiment information to the server via the communication network. The input is pre-processed text data and extracted sentiment information, and the output is a data packet that packages this information. This data reaches the server via the network.

[0162] Step 4:

[0163] The server analyzes the received data using a generative AI model. At this stage, the input is the transmitted data packets. The server uses the generative AI model, combining natural language processing and machine learning algorithms, to process the data and identify potential risks. The output consists of a risk assessment and analysis results based on the user's emotional state.

[0164] Step 5:

[0165] The server generates an alarm signal and notifies the terminal. The input is the analysis result, and the output is an alarm signal tailored to the user's situation. This alarm signal takes the user's emotional state into account and is generated with appropriate content and urgency.

[0166] Step 6:

[0167] The terminal displays the received alarm signal on the user interface. The input is the alarm signal, and the output is the warning message presented to the user. The terminal notifies the user via display or audio, prompting them to take appropriate action. Specifically, a message such as "This information may contain risks" is displayed on the screen.

[0168] (Application Example 2)

[0169] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0170] There is a need to effectively protect users from potential dangers such as inappropriate online information and phishing scams. However, conventional methods have difficulty providing dynamic warnings that take into account the user's psychological state, and there are limitations to how well they can support users in making appropriate decisions.

[0171] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0172] This invention includes a server that uses an emotion engine to analyze the user's psychological state and dynamically adjust the content of warning signals, a means for storing the recorded information in a storage device and sharing the information with other organizations via a system, and a means for constructing prompt sentences using a generative AI model. This makes it possible to provide appropriate and detailed warnings and countermeasures that are tailored to the user's psychological state, thereby enhancing online safety.

[0173] An "information processing device" is a device that receives user input, monitors information containing specific keywords, and retrieves that information.

[0174] A "communication network" is a network infrastructure that connects multiple information processing devices and computing devices to send and receive information and data.

[0175] A "computational processing unit" is a central processing unit that receives data transmitted through a communication network, analyzes it using a predictive model, and determines the level of risk.

[0176] A "predictive model" is a set of algorithms and computational methods built using natural language processing and machine learning techniques to determine risk from data.

[0177] A "human-machine interface" is a means of interaction that allows an information processing device to display information to a user or to receive input from a user.

[0178] An "emotion engine" is a part of the algorithms and software used to analyze the user's psychological state from their actions and interactions, and to dynamically adjust the content of warning signals.

[0179] A "storage device" is a data storage device used to save records of information so that it can be used later.

[0180] A "generative AI model" is an artificial intelligence model designed to automatically generate appropriate messages based on the user's psychological state.

[0181] A "prompt message" is the input text used by a generative AI model to generate an appropriate warning message.

[0182] This invention is constructed as a system incorporating an information processing device, a computing device, a communication network, and an emotion engine. The information processing device monitors the user's daily activities and acquires information containing specific keywords. For example, if a user receives a message such as "verify credit card details" on social media, this information is detected.

[0183] Information acquired by the information processing device is pre-processed and transmitted to the computing device via the communication network. The computing device analyzes the received information using a predictive model and determines the risk using natural language processing and machine learning algorithms. In this risk determination, the emotion engine analyzes the user's psychological state and dynamically adjusts the warning signals.

[0184] Warning signals tailored to the user's psychological state are generated through prompt messages using a generative AI model and notified to the information processing device. The information processing device displays these warning signals to the user through a human-machine interface, indicating the optimal course of action for the user. This provides real-time information on necessary safety measures and additional details.

[0185] As a concrete example, a possible prompt message for a generative AI model might be: "Based on the keywords 'credit card' and 'verify details' in the message received by the user, the sentiment engine has detected anxiety. Please generate an appropriate warning message and notify the user."

[0186] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0187] Step 1:

[0188] The device monitors the user's activity on social media and messaging apps and retrieves information containing specific keywords. This process takes text data received by the user as input and extracts and outputs relevant content through keyword filtering.

[0189] Step 2:

[0190] The terminal preprocesses the extracted information and converts it into a structured data format. The input is the raw text data obtained in step 1. At this stage, the data is tokenized, unnecessary information is removed as needed, and it is prepared for subsequent analysis. The results of this processing are output as intermediate data.

[0191] Step 3:

[0192] The terminal transmits pre-processed data to the server (processing unit) via the communication network. Here, intermediate data serves as input, and the data is provided to the server safely and quickly.

[0193] Step 4:

[0194] The server uses a predictive model to analyze the information's potential risks based on the received data. Through data analysis using natural language processing and machine learning algorithms, it scores the data's potential risk level and outputs a risk assessment result.

[0195] Step 5:

[0196] The server uses an emotion engine to analyze the user's psychological state and generates a warning signal based on the assessment result. The input is the assessment result from step 4 and the user's emotion data. The output is a warning message tailored to the user.

[0197] Step 6:

[0198] Using a generative AI model, prompt statements are created based on warning signals, and individual warning messages are concretized. Here, the prompt statement serves as input, and dynamically generated warning messages are output.

[0199] Step 7:

[0200] The server sends the generated warning message to the terminal, which then presents it to the user through a human-machine interface. The user receives this warning message and can then review the suggested countermeasures and actions.

[0201] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0202] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0203] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0204] [Second Embodiment]

[0205] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0206] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0207] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0208] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0209] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0210] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0211] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0212] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0213] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0214] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0215] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0216] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0217] The system of the present invention operates in a network environment including information terminals, servers, and databases. The information terminal used by the user collects data in real time via SNS or web browsers and filters it using specific keywords as triggers. The data obtained through this filtering process is converted to a standard data format through preprocessing and transmitted to the server using a secure protocol.

[0218] The server performs analysis using an AI model based on the received data. This AI model is built using natural language processing and machine learning algorithms to determine the risks within the data. Based on the analysis results, a warning signal is generated for information deemed to be high risk and sent to the information terminal. The warning is accompanied by an appropriate message and information on a contact point to enable the user to take immediate action.

[0219] On the user's information terminal, an alert message is displayed through the user interface based on the received warning signal. This allows the user to recognize potential dangers and take necessary actions. Through this series of processes, the system helps prevent crimes committed by users while using the internet.

[0220] As a concrete example, when a user sees a post on social media containing text such as "easy high-paying job" or "secret part-time job," the information is filtered and analyzed by AI. If it is determined to be high-risk, a warning message such as "This job posting is dangerous" will be displayed on the device, and a link to access more specific precautions and additional information will be provided. Through this process, users can effectively avoid potential online dangers.

[0221] The following describes the processing flow.

[0222] Step 1:

[0223] The device monitors the user's activity on social media and in their browser, and detects content containing specific keywords. The detected data is filtered and then recorded for collection.

[0224] Step 2:

[0225] The device preprocesses the data collected through filtering and converts it into a data format (e.g., JSON) that can be analyzed by an AI model. The converted data is then sent to the server via a secure connection.

[0226] Step 3:

[0227] The server places the received data into an analysis queue and runs the AI ​​model. The AI ​​model uses natural language processing and machine learning algorithms to analyze patterns in the data and determine the potential for danger.

[0228] Step 4:

[0229] The server calculates a risk score based on the analysis results and generates a warning signal if the threshold is exceeded. The warning includes specific risk information, as well as information on potential countermeasures and contact points for consultation.

[0230] Step 5:

[0231] The terminal receives a warning signal from the server. Based on this warning signal, the terminal's user interface displays an alert to the user and, if necessary, provides additional safety information and links.

[0232] Step 6:

[0233] Users can check alerts displayed on their devices and, if necessary, decide on their next course of action based on the information provided. This promotes awareness of crime risks and encourages preventative actions.

[0234] (Example 1)

[0235] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0236] In modern internet use, users have access to a wide variety of information, but it is especially important to prevent exposure to dangerous information found on social media and websites. However, it is extremely difficult for users to manually monitor all information and assess its risks, thus increasing the need for systems that efficiently and automatically identify and warn about dangerous information.

[0237] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0238] In this invention, the server includes means for monitoring and acquiring information containing specified words or phrases, means for transmitting the pre-processed information to a processing unit via a communication network, and means for evaluating the risk using a generative model. This makes it possible to automatically identify dangerous information from information accessed by users on the internet and issue warnings immediately.

[0239] An "information processing device" is a device used by a user that has the ability to monitor and acquire information containing specified words or phrases.

[0240] "Specified terms" refer to keywords or phrases used to identify specific information as a trigger.

[0241] "Communication network" refers to the infrastructure of the internet and other networks used to send and receive information.

[0242] A "processing device" refers to a computer server or a component that forms part of a system used to analyze received information and perform risk assessments.

[0243] "Preprocessing" refers to the initial data processing process to convert raw information data into a format suitable for analysis.

[0244] "Generative models" refer to AI technologies that use natural language processing and machine learning algorithms to analyze data and assess risk.

[0245] "User interface" refers to the interface through which a user receives warnings and information from a system via an information processing device.

[0246] "Storage device" refers to a database or file system used to store information within a system.

[0247] To implement this invention, it is necessary to construct a network environment that includes an information processing device, a communication network, a processing device, and a storage device. The information processing device refers to a device such as a smartphone or personal computer that a user uses on a daily basis, and has the function of monitoring and acquiring information containing specific designated words or phrases. This processing includes data collection functions via web browsers and social networking services (SNS) applications.

[0248] The information processing device preprocesses the data into a parseable format and transmits this information to the processing device via a communication network. The HTTPS protocol is used for the communication network to ensure security.

[0249] The processing unit operates a generative model to evaluate the risk level of the received information. This generative model is an AI system that combines natural language processing techniques and machine learning algorithms. Specifically, the processing unit utilizes Python's TensorFlow and NLTK for analysis. Information deemed highly risky through this analysis is notified to the information processing unit as a warning signal via the user interface.

[0250] Users receive warnings from the information processing device at the user interface, allowing them to recognize potential dangers and take necessary actions. For example, if a user sees posts on social media such as "easy high-paying jobs" or "secret part-time jobs," the information may be immediately filtered, and a generative model may determine that it is high-risk. In that case, a warning such as "This job posting is dangerous" will be displayed on the device. This process allows users to appropriately avoid online risks.

[0251] A concrete example of a prompt message to input into a generative AI model would be something like, "Assess the risk of this message and determine if it poses a danger." This allows the generative model to efficiently assess the danger of the information and issue appropriate warnings.

[0252] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0253] Step 1:

[0254] Users collect information through social media and websites using information processing devices. This collected data is stored on the device as raw data. At this stage, it is not determined whether or not it contains specific designated words or phrases.

[0255] Step 2:

[0256] The terminal filters the entire collected raw data using pre-configured specified phrases (e.g., "easy high-paying job," "secret part-time job") as triggers. The filtering process is performed using regular expressions or keyword matching algorithms, and data matching the triggers is extracted. The output at this point is the filtered data.

[0257] Step 3:

[0258] The terminal preprocesses the filtered data into a standard JSON format and sends that formatted data to the server using the secure HTTPS protocol. The output resulting from the preprocessing is structured formatted data.

[0259] Step 4:

[0260] The server receives the formatted data and inputs it into the generative AI model. The generative AI model analyzes the data using natural language processing and performs a risk assessment. In this analysis process, the generative AI model calculates a risk score from the text content and evaluates the danger of the information based on that score. The risk assessment result is obtained as output.

[0261] Step 5:

[0262] Based on the risk assessment results, the server generates a warning signal for information deemed to be high-risk. This warning includes a specific message to immediately alert the user. The output at this step is a warning signal.

[0263] Step 6:

[0264] The terminal receives a warning signal from the server and displays an alert message to the user through the user interface. Based on this signal, the user reviews the detailed warning and recognizes the potential danger. The displayed message may also include links or instructions prompting further action. This output is the warning display to the user.

[0265] (Application Example 1)

[0266] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0267] In today's information and communication environment, the threat of phishing scams and malware through links in emails and messages received by users is increasing. There is a need to provide effective methods to ensure users can use information safely against such online dangers. However, there are limited appropriate technical means to immediately detect these threats and warn users about the dangers. Therefore, the challenge is to provide new measures to enable secure information communication.

[0268] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0269] In this invention, the server includes means for automatically detecting links in communications received by the user and evaluating their security, means for generating and notifying warnings regarding links deemed dangerous based on the analysis results, and means for displaying the warning signals through a user interface. This allows the user to check the security of links in real time and prevent information leaks and attacks on devices caused by malicious links.

[0270] An "information processing device" is a general term for electronic devices that have the functions of collecting, analyzing, and displaying data.

[0271] "Communication network" refers to the entire network infrastructure used for sending and receiving information.

[0272] A "processing unit" is a part of a server or computer that processes data and performs calculations.

[0273] A "computational model" refers to an algorithm used to analyze data and support decision-making based on the results.

[0274] A "user interface" refers to the visual and operational points of contact that users use when operating digital devices.

[0275] "Storage device" refers to all digital storage media that can store information and retrieve it as needed.

[0276] An "organization" refers to a group or entity with a specific purpose, and is the entity that shares information.

[0277] The system for realizing this invention operates in a network environment including an information processing device, a computing device, and a communication network. The information processing device has a function to monitor the content of communications received by the user in real time and to automatically detect links. The detected links are pre-processed for security evaluation and transmitted to the computing device via the communication network.

[0278] The computing unit analyzes the risk of a link using a computational model. This model is built using natural language processing and machine learning algorithms to determine whether a link may be a phishing or malware link. If the link is deemed unsafe, the computing unit generates a warning signal and notifies the information processing unit again via the communication network.

[0279] The information processing device displays the received warning signal to the user through a user interface. This process allows the user to immediately take precautions against dangerous links, thereby preventing information leaks and attacks on devices.

[0280] In terms of specific hardware, smart devices (e.g., Android and iOS devices) are used as information processing units, and servers are used as computing units. On the software side, TensorFlow or PyTorch are used for natural language processing, and Swift (for iOS) or Java (for Android) are used for the user interface.

[0281] As a specific example, an instruction "Analyze the links in the incoming emails and display a warning message regarding the risky links." is used as a prompt sentence, and a process of displaying a warning to the user about links with a risk of phishing fraud is carried out. In this way, even in the communications that the user receives daily, information processing with enhanced security can be realized.

[0282] The flow of the specific process in Application Example 1 will be described with reference to FIG. 12.

[0283] Step 1:

[0284] The terminal monitors in real time the emails and messages received by the user. Using the text data received by the user as input, the link URLs are automatically extracted from it. The output is a list of the extracted URLs.

[0285] Step 2:

[0286] The terminal preprocesses the extracted URLs. Receiving the URL list as input, it converts the data into a standard format. This makes it easier to transmit to the computing device via the communication network. The output is a list of the preprocessed URLs.

[0287] [[ID=2X]]Step 3:

[0288] The server receives the list of preprocessed URLs transmitted from the terminal through the communication network. The input is the URL list, and based on this, the security of each URL is evaluated using a calculation model. Natural language processing and machine learning algorithms are applied to data processing to determine the risk. The output is the security determination result of each URL.

[0289] Step 4:

[0290] The server generates a warning signal for URLs deemed high-risk based on the analysis results from a computational model. The input is the safety assessment result, and the output is the warning signal. This warning signal is transmitted to the terminal via the communication network.

[0291] Step 5:

[0292] The terminal receives a warning signal sent from the server. The input is the warning signal, which is displayed to the user as a warning message through the user interface. The output is the displayed warning message.

[0293] Step 6:

[0294] Users are alerted to warning messages and cautioned against dangerous links. This allows them to take action to ensure their online safety by avoiding clicking on links if necessary. The input is the warning message, and the output is the appropriate action taken by the user.

[0295] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0296] This invention comprises a system incorporating an information terminal, a server, a database, and an emotion engine. The information terminal monitors the user's activity on social media and browsers and collects data based on specific keywords in real time. In addition, it is equipped with an emotion engine that extracts emotion information from the user's actions and interactions. The collected data and emotion information undergo preprocessing and are transmitted to the server via the network.

[0297] The server analyzes the received data using an AI model and determines the level of risk using natural language processing and machine learning algorithms. It also dynamically adjusts the content and presentation method of warning signals by taking into account the user's emotional information analyzed by an emotion engine. Warning signals are generated in a way that matches the user's emotional state and are sent to the information terminal.

[0298] The information terminal receives warning signals transmitted from the server and displays alerts through the user interface based on these signals. Users are presented with messages and options appropriate to their situation, making it easier for them to choose the optimal course of action.

[0299] As a concrete example, consider a scenario where a user finds phrases like "high pay" or "special job offer" on social media. The information terminal collects this data and uses an emotion engine to analyze whether the user is feeling anxious or suspicious. The server generates a warning, taking into account both the text's potential dangers and the sentiment analysis. For example, if the user is wary, more detailed information and specific countermeasures are presented. In this way, the system can respond flexibly, taking into account the user's psychological state, and protect them from potential online dangers.

[0300] The following describes the processing flow.

[0301] Step 1:

[0302] The device monitors the user's activities on social media and in their browser in real time, automatically detecting posts and messages containing specific keywords. In addition, an emotion engine uses user interactions such as facial expressions and operation speed to infer the user's emotional state.

[0303] Step 2:

[0304] The terminal sets the data collected through the filter and the estimated emotion information, preprocesses them, and converts them into an integrated data format. This data is batch processed to minimize latency as much as possible and sent to the server via the network.

[0305] Step 3:

[0306] The server adds the received data to the analysis queue and activates the AI model. The AI model scrutinizes the data by leveraging natural language processing techniques to identify potential risks within the text. At the same time, it analyzes the emotion data to evaluate the user's psychological state.

[0307] Step 4:

[0308] The server synthesizes the risk score and the emotion analysis result to adjust the content of the warning signal. For example, if the user already strongly feels the risk, a warning including specific countermeasures or highly important information is generated.

[0309] Step 5:

[0310] The terminal receives the appropriately customized warning signal and immediately notifies the user. The user interface of the terminal presents this to the user as a visual or audio message, indicating guidelines for appropriate actions.

[0311] Step 6:

[0312] The user checks the information provided by the terminal and decides on specific actions regarding the risks faced. The terminal also continuously collects the user's subsequent action data and prepares to return it to the system as feedback for improvement.

[0313] (Example 2)

[0314] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0315] In modern society, the information users encounter online is vast and diverse, and can sometimes contain potentially dangerous content. Traditional methods have been insufficient in detecting the dangers of this information and providing warnings tailored to users' emotional states. Therefore, there is a need for a system that allows users to quickly and accurately identify potential online risks.

[0316] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0317] In this invention, the server includes a function for extracting user emotional information, means for generating emotional information, means for analyzing the information using a generation AI model to determine potential dangers, and means for generating an alarm signal based on the analysis results and notifying an information processing device. This enables an automatic and dynamic response to potentially dangerous information, taking into account the user's psychological state.

[0318] An "information processing device" is a network device used by a user, and is a means of monitoring the user's online behavior and collecting and processing specific data.

[0319] "User" refers to the human entity that operates the information processing device, and is the entity that plays the role of both the source of information and the recipient of its intended purpose within the system.

[0320] "Linguistic expressions" refer to specific keywords or phrases contained in information on a network, and are data elements that trigger risk assessments.

[0321] "Emotional information" refers to information representing the emotional state of a user, extracted from their behavior and interactions acquired through an information processing device.

[0322] A "communication network" is a network infrastructure that enables the transmission and reception of data between an information processing device and an analysis device.

[0323] An "analysis device" is a server system that processes received information using an AI model to analyze potential risks.

[0324] A "generative AI model" is a program or system architecture that utilizes natural language processing and machine learning algorithms to derive useful insights from data.

[0325] A "warning signal" is a warning message generated by an analysis device and notified to an information processing device; it is information that serves as an indicator to inform users of potential risks.

[0326] A "command provision device" is a user interface incorporated into an information processing device, designed to effectively display alarm signals to the user.

[0327] An "information set" is a storage system for saving information and analysis results obtained by an analysis device, and includes a database that enables information sharing with other components.

[0328] This invention is a system implemented using an information processing device, an analysis device, and related software and hardware. The information processing device uses smartphones and computers as devices to monitor user behavior and collect information including specific linguistic expressions. These devices collect data and generate sentiment information in real time via software such as web browsers and social networking applications.

[0329] When a user uses social media, the information processing device identifies posts containing specific keywords and extracts the user's emotional information using an emotion engine. The emotion engine analyzes psychological indicators derived from the user's activity history and posting content.

[0330] The analysis device receives data collected via a communication network and performs analysis using a generative AI model. During this process, it utilizes software libraries such as TensorFlow and PyTorch to perform assessments of potential risks using natural language processing and machine learning. From the analysis results, an alarm signal is generated that takes into account the user's psychological state.

[0331] In information processing systems, received alarm signals are displayed on the user interface. A specific example of this display is a message such as, "This information may contain risks. Please review the details." This alert is communicated to the user via display or audio output, prompting appropriate action.

[0332] Examples of prompt messages are as follows:

[0333] "We want to build a system that detects specific keywords on social media and adjusts warnings based on the user's emotional state. Please explain how this system works."

[0334] As a result, users are protected from potential dangers they may encounter online, and the system is able to respond flexibly and dynamically, taking into account the user's psychological state.

[0335] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0336] Step 1:

[0337] The device monitors user behavior. Specifically, the device analyzes information accessed by the user on social media and browsers, and collects input text data containing specific keywords. This input data includes the user's browsing history and posted content. The collected data is filtered and preprocessed into a format suitable for analysis. The output of the preprocessing is text data formatted for analysis.

[0338] Step 2:

[0339] The device generates user emotion information. The device utilizes an emotion engine to generate emotion data based on user actions and keywords in text. The input for this process consists of the user's emotional responses and action history. The emotion engine analyzes the user's input data using natural language processing techniques and outputs data indicating the emotional state. This output represents the user's current emotional state using numerical values ​​and categories.

[0340] Step 3:

[0341] The terminal transmits collected text data and sentiment information to the server via the communication network. The input is pre-processed text data and extracted sentiment information, and the output is a data packet that packages this information. This data reaches the server via the network.

[0342] Step 4:

[0343] The server analyzes the received data using a generative AI model. At this stage, the input is the transmitted data packets. The server uses the generative AI model, combining natural language processing and machine learning algorithms, to process the data and identify potential risks. The output consists of a risk assessment and analysis results based on the user's emotional state.

[0344] Step 5:

[0345] The server generates an alarm signal and notifies the terminal. The input is the analysis result, and the output is an alarm signal tailored to the user's situation. This alarm signal takes the user's emotional state into account and is generated with appropriate content and urgency.

[0346] Step 6:

[0347] The terminal displays the received alarm signal on the user interface. The input is the alarm signal, and the output is the warning message presented to the user. The terminal notifies the user via display or audio, prompting them to take appropriate action. Specifically, a message such as "This information may contain risks" is displayed on the screen.

[0348] (Application Example 2)

[0349] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0350] There is a need to effectively protect users from potential dangers such as inappropriate online information and phishing scams. However, conventional methods have difficulty providing dynamic warnings that take into account the user's psychological state, and there are limitations to how well they can support users in making appropriate decisions.

[0351] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0352] This invention includes a server that uses an emotion engine to analyze the user's psychological state and dynamically adjust the content of warning signals, a means for storing the recorded information in a storage device and sharing the information with other organizations via a system, and a means for constructing prompt sentences using a generative AI model. This makes it possible to provide appropriate and detailed warnings and countermeasures that are tailored to the user's psychological state, thereby enhancing online safety.

[0353] An "information processing device" is a device that receives user input, monitors information containing specific keywords, and retrieves that information.

[0354] A "communication network" is a network infrastructure that connects multiple information processing devices and computing devices to send and receive information and data.

[0355] A "computational processing unit" is a central processing unit that receives data transmitted through a communication network, analyzes it using a predictive model, and determines the level of risk.

[0356] A "predictive model" is a set of algorithms and computational methods built using natural language processing and machine learning techniques to determine risk from data.

[0357] A "human-machine interface" is a means of interaction that allows an information processing device to display information to a user or to receive input from a user.

[0358] An "emotion engine" is a part of the algorithms and software used to analyze the user's psychological state from their actions and interactions, and to dynamically adjust the content of warning signals.

[0359] A "storage device" is a data storage device used to save records of information so that it can be used later.

[0360] A "generative AI model" is an artificial intelligence model designed to automatically generate appropriate messages based on the user's psychological state.

[0361] A "prompt message" is the input text used by a generative AI model to generate an appropriate warning message.

[0362] This invention is constructed as a system incorporating an information processing device, a computing device, a communication network, and an emotion engine. The information processing device monitors the user's daily activities and acquires information containing specific keywords. For example, if a user receives a message such as "verify credit card details" on social media, this information is detected.

[0363] Information acquired by the information processing device is pre-processed and transmitted to the computing device via the communication network. The computing device analyzes the received information using a predictive model and determines the risk using natural language processing and machine learning algorithms. In this risk determination, the emotion engine analyzes the user's psychological state and dynamically adjusts the warning signals.

[0364] Warning signals tailored to the user's psychological state are generated through prompt messages using a generative AI model and notified to the information processing device. The information processing device displays these warning signals to the user through a human-machine interface, indicating the optimal course of action for the user. This provides real-time information on necessary safety measures and additional details.

[0365] As a concrete example, a possible prompt message for a generative AI model might be: "Based on the keywords 'credit card' and 'verify details' in the message received by the user, the sentiment engine has detected anxiety. Please generate an appropriate warning message and notify the user."

[0366] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0367] Step 1:

[0368] The device monitors the user's activity on social media and messaging apps and retrieves information containing specific keywords. This process takes text data received by the user as input and extracts and outputs relevant content through keyword filtering.

[0369] Step 2:

[0370] The terminal preprocesses the extracted information and converts it into a structured data format. The input is the raw text data obtained in step 1. At this stage, the data is tokenized, unnecessary information is removed as needed, and it is prepared for subsequent analysis. The results of this processing are output as intermediate data.

[0371] Step 3:

[0372] The terminal transmits pre-processed data to the server (processing unit) via the communication network. Here, intermediate data serves as input, and the data is provided to the server safely and quickly.

[0373] Step 4:

[0374] The server uses a predictive model to analyze the information's potential risks based on the received data. Through data analysis using natural language processing and machine learning algorithms, it scores the data's potential risk level and outputs a risk assessment result.

[0375] Step 5:

[0376] The server uses an emotion engine to analyze the user's psychological state and generates a warning signal based on the assessment result. The input is the assessment result from step 4 and the user's emotion data. The output is a warning message tailored to the user.

[0377] Step 6:

[0378] Using a generative AI model, prompt statements are created based on warning signals, and individual warning messages are concretized. Here, the prompt statement serves as input, and dynamically generated warning messages are output.

[0379] Step 7:

[0380] The server sends the generated warning message to the terminal, which then presents it to the user through a human-machine interface. The user receives this warning message and can then review the suggested countermeasures and actions.

[0381] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0382] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0383] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0384] [Third Embodiment]

[0385] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0386] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0387] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0388] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0389] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0390] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0391] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0392] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0393] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0394] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0395] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0396] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0397] The system of the present invention operates in a network environment including information terminals, servers, and databases. The information terminal used by the user collects data in real time via SNS or web browsers and filters it using specific keywords as triggers. The data obtained through this filtering process is converted to a standard data format through preprocessing and transmitted to the server using a secure protocol.

[0398] The server performs analysis using an AI model based on the received data. This AI model is built using natural language processing and machine learning algorithms to determine the risks within the data. Based on the analysis results, a warning signal is generated for information deemed to be high risk and sent to the information terminal. The warning is accompanied by an appropriate message and information on a contact point to enable the user to take immediate action.

[0399] On the user's information terminal, an alert message is displayed through the user interface based on the received warning signal. This allows the user to recognize potential dangers and take necessary actions. Through this series of processes, the system helps prevent crimes committed by users while using the internet.

[0400] As a concrete example, when a user sees a post on social media containing text such as "easy high-paying job" or "secret part-time job," the information is filtered and analyzed by AI. If it is determined to be high-risk, a warning message such as "This job posting is dangerous" will be displayed on the device, and a link to access more specific precautions and additional information will be provided. Through this process, users can effectively avoid potential online dangers.

[0401] The following describes the processing flow.

[0402] Step 1:

[0403] The device monitors the user's activity on social media and in their browser, and detects content containing specific keywords. The detected data is filtered and then recorded for collection.

[0404] Step 2:

[0405] The device preprocesses the data collected through filtering and converts it into a data format (e.g., JSON) that can be analyzed by an AI model. The converted data is then sent to the server via a secure connection.

[0406] Step 3:

[0407] The server places the received data into an analysis queue and runs the AI ​​model. The AI ​​model uses natural language processing and machine learning algorithms to analyze patterns in the data and determine the potential for danger.

[0408] Step 4:

[0409] The server calculates a risk score based on the analysis results and generates a warning signal if the threshold is exceeded. The warning includes specific risk information, as well as information on potential countermeasures and contact points for consultation.

[0410] Step 5:

[0411] The terminal receives a warning signal from the server. Based on this warning signal, the terminal's user interface displays an alert to the user and, if necessary, provides additional safety information and links.

[0412] Step 6:

[0413] Users can check alerts displayed on their devices and, if necessary, decide on their next course of action based on the information provided. This promotes awareness of crime risks and preventative actions.

[0414] (Example 1)

[0415] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0416] In modern internet use, users have access to a wide variety of information, but it is especially important to prevent exposure to dangerous information found on social media and websites. However, it is extremely difficult for users to manually monitor all information and assess its risks, thus increasing the need for systems that efficiently and automatically identify and warn about dangerous information.

[0417] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0418] In this invention, the server includes means for monitoring and acquiring information containing specified words or phrases, means for transmitting the pre-processed information to a processing unit via a communication network, and means for evaluating the risk using a generative model. This makes it possible to automatically identify dangerous information from information accessed by users on the internet and issue warnings immediately.

[0419] An "information processing device" is a device used by a user that has the ability to monitor and acquire information containing specified words or phrases.

[0420] "Specified terms" refer to keywords or phrases used to identify specific information as a trigger.

[0421] "Communication network" refers to the infrastructure of the internet and other networks used to send and receive information.

[0422] A "processing device" refers to a computer server or a component that forms part of a system used to analyze received information and perform risk assessments.

[0423] "Preprocessing" refers to the initial data processing process to convert raw information data into a format suitable for analysis.

[0424] "Generative models" refer to AI technologies that use natural language processing and machine learning algorithms to analyze data and assess risk.

[0425] "User interface" refers to the interface through which a user receives warnings and information from a system via an information processing device.

[0426] "Storage device" refers to a database or file system used to store information within a system.

[0427] To implement this invention, it is necessary to construct a network environment that includes an information processing device, a communication network, a processing device, and a storage device. The information processing device refers to a device such as a smartphone or personal computer that a user uses on a daily basis, and has the function of monitoring and acquiring information containing specific designated words or phrases. This processing includes data collection functions via web browsers and social networking services (SNS) applications.

[0428] The information processing device preprocesses the data into a parseable format and transmits this information to the processing device via a communication network. The HTTPS protocol is used for the communication network to ensure security.

[0429] The processing unit operates a generative model to evaluate the risk level of the received information. This generative model is an AI system that combines natural language processing techniques and machine learning algorithms. Specifically, the processing unit utilizes Python's TensorFlow and NLTK for analysis. Information deemed highly risky through this analysis is notified to the information processing unit as a warning signal via the user interface.

[0430] Users receive warnings from the information processing device at the user interface, allowing them to recognize potential dangers and take necessary actions. For example, if a user sees posts on social media such as "easy high-paying jobs" or "secret part-time jobs," the information may be immediately filtered, and a generative model may determine that it is high-risk. In that case, a warning such as "This job posting is dangerous" will be displayed on the device. This process allows users to appropriately avoid online risks.

[0431] A concrete example of a prompt message to input into a generative AI model would be something like, "Assess the risk of this message and determine if it poses a danger." This allows the generative model to efficiently assess the danger of the information and issue appropriate warnings.

[0432] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0433] Step 1:

[0434] Users collect information through social media and websites using information processing devices. This collected data is stored on the device as raw data. At this stage, it is not determined whether or not it contains specific designated words or phrases.

[0435] Step 2:

[0436] The terminal filters the entire collected raw data using pre-configured specified phrases (e.g., "easy high-paying job," "secret part-time job") as triggers. The filtering process is performed using regular expressions or keyword matching algorithms, and data matching the triggers is extracted. The output at this point is the filtered data.

[0437] Step 3:

[0438] The terminal preprocesses the filtered data into a standard JSON format and sends that formatted data to the server using the secure HTTPS protocol. The output resulting from the preprocessing is structured formatted data.

[0439] Step 4:

[0440] The server receives the formatted data and inputs it into the generative AI model. The generative AI model analyzes the data using natural language processing and performs a risk assessment. In this analysis process, the generative AI model calculates a risk score from the text content and evaluates the danger of the information based on that score. The risk assessment result is obtained as output.

[0441] Step 5:

[0442] Based on the risk assessment results, the server generates a warning signal for information deemed to be high-risk. This warning includes a specific message to immediately alert the user. The output at this step is a warning signal.

[0443] Step 6:

[0444] The terminal receives a warning signal from the server and displays an alert message to the user through the user interface. Based on this signal, the user reviews the detailed warning and recognizes the potential danger. The displayed message may also include links or instructions prompting further action. This output is the warning display to the user.

[0445] (Application Example 1)

[0446] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0447] In today's information and communication environment, the threat of phishing scams and malware through links in emails and messages received by users is increasing. There is a need to provide effective methods to ensure users can use information safely against such online dangers. However, there are limited appropriate technical means to immediately detect these threats and warn users about the dangers. Therefore, the challenge is to provide new measures to enable secure information communication.

[0448] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0449] In this invention, the server includes means for automatically detecting links in communications received by the user and evaluating their security, means for generating and notifying warnings regarding links deemed dangerous based on the analysis results, and means for displaying the warning signals through a user interface. This allows the user to check the security of links in real time and prevent information leaks and attacks on devices caused by malicious links.

[0450] An "information processing device" is a general term for electronic devices that have the functions of collecting, analyzing, and displaying data.

[0451] "Communication network" refers to the entire network infrastructure used for sending and receiving information.

[0452] A "processing unit" is a part of a server or computer that processes data and performs calculations.

[0453] A "computational model" refers to an algorithm used to analyze data and support decision-making based on the results.

[0454] A "user interface" refers to the visual and operational points of contact that users use when operating digital devices.

[0455] "Storage device" refers to all digital storage media that can store information and retrieve it as needed.

[0456] An "organization" refers to a group or entity with a specific purpose, and is the entity that shares information.

[0457] The system for realizing this invention operates in a network environment including an information processing device, a computing device, and a communication network. The information processing device has a function to monitor the content of communications received by the user in real time and to automatically detect links. The detected links are pre-processed for security evaluation and transmitted to the computing device via the communication network.

[0458] The computing unit analyzes the risk of a link using a computational model. This model is built using natural language processing and machine learning algorithms to determine whether a link may be a phishing or malware link. If the link is deemed unsafe, the computing unit generates a warning signal and notifies the information processing unit again via the communication network.

[0459] The information processing device displays the received warning signal to the user through a user interface. This process allows the user to immediately take precautions against dangerous links, thereby preventing information leaks and attacks on devices.

[0460] In terms of specific hardware, smart devices (e.g., Android and iOS devices) are used as information processing units, and servers are used as computing units. On the software side, TensorFlow or PyTorch are used for natural language processing, and Swift (for iOS) or Java (for Android) are used for the user interface.

[0461] As a concrete example, the prompt "Analyze links in new emails and display warning messages for potentially dangerous links" is used to implement a process that displays a warning to the user about links that pose a risk of phishing scams. In this way, users can achieve enhanced security in the communications they receive on a daily basis.

[0462] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0463] Step 1:

[0464] The device monitors emails and messages received by the user in real time. It uses the text data received by the user as input and automatically extracts URL links from it. The output is a list of the extracted URLs.

[0465] Step 2:

[0466] The terminal preprocesses the extracted URLs. It receives a list of URLs as input and converts the data into a standard format. This makes it easier to send to the computing device over the communication network. The output is a list of preprocessed URLs.

[0467] Step 3:

[0468] The server receives a list of pre-processed URLs sent from terminals via the communication network. The input is a list of URLs, and a computational model is used to evaluate the security of each URL based on this list. Natural language processing and machine learning algorithms are applied to the data processing to determine the risk. The output is the security evaluation result for each URL.

[0469] Step 4:

[0470] The server generates a warning signal for URLs deemed high-risk based on the analysis results from a computational model. The input is the safety assessment result, and the output is the warning signal. This warning signal is transmitted to the terminal via the communication network.

[0471] Step 5:

[0472] The terminal receives a warning signal sent from the server. The input is the warning signal, which is displayed to the user as a warning message through the user interface. The output is the displayed warning message.

[0473] Step 6:

[0474] Users are alerted to warning messages and cautioned against dangerous links. This allows them to take action to ensure their online safety by avoiding clicking on links if necessary. The input is the warning message, and the output is the appropriate action taken by the user.

[0475] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0476] This invention comprises a system incorporating an information terminal, a server, a database, and an emotion engine. The information terminal monitors the user's activity on social media and browsers and collects data based on specific keywords in real time. In addition, it is equipped with an emotion engine that extracts emotion information from the user's actions and interactions. The collected data and emotion information undergo preprocessing and are transmitted to the server via the network.

[0477] The server analyzes the received data using an AI model and determines the level of risk using natural language processing and machine learning algorithms. It also dynamically adjusts the content and presentation method of warning signals by taking into account the user's emotional information analyzed by an emotion engine. Warning signals are generated in a way that matches the user's emotional state and are sent to the information terminal.

[0478] The information terminal receives warning signals transmitted from the server and displays alerts through the user interface based on these signals. Users are presented with messages and options appropriate to their situation, making it easier for them to choose the optimal course of action.

[0479] As a concrete example, consider a scenario where a user finds phrases like "high pay" or "special job offer" on social media. The information terminal collects this data and uses an emotion engine to analyze whether the user is feeling anxious or suspicious. The server generates a warning, taking into account both the text's potential dangers and the sentiment analysis. For example, if the user is wary, more detailed information and specific countermeasures are presented. In this way, the system can respond flexibly, taking into account the user's psychological state, and protect them from potential online dangers.

[0480] The following describes the processing flow.

[0481] Step 1:

[0482] The device monitors the user's activities on social media and in their browser in real time, automatically detecting posts and messages containing specific keywords. In addition, an emotion engine uses user interactions such as facial expressions and operation speed to infer the user's emotional state.

[0483] Step 2:

[0484] The terminal combines the data collected through filters with estimated sentiment information, preprocesses it, and converts it into an integrated data format. This data is batch-processed to minimize latency and sent to the server over the network.

[0485] Step 3:

[0486] The server adds the received data to the analysis queue and activates the AI ​​model. The AI ​​model uses natural language processing techniques to scrutinize the data and identify potential dangers within the text. Simultaneously, it analyzes emotional data to assess the user's psychological state.

[0487] Step 4:

[0488] The server combines the risk score and sentiment analysis results to adjust the content of the warning signal. For example, if the user already feels a strong sense of risk, a warning will be generated that includes specific countermeasures and important information.

[0489] Step 5:

[0490] The device receives appropriately customized warning signals and immediately notifies the user. The device's user interface presents this to the user as a visual or audio message, providing guidance for appropriate action.

[0491] Step 6:

[0492] The user reviews the information provided by the device and decides on specific actions to take in response to the risks they face. The device also continuously collects data on the user's subsequent actions and prepares to return it to the system as feedback for improvement.

[0493] (Example 2)

[0494] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0495] In modern society, the information users encounter online is vast and diverse, and can sometimes contain potentially dangerous content. Traditional methods have been insufficient in detecting the dangers of this information and providing warnings tailored to users' emotional states. Therefore, there is a need for a system that allows users to quickly and accurately identify potential online risks.

[0496] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0497] In this invention, the server includes a function for extracting user emotional information, means for generating emotional information, means for analyzing the information using a generation AI model to determine potential dangers, and means for generating an alarm signal based on the analysis results and notifying an information processing device. This enables an automatic and dynamic response to potentially dangerous information, taking into account the user's psychological state.

[0498] An "information processing device" is a network device used by a user, and is a means of monitoring the user's online behavior and collecting and processing specific data.

[0499] "User" refers to the human entity that operates the information processing device, and is the entity that plays the role of both the source of information and the recipient of its intended purpose within the system.

[0500] "Linguistic expressions" refer to specific keywords or phrases contained in information on a network, and are data elements that trigger risk assessments.

[0501] "Emotional information" refers to information representing the emotional state of a user, extracted from their behavior and interactions acquired through an information processing device.

[0502] A "communication network" is a network infrastructure that enables the transmission and reception of data between an information processing device and an analysis device.

[0503] An "analysis device" is a server system that processes received information using an AI model to analyze potential risks.

[0504] A "generative AI model" is a program or system architecture that utilizes natural language processing and machine learning algorithms to derive useful insights from data.

[0505] A "warning signal" is a warning message generated by an analysis device and notified to an information processing device; it is information that serves as an indicator to inform users of potential risks.

[0506] A "command provision device" is a user interface incorporated into an information processing device, designed to effectively display alarm signals to the user.

[0507] An "information set" is a storage system for saving information and analysis results obtained by an analysis device, and includes a database that enables information sharing with other components.

[0508] This invention is a system implemented using an information processing device, an analysis device, and related software and hardware. The information processing device uses smartphones and computers as devices to monitor user behavior and collect information including specific linguistic expressions. These devices collect data and generate sentiment information in real time via software such as web browsers and social networking applications.

[0509] When a user uses social media, the information processing device identifies posts containing specific keywords and extracts the user's emotional information using an emotion engine. The emotion engine analyzes psychological indicators derived from the user's activity history and posting content.

[0510] The analysis device receives data collected via a communication network and performs analysis using a generative AI model. During this process, it utilizes software libraries such as TensorFlow and PyTorch to perform assessments of potential risks using natural language processing and machine learning. From the analysis results, an alarm signal is generated that takes into account the user's psychological state.

[0511] In information processing systems, received alarm signals are displayed on the user interface. A specific example of this display is a message such as, "This information may contain risks. Please review the details." This alert is communicated to the user via display or audio output, prompting appropriate action.

[0512] Examples of prompt messages are as follows:

[0513] "We want to build a system that detects specific keywords on social media and adjusts warnings based on the user's emotional state. Please explain how this system works."

[0514] As a result, users are protected from potential dangers they may encounter online, and the system is able to respond flexibly and dynamically, taking into account the user's psychological state.

[0515] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0516] Step 1:

[0517] The device monitors user behavior. Specifically, the device analyzes information accessed by the user on social media and browsers, and collects input text data containing specific keywords. This input data includes the user's browsing history and posted content. The collected data is filtered and preprocessed into a format suitable for analysis. The output of the preprocessing is text data formatted for analysis.

[0518] Step 2:

[0519] The device generates user emotion information. The device utilizes an emotion engine to generate emotion data based on user actions and keywords in text. The input for this process consists of the user's emotional responses and action history. The emotion engine analyzes the user's input data using natural language processing techniques and outputs data indicating the emotional state. This output represents the user's current emotional state using numerical values ​​and categories.

[0520] Step 3:

[0521] The terminal transmits collected text data and sentiment information to the server via the communication network. The input is pre-processed text data and extracted sentiment information, and the output is a data packet that packages this information. This data reaches the server via the network.

[0522] Step 4:

[0523] The server analyzes the received data using a generative AI model. At this stage, the input is the transmitted data packets. The server uses the generative AI model, combining natural language processing and machine learning algorithms, to process the data and identify potential risks. The output consists of a risk assessment and analysis results based on the user's emotional state.

[0524] Step 5:

[0525] The server generates an alarm signal and notifies the terminal. The input is the analysis result, and the output is an alarm signal tailored to the user's situation. This alarm signal takes the user's emotional state into account and is generated with appropriate content and urgency.

[0526] Step 6:

[0527] The terminal displays the received alarm signal on the user interface. The input is the alarm signal, and the output is the warning message presented to the user. The terminal notifies the user via display or audio, prompting them to take appropriate action. Specifically, a message such as "This information may contain risks" is displayed on the screen.

[0528] (Application Example 2)

[0529] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0530] There is a need to effectively protect users from potential dangers such as inappropriate online information and phishing scams. However, conventional methods have difficulty providing dynamic warnings that take into account the user's psychological state, and there are limitations to how well they can support users in making appropriate decisions.

[0531] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0532] This invention includes a server that uses an emotion engine to analyze the user's psychological state and dynamically adjust the content of warning signals, a means for storing the recorded information in a storage device and sharing the information with other organizations via a system, and a means for constructing prompt sentences using a generative AI model. This makes it possible to provide appropriate and detailed warnings and countermeasures that are tailored to the user's psychological state, thereby enhancing online safety.

[0533] An "information processing device" is a device that receives user input, monitors information containing specific keywords, and retrieves that information.

[0534] A "communication network" is a network infrastructure that connects multiple information processing devices and computing devices to send and receive information and data.

[0535] A "computational processing unit" is a central processing unit that receives data transmitted through a communication network, analyzes it using a predictive model, and determines the level of risk.

[0536] A "predictive model" is a set of algorithms and computational methods built using natural language processing and machine learning techniques to determine risk from data.

[0537] A "human-machine interface" is a means of interaction that allows an information processing device to display information to a user or to receive input from a user.

[0538] An "emotion engine" is a part of the algorithms and software used to analyze the user's psychological state from their actions and interactions, and to dynamically adjust the content of warning signals.

[0539] A "storage device" is a data storage device used to save records of information so that it can be used later.

[0540] A "generative AI model" is an artificial intelligence model designed to automatically generate appropriate messages based on the user's psychological state.

[0541] A "prompt message" is the input text used by a generative AI model to generate an appropriate warning message.

[0542] This invention is constructed as a system incorporating an information processing device, a computing device, a communication network, and an emotion engine. The information processing device monitors the user's daily activities and acquires information containing specific keywords. For example, if a user receives a message such as "verify credit card details" on social media, this information is detected.

[0543] Information acquired by the information processing device is pre-processed and transmitted to the computing device via the communication network. The computing device analyzes the received information using a predictive model and determines the risk using natural language processing and machine learning algorithms. In this risk determination, the emotion engine analyzes the user's psychological state and dynamically adjusts the warning signals.

[0544] Warning signals tailored to the user's psychological state are generated through prompt messages using a generative AI model and notified to the information processing device. The information processing device displays these warning signals to the user through a human-machine interface, indicating the optimal course of action for the user. This provides real-time information on necessary safety measures and additional details.

[0545] As a concrete example, a possible prompt message for a generative AI model might be: "Based on the keywords 'credit card' and 'verify details' in the message received by the user, the sentiment engine has detected anxiety. Please generate an appropriate warning message and notify the user."

[0546] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0547] Step 1:

[0548] The device monitors the user's activity on social media and messaging apps and retrieves information containing specific keywords. This process takes text data received by the user as input and extracts and outputs relevant content through keyword filtering.

[0549] Step 2:

[0550] The terminal preprocesses the extracted information and converts it into a structured data format. The input is the raw text data obtained in step 1. At this stage, the data is tokenized, unnecessary information is removed as needed, and it is prepared for subsequent analysis. The results of this processing are output as intermediate data.

[0551] Step 3:

[0552] The terminal transmits pre-processed data to the server (processing unit) via the communication network. Here, intermediate data serves as input, and the data is provided to the server safely and quickly.

[0553] Step 4:

[0554] The server uses a predictive model to analyze the information's potential risks based on the received data. Through data analysis using natural language processing and machine learning algorithms, it scores the data's potential risk level and outputs a risk assessment result.

[0555] Step 5:

[0556] The server uses an emotion engine to analyze the user's psychological state and generates a warning signal based on the assessment result. The input is the assessment result from step 4 and the user's emotion data. The output is a warning message tailored to the user.

[0557] Step 6:

[0558] Using a generative AI model, prompt statements are created based on warning signals, and individual warning messages are concretized. Here, the prompt statement serves as input, and dynamically generated warning messages are output.

[0559] Step 7:

[0560] The server sends the generated warning message to the terminal, which then presents it to the user through a human-machine interface. The user receives this warning message and can then review the suggested countermeasures and actions.

[0561] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0562] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0563] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0564] [Fourth Embodiment]

[0565] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0566] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0567] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0568] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0569] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0570] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0571] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0572] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0573] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0574] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0575] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0576] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0577] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0578] The system of the present invention operates in a network environment including information terminals, servers, and databases. The information terminal used by the user collects data in real time via SNS or web browsers and filters it using specific keywords as triggers. The data obtained through this filtering process is converted to a standard data format through preprocessing and transmitted to the server using a secure protocol.

[0579] The server performs analysis using an AI model based on the received data. This AI model is built using natural language processing and machine learning algorithms to determine the risks within the data. Based on the analysis results, a warning signal is generated for information deemed to be high risk and sent to the information terminal. The warning is accompanied by an appropriate message and information on a contact point to enable the user to take immediate action.

[0580] On the user's information terminal, an alert message is displayed through the user interface based on the received warning signal. This allows the user to recognize potential dangers and take necessary actions. Through this series of processes, the system helps prevent crimes committed by users while using the internet.

[0581] As a concrete example, when a user sees a post on social media containing text such as "easy high-paying job" or "secret part-time job," the information is filtered and analyzed by AI. If it is determined to be high-risk, a warning message such as "This job posting is dangerous" will be displayed on the device, and a link to access more specific precautions and additional information will be provided. Through this process, users can effectively avoid potential online dangers.

[0582] The following describes the processing flow.

[0583] Step 1:

[0584] The device monitors the user's activity on social media and in their browser, and detects content containing specific keywords. The detected data is filtered and then recorded for collection.

[0585] Step 2:

[0586] The device preprocesses the data collected through filtering and converts it into a data format (e.g., JSON) that can be analyzed by an AI model. The converted data is then sent to the server via a secure connection.

[0587] Step 3:

[0588] The server places the received data into an analysis queue and runs the AI ​​model. The AI ​​model uses natural language processing and machine learning algorithms to analyze patterns in the data and determine the potential for danger.

[0589] Step 4:

[0590] The server calculates a risk score based on the analysis results and generates a warning signal if the threshold is exceeded. The warning includes specific risk information, as well as information on potential countermeasures and contact points for consultation.

[0591] Step 5:

[0592] The terminal receives a warning signal from the server. Based on this warning signal, the terminal's user interface displays an alert to the user and, if necessary, provides additional safety information and links.

[0593] Step 6:

[0594] Users can check alerts displayed on their devices and, if necessary, decide on their next course of action based on the information provided. This promotes awareness of crime risks and preventative actions.

[0595] (Example 1)

[0596] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0597] In modern internet use, users have access to a wide variety of information, but it is especially important to prevent exposure to dangerous information found on social media and websites. However, it is extremely difficult for users to manually monitor all information and assess its risks, thus increasing the need for systems that efficiently and automatically identify and warn about dangerous information.

[0598] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0599] In this invention, the server includes means for monitoring and acquiring information containing specified words or phrases, means for transmitting the pre-processed information to a processing unit via a communication network, and means for evaluating the risk using a generative model. This makes it possible to automatically identify dangerous information from information accessed by users on the internet and issue warnings immediately.

[0600] An "information processing device" is a device used by a user that has the ability to monitor and acquire information containing specified words or phrases.

[0601] "Specified terms" refer to keywords or phrases used to identify specific information as a trigger.

[0602] "Communication network" refers to the infrastructure of the internet and other networks used to send and receive information.

[0603] A "processing device" refers to a computer server or a component that forms part of a system used to analyze received information and perform risk assessments.

[0604] "Preprocessing" refers to the initial data processing process to convert raw information data into a format suitable for analysis.

[0605] "Generative models" refer to AI technologies that use natural language processing and machine learning algorithms to analyze data and assess risk.

[0606] "User interface" refers to the interface through which a user receives warnings and information from a system via an information processing device.

[0607] "Storage device" refers to a database or file system used to store information within a system.

[0608] To implement this invention, it is necessary to construct a network environment that includes an information processing device, a communication network, a processing device, and a storage device. The information processing device refers to a device such as a smartphone or personal computer that a user uses on a daily basis, and has the function of monitoring and acquiring information containing specific designated words or phrases. This processing includes data collection functions via web browsers and social networking services (SNS) applications.

[0609] The information processing device preprocesses the data into a parseable format and transmits this information to the processing device via a communication network. The HTTPS protocol is used for the communication network to ensure security.

[0610] The processing unit operates a generative model to evaluate the risk level of the received information. This generative model is an AI system that combines natural language processing techniques and machine learning algorithms. Specifically, the processing unit utilizes Python's TensorFlow and NLTK for analysis. Information deemed highly risky through this analysis is notified to the information processing unit as a warning signal via the user interface.

[0611] Users receive warnings from the information processing device at the user interface, allowing them to recognize potential dangers and take necessary actions. For example, if a user sees posts on social media such as "easy high-paying jobs" or "secret part-time jobs," the information may be immediately filtered, and a generative model may determine that it is high-risk. In that case, a warning such as "This job posting is dangerous" will be displayed on the device. This process allows users to appropriately avoid online risks.

[0612] A concrete example of a prompt message to input into a generative AI model would be something like, "Assess the risk of this message and determine if it poses a danger." This allows the generative model to efficiently assess the danger of the information and issue appropriate warnings.

[0613] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0614] Step 1:

[0615] Users collect information through social media and websites using information processing devices. This collected data is stored on the device as raw data. At this stage, it is not determined whether or not it contains specific designated words or phrases.

[0616] Step 2:

[0617] The terminal filters the entire collected raw data using pre-configured specified phrases (e.g., "easy high-paying job," "secret part-time job") as triggers. The filtering process is performed using regular expressions or keyword matching algorithms, and data matching the triggers is extracted. The output at this point is the filtered data.

[0618] Step 3:

[0619] The terminal preprocesses the filtered data into a standard JSON format and sends that formatted data to the server using the secure HTTPS protocol. The output resulting from the preprocessing is structured formatted data.

[0620] Step 4:

[0621] The server receives the formatted data and inputs it into the generative AI model. The generative AI model analyzes the data using natural language processing and performs a risk assessment. In this analysis process, the generative AI model calculates a risk score from the text content and evaluates the danger of the information based on that score. The risk assessment result is obtained as output.

[0622] Step 5:

[0623] Based on the risk assessment results, the server generates a warning signal for information deemed to be high-risk. This warning includes a specific message to immediately alert the user. The output at this step is a warning signal.

[0624] Step 6:

[0625] The terminal receives a warning signal from the server and displays an alert message to the user through the user interface. Based on this signal, the user reviews the detailed warning and recognizes the potential danger. The displayed message may also include links or instructions prompting further action. This output is the warning display to the user.

[0626] (Application Example 1)

[0627] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0628] In today's information and communication environment, the threat of phishing scams and malware through links in emails and messages received by users is increasing. There is a need to provide effective methods to ensure users can use information safely against such online dangers. However, there are limited appropriate technical means to immediately detect these threats and warn users about the dangers. Therefore, the challenge is to provide new measures to enable secure information communication.

[0629] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0630] In this invention, the server includes means for automatically detecting links in communications received by the user and evaluating their security, means for generating and notifying warnings regarding links deemed dangerous based on the analysis results, and means for displaying the warning signals through a user interface. This allows the user to check the security of links in real time and prevent information leaks and attacks on devices caused by malicious links.

[0631] An "information processing device" is a general term for electronic devices that have the functions of collecting, analyzing, and displaying data.

[0632] "Communication network" refers to the entire network infrastructure used for sending and receiving information.

[0633] A "processing unit" is a part of a server or computer that processes data and performs calculations.

[0634] A "computational model" refers to an algorithm used to analyze data and support decision-making based on the results.

[0635] A "user interface" refers to the visual and operational points of contact that users use when operating digital devices.

[0636] "Storage device" refers to all digital storage media that can store information and retrieve it as needed.

[0637] An "organization" refers to a group or entity with a specific purpose, and is the entity that shares information.

[0638] The system for realizing this invention operates in a network environment including an information processing device, a computing device, and a communication network. The information processing device has a function to monitor the content of communications received by the user in real time and to automatically detect links. The detected links are pre-processed for security evaluation and transmitted to the computing device via the communication network.

[0639] The computing unit analyzes the risk of a link using a computational model. This model is built using natural language processing and machine learning algorithms to determine whether a link may be a phishing or malware link. If the link is deemed unsafe, the computing unit generates a warning signal and notifies the information processing unit again via the communication network.

[0640] The information processing device displays the received warning signal to the user through a user interface. This process allows the user to immediately take precautions against dangerous links, thereby preventing information leaks and attacks on devices.

[0641] In terms of specific hardware, smart devices (e.g., Android and iOS devices) are used as information processing units, and servers are used as computing units. On the software side, TensorFlow or PyTorch are used for natural language processing, and Swift (for iOS) or Java (for Android) are used for the user interface.

[0642] As a concrete example, the prompt "Analyze links in new emails and display warning messages for potentially dangerous links" is used to implement a process that displays a warning to the user about links that pose a risk of phishing scams. In this way, users can achieve enhanced security in the communications they receive on a daily basis.

[0643] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0644] Step 1:

[0645] The device monitors emails and messages received by the user in real time. It uses the text data received by the user as input and automatically extracts link URLs from it. The output is a list of the extracted URLs.

[0646] Step 2:

[0647] The terminal preprocesses the extracted URLs. It receives a list of URLs as input and converts the data into a standard format. This makes it easier to send to the computing device over the communication network. The output is a list of preprocessed URLs.

[0648] Step 3:

[0649] The server receives a list of pre-processed URLs sent from terminals via the communication network. The input is a list of URLs, and a computational model is used to evaluate the security of each URL based on this list. Natural language processing and machine learning algorithms are applied to the data processing to determine the risk. The output is the security evaluation result for each URL.

[0650] Step 4:

[0651] The server generates a warning signal for URLs deemed high-risk based on the analysis results from a computational model. The input is the safety assessment result, and the output is the warning signal. This warning signal is transmitted to the terminal via the communication network.

[0652] Step 5:

[0653] The terminal receives a warning signal sent from the server. The input is the warning signal, which is displayed to the user as a warning message through the user interface. The output is the displayed warning message.

[0654] Step 6:

[0655] Users are alerted to warning messages and cautioned about dangerous links. This allows them to take action to ensure their online safety by avoiding clicking on links if necessary. The input is the warning message, and the output is the appropriate action taken by the user.

[0656] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0657] This invention comprises a system incorporating an information terminal, a server, a database, and an emotion engine. The information terminal monitors the user's activity on social media and browsers and collects data based on specific keywords in real time. In addition, it is equipped with an emotion engine that extracts emotion information from the user's actions and interactions. The collected data and emotion information undergo preprocessing and are transmitted to the server via the network.

[0658] The server analyzes the received data using an AI model and determines the level of risk using natural language processing and machine learning algorithms. It also dynamically adjusts the content and presentation method of warning signals by taking into account the user's emotional information analyzed by an emotion engine. Warning signals are generated in a way that matches the user's emotional state and are sent to the information terminal.

[0659] The information terminal receives warning signals transmitted from the server and displays alerts through the user interface based on these signals. Users are presented with messages and options appropriate to their situation, making it easier for them to choose the optimal course of action.

[0660] As a concrete example, consider a scenario where a user finds phrases like "high pay" or "special job offer" on social media. The information terminal collects this data and uses an emotion engine to analyze whether the user is feeling anxious or suspicious. The server generates a warning, taking into account both the text's potential dangers and the sentiment analysis. For example, if the user is wary, more detailed information and specific countermeasures are presented. In this way, the system can respond flexibly, taking into account the user's psychological state, and protect them from potential online dangers.

[0661] The following describes the processing flow.

[0662] Step 1:

[0663] The device monitors the user's activities on social media and in their browser in real time, automatically detecting posts and messages containing specific keywords. In addition, an emotion engine uses user interactions such as facial expressions and operation speed to infer the user's emotional state.

[0664] Step 2:

[0665] The terminal combines the data collected through filters with estimated sentiment information, preprocesses it, and converts it into an integrated data format. This data is batch-processed to minimize latency and sent to the server over the network.

[0666] Step 3:

[0667] The server adds the received data to the analysis queue and activates the AI ​​model. The AI ​​model uses natural language processing techniques to scrutinize the data and identify potential dangers within the text. Simultaneously, it analyzes emotional data to assess the user's psychological state.

[0668] Step 4:

[0669] The server combines the risk score and sentiment analysis results to adjust the content of the warning signal. For example, if the user already feels a strong sense of risk, a warning will be generated that includes specific countermeasures and important information.

[0670] Step 5:

[0671] The device receives appropriately customized warning signals and immediately notifies the user. The device's user interface presents this to the user as a visual or audio message, providing guidance for appropriate action.

[0672] Step 6:

[0673] The user reviews the information provided by the device and decides on specific actions to take in response to the risks they face. The device also continuously collects data on the user's subsequent actions and prepares to return it to the system as feedback for improvement.

[0674] (Example 2)

[0675] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0676] In modern society, the information users encounter online is vast and diverse, and can sometimes contain potentially dangerous content. Traditional methods have been insufficient in detecting the dangers of this information and providing warnings tailored to users' emotional states. Therefore, there is a need for a system that allows users to quickly and accurately identify potential online risks.

[0677] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0678] In this invention, the server includes a function for extracting user emotional information, means for generating emotional information, means for analyzing the information using a generation AI model to determine potential dangers, and means for generating an alarm signal based on the analysis results and notifying an information processing device. This enables an automatic and dynamic response to potentially dangerous information, taking into account the user's psychological state.

[0679] An "information processing device" is a network device used by a user, and is a means of monitoring the user's online behavior and collecting and processing specific data.

[0680] "User" refers to the human entity that operates the information processing device, and is the entity that plays the role of both the source of information and the recipient of its intended purpose within the system.

[0681] "Linguistic expressions" refer to specific keywords or phrases contained in information on a network, and are data elements that trigger risk assessments.

[0682] "Emotional information" refers to information representing the emotional state of a user, extracted from their behavior and interactions acquired through an information processing device.

[0683] A "communication network" is a network infrastructure that enables the transmission and reception of data between an information processing device and an analysis device.

[0684] An "analysis device" is a server system that processes received information using an AI model to analyze potential risks.

[0685] A "generative AI model" is a program or system architecture that utilizes natural language processing and machine learning algorithms to derive useful insights from data.

[0686] A "warning signal" is a warning message generated by an analysis device and notified to an information processing device; it is information that serves as an indicator to inform users of potential risks.

[0687] A "command provision device" is a user interface incorporated into an information processing device, designed to effectively display alarm signals to the user.

[0688] An "information set" is a storage system for saving information and analysis results obtained by an analysis device, and includes a database that enables information sharing with other components.

[0689] This invention is a system implemented using an information processing device, an analysis device, and related software and hardware. The information processing device uses smartphones and computers as devices to monitor user behavior and collect information including specific linguistic expressions. These devices collect data and generate sentiment information in real time via software such as web browsers and social networking applications.

[0690] When a user uses social media, the information processing device identifies posts containing specific keywords and extracts the user's emotional information using an emotion engine. The emotion engine analyzes psychological indicators derived from the user's activity history and posting content.

[0691] The analysis device receives data collected via a communication network and performs analysis using a generative AI model. During this process, it utilizes software libraries such as TensorFlow and PyTorch to perform assessments of potential risks using natural language processing and machine learning. From the analysis results, an alarm signal is generated that takes into account the user's psychological state.

[0692] In information processing systems, received alarm signals are displayed on the user interface. A specific example of this display is a message such as, "This information may contain risks. Please review the details." This alert is communicated to the user via display or audio output, prompting appropriate action.

[0693] Examples of prompt messages are as follows:

[0694] "We want to build a system that detects specific keywords on social media and adjusts warnings based on the user's emotional state. Please explain how this system works."

[0695] As a result, users are protected from potential dangers they may encounter online, and the system is able to respond flexibly and dynamically, taking into account the user's psychological state.

[0696] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0697] Step 1:

[0698] The device monitors user behavior. Specifically, the device analyzes information accessed by the user on social media and browsers, and collects input text data containing specific keywords. This input data includes the user's browsing history and posted content. The collected data is filtered and preprocessed into a format suitable for analysis. The output of the preprocessing is text data formatted for analysis.

[0699] Step 2:

[0700] The device generates user emotion information. The device utilizes an emotion engine to generate emotion data based on user actions and keywords in text. The input for this process consists of the user's emotional responses and action history. The emotion engine analyzes the user's input data using natural language processing techniques and outputs data indicating the emotional state. This output represents the user's current emotional state using numerical values ​​and categories.

[0701] Step 3:

[0702] The terminal transmits collected text data and sentiment information to the server via the communication network. The input is pre-processed text data and extracted sentiment information, and the output is a data packet that packages this information. This data reaches the server via the network.

[0703] Step 4:

[0704] The server analyzes the received data using a generative AI model. At this stage, the input is the transmitted data packets. The server uses the generative AI model, combining natural language processing and machine learning algorithms, to process the data and identify potential risks. The output consists of a risk assessment and analysis results based on the user's emotional state.

[0705] Step 5:

[0706] The server generates an alarm signal and notifies the terminal. The input is the analysis result, and the output is an alarm signal tailored to the user's situation. This alarm signal takes the user's emotional state into account and is generated with appropriate content and urgency.

[0707] Step 6:

[0708] The terminal displays the received alarm signal on the user interface. The input is the alarm signal, and the output is the warning message presented to the user. The terminal notifies the user via display or audio, prompting them to take appropriate action. Specifically, a message such as "This information may contain risks" is displayed on the screen.

[0709] (Application Example 2)

[0710] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0711] There is a need to effectively protect users from potential dangers such as inappropriate online information and phishing scams. However, conventional methods have difficulty providing dynamic warnings that take into account the user's psychological state, and there are limitations to how well they can support users in making appropriate decisions.

[0712] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0713] This invention includes a server that uses an emotion engine to analyze the user's psychological state and dynamically adjust the content of warning signals, a means for storing the recorded information in a storage device and sharing the information with other organizations via a system, and a means for constructing prompt sentences using a generative AI model. This makes it possible to provide appropriate and detailed warnings and countermeasures that are tailored to the user's psychological state, thereby enhancing online safety.

[0714] An "information processing device" is a device that receives user input, monitors information containing specific keywords, and retrieves that information.

[0715] A "communication network" is a network infrastructure that connects multiple information processing devices and computing devices to send and receive information and data.

[0716] A "computational processing unit" is a central processing unit that receives data transmitted through a communication network, analyzes it using a predictive model, and determines the level of risk.

[0717] A "predictive model" is a set of algorithms and computational methods built using natural language processing and machine learning techniques to determine risk from data.

[0718] A "human-machine interface" is a means of interaction that allows an information processing device to display information to a user or to receive input from a user.

[0719] An "emotion engine" is a part of the algorithms and software used to analyze the user's psychological state from their actions and interactions, and to dynamically adjust the content of warning signals.

[0720] A "storage device" is a data storage device used to save records of information so that it can be used later.

[0721] A "generative AI model" is an artificial intelligence model designed to automatically generate appropriate messages based on the user's psychological state.

[0722] A "prompt message" is the input text used by a generative AI model to generate an appropriate warning message.

[0723] This invention is constructed as a system incorporating an information processing device, a computing device, a communication network, and an emotion engine. The information processing device monitors the user's daily activities and acquires information containing specific keywords. For example, if a user receives a message such as "verify credit card details" on social media, this information is detected.

[0724] Information acquired by the information processing device is pre-processed and transmitted to the computing device via the communication network. The computing device analyzes the received information using a predictive model and determines the risk using natural language processing and machine learning algorithms. In this risk determination, the emotion engine analyzes the user's psychological state and dynamically adjusts the warning signals.

[0725] Warning signals tailored to the user's psychological state are generated through prompt messages using a generative AI model and notified to the information processing device. The information processing device displays these warning signals to the user through a human-machine interface, indicating the optimal course of action for the user. This provides real-time information on necessary safety measures and additional details.

[0726] As a concrete example, a possible prompt message for a generative AI model might be: "Based on the keywords 'credit card' and 'verify details' in the message received by the user, the sentiment engine has detected anxiety. Please generate an appropriate warning message and notify the user."

[0727] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0728] Step 1:

[0729] The device monitors the user's activity on social media and messaging apps and retrieves information containing specific keywords. This process takes text data received by the user as input and extracts and outputs relevant content through keyword filtering.

[0730] Step 2:

[0731] The terminal preprocesses the extracted information and converts it into a structured data format. The input is the raw text data obtained in step 1. At this stage, the data is tokenized, unnecessary information is removed as needed, and it is prepared for subsequent analysis. The results of this processing are output as intermediate data.

[0732] Step 3:

[0733] The terminal transmits pre-processed data to the server (processing unit) via the communication network. Here, intermediate data serves as input, and the data is provided to the server safely and quickly.

[0734] Step 4:

[0735] The server uses a predictive model to analyze the information's potential risks based on the received data. Through data analysis using natural language processing and machine learning algorithms, it scores the data's potential risk level and outputs a risk assessment result.

[0736] Step 5:

[0737] The server uses an emotion engine to analyze the user's psychological state and generates a warning signal based on the assessment result. The input is the assessment result from step 4 and the user's emotion data. The output is a warning message tailored to the user.

[0738] Step 6:

[0739] Using a generative AI model, prompt statements are created based on warning signals, and individual warning messages are concretized. Here, the prompt statement serves as input, and dynamically generated warning messages are output.

[0740] Step 7:

[0741] The server sends the generated warning message to the terminal, which then presents it to the user through a human-machine interface. The user receives this warning message and can then review the suggested countermeasures and actions.

[0742] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0743] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0744] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0745] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0746] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0747] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0748] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0749] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0750] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0751] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0752] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0753] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0754] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0755] 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.

[0756] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0757] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0758] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0759] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0760] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0761] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0762] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0763] The following is further disclosed regarding the embodiments described above.

[0764] (Claim 1)

[0765] A means for monitoring data containing specific keywords on an information terminal and collecting this data,

[0766] A means of sending pre-processed data to a server via a network,

[0767] A means of receiving transmitted data and analyzing the risks using an AI model,

[0768] A means for generating a warning signal based on the analysis results and notifying an information terminal,

[0769] An information terminal provides means for displaying a received warning signal through a user interface,

[0770] A system that includes this.

[0771] (Claim 2)

[0772] The system according to claim 1, further comprising means for constructing an AI model for the server to determine risk using natural language processing or machine learning algorithms.

[0773] (Claim 3)

[0774] The system according to claim 1, wherein the server has means for storing records of information in a database and for sharing information with other organizations via a system.

[0775] "Example 1"

[0776] (Claim 1)

[0777] An information processing device includes means for monitoring information containing a specified word or phrase and acquiring this information.

[0778] A means for transmitting pre-processed information to a processing unit via a communication network,

[0779] A means for receiving transmitted information and evaluating the risk using a generative model,

[0780] A means for generating a warning signal based on the evaluation results and notifying an information processing device,

[0781] An information processing device includes means for displaying a received warning signal through a user interface,

[0782] A system that includes this.

[0783] (Claim 2)

[0784] The system according to claim 1, comprising means for constructing a generative model for determining risk using language processing or machine learning techniques.

[0785] (Claim 3)

[0786] The system according to claim 1, wherein the processing device has means for recording information and storing it in a storage device, and for sharing information with other organizations via a system.

[0787] "Application Example 1"

[0788] (Claim 1)

[0789] An information processing device includes means for monitoring information containing a specific instruction word and collecting this information,

[0790] A means for transmitting pre-processed information to a computing device via a communication network,

[0791] A means of receiving transmitted information and analyzing the risks using a computational model,

[0792] A means for generating a warning signal based on the analysis results and notifying an information processing device,

[0793] An information processing device provides means for displaying a received warning signal through a user interface,

[0794] A means for automatically detecting links in communications received by a user and evaluating their security,

[0795] A means of displaying warnings about links that have been identified as dangerous,

[0796] A system that includes this.

[0797] (Claim 2)

[0798] The system according to claim 1, further comprising means for constructing an AI model for determining risk using natural language processing or machine learning algorithms.

[0799] (Claim 3)

[0800] The system according to claim 1, comprising a computing device for recording information and storing it in a storage device, and means for sharing information with other organizations via a system.

[0801] "Example 2 of combining an emotion engine"

[0802] (Claim 1)

[0803] An information processing device includes means for monitoring a user's behavior on a network and collecting information including specific linguistic expressions,

[0804] It has a function to extract user emotional information and a means to generate emotional information,

[0805] A means for transmitting pre-processed information and emotional information to an analysis device via a communication network,

[0806] A means for analyzing information received by an analysis device using a generating AI model to determine potential risks,

[0807] A means by which an analysis device generates an alarm signal based on the analysis results and notifies an information processing device,

[0808] An information processing device includes means for displaying a received alarm signal through an instruction providing device,

[0809] A system that includes this.

[0810] (Claim 2)

[0811] The system according to claim 1, further comprising means for constructing a generative AI model for determining risk using natural language processing or machine learning techniques.

[0812] (Claim 3)

[0813] The system according to claim 1, wherein the analysis device has means for recording information and storing it in an information collection, and for sharing information with other components via a system.

[0814] "Application example 2 when combining with an emotional engine"

[0815] (Claim 1)

[0816] An information processing device includes means for monitoring information containing a specific keyword and acquiring this information.

[0817] Means for transmitting pre-processed information to a computing device via a communication network,

[0818] A means for receiving transmitted information and analyzing the risk using a predictive model,

[0819] A means for generating a warning signal based on the analysis results and notifying an information processing device,

[0820] An information processing device includes means for displaying a received warning signal through a human-machine interface,

[0821] A means of using an emotion engine to analyze the user's psychological state and dynamically adjust the content of warning signals,

[0822] A system that includes this.

[0823] (Claim 2)

[0824] The system according to claim 1, further comprising means for constructing a predictive model for determining risk using natural language processing or machine learning techniques.

[0825] (Claim 3)

[0826] The system according to claim 1, comprising a means for a computing device to record information and store it in a storage device, and for sharing information with other organizations via a system, as well as a means for constructing prompt sentences using a generative AI model that automatically generates messages that match the user's psychological state. [Explanation of Symbols]

[0827] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. An information processing device includes means for monitoring information containing a specific instruction word and collecting this information, A means for transmitting pre-processed information to a computing device via a communication network, A means of receiving transmitted information and analyzing the risks using a computational model, A means for generating a warning signal based on the analysis results and notifying an information processing device, An information processing device provides means for displaying a received warning signal through a user interface, A means for automatically detecting links in communications received by a user and evaluating their security, A means of displaying warnings about links that have been identified as dangerous, A system that includes this.

2. The system according to claim 1, further comprising means for constructing an AI model for determining risk using natural language processing or machine learning algorithms.

3. The system according to claim 1, comprising a computing device for recording information and storing it in a storage device, and a means for sharing information with other organizations via a system.

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