system

Abstract

We provide the system. [Solution] Means for collecting data via information and communication networks, A means of analyzing collected data and assessing potential risks, A means of notifying the user of a warning based on the analysis results, A system that includes this.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Unexamined Patent Application Publication No. 2022 - 180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In the modern information and communication environment, the risk that individuals, especially young people, are involved in illegal part - time job recruitment is increasing. In particular, the information on the Internet is huge and diverse, and it is difficult for individual users to appropriately judge its credibility. As a result, there is a problem that young people unknowingly participate in criminal acts. It is required to prevent such criminal victimization. 【Means for Solving the Problems】 【0005】 This invention provides a system that includes means for collecting data via an information and communication network, means for analyzing the collected data and evaluating potential risks, and means for notifying the user of a warning based on the analysis results. The analysis means accurately identifies risk factors by scrutinizing the information using natural language processing technology. The warning notification means provides a link to a consultation service that corresponds to the notification, enabling the user to take prompt and appropriate action. This makes it possible to reduce the risk of users becoming involved in fraudulent part-time job information. 【0006】 An "information and communication network" is a digital communication system that connects multiple computers and devices for sending and receiving data. 【0007】 "Means of data collection" refers to a system for automatically or manually acquiring and storing relevant information from information sources on a network. 【0008】 "Means of analysis and assessment of potential risks" refers to the process of using collected data to evaluate it through specific algorithms or models and determine the presence of risk factors. 【0009】 "Means of notifying users of warnings" refers to a system that sends messages to users to inform them of risky information or events based on analysis results. 【0010】 "Natural language processing technology" refers to the techniques and processes used by computers to understand, interpret, and generate human language. 【0011】 A "consultation desk" is a point of contact, support center, or contact point that users can access to receive advice or assistance regarding problems or questions. [Brief explanation of the drawing] 【0012】 [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 the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, when an emotion engine is combined. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention] 【0013】 Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings. 【0014】 First, the terms used in the following description will be explained. 【0015】 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 a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), etc. 【0016】 In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0017】 In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc. 【0018】 In the following embodiments, a labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc. 【0019】 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." 【0020】 [First Embodiment] 【0021】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0022】 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. 【0023】 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). 【0024】 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. 【0025】 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. 【0026】 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. 【0027】 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. 【0028】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0029】 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. 【0030】 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. 【0031】 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. 【0032】 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". 【0033】 This invention provides a system that protects users from fraudulent part-time job solicitations using information and communication networks. The system primarily consists of a server, a terminal, and a user. The server collects information from sources such as social networking services (SNS) and job search websites via the internet. This collection is performed using periodic API calls and web scraping techniques. 【0034】 The device incorporates an AI analysis module equipped with natural language processing technology, which performs analysis based on information sent from the server. This AI module uses machine learning algorithms to evaluate relevant posts and score their potential risk. The analyzed data is visually displayed to the user on the device. For information assessed as high risk, the device immediately displays a warning message and provides a link to a support service to ensure safety. 【0035】 Users can examine the information they receive a warning about and use the provided link to contact the appropriate support center. For example, when a user encounters a job posting on an online bulletin board, their device evaluates the information in real time, and if a risk is identified, it immediately displays a warning such as, "This information is high risk. Please contact the support center for details." 【0036】 Key features of this system include the provision of enhanced platform security through information anonymization, feedback loops, and multi-layered data analysis. Information sharing between companies is managed by servers, and high-risk information is shared with other companies, strengthening the overall security network. This creates an environment where users can confidently access information on the internet. 【0037】 The following describes the processing flow. 【0038】 Step 1: 【0039】 The server uses the information and communication network to periodically collect publicly available data through APIs of social networking services and job search websites. The data collected includes posts and comments containing specific keywords, and this information is stored in a database. 【0040】 Step 2: 【0041】 The terminal receives data sent from the server and analyzes it using an AI model equipped with natural language processing technology. The analysis module examines the text in the data and scores the risk by recognizing linguistic patterns. 【0042】 Step 3: 【0043】 The device immediately notifies the user of the analysis results. For information where the risk exceeds a certain threshold, a visual warning message is displayed. This message includes an overview of the risk factors and a link to a consultation service to encourage prompt action. 【0044】 Step 4: 【0045】 Users can review the warning message and, if necessary, click the provided link to contact the designated support center. User feedback is sent to the server via the device and recorded in the database. 【0046】 Step 5: 【0047】 The server collects user feedback and uses it to improve the AI ​​model. Learning based on this feedback improves analysis accuracy and warning accuracy. Additionally, high-risk information is shared with other communication networks, and the reference database is updated. 【0048】 Step 6: 【0049】 The server manages information sharing among multiple companies and provides anonymized data in conjunction with other recruitment services. This ensures that common risk mitigation measures are implemented across each company's platform, contributing to improved information security. 【0050】 (Example 1) 【0051】 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." 【0052】 Numerous fraudulent job postings and scams exist on the internet, posing significant risks to users. Traditional methods require users to verify the authenticity of information themselves; therefore, a system is needed that can quickly and accurately detect fraudulent information and provide appropriate warnings. 【0053】 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. 【0054】 In this invention, the server includes means for collecting data via an information and communication network, means for analyzing the collected data, scoring the risk level of the posted content using a machine learning algorithm, and evaluating the potential risk, and means for notifying the user of a warning and providing a link to a consultation service to ensure safety based on the analysis results. This enables users to quickly recognize malicious information and ensure safe internet use. 【0055】 An "information processing device" is a device that has the function of receiving, analyzing, and sharing information via a computer network. 【0056】 "Information and communication network" refers to all infrastructure used for sending and receiving data, and includes the internet. 【0057】 "Means of collecting data" refers to techniques for obtaining data from internet sources through API calls or web scraping. 【0058】 A "machine learning algorithm" is a set of techniques for learning patterns from data and performing predictions and classifications. 【0059】 "Scoring" is the process of assigning numerical values ​​to target data based on specific criteria and evaluating its importance and risk. 【0060】 "Natural language processing technology" refers to the technology used to analyze, understand, and generate human language using computers. 【0061】 A "means of notifying warnings" refers to a mechanism for visually or audibly alerting users based on a risk assessment. 【0062】 "Methods for strengthening secure networks" refer to technologies that share high-risk information with other information processing devices and build a multi-layered security system. 【0063】 This invention is a system that utilizes an information and communication network, and is particularly intended to protect users from fraudulent recruitment for part-time work. The main components are a server, a terminal, and a user. 【0064】 The server is responsible for collecting information from sources on the internet, such as social networking services and job search websites. This process involves periodic API calls and web scraping techniques. Specifically, programming languages ​​such as Python are used, and libraries like Beautiful Soup and Selenium are employed to implement web scraping. This allows the server to efficiently retrieve information and prepare it for transfer to the terminal. 【0065】 The device analyzes received information using natural language processing and machine learning algorithms. For natural language processing, libraries such as NLTK and spaCy, which can be built using Python, are particularly utilized. For machine learning algorithms, scikit-learn and TENSORFLOW® are used. Using these tools, the device analyzes the collected information, scores the risk, and displays it visually to the user. Additionally, if the risk assessment is high, the device immediately displays a warning message to the user. This message includes a link to a support service for improving security. 【0066】 Users can review warnings from their device and use the provided links to take safe action. For example, if a user finds a suspicious job posting online, the device will evaluate the information in real time, and if it determines that it is dangerous, a warning will be displayed stating, "This information is high risk. Please contact our support desk for details." 【0067】 As an example of a specific prompt, the following would be input to the generative AI model: 【0068】 "Evaluate the content of social media posts to determine if they may be fraudulent part-time jobs. The post is as follows: 'High pay in a short time! Apply now! Details here.'" 【0069】 In this way, users can respond quickly to fraudulent information, creating an environment where they can use information on the internet with peace of mind. 【0070】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0071】 Step 1: 【0072】 The server collects data from social networking services (SNS) and job search websites on the internet via the information and communication network. During this process, the server periodically uses API calls and web scraping techniques to obtain information. It uses URLs and API endpoints as input and outputs data in HTML or JSON format. Specifically, the server uses Python's Beautiful Soup library to parse web pages and extract the necessary text information. 【0073】 Step 2: 【0074】 The server transfers the collected data to the terminal. This process uses a secure communication protocol to transmit the data. The input is HTML or JSON data obtained in step 1, and the output is data in the same format sent to the terminal. Specifically, the server uses SSL / TLS encryption to ensure the security of the data during the transfer. 【0075】 Step 3: 【0076】 The terminal analyzes the received data using a natural language processing module. This process takes in HTML and JSON data as input, extracts text data, and analyzes it. The output is a risk score for the posted content. Specifically, this involves using Python's NLTK and spaCy to perform morphological analysis and semantic analysis of sentences. 【0077】 Step 4: 【0078】 The terminal uses a machine learning algorithm to score and assess risk. The input is the analysis results obtained in step 3, and the output is the risk assessment score for each post. Specifically, it uses scikit-learn to quantify the risk level of a post using a model based on past fraud case data. 【0079】 Step 5: 【0080】 The device displays a warning to the user based on a risk assessment. The input is a risk assessment score, and the output is a visual warning message. Specifically, the device displays a warning message on the user interface stating "This information is high risk" and provides the user with a link to a contact point for ensuring safety. 【0081】 Step 6: 【0082】 When a user receives a warning, they use the link to take appropriate action. The input is the warning message and link information from the device, and the output is the user's action of accessing the support center. Specifically, when the user clicks the link, the support center page opens in the browser. 【0083】 Step 7: 【0084】 The server shares information deemed high-risk with other servers to strengthen the security network. The input is the risk assessment results from step 4, and the output is the transmission of warning information to other information processing devices. Specifically, it synchronizes data between servers and updates the list of malicious information in cooperation with partner companies. 【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】 Given the current situation where fraudulent information on the internet, particularly scams and unethical solicitations via job postings, is on the rise, there is a growing need for systems that reduce the risk of users encountering fraudulent information and enable them to use information safely. However, traditional methods have problems such as being time-consuming or having low accuracy in information gathering and evaluation. As a result, there is a challenge in that it is difficult for users to grasp the risks in real time. 【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 acquiring data via an information and communication network, means for processing the acquired information and evaluating potential risks, means for presenting a warning to the user based on the processing results, and means for detecting and immediately displaying the content being viewed at the time of the warning. This enables users to effectively avoid risks from malicious information on the internet in real time and to use information safely. 【0090】 "Information and communication network" refers to the entire communication infrastructure used to send and receive data and information between distant locations. 【0091】 "Means of acquiring data" refers to the processes and technologies used to collect desired information via information and communication networks. 【0092】 "Means of processing information and assessing potential risks" refers to the technologies and methods used to analyze acquired data and assess the potential risks contained within it. 【0093】 "Means of providing warnings to users based on processing results" refers to a system for alerting users about risks based on the results of information analysis. 【0094】 "Means of detecting and instantly displaying what a user is viewing" refers to technology that instantly grasps the information a user is currently viewing and provides corresponding information at that moment. 【0095】 To implement this invention, it is necessary to build a system in which a server and a terminal cooperate to process information. The server collects data from various sources via the internet and utilizes machine learning models to analyze that data. Specifically, it builds APIs using Python or Flask and obtains information through web scraping using BeautifulSoup. Then, it processes the data using a machine learning framework such as TensorFlow and evaluates potential risks. Warning information generated based on this evaluation is sent from the server to the terminal. 【0096】 The terminal displays notifications based on warning information received from the server, responding immediately to the information the user is currently viewing. These notifications include links to advice channels to ensure user safety. If a user encounters fraudulent job postings, the terminal automatically evaluates the content and issues a warning if necessary. This system protects users from malicious information on the internet, enabling safe communication. 【0097】 As a concrete example, when a user is viewing a job posting on a recruitment site that advertises "high pay," the device analyzes it in real time and immediately displays a warning message saying, "This information may be dangerous. Please contact our advisory desk for details." This allows the user to take appropriate action. 【0098】 An example of a prompt message for a generative AI model is, "Identify any unnatural elements in this job posting and assess the risks." 【0099】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0100】 Step 1: 【0101】 The server retrieves data from social networking services and job search sites via the information and communication network. Specifically, it collects information through API calls and web scraping using BeautifulSoup. The input is the URL of each website, and the output is the raw text data extracted from that site. 【0102】 Step 2: 【0103】 The server analyzes the acquired text data using a machine learning model based on TensorFlow. The server takes the text data as input and calculates a risk score based on it. The output is the risk score for each piece of information. This score serves as an indicator for evaluating potential risks. 【0104】 Step 3: 【0105】 The server generates a warning message for the information the user is viewing, based on the analyzed risk score. The input is the risk score, and the output is the corresponding warning message. For example, if the score is high, a message such as "This information is dangerous" is generated. 【0106】 Step 4: 【0107】 The terminal receives warning messages from the server and displays them immediately on the user interface. The input is the warning message, and the output is a visual warning displayed to the user. This allows the user to immediately recognize the danger. 【0108】 Step 5: 【0109】 Users can click on links to the provided advisory services in response to warning messages displayed on their devices. The input is the user's action (click), and the output is a connection to the advisory service via the link. This process allows users to take further action. 【0110】 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. 【0111】 This invention personalizes the way warnings are delivered by incorporating an emotion engine into a system that warns users of potential risks, such as fraudulent part-time job information. The invention operates via an information and communication network and consists of a server, a terminal, and a user. 【0112】 The server collects publicly available data from social media and job sites via the internet and stores it in a database. This information is then filtered based on posts and comments containing specific keywords. 【0113】 The device is equipped with an AI analysis module that uses natural language processing technology, as well as an emotion engine. The AI ​​analysis module analyzes information sent from the server and scores potential risks based on that information. The emotion engine has the function of recognizing the user's emotional state in real time based on the user's input information and interactions. 【0114】 By combining analysis results with the emotion engine's judgment, the device provides the user with the most appropriate warning. For example, if the emotion engine determines that the user is depressed, the device will provide reassuring language and support information in addition to the usual warning message. Furthermore, even when a high-priority risk is recognized, the strength of the warning will be adjusted according to the user's emotional state. 【0115】 As a concrete example, suppose a user views a suspicious advertisement, and the device detects something suspicious about the advertisement, while the emotion engine simultaneously recognizes the user's anxious expression. The device then displays a warning message directly on the screen saying, "This information is risky. If you are concerned, please use this link for support," prompting the user to access a support service. 【0116】 Thus, this system, which includes an emotion engine, enables flexible risk warnings tailored to individual users, improving the user experience and providing more effective protection against fraudulent part-time job information. 【0117】 The following describes the processing flow. 【0118】 Step 1: 【0119】 The server periodically collects publicly available data from APIs of social networking services and job search sites via the information and communication network. During collection, it filters the data based on specific risky keywords (e.g., high income, daily pay, etc.) and stores the information in a database. 【0120】 Step 2: 【0121】 The server sends the collected data to the terminal for analysis. The transmitted data includes filtered posts and comments. 【0122】 Step 3: 【0123】 The data received by the device is processed by an AI analysis module, and the content of each post is evaluated using natural language processing technology. In this step, the contextual risk level is scored, and the results are temporarily stored. 【0124】 Step 4: 【0125】 The emotion engine built into the device recognizes the user's emotional state from their input and interactions. For example, it analyzes emotions such as reassurance, anxiety, and excitement in real time from the user's facial expressions and voice input. 【0126】 Step 5: 【0127】 The device integrates the scoring results from the AI ​​analysis module with the recognition results from the emotion engine. This determines the appropriate warning content and display method for the user's current emotional state. 【0128】 Step 6: 【0129】 The device notifies the user of any determined warnings. For example, if the emotion engine determines that the user is anxious, it adds reassuring words to the warning message, such as "Don't worry, support is available here." 【0130】 Step 7: 【0131】 This system allows users to review warnings and, if necessary, click on the provided links to contact support. The user's chosen action is sent as feedback to the server via the terminal for future system improvements. 【0132】 Step 8: 【0133】 The server aggregates user feedback and interaction data, which is used to improve the accuracy of the AI ​​analysis module and emotion engine. Through continuous learning, the system becomes more effective over time. 【0134】 (Example 2) 【0135】 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 will be referred to as the "terminal." 【0136】 In recent years, much of the information provided through information and communication networks contains potential risks. However, a problem exists in that users often struggle to receive appropriate warnings tailored to their individual emotional states and circumstances regarding this risk information. In particular, uniform warning messages are ineffective in prompting users to take appropriate action, and their effectiveness in improving the user experience and providing protection is limited. 【0137】 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. 【0138】 In this invention, the server includes means for collecting data via an information and communication network, means for analyzing the collected data and evaluating potential risks, means for recognizing the user's emotional state in real time based on the analysis results and optimally adjusting warnings, and means for notifying the user of the generated warning message. This enables flexible and effective risk warnings tailored to each user's emotional state, improving the user experience and providing more effective protection against fraudulent information. 【0139】 An "information and communication network" is a communication infrastructure that interconnects computers, servers, and other devices for sending and receiving data. 【0140】 "Means of data collection" refers to the functionality of hardware and software that retrieve information from network sources based on specific conditions. 【0141】 "Means of analyzing data and assessing potential risks" refers to the technology that supports the process of processing collected information and identifying and quantifying the risks and problems contained in that information. 【0142】 "A means of recognizing the user's emotional state in real time and optimally adjusting warnings" refers to a technology that instantly determines the user's current psychological state and dynamically changes the content and expression of warnings according to that state. 【0143】 "Means of notifying users of warning messages" refers to methods or devices for presenting users with risk information, enabling them to take immediate action. 【0144】 This invention comprises a system that provides users with necessary information via an information and communication network. The system primarily consists of a server, a terminal, and a user. The roles and technical characteristics of each component are described below. 【0145】 First, the server collects data from multiple sources via the internet. Specifically, it collects publicly available information from social networking services and job posting sites, filters this information based on specific keywords, and stores it in a database. Servers are often implemented using programming languages ​​such as Python or JavaScript. 【0146】 Next, the terminal is responsible for receiving data sent from the server. The received data is analyzed by an AI analysis module using natural language processing technology within the terminal. This AI analysis module uses libraries such as TensorFlow and PyTorch. This module processes the received information to assess potential risks and performs risk scoring. 【0147】 Furthermore, the device is equipped with an emotion engine that recognizes the user's emotional state in real time based on the user's input information. The emotion engine analyzes the user's facial expressions and voice using the camera and microphone, and updates the emotion model as needed. 【0148】 By combining analysis results with the emotion engine's judgment, the device generates individually customized warning messages for each user. For example, if the emotion engine determines that the user is feeling down, it adds reassuring phrases and support information in addition to the usual warning message. Furthermore, even when a high-priority risk is recognized, the strength of the warning is adjusted according to the user's emotions. 【0149】 For example, suppose a user is viewing a suspicious job advertisement, and the device detects a risk in the advertisement information, while the emotion engine simultaneously recognizes that the user is showing signs of anxiety. In this case, the device will directly display a message on the screen saying, "This information is risky. If you are concerned, please use this link for support," encouraging the user to proceed with confidence. 【0150】 An example of a prompt message is: "Based on data collected from social media, generate appropriate warning messages for each user's current emotional state. If the user is feeling down, include additional information to provide reassurance." 【0151】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0152】 Step 1: 【0153】 The server collects data publicly available from social networking services (SNS) and job search websites via information and communication networks. The input is text data from the internet, which the server obtains using scraping techniques. The collected data is filtered based on specific keywords and stored in a database as output. Specifically, an automated script using a Python library runs periodically to collect the necessary data. 【0154】 Step 2: 【0155】 The terminal receives filtered data sent from the server. The input is the data collected in step 1. The terminal's AI analysis module analyzes this data using natural language processing techniques, performing evaluations based on word frequency and sentiment analysis models. The output is a potential risk score, and the analyzed data is obtained as numerical information indicating the level of risk. Specifically, text analysis and scoring are performed using NLTK and spaCy. 【0156】 Step 3: 【0157】 The device utilizes an emotion engine to recognize the user's emotional state in real time. Input consists of the user's facial expressions and voice, acquired through the device's camera and microphone. The emotion engine analyzes the acquired data to determine the user's emotions. Output is a tag or score indicating the user's emotional state. Specifically, a TensorFlow model analyzes the user's facial expressions and voice data to determine their emotional state in real time. 【0158】 Step 4: 【0159】 The device generates a warning message based on the analyzed risk score and the user's emotional state. The inputs are the risk score obtained in step 2 and the emotional state in step 3. The device uses a generation AI model to create a warning message in response to the prompt, and the message includes customized wording based on the user's situation. The output is the warning message displayed to the user. Specifically, the warning message is dynamically generated in response to the prompt and visually presented on the screen. 【0160】 Step 5: 【0161】 The user reviews the warning message displayed on the device and clicks on links to request additional information or support as needed. The input is the warning message generated in step 4. The user's action connects them to a support desk. The output is the user's action and the subsequent support provided. Specifically, a web browser opens displaying relevant information, and if support is needed, more detailed guidance is provided. 【0162】 (Application Example 2) 【0163】 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". 【0164】 Conventional warning systems struggle to appropriately recognize and address the individual emotional state of each user, resulting in warnings that are not optimal for the user. This can lead to inaccurate warnings, potentially exacerbating user anxiety and negatively impacting the user experience. 【0165】 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. 【0166】 In this invention, the server includes means for collecting data via an information and communication network, means for analyzing the collected data and evaluating potential risks, and means for recognizing the user's emotional state and adjusting the content and intensity of warnings based on that emotional state. This enables the provision of personalized warnings tailored to the user, improving the user experience and providing information in an appropriate format. 【0167】 An "information and communication network" is a technological foundation that enables the transmission and reception of data, and is a system that uses the internet to exchange information between multiple devices. 【0168】 "Means of data collection" refers to the function of acquiring various data from external sources via information and communication networks and making it available for use within the system. 【0169】 "Potential risks" refer to unseen dangers or disadvantages that users may encounter in information or situations they might come into contact with. 【0170】 "Means of analysis" refers to the process technology of analyzing collected data and interpreting its content and meaning. 【0171】 "Means of notifying users of warnings" refers to a function that sends messages or alerts to users based on analysis results to draw their attention. 【0172】 "Means for recognizing a user's emotional state" refers to a function that determines the user's emotional state in real time based on their interactions and input information. 【0173】 "Means of adjusting the content and intensity of warnings based on emotional state" refers to the process of changing the way warnings are delivered and the depth of their content, taking into account the recognized emotions of the user. 【0174】 This invention relates to the realization of a system that collects data via an information and communication network and provides warnings that take user sentiment into consideration based on the analysis results. The server collects data via the internet and obtains information from social networking services and job search websites. The collected data is analyzed by an analysis module using natural language processing technology, and potential risks are evaluated. 【0175】 The device incorporates an AI analysis module that scores potential risks based on information transmitted from the analysis module, and an emotion engine that processes the user's facial expressions and input information in real time. The emotion engine recognizes the user's emotional state and uses that information to adjust the content and intensity of warnings. For example, if the device detects that the user is anxious, it will change the normal warning message to reassuring wording and add a link to a support center to help alleviate the user's anxiety. 【0176】 For example, if a user expresses concern after accessing fraudulent part-time job information, the device will assess the risk of that information and display a message such as, "This information is risky. If you are concerned, please seek support via this link." In this way, flexible risk warnings tailored to the user's emotional state become possible, resulting in more effective protection. 【0177】 An example of a prompt message for a generative AI model would be: "I feel uneasy about a certain website. Please assess the risks and display reassuring support information." 【0178】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0179】 Step 1: 【0180】 The server collects data from social networking services (SNS) and job search websites via information and communication networks. It uses access information to external data sources as input and outputs collected text data. This collection process utilizes APIs and web crawling technologies to periodically retrieve relevant information. 【0181】 Step 2: 【0182】 The terminal receives text data sent from the server and performs analysis using natural language processing technology. The input is the collected text data, and the output is a score indicating the analyzed topic and risk level. Specifically, the text is tokenized, and relationships and risk factors are extracted using big data analysis algorithms. 【0183】 Step 3: 【0184】 The emotion engine built into the device processes the user's facial expressions and input data in real time to recognize their emotional state. Input is user interaction data (camera footage, keyboard input, etc.), and output is the recognized emotional state. A machine learning model is used for emotion recognition, extracting emotional characteristics from the data. 【0185】 Step 4: 【0186】 The device combines the analyzed risk score with the user's emotional state to generate the optimal warning message. The input is the analyzed risk score and the user's emotional state, and the output is the adjusted warning message. In this step, a generative AI model is used to create a customized message based on the prompt text. 【0187】 Step 5: 【0188】 The device displays appropriate warning messages to the user and, if necessary, provides links to support services. The input is the warning message, and the output is the information displayed on the user's screen. Throughout this process, information is provided in a visually clear and easy-to-understand format via the user interface. 【0189】 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. 【0190】 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. 【0191】 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. 【0192】 [Second Embodiment] 【0193】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0194】 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. 【0195】 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). 【0196】 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. 【0197】 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. 【0198】 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). 【0199】 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. 【0200】 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. 【0201】 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. 【0202】 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. 【0203】 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. 【0204】 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". 【0205】 This invention provides a system that protects users from fraudulent part-time job solicitations using information and communication networks. The system primarily consists of a server, a terminal, and a user. The server collects information from sources such as social networking services (SNS) and job search websites via the internet. This collection is performed using periodic API calls and web scraping techniques. 【0206】 The device incorporates an AI analysis module equipped with natural language processing technology, which performs analysis based on information sent from the server. This AI module uses machine learning algorithms to evaluate relevant posts and score their potential risk. The analyzed data is visually displayed to the user on the device. For information assessed as high risk, the device immediately displays a warning message and provides a link to a support service to ensure safety. 【0207】 Users can examine the information they receive a warning about and use the provided link to contact the appropriate support center. For example, when a user encounters a job posting on an online bulletin board, their device evaluates the information in real time, and if a risk is identified, it immediately displays a warning such as, "This information is high risk. Please contact the support center for details." 【0208】 Key features of this system include the provision of enhanced platform security through information anonymization, feedback loops, and multi-layered data analysis. Information sharing between companies is managed by servers, and high-risk information is shared with other companies, strengthening the overall security network. This creates an environment where users can confidently access information on the internet. 【0209】 The following describes the processing flow. 【0210】 Step 1: 【0211】 The server uses the information and communication network to periodically collect publicly available data through APIs of social networking services and job search websites. The data collected includes posts and comments containing specific keywords, and this information is stored in a database. 【0212】 Step 2: 【0213】 The terminal receives data sent from the server and analyzes it using an AI model equipped with natural language processing technology. The analysis module examines the text in the data and scores the risk by recognizing linguistic patterns. 【0214】 Step 3: 【0215】 The device immediately notifies the user of the analysis results. For information where the risk exceeds a certain threshold, a visual warning message is displayed. This message includes an overview of the risk factors and a link to a consultation service to encourage prompt action. 【0216】 Step 4: 【0217】 Users can review the warning message and, if necessary, click the provided link to contact the designated support center. User feedback is sent to the server via the device and recorded in the database. 【0218】 Step 5: 【0219】 The server collects user feedback and uses it to improve the AI ​​model. Learning based on this feedback improves analysis accuracy and warning accuracy. Additionally, high-risk information is shared with other communication networks, and the reference database is updated. 【0220】 Step 6: 【0221】 The server manages information sharing among multiple companies and provides anonymized data in conjunction with other recruitment services. This ensures that common risk mitigation measures are implemented across each company's platform, contributing to improved information security. 【0222】 (Example 1) 【0223】 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." 【0224】 Numerous fraudulent job postings and scams exist on the internet, posing significant risks to users. Traditional methods require users to verify the authenticity of information themselves; therefore, a system is needed that can quickly and accurately detect fraudulent information and provide appropriate warnings. 【0225】 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. 【0226】 In this invention, the server includes means for collecting data via an information and communication network, means for analyzing the collected data, scoring the risk level of the posted content using a machine learning algorithm, and evaluating the potential risk, and means for notifying the user of a warning and providing a link to a consultation service to ensure safety based on the analysis results. This enables users to quickly recognize malicious information and ensure safe internet use. 【0227】 An "information processing device" is a device that has the function of receiving, analyzing, and sharing information via a computer network. 【0228】 "Information and communication network" refers to all infrastructure used for sending and receiving data, and includes the internet. 【0229】 "Means of collecting data" refers to techniques for obtaining data from internet sources through API calls or web scraping. 【0230】 A "machine learning algorithm" is a set of techniques for learning patterns from data and performing predictions and classifications. 【0231】 "Scoring" is the process of assigning numerical values ​​to target data based on specific criteria and evaluating its importance and risk. 【0232】 "Natural language processing technology" refers to the technology used to analyze, understand, and generate human language using computers. 【0233】 A "means of notifying warnings" refers to a mechanism for visually or audibly alerting users based on a risk assessment. 【0234】 "Methods for strengthening secure networks" refer to technologies that share high-risk information with other information processing devices and build a multi-layered security system. 【0235】 This invention is a system that utilizes an information and communication network, and is particularly intended to protect users from fraudulent recruitment for part-time work. The main components are a server, a terminal, and a user. 【0236】 The server is responsible for collecting information from sources on the internet, such as social networking services and job search websites. This process involves periodic API calls and web scraping techniques. Specifically, programming languages ​​such as Python are used, and libraries like Beautiful Soup and Selenium are employed to implement web scraping. This allows the server to efficiently retrieve information and prepare it for transfer to the terminal. 【0237】 The device analyzes received information using natural language processing techniques and machine learning algorithms. For natural language processing, libraries such as NLTK and spaCy, which can be built using Python, are utilized. For machine learning algorithms, scikit-learn and TensorFlow are used. Using these methods, the device analyzes the collected information, scores the risk, and displays it visually to the user. Additionally, if the risk assessment is high, the device immediately displays a warning message to the user. This message includes a link to a support service for improving security. 【0238】 Users can review warnings from their device and use the provided links to take safe action. For example, if a user finds a suspicious job posting online, the device will evaluate the information in real time, and if it determines that it is dangerous, a warning will be displayed stating, "This information is high risk. Please contact our support desk for details." 【0239】 As an example of a specific prompt, the following would be input to the generative AI model: 【0240】 "Evaluate the content of social media posts to determine if they may be fraudulent part-time jobs. The post is as follows: 'High pay in a short time! Apply now! Details here.'" 【0241】 In this way, users can respond quickly to fraudulent information, creating an environment where they can use information on the internet with peace of mind. 【0242】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0243】 Step 1: 【0244】 The server collects data from social networking services (SNS) and job search websites on the internet via the information and communication network. During this process, the server periodically uses API calls and web scraping techniques to obtain information. It uses URLs and API endpoints as input and outputs data in HTML or JSON format. Specifically, the server uses Python's Beautiful Soup library to parse web pages and extract the necessary text information. 【0245】 Step 2: 【0246】 The server transfers the collected data to the terminal. This process uses a secure communication protocol to transmit the data. The input is HTML or JSON data obtained in step 1, and the output is data in the same format sent to the terminal. Specifically, the server uses SSL / TLS encryption to ensure the security of the data during the transfer. 【0247】 Step 3: 【0248】 The terminal analyzes the received data using a natural language processing module. This process takes in HTML and JSON data as input, extracts text data, and analyzes it. The output is a risk score for the posted content. Specifically, this involves using Python's NLTK and spaCy to perform morphological analysis and semantic analysis of sentences. 【0249】 Step 4: 【0250】 The terminal uses a machine learning algorithm to score and assess risk. The input is the analysis results obtained in step 3, and the output is the risk assessment score for each post. Specifically, it uses scikit-learn to quantify the risk level of a post using a model based on past fraud case data. 【0251】 Step 5: 【0252】 The device displays a warning to the user based on a risk assessment. The input is a risk assessment score, and the output is a visual warning message. Specifically, the device displays a warning message on the user interface stating "This information is high risk" and provides the user with a link to a contact point for ensuring safety. 【0253】 Step 6: 【0254】 When a user receives a warning, they use the link to take appropriate action. The input is the warning message and link information from the device, and the output is the user's action of accessing the support center. Specifically, when the user clicks the link, the support center page opens in the browser. 【0255】 Step 7: 【0256】 The server shares information deemed high-risk with other servers to strengthen the security network. The input is the risk assessment results from step 4, and the output is the transmission of warning information to other information processing devices. Specifically, it synchronizes data between servers and updates the list of malicious information in cooperation with partner companies. 【0257】 (Application Example 1) 【0258】 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." 【0259】 Given the current situation where fraudulent information on the internet, particularly scams and unethical solicitations via job postings, is on the rise, there is a growing need for systems that reduce the risk of users encountering fraudulent information and enable them to use information safely. However, traditional methods have problems such as being time-consuming or having low accuracy in information gathering and evaluation. As a result, there is a challenge in that it is difficult for users to grasp the risks in real time. 【0260】 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. 【0261】 In this invention, the server includes means for acquiring data via an information and communication network, means for processing the acquired information and evaluating potential risks, means for presenting a warning to the user based on the processing results, and means for detecting and immediately displaying the content being viewed at the time of the warning. This enables users to effectively avoid risks from malicious information on the internet in real time and to use information safely. 【0262】 "Information and communication network" refers to the entire communication infrastructure used to send and receive data and information between distant locations. 【0263】 "Means of acquiring data" refers to the processes and technologies used to collect desired information via information and communication networks. 【0264】 "Means of processing information and assessing potential risks" refers to the technologies and methods used to analyze acquired data and assess the potential risks contained within it. 【0265】 "Means of providing warnings to users based on processing results" refers to a system for alerting users about risks based on the results of information analysis. 【0266】 "Means of detecting and instantly displaying what a user is viewing" refers to technology that instantly grasps the information a user is currently viewing and provides corresponding information at that moment. 【0267】 To implement this invention, it is necessary to build a system in which a server and a terminal cooperate to process information. The server collects data from various sources via the internet and utilizes machine learning models to analyze that data. Specifically, it builds APIs using Python or Flask and obtains information through web scraping using BeautifulSoup. Then, it processes the data using a machine learning framework such as TensorFlow and evaluates potential risks. Warning information generated based on this evaluation is sent from the server to the terminal. 【0268】 The terminal displays notifications based on warning information received from the server, responding immediately to the information the user is currently viewing. These notifications include links to advice channels to ensure user safety. If a user encounters fraudulent job postings, the terminal automatically evaluates the content and issues a warning if necessary. This system protects users from malicious information on the internet, enabling safe communication. 【0269】 As a concrete example, when a user is viewing a job posting on a recruitment site that advertises "high pay," the device analyzes it in real time and immediately displays a warning message saying, "This information may be dangerous. Please contact our advisory desk for details." This allows the user to take appropriate action. 【0270】 An example of a prompt message for a generative AI model is, "Identify any unnatural elements in this job posting and assess the risks." 【0271】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0272】 Step 1: 【0273】 The server retrieves data from social networking services and job search sites via the information and communication network. Specifically, it collects information through API calls and web scraping using BeautifulSoup. The input is the URL of each website, and the output is the raw text data extracted from that site. 【0274】 Step 2: 【0275】 The server analyzes the acquired text data using a machine learning model based on TensorFlow. The server takes the text data as input and calculates a risk score based on it. The output is the risk score for each piece of information. This score serves as an indicator for evaluating potential risks. 【0276】 Step 3: 【0277】 The server generates a warning message for the information the user is viewing, based on the analyzed risk score. The input is the risk score, and the output is the corresponding warning message. For example, if the score is high, a message such as "This information is dangerous" is generated. 【0278】 Step 4: 【0279】 The terminal receives warning messages from the server and displays them immediately on the user interface. The input is the warning message, and the output is a visual warning displayed to the user. This allows the user to immediately recognize the danger. 【0280】 Step 5: 【0281】 Users can click on links to the provided advisory services in response to warning messages displayed on their devices. The input is the user's action (click), and the output is a connection to the advisory service via the link. This process allows users to take further action. 【0282】 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. 【0283】 This invention personalizes the way warnings are delivered by incorporating an emotion engine into a system that warns users of potential risks, such as fraudulent part-time job information. The invention operates via an information and communication network and consists of a server, a terminal, and a user. 【0284】 The server collects public data from SNS and job hunting websites through the Internet and stores it in a database. This information is filtered based on posts and comments with specific keywords. 【0285】 In addition to the AI analysis module using natural language processing technology, the terminal is equipped with an emotion engine. The AI analysis module analyzes the information sent from the server and scores potential risks based on its content. The emotion engine has the function of recognizing the user's emotional state in real time based on the user's input information and interactions. 【0286】 By combining the analysis results and the judgment of the emotion engine, the terminal warns the user in the most appropriate way. For example, when the emotion engine determines that the user is depressed, the terminal provides reassuring words and support information in addition to the normal warning message. Also, even when a highly urgent risk is recognized, the intensity of the warning is adjusted according to the user's emotional state. 【0287】 As a specific example, when the user views a suspicious advertisement, assume that the terminal detects an abnormal smell from the advertisement, and at the same time the emotion engine recognizes the user's anxious expression. The terminal directly displays a warning message on the screen saying, "This information is risky. If you are worried, please get support from this link" and prompts access to the consultation window. 【0288】 In this way, this system including the emotion engine enables flexible risk warnings according to individual users, improves the user experience, and realizes more effective defense against illegal part-time job information. 【0289】 The following describes the processing flow. 【0290】 Step 1: 【0291】 The server periodically collects publicly available data from APIs of social networking services and job search websites via the information and communication network. During collection, it filters the data based on specific risky keywords (e.g., high income, daily pay, etc.) and stores that information in a database. 【0292】 Step 2: 【0293】 The server sends the collected data to the terminal for analysis. The transmitted data includes filtered posts and comments. 【0294】 Step 3: 【0295】 The data received by the device is processed by an AI analysis module, and the content of each post is evaluated using natural language processing technology. In this step, the contextual risk level is scored, and the results are temporarily stored. 【0296】 Step 4: 【0297】 The emotion engine built into the device recognizes the user's emotional state from their input and interactions. For example, it analyzes emotions such as reassurance, anxiety, and excitement in real time from the user's facial expressions and voice input. 【0298】 Step 5: 【0299】 The device integrates the scoring results from the AI ​​analysis module with the recognition results from the emotion engine. This determines the appropriate warning content and display method for the user's current emotional state. 【0300】 Step 6: 【0301】 The device notifies the user of any determined warnings. For example, if the emotion engine determines that the user is anxious, it adds reassuring words to the warning message, such as "Don't worry, support is available here." 【0302】 Step 7: 【0303】 Prepare for the case where the user checks the warning and clicks on the presented link if necessary to consult the consultation window. The user's selected action is transmitted as feedback to the server through the terminal for subsequent system improvement. 【0304】 Step 8: 【0305】 The server aggregates the user's feedback and interaction data and uses it to improve the accuracy of the AI analysis module and the emotion engine. Through continuous learning, the system enhances its effectiveness over time. 【0306】 (Example 2) 【0307】 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". 【0308】 In recent years, among the information provided through information and communication networks, many contain potential risks. However, there is a problem that it is difficult for users to receive appropriate warnings according to their individual emotional states and situations regarding this risk information. In particular, with a uniform warning message, it is difficult to prompt users to take appropriate actions, and the improvement of the user experience and the defensive effect are also limited. 【0309】 The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0310】 In this invention, the server includes means for collecting data via an information and communication network, means for analyzing the collected data and evaluating potential risks, means for recognizing the user's emotional state in real time based on the analysis results and optimally adjusting warnings, and means for notifying the user of the generated warning message. This enables flexible and effective risk warnings tailored to each user's emotional state, improving the user experience and providing more effective protection against fraudulent information. 【0311】 An "information and communication network" is a communication infrastructure that interconnects computers, servers, and other devices for sending and receiving data. 【0312】 "Means of data collection" refers to the functionality of hardware and software that retrieve information from network sources based on specific conditions. 【0313】 "Means of analyzing data and assessing potential risks" refers to the technologies that support the process of processing collected information and identifying and quantifying the risks and problems contained in that information. 【0314】 "A means of recognizing the user's emotional state in real time and optimally adjusting warnings" refers to a technology that instantly determines the user's current psychological state and dynamically changes the content and expression of warnings according to that state. 【0315】 "Means of notifying users of warning messages" refers to methods or devices for presenting users with risk information, enabling them to take immediate action. 【0316】 This invention comprises a system that provides users with necessary information via an information and communication network. The system primarily consists of a server, a terminal, and a user. The roles and technical characteristics of each component are described below. 【0317】 First, the server collects data from multiple sources via the internet. Specifically, it collects publicly available information from social networking services and job posting sites, filters this information based on specific keywords, and stores it in a database. Servers are often implemented using programming languages ​​such as Python or JavaScript. 【0318】 Next, the terminal is responsible for receiving data sent from the server. The received data is analyzed by an AI analysis module using natural language processing technology within the terminal. This AI analysis module uses libraries such as TensorFlow and PyTorch. This module processes the received information to assess potential risks and performs risk scoring. 【0319】 Furthermore, the device is equipped with an emotion engine that recognizes the user's emotional state in real time based on the user's input information. The emotion engine analyzes the user's facial expressions and voice using the camera and microphone, and updates the emotion model as needed. 【0320】 By combining analysis results with the emotion engine's judgment, the device generates individually customized warning messages for each user. For example, if the emotion engine determines that the user is feeling down, it adds reassuring phrases and support information in addition to the usual warning message. Furthermore, even when a high-priority risk is recognized, the strength of the warning is adjusted according to the user's emotions. 【0321】 For example, suppose a user is viewing a suspicious job advertisement, and the device detects a risk in the advertisement information, while the emotion engine simultaneously recognizes that the user is showing signs of anxiety. In this case, the device will directly display a message on the screen saying, "This information is risky. If you are concerned, please use this link for support," encouraging the user to proceed with confidence. 【0322】 An example of a prompt message is: "Based on data collected from social media, generate appropriate warning messages for each user's current emotional state. If the user is feeling down, include additional information to provide reassurance." 【0323】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0324】 Step 1: 【0325】 The server collects data publicly available from social networking services (SNS) and job search websites via information and communication networks. The input is text data from the internet, which the server obtains using scraping techniques. The collected data is filtered based on specific keywords and stored in a database as output. Specifically, an automated script using a Python library runs periodically to collect the necessary data. 【0326】 Step 2: 【0327】 The terminal receives filtered data sent from the server. The input is the data collected in step 1. The terminal's AI analysis module analyzes this data using natural language processing techniques, performing evaluations based on word frequency and sentiment analysis models. The output is a potential risk score, and the analyzed data is obtained as numerical information indicating the level of risk. Specifically, text analysis and scoring are performed using NLTK and spaCy. 【0328】 Step 3: 【0329】 The device utilizes an emotion engine to recognize the user's emotional state in real time. Input consists of the user's facial expressions and voice, acquired through the device's camera and microphone. The emotion engine analyzes the acquired data to determine the user's emotions. Output is a tag or score indicating the user's emotional state. Specifically, a TensorFlow model analyzes the user's facial expressions and voice data to determine their emotional state in real time. 【0330】 Step 4: 【0331】 The device generates a warning message based on the analyzed risk score and the user's emotional state. The inputs are the risk score obtained in step 2 and the emotional state in step 3. The device uses a generation AI model to create a warning message in response to the prompt, and the message includes customized wording based on the user's situation. The output is the warning message displayed to the user. Specifically, the warning message is dynamically generated in response to the prompt and visually presented on the screen. 【0332】 Step 5: 【0333】 The user reviews the warning message displayed on the device and clicks on links to request additional information or support as needed. The input is the warning message generated in step 4. The user's action connects them to a support desk. The output is the user's action and the subsequent support provided. Specifically, a web browser opens displaying relevant information, and if support is needed, more detailed guidance is provided. 【0334】 (Application Example 2) 【0335】 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." 【0336】 Conventional warning systems struggle to appropriately recognize and address the individual emotional state of each user, resulting in warnings that are not optimal for the user. This can lead to inaccurate warnings, potentially exacerbating user anxiety and negatively impacting the user experience. 【0337】 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. 【0338】 In this invention, the server includes means for collecting data via an information and communication network, means for analyzing the collected data and evaluating potential risks, and means for recognizing the user's emotional state and adjusting the content and intensity of warnings based on that emotional state. This enables the provision of personalized warnings tailored to the user, improving the user experience and providing information in an appropriate format. 【0339】 An "information and communication network" is a technological foundation that enables the transmission and reception of data, and is a system that uses the internet to exchange information between multiple devices. 【0340】 "Means of data collection" refers to the function of acquiring various data from external sources via information and communication networks and making it available for use within the system. 【0341】 "Potential risks" refer to unseen dangers or disadvantages that users may encounter in information or situations they might come into contact with. 【0342】 "Means of analysis" refers to the process technology of analyzing collected data and interpreting its content and meaning. 【0343】 "Means of notifying users of warnings" refers to a function that sends messages or alerts to users based on analysis results to draw their attention. 【0344】 "Means for recognizing a user's emotional state" refers to a function that determines the user's emotional state in real time based on their interactions and input information. 【0345】 "Means of adjusting the content and intensity of warnings based on emotional state" refers to the process of changing the way warnings are delivered and the depth of their content, taking into account the recognized emotions of the user. 【0346】 This invention relates to the realization of a system that collects data via an information and communication network and provides warnings that take user sentiment into consideration based on the analysis results. The server collects data via the internet and obtains information from social networking services and job search websites. The collected data is analyzed by an analysis module using natural language processing technology, and potential risks are evaluated. 【0347】 The device incorporates an AI analysis module that scores potential risks based on information transmitted from the analysis module, and an emotion engine that processes the user's facial expressions and input information in real time. The emotion engine recognizes the user's emotional state and uses that information to adjust the content and intensity of warnings. For example, if the device detects that the user is anxious, it will change the normal warning message to reassuring wording and add a link to a support center to help alleviate the user's anxiety. 【0348】 For example, if a user expresses concern after accessing fraudulent part-time job information, the device will assess the risk of that information and display a message such as, "This information is risky. If you are concerned, please seek support via this link." In this way, flexible risk warnings tailored to the user's emotional state become possible, resulting in more effective protection. 【0349】 An example of a prompt message for a generative AI model would be: "I feel uneasy about a certain website. Please assess the risks and display reassuring support information." 【0350】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0351】 Step 1: 【0352】 The server collects data from social networking services (SNS) and job search websites via information and communication networks. It uses access information to external data sources as input and outputs collected text data. This collection process utilizes APIs and web crawling technologies to periodically retrieve relevant information. 【0353】 Step 2: 【0354】 The terminal receives text data sent from the server and performs analysis using natural language processing technology. The input is the collected text data, and the output is a score indicating the analyzed topic and risk level. Specifically, the text is tokenized, and relationships and risk factors are extracted using big data analysis algorithms. 【0355】 Step 3: 【0356】 The emotion engine built into the device processes the user's facial expressions and input data in real time to recognize their emotional state. Input is user interaction data (camera footage, keyboard input, etc.), and output is the recognized emotional state. A machine learning model is used for emotion recognition, extracting emotional characteristics from the data. 【0357】 Step 4: 【0358】 The device combines the analyzed risk score with the user's emotional state to generate the optimal warning message. The input is the analyzed risk score and the user's emotional state, and the output is the adjusted warning message. In this step, a generative AI model is used to create a customized message based on the prompt text. 【0359】 Step 5: 【0360】 The device displays appropriate warning messages to the user and, if necessary, provides links to support services. The input is the warning message, and the output is the information displayed on the user's screen. Throughout this process, information is provided in a visually clear and easy-to-understand format via the user interface. 【0361】 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. 【0362】 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. 【0363】 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. 【0364】 [Third Embodiment] 【0365】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0366】 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. 【0367】 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). 【0368】 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. 【0369】 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. 【0370】 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). 【0371】 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. 【0372】 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. 【0373】 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. 【0374】 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. 【0375】 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. 【0376】 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". 【0377】 This invention provides a system that protects users from fraudulent part-time job solicitations using information and communication networks. The system primarily consists of a server, a terminal, and a user. The server collects information from sources such as social networking services (SNS) and job search websites via the internet. This collection is performed using periodic API calls and web scraping techniques. 【0378】 The device incorporates an AI analysis module equipped with natural language processing technology, which performs analysis based on information sent from the server. This AI module uses machine learning algorithms to evaluate relevant posts and score their potential risk. The analyzed data is visually displayed to the user on the device. For information assessed as high risk, the device immediately displays a warning message and provides a link to a support service to ensure safety. 【0379】 Users can examine the information they receive a warning about and use the provided link to contact the appropriate support center. For example, when a user encounters a job posting on an online bulletin board, their device evaluates the information in real time, and if a risk is identified, it immediately displays a warning such as, "This information is high risk. Please contact the support center for details." 【0380】 Key features of this system include the provision of enhanced platform security through information anonymization, feedback loops, and multi-layered data analysis. Information sharing between companies is managed by servers, and high-risk information is shared with other companies, strengthening the overall security network. This creates an environment where users can confidently access information on the internet. 【0381】 The following describes the processing flow. 【0382】 Step 1: 【0383】 The server uses the information and communication network to periodically collect publicly available data through APIs of social networking services and job search websites. The data collected includes posts and comments containing specific keywords, and this information is stored in a database. 【0384】 Step 2: 【0385】 The terminal receives data sent from the server and analyzes it using an AI model equipped with natural language processing technology. The analysis module examines the text in the data and scores the risk by recognizing linguistic patterns. 【0386】 Step 3: 【0387】 The device immediately notifies the user of the analysis results. For information where the risk exceeds a certain threshold, a visual warning message is displayed. This message includes an overview of the risk factors and a link to a consultation service to encourage prompt action. 【0388】 Step 4: 【0389】 Users can review the warning message and, if necessary, click the provided link to contact the designated support center. User feedback is sent to the server via the device and recorded in the database. 【0390】 Step 5: 【0391】 The server collects user feedback and uses it to improve the AI ​​model. Learning based on this feedback improves analysis accuracy and warning accuracy. Additionally, high-risk information is shared with other communication networks, and the reference database is updated. 【0392】 Step 6: 【0393】 The server manages information sharing among multiple companies and provides anonymized data in conjunction with other recruitment services. This ensures that common risk mitigation measures are implemented across each company's platform, contributing to improved information security. 【0394】 (Example 1) 【0395】 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." 【0396】 Numerous fraudulent job postings and scams exist on the internet, posing significant risks to users. Traditional methods require users to verify the authenticity of information themselves; therefore, a system is needed that can quickly and accurately detect fraudulent information and provide appropriate warnings. 【0397】 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. 【0398】 In this invention, the server includes means for collecting data via an information and communication network, means for analyzing the collected data, scoring the risk level of the posted content using a machine learning algorithm, and evaluating the potential risk, and means for notifying the user of a warning and providing a link to a consultation service to ensure safety based on the analysis results. This enables users to quickly recognize malicious information and ensure safe internet use. 【0399】 An "information processing device" is a device that has the function of receiving, analyzing, and sharing information via a computer network. 【0400】 "Information and communication network" refers to all infrastructure used for sending and receiving data, and includes the internet. 【0401】 "Means of collecting data" refers to techniques for obtaining data from internet sources through API calls or web scraping. 【0402】 A "machine learning algorithm" is a set of techniques for learning patterns from data and performing predictions and classifications. 【0403】 "Scoring" is the process of assigning numerical values ​​to target data based on specific criteria and evaluating its importance and risk. 【0404】 "Natural language processing technology" refers to the technology used to analyze, understand, and generate human language using computers. 【0405】 A "means of notifying warnings" refers to a mechanism for visually or audibly alerting users based on a risk assessment. 【0406】 "Methods for strengthening secure networks" refer to technologies that share high-risk information with other information processing devices and build a multi-layered security system. 【0407】 This invention is a system that utilizes an information and communication network, and is particularly intended to protect users from fraudulent recruitment for part-time work. The main components are a server, a terminal, and a user. 【0408】 The server is responsible for collecting information from sources on the internet, such as social networking services and job search websites. This process involves periodic API calls and web scraping techniques. Specifically, programming languages ​​such as Python are used, and libraries like Beautiful Soup and Selenium are employed to implement web scraping. This allows the server to efficiently retrieve information and prepare it for transfer to the terminal. 【0409】 The device analyzes received information using natural language processing techniques and machine learning algorithms. For natural language processing, libraries such as NLTK and spaCy, which can be built using Python, are utilized. For machine learning algorithms, scikit-learn and TensorFlow are used. Using these methods, the device analyzes the collected information, scores the risk, and displays it visually to the user. Additionally, if the risk assessment is high, the device immediately displays a warning message to the user. This message includes a link to a support service for improving security. 【0410】 Users can review warnings from their device and use the provided links to take safe action. For example, if a user finds a suspicious job posting online, the device will evaluate the information in real time, and if it determines that it is dangerous, a warning will be displayed stating, "This information is high risk. Please contact our support desk for details." 【0411】 As an example of a specific prompt, the following would be input to the generative AI model: 【0412】 "Evaluate the content of social media posts to determine if they may be fraudulent part-time jobs. The post is as follows: 'High pay in a short time! Apply now! Details here.'" 【0413】 In this way, users can respond quickly to fraudulent information, creating an environment where they can use information on the internet with peace of mind. 【0414】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0415】 Step 1: 【0416】 The server collects data from social networking services (SNS) and job search websites on the internet via the information and communication network. During this process, the server periodically uses API calls and web scraping techniques to obtain information. It uses URLs and API endpoints as input and outputs data in HTML or JSON format. Specifically, the server uses Python's Beautiful Soup library to parse web pages and extract the necessary text information. 【0417】 Step 2: 【0418】 The server transfers the collected data to the terminal. This process uses a secure communication protocol to transmit the data. The input is HTML or JSON data obtained in step 1, and the output is data in the same format sent to the terminal. Specifically, the server uses SSL / TLS encryption to ensure the security of the data during the transfer. 【0419】 Step 3: 【0420】 The terminal analyzes the received data using a natural language processing module. This process takes in HTML and JSON data as input, extracts text data, and analyzes it. The output is a risk score for the posted content. Specifically, this involves using Python's NLTK and spaCy to perform morphological analysis and semantic analysis of sentences. 【0421】 Step 4: 【0422】 The terminal uses a machine learning algorithm to score and assess risk. The input is the analysis results obtained in step 3, and the output is the risk assessment score for each post. Specifically, it uses scikit-learn to quantify the risk level of a post using a model based on past fraud case data. 【0423】 Step 5: 【0424】 The device displays a warning to the user based on a risk assessment. The input is a risk assessment score, and the output is a visual warning message. Specifically, the device displays a warning message on the user interface stating "This information is high risk" and provides the user with a link to a contact point for ensuring safety. 【0425】 Step 6: 【0426】 When a user receives a warning, they use the link to take appropriate action. The input is the warning message and link information from the device, and the output is the user's action of accessing the support center. Specifically, when the user clicks the link, the support center page opens in the browser. 【0427】 Step 7: 【0428】 The server shares information deemed high-risk with other servers to strengthen the security network. The input is the risk assessment results from step 4, and the output is the transmission of warning information to other information processing devices. Specifically, it synchronizes data between servers and updates the list of malicious information in cooperation with partner companies. 【0429】 (Application Example 1) 【0430】 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." 【0431】 Given the current situation where fraudulent information on the internet, particularly scams and unethical solicitations via job postings, is on the rise, there is a growing need for systems that reduce the risk of users encountering fraudulent information and enable them to use information safely. However, traditional methods have problems such as being time-consuming or having low accuracy in information gathering and evaluation. As a result, there is a challenge in that it is difficult for users to grasp the risks in real time. 【0432】 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. 【0433】 In this invention, the server includes means for acquiring data via an information and communication network, means for processing the acquired information and evaluating potential risks, means for presenting a warning to the user based on the processing results, and means for detecting and immediately displaying the content being viewed at the time of the warning. This enables users to effectively avoid risks from malicious information on the internet in real time and to use information safely. 【0434】 "Information and communication network" refers to the entire communication infrastructure used to send and receive data and information between distant locations. 【0435】 "Means of acquiring data" refers to the processes and technologies used to collect desired information via information and communication networks. 【0436】 "Means of processing information and assessing potential risks" refers to the technologies and methods used to analyze acquired data and assess the potential risks contained within it. 【0437】 "Means of providing warnings to users based on processing results" refers to a system for alerting users about risks based on the results of information analysis. 【0438】 "Means of detecting and instantly displaying what a user is viewing" refers to technology that instantly grasps the information a user is currently viewing and provides corresponding information at that moment. 【0439】 To implement this invention, it is necessary to build a system in which a server and a terminal cooperate to process information. The server collects data from various sources via the internet and utilizes machine learning models to analyze that data. Specifically, it builds APIs using Python or Flask and obtains information through web scraping using BeautifulSoup. Then, it processes the data using a machine learning framework such as TensorFlow and evaluates potential risks. Warning information generated based on this evaluation is sent from the server to the terminal. 【0440】 The terminal displays notifications based on warning information received from the server, responding immediately to the information the user is currently viewing. These notifications include links to advice channels to ensure user safety. If a user encounters fraudulent job postings, the terminal automatically evaluates the content and issues a warning if necessary. This system protects users from malicious information on the internet, enabling safe communication. 【0441】 As a concrete example, when a user is viewing a job posting on a recruitment site that advertises "high pay," the device analyzes it in real time and immediately displays a warning message saying, "This information may be dangerous. Please contact our advisory desk for details." This allows the user to take appropriate action. 【0442】 An example of a prompt message for a generative AI model is, "Identify any unnatural elements in this job posting and assess the risks." 【0443】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0444】 Step 1: 【0445】 The server retrieves data from social networking services and job search sites via the information and communication network. Specifically, it collects information through API calls and web scraping using BeautifulSoup. The input is the URL of each website, and the output is the raw text data extracted from that site. 【0446】 Step 2: 【0447】 The server analyzes the acquired text data using a machine learning model based on TensorFlow. The server takes the text data as input and calculates a risk score based on it. The output is the risk score for each piece of information. This score serves as an indicator for evaluating potential risks. 【0448】 Step 3: 【0449】 The server generates a warning message for the information the user is viewing, based on the analyzed risk score. The input is the risk score, and the output is the corresponding warning message. For example, if the score is high, a message such as "This information is dangerous" is generated. 【0450】 Step 4: 【0451】 The terminal receives warning messages from the server and displays them immediately on the user interface. The input is the warning message, and the output is a visual warning displayed to the user. This allows the user to immediately recognize the danger. 【0452】 Step 5: 【0453】 Users can click on links to the provided advisory services in response to warning messages displayed on their devices. The input is the user's action (click), and the output is a connection to the advisory service via the link. This process allows users to take further action. 【0454】 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. 【0455】 This invention personalizes the way warnings are delivered by incorporating an emotion engine into a system that warns users of potential risks, such as fraudulent part-time job information. The invention operates via an information and communication network and consists of a server, a terminal, and a user. 【0456】 The server collects publicly available data from social media and job sites via the internet and stores it in a database. This information is then filtered based on posts and comments containing specific keywords. 【0457】 The device is equipped with an AI analysis module that uses natural language processing technology, as well as an emotion engine. The AI ​​analysis module analyzes information sent from the server and scores potential risks based on that information. The emotion engine has the function of recognizing the user's emotional state in real time based on the user's input information and interactions. 【0458】 By combining analysis results with the emotion engine's judgment, the device provides the user with the most appropriate warning. For example, if the emotion engine determines that the user is depressed, the device will provide reassuring language and support information in addition to the usual warning message. Furthermore, even when a high-priority risk is recognized, the strength of the warning will be adjusted according to the user's emotional state. 【0459】 As a concrete example, suppose a user views a suspicious advertisement, and the device detects something suspicious about the advertisement, while the emotion engine simultaneously recognizes the user's anxious expression. The device then displays a warning message directly on the screen saying, "This information is risky. If you are concerned, please use this link for support," prompting the user to access a support service. 【0460】 Thus, this system, which includes an emotion engine, enables flexible risk warnings tailored to individual users, improving the user experience and providing more effective protection against fraudulent part-time job information. 【0461】 The following describes the processing flow. 【0462】 Step 1: 【0463】 The server periodically collects publicly available data from APIs of social networking services and job search websites via the information and communication network. During collection, it filters the data based on specific risky keywords (e.g., high income, daily pay, etc.) and stores that information in a database. 【0464】 Step 2: 【0465】 The server sends the collected data to the terminal for analysis. The transmitted data includes filtered posts and comments. 【0466】 Step 3: 【0467】 The data received by the device is processed by an AI analysis module, and the content of each post is evaluated using natural language processing technology. In this step, the contextual risk level is scored, and the results are temporarily stored. 【0468】 Step 4: 【0469】 The emotion engine built into the device recognizes the user's emotional state from their input and interactions. For example, it analyzes emotions such as reassurance, anxiety, and excitement in real time from the user's facial expressions and voice input. 【0470】 Step 5: 【0471】 The device integrates the scoring results from the AI ​​analysis module with the recognition results from the emotion engine. This determines the appropriate warning content and display method for the user's current emotional state. 【0472】 Step 6: 【0473】 The device notifies the user of any determined warnings. For example, if the emotion engine determines that the user is anxious, it adds reassuring words to the warning message, such as "Don't worry, support is available here." 【0474】 Step 7: 【0475】 This system allows users to review warnings and, if necessary, click on the provided links to contact support. The user's chosen action is sent as feedback to the server via the terminal for future system improvements. 【0476】 Step 8: 【0477】 The server aggregates user feedback and interaction data, which is used to improve the accuracy of the AI ​​analysis module and emotion engine. Through continuous learning, the system becomes more effective over time. 【0478】 (Example 2) 【0479】 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." 【0480】 In recent years, much of the information provided through information and communication networks contains potential risks. However, a problem exists in that users often struggle to receive appropriate warnings tailored to their individual emotional states and circumstances regarding this risk information. In particular, uniform warning messages are ineffective in prompting users to take appropriate action, and their effectiveness in improving the user experience and providing protection is limited. 【0481】 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. 【0482】 In this invention, the server includes means for collecting data via an information and communication network, means for analyzing the collected data and evaluating potential risks, means for recognizing the user's emotional state in real time based on the analysis results and optimally adjusting warnings, and means for notifying the user of the generated warning message. This enables flexible and effective risk warnings tailored to each user's emotional state, improving the user experience and providing more effective protection against fraudulent information. 【0483】 An "information and communication network" is a communication infrastructure that interconnects computers, servers, and other devices for sending and receiving data. 【0484】 "Means of data collection" refers to the functionality of hardware and software that retrieve information from network sources based on specific conditions. 【0485】 "Means of analyzing data and assessing potential risks" refers to the technologies that support the process of processing collected information and identifying and quantifying the risks and problems contained in that information. 【0486】 "A means of recognizing the user's emotional state in real time and optimally adjusting warnings" refers to a technology that instantly determines the user's current psychological state and dynamically changes the content and expression of warnings according to that state. 【0487】 "Means of notifying users of warning messages" refers to methods or devices for presenting users with risk information, enabling them to take immediate action. 【0488】 This invention comprises a system that provides users with necessary information via an information and communication network. The system primarily consists of a server, a terminal, and a user. The roles and technical characteristics of each component are described below. 【0489】 First, the server collects data from multiple sources via the internet. Specifically, it collects publicly available information from social networking services and job posting sites, filters this information based on specific keywords, and stores it in a database. Servers are often implemented using programming languages ​​such as Python or JavaScript. 【0490】 Next, the terminal is responsible for receiving data sent from the server. The received data is analyzed by an AI analysis module using natural language processing technology within the terminal. This AI analysis module uses libraries such as TensorFlow and PyTorch. This module processes the received information to assess potential risks and performs risk scoring. 【0491】 Furthermore, the device is equipped with an emotion engine that recognizes the user's emotional state in real time based on the user's input information. The emotion engine analyzes the user's facial expressions and voice using the camera and microphone, and updates the emotion model as needed. 【0492】 By combining analysis results with the emotion engine's judgment, the device generates individually customized warning messages for each user. For example, if the emotion engine determines that the user is feeling down, it adds reassuring phrases and support information in addition to the usual warning message. Furthermore, even when a high-priority risk is recognized, the strength of the warning is adjusted according to the user's emotions. 【0493】 For example, suppose a user is viewing a suspicious job advertisement, and the device detects a risk in the advertisement information, while the emotion engine simultaneously recognizes that the user is showing signs of anxiety. In this case, the device will directly display a message on the screen saying, "This information is risky. If you are concerned, please use this link for support," encouraging the user to proceed with confidence. 【0494】 An example of a prompt message is: "Based on data collected from social media, generate appropriate warning messages for each user's current emotional state. If the user is feeling down, include additional information to provide reassurance." 【0495】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0496】 Step 1: 【0497】 The server collects data publicly available from social networking services (SNS) and job search websites via information and communication networks. The input is text data from the internet, which the server obtains using scraping techniques. The collected data is filtered based on specific keywords and stored in a database as output. Specifically, an automated script using a Python library runs periodically to collect the necessary data. 【0498】 Step 2: 【0499】 The terminal receives filtered data sent from the server. The input is the data collected in step 1. The terminal's AI analysis module analyzes this data using natural language processing techniques, performing evaluations based on word frequency and sentiment analysis models. The output is a potential risk score, and the analyzed data is obtained as numerical information indicating the level of risk. Specifically, text analysis and scoring are performed using NLTK and spaCy. 【0500】 Step 3: 【0501】 The device utilizes an emotion engine to recognize the user's emotional state in real time. Input consists of the user's facial expressions and voice, acquired through the device's camera and microphone. The emotion engine analyzes the acquired data to determine the user's emotions. Output is a tag or score indicating the user's emotional state. Specifically, a TensorFlow model analyzes the user's facial expressions and voice data to determine their emotional state in real time. 【0502】 Step 4: 【0503】 The device generates a warning message based on the analyzed risk score and the user's emotional state. The inputs are the risk score obtained in step 2 and the emotional state in step 3. The device uses a generation AI model to create a warning message in response to the prompt, and the message includes customized wording based on the user's situation. The output is the warning message displayed to the user. Specifically, the warning message is dynamically generated in response to the prompt and visually presented on the screen. 【0504】 Step 5: 【0505】 The user reviews the warning message displayed on the device and clicks on links to request additional information or support as needed. The input is the warning message generated in step 4. The user's action connects them to a support desk. The output is the user's action and the subsequent support provided. Specifically, a web browser opens displaying relevant information, and if support is needed, more detailed guidance is provided. 【0506】 (Application Example 2) 【0507】 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." 【0508】 Conventional warning systems struggle to appropriately recognize and address the individual emotional state of each user, resulting in warnings that are not optimal for the user. This can lead to inaccurate warnings, potentially exacerbating user anxiety and negatively impacting the user experience. 【0509】 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. 【0510】 In this invention, the server includes means for collecting data via an information and communication network, means for analyzing the collected data and evaluating potential risks, and means for recognizing the user's emotional state and adjusting the content and intensity of warnings based on that emotional state. This enables the provision of personalized warnings tailored to the user, improving the user experience and providing information in an appropriate format. 【0511】 An "information and communication network" is a technological foundation that enables the transmission and reception of data, and is a system that uses the internet to exchange information between multiple devices. 【0512】 "Means of data collection" refers to the function of acquiring various data from external sources via information and communication networks and making it available for use within the system. 【0513】 "Potential risks" refer to unseen dangers or disadvantages that users may encounter in information or situations they might come into contact with. 【0514】 "Means of analysis" refers to the process technology of analyzing collected data and interpreting its content and meaning. 【0515】 "Means of notifying users of warnings" refers to a function that sends messages or alerts to users based on analysis results to draw their attention. 【0516】 "Means for recognizing a user's emotional state" refers to a function that determines the user's emotional state in real time based on their interactions and input information. 【0517】 "Means of adjusting the content and intensity of warnings based on emotional state" refers to the process of changing the way warnings are delivered and the depth of their content, taking into account the recognized emotions of the user. 【0518】 This invention relates to the realization of a system that collects data via an information and communication network and provides warnings that take user sentiment into consideration based on the analysis results. The server collects data via the internet and obtains information from social networking services and job search websites. The collected data is analyzed by an analysis module using natural language processing technology, and potential risks are evaluated. 【0519】 The device incorporates an AI analysis module that scores potential risks based on information transmitted from the analysis module, and an emotion engine that processes the user's facial expressions and input information in real time. The emotion engine recognizes the user's emotional state and uses that information to adjust the content and intensity of warnings. For example, if the device detects that the user is anxious, it will change the normal warning message to reassuring wording and add a link to a support center to help alleviate the user's anxiety. 【0520】 For example, if a user expresses concern after accessing fraudulent part-time job information, the device will assess the risk of that information and display a message such as, "This information is risky. If you are concerned, please seek support via this link." In this way, flexible risk warnings tailored to the user's emotional state become possible, resulting in more effective protection. 【0521】 An example of a prompt message for a generative AI model would be: "I feel uneasy about a certain website. Please assess the risks and display reassuring support information." 【0522】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0523】 Step 1: 【0524】 The server collects data from social networking services (SNS) and job search websites via information and communication networks. It uses access information to external data sources as input and outputs collected text data. This collection process utilizes APIs and web crawling technologies to periodically retrieve relevant information. 【0525】 Step 2: 【0526】 The terminal receives text data sent from the server and performs analysis using natural language processing technology. The input is the collected text data, and the output is a score indicating the analyzed topic and risk level. Specifically, the text is tokenized, and relationships and risk factors are extracted using big data analysis algorithms. 【0527】 Step 3: 【0528】 The emotion engine built into the device processes the user's facial expressions and input data in real time to recognize their emotional state. Input is user interaction data (camera footage, keyboard input, etc.), and output is the recognized emotional state. A machine learning model is used for emotion recognition, extracting emotional characteristics from the data. 【0529】 Step 4: 【0530】 The device combines the analyzed risk score with the user's emotional state to generate the optimal warning message. The input is the analyzed risk score and the user's emotional state, and the output is the adjusted warning message. In this step, a generative AI model is used to create a customized message based on the prompt text. 【0531】 Step 5: 【0532】 The device displays appropriate warning messages to the user and, if necessary, provides links to support services. The input is the warning message, and the output is the information displayed on the user's screen. Throughout this process, information is provided in a visually clear and easy-to-understand format via the user interface. 【0533】 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. 【0534】 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. 【0535】 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. 【0536】 [Fourth Embodiment] 【0537】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0538】 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. 【0539】 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). 【0540】 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. 【0541】 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. 【0542】 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). 【0543】 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. 【0544】 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. 【0545】 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. 【0546】 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. 【0547】 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. 【0548】 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. 【0549】 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". 【0550】 This invention provides a system that protects users from fraudulent part-time job solicitations using information and communication networks. The system primarily consists of a server, a terminal, and a user. The server collects information from sources such as social networking services (SNS) and job search websites via the internet. This collection is performed using periodic API calls and web scraping techniques. 【0551】 The device incorporates an AI analysis module equipped with natural language processing technology, which performs analysis based on information sent from the server. This AI module uses machine learning algorithms to evaluate relevant posts and score their potential risk. The analyzed data is visually displayed to the user on the device. For information assessed as high risk, the device immediately displays a warning message and provides a link to a support service to ensure safety. 【0552】 Users can examine the information they receive a warning about and use the provided link to contact the appropriate support center. For example, when a user encounters a job posting on an online bulletin board, their device evaluates the information in real time, and if a risk is identified, it immediately displays a warning such as, "This information is high risk. Please contact the support center for details." 【0553】 Key features of this system include the provision of enhanced platform security through information anonymization, feedback loops, and multi-layered data analysis. Information sharing between companies is managed by servers, and high-risk information is shared with other companies, strengthening the overall security network. This creates an environment where users can confidently access information on the internet. 【0554】 The following describes the processing flow. 【0555】 Step 1: 【0556】 The server uses the information and communication network to periodically collect publicly available data through APIs of social networking services and job search websites. The data collected includes posts and comments containing specific keywords, and this information is stored in a database. 【0557】 Step 2: 【0558】 The terminal receives data sent from the server and analyzes it using an AI model equipped with natural language processing technology. The analysis module examines the text in the data and scores the risk by recognizing linguistic patterns. 【0559】 Step 3: 【0560】 The device immediately notifies the user of the analysis results. For information where the risk exceeds a certain threshold, a visual warning message is displayed. This message includes an overview of the risk factors and a link to a consultation service to encourage prompt action. 【0561】 Step 4: 【0562】 Users can review the warning message and, if necessary, click the provided link to contact the designated support center. User feedback is sent to the server via the device and recorded in the database. 【0563】 Step 5: 【0564】 The server collects user feedback and uses it to improve the AI ​​model. Learning based on this feedback improves analysis accuracy and warning accuracy. Additionally, high-risk information is shared with other communication networks, and the reference database is updated. 【0565】 Step 6: 【0566】 The server manages information sharing among multiple companies and provides anonymized data in conjunction with other recruitment services. This ensures that common risk mitigation measures are implemented across each company's platform, contributing to improved information security. 【0567】 (Example 1) 【0568】 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". 【0569】 Numerous fraudulent job postings and scams exist on the internet, posing significant risks to users. Traditional methods require users to verify the authenticity of information themselves; therefore, a system is needed that can quickly and accurately detect fraudulent information and provide appropriate warnings. 【0570】 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. 【0571】 In this invention, the server includes means for collecting data via an information and communication network, means for analyzing the collected data, scoring the risk level of the posted content using a machine learning algorithm, and evaluating the potential risk, and means for notifying the user of a warning and providing a link to a consultation service to ensure safety based on the analysis results. This enables users to quickly recognize malicious information and ensure safe internet use. 【0572】 An "information processing device" is a device that has the function of receiving, analyzing, and sharing information via a computer network. 【0573】 "Information and communication network" refers to all infrastructure used for sending and receiving data, and includes the internet. 【0574】 "Means of collecting data" refers to techniques for obtaining data from internet sources through API calls or web scraping. 【0575】 A "machine learning algorithm" is a set of techniques for learning patterns from data and performing predictions and classifications. 【0576】 "Scoring" is the process of assigning numerical values ​​to target data based on specific criteria and evaluating its importance and risk. 【0577】 "Natural language processing technology" refers to the technology used to analyze, understand, and generate human language using computers. 【0578】 A "means of notifying warnings" refers to a mechanism for visually or audibly alerting users based on a risk assessment. 【0579】 "Methods for strengthening secure networks" refer to technologies that share high-risk information with other information processing devices and build a multi-layered security system. 【0580】 This invention is a system that utilizes an information and communication network, and is particularly intended to protect users from fraudulent recruitment for part-time work. The main components are a server, a terminal, and a user. 【0581】 The server is responsible for collecting information from sources on the internet, such as social networking services and job search websites. This process involves periodic API calls and web scraping techniques. Specifically, programming languages ​​such as Python are used, and libraries like Beautiful Soup and Selenium are employed to implement web scraping. This allows the server to efficiently retrieve information and prepare it for transfer to the terminal. 【0582】 The device analyzes received information using natural language processing techniques and machine learning algorithms. For natural language processing, libraries such as NLTK and spaCy, which can be built using Python, are utilized. For machine learning algorithms, scikit-learn and TensorFlow are used. Using these methods, the device analyzes the collected information, scores the risk, and displays it visually to the user. Additionally, if the risk assessment is high, the device immediately displays a warning message to the user. This message includes a link to a support service for improving security. 【0583】 Users can review warnings from their device and use the provided links to take safe action. For example, if a user finds a suspicious job posting online, the device will evaluate the information in real time, and if it determines that it is dangerous, a warning will be displayed stating, "This information is high risk. Please contact our support desk for details." 【0584】 As an example of a specific prompt, the following would be input to the generative AI model: 【0585】 "Evaluate the content of social media posts to determine if they may be fraudulent part-time jobs. The post is as follows: 'High pay in a short time! Apply now! Details here.'" 【0586】 In this way, users can respond quickly to fraudulent information, creating an environment where they can use information on the internet with peace of mind. 【0587】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0588】 Step 1: 【0589】 The server collects data from social networking services (SNS) and job search websites on the internet via the information and communication network. During this process, the server periodically uses API calls and web scraping techniques to obtain information. It uses URLs and API endpoints as input and outputs data in HTML or JSON format. Specifically, the server uses Python's Beautiful Soup library to parse web pages and extract the necessary text information. 【0590】 Step 2: 【0591】 The server transfers the collected data to the terminal. This process uses a secure communication protocol to transmit the data. The input is HTML or JSON data obtained in step 1, and the output is data in the same format sent to the terminal. Specifically, the server uses SSL / TLS encryption to ensure the security of the data during the transfer. 【0592】 Step 3: 【0593】 The terminal analyzes the received data using a natural language processing module. This process takes in HTML and JSON data as input, extracts text data, and analyzes it. The output is a risk score for the posted content. Specifically, this involves using Python's NLTK and spaCy to perform morphological analysis and semantic analysis of sentences. 【0594】 Step 4: 【0595】 The terminal uses a machine learning algorithm to score and assess risk. The input is the analysis results obtained in step 3, and the output is the risk assessment score for each post. Specifically, it uses scikit-learn to quantify the risk level of a post using a model based on past fraud case data. 【0596】 Step 5: 【0597】 The device displays a warning to the user based on a risk assessment. The input is a risk assessment score, and the output is a visual warning message. Specifically, the device displays a warning message on the user interface stating "This information is high risk" and provides the user with a link to a contact point for ensuring safety. 【0598】 Step 6: 【0599】 When a user receives a warning, they use the link to take appropriate action. The input is the warning message and link information from the device, and the output is the user's action of accessing the support center. Specifically, when the user clicks the link, the support center page opens in the browser. 【0600】 Step 7: 【0601】 The server shares information deemed high-risk with other servers to strengthen the security network. The input is the risk assessment results from step 4, and the output is the transmission of warning information to other information processing devices. Specifically, it synchronizes data between servers and updates the list of malicious information in cooperation with partner companies. 【0602】 (Application Example 1) 【0603】 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". 【0604】 Given the current situation where fraudulent information on the internet, particularly scams and unethical solicitations via job postings, is on the rise, there is a growing need for systems that reduce the risk of users encountering fraudulent information and enable them to use information safely. However, traditional methods have problems such as being time-consuming or having low accuracy in information gathering and evaluation. As a result, there is a challenge in that it is difficult for users to grasp the risks in real time. 【0605】 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. 【0606】 In this invention, the server includes means for acquiring data via an information and communication network, means for processing the acquired information and evaluating potential risks, means for presenting a warning to the user based on the processing results, and means for detecting and immediately displaying the content being viewed at the time of the warning. This enables users to effectively avoid risks from malicious information on the internet in real time and to use information safely. 【0607】 "Information and communication network" refers to the entire communication infrastructure used to send and receive data and information between distant locations. 【0608】 "Means of acquiring data" refers to the processes and technologies used to collect desired information via information and communication networks. 【0609】 "Means of processing information and assessing potential risks" refers to the technologies and methods used to analyze acquired data and assess the potential risks contained within it. 【0610】 "Means of providing warnings to users based on processing results" refers to a system for alerting users about risks based on the results of information analysis. 【0611】 "Means of detecting and instantly displaying what a user is viewing" refers to technology that instantly grasps the information a user is currently viewing and provides corresponding information at that moment. 【0612】 To implement this invention, it is necessary to build a system in which a server and a terminal cooperate to process information. The server collects data from various sources via the internet and utilizes machine learning models to analyze that data. Specifically, it builds APIs using Python or Flask and obtains information through web scraping using BeautifulSoup. Then, it processes the data using a machine learning framework such as TensorFlow and evaluates potential risks. Warning information generated based on this evaluation is sent from the server to the terminal. 【0613】 The terminal displays notifications based on warning information received from the server, responding immediately to the information the user is currently viewing. These notifications include links to advice channels to ensure user safety. If a user encounters fraudulent job postings, the terminal automatically evaluates the content and issues a warning if necessary. This system protects users from malicious information on the internet, enabling safe communication. 【0614】 As a concrete example, when a user is viewing a job posting on a recruitment site that advertises "high pay," the device analyzes it in real time and immediately displays a warning message saying, "This information may be dangerous. Please contact our advisory desk for details." This allows the user to take appropriate action. 【0615】 An example of a prompt message for a generative AI model is, "Identify any unnatural elements in this job posting and assess the risks." 【0616】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0617】 Step 1: 【0618】 The server retrieves data from social networking services and job search sites via the information and communication network. Specifically, it collects information through API calls and web scraping using BeautifulSoup. The input is the URL of each website, and the output is the raw text data extracted from that site. 【0619】 Step 2: 【0620】 The server analyzes the acquired text data using a machine learning model based on TensorFlow. The server takes the text data as input and calculates a risk score based on it. The output is the risk score for each piece of information. This score serves as an indicator for evaluating potential risks. 【0621】 Step 3: 【0622】 The server generates a warning message for the information the user is viewing, based on the analyzed risk score. The input is the risk score, and the output is the corresponding warning message. For example, if the score is high, a message such as "This information is dangerous" is generated. 【0623】 Step 4: 【0624】 The terminal receives warning messages from the server and displays them immediately on the user interface. The input is the warning message, and the output is a visual warning displayed to the user. This allows the user to immediately recognize the danger. 【0625】 Step 5: 【0626】 Users can click on links to the provided advisory services in response to warning messages displayed on their devices. The input is the user's action (click), and the output is a connection to the advisory service via the link. This process allows users to take further action. 【0627】 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. 【0628】 This invention personalizes the way warnings are delivered by incorporating an emotion engine into a system that warns users of potential risks, such as fraudulent part-time job information. The invention operates via an information and communication network and consists of a server, a terminal, and a user. 【0629】 The server collects publicly available data from social media and job sites via the internet and stores it in a database. This information is then filtered based on posts and comments containing specific keywords. 【0630】 The device is equipped with an AI analysis module that uses natural language processing technology, as well as an emotion engine. The AI ​​analysis module analyzes information sent from the server and scores potential risks based on that information. The emotion engine has the function of recognizing the user's emotional state in real time based on the user's input information and interactions. 【0631】 By combining analysis results with the emotion engine's judgment, the device provides the user with the most appropriate warning. For example, if the emotion engine determines that the user is depressed, the device will provide reassuring language and support information in addition to the usual warning message. Furthermore, even when a high-priority risk is recognized, the strength of the warning will be adjusted according to the user's emotional state. 【0632】 As a concrete example, suppose a user views a suspicious advertisement, and the device detects something suspicious about the advertisement, while the emotion engine simultaneously recognizes the user's anxious expression. The device then displays a warning message directly on the screen saying, "This information is risky. If you are concerned, please use this link for support," prompting the user to access a support service. 【0633】 Thus, this system, which includes an emotion engine, enables flexible risk warnings tailored to individual users, improving the user experience and providing more effective protection against fraudulent part-time job information. 【0634】 The following describes the processing flow. 【0635】 Step 1: 【0636】 The server periodically collects publicly available data from APIs of social networking services and job search websites via the information and communication network. During collection, it filters the data based on specific risky keywords (e.g., high income, daily pay, etc.) and stores that information in a database. 【0637】 Step 2: 【0638】 The server sends the collected data to the terminal for analysis. The transmitted data includes filtered posts and comments. 【0639】 Step 3: 【0640】 The data received by the device is processed by an AI analysis module, and the content of each post is evaluated using natural language processing technology. In this step, the contextual risk level is scored, and the results are temporarily stored. 【0641】 Step 4: 【0642】 The emotion engine built into the device recognizes the user's emotional state from their input and interactions. For example, it analyzes emotions such as reassurance, anxiety, and excitement in real time from the user's facial expressions and voice input. 【0643】 Step 5: 【0644】 The device integrates the scoring results from the AI ​​analysis module with the recognition results from the emotion engine. This determines the appropriate warning content and display method for the user's current emotional state. 【0645】 Step 6: 【0646】 The device notifies the user of any determined warnings. For example, if the emotion engine determines that the user is anxious, it adds reassuring words to the warning message, such as "Don't worry, support is available here." 【0647】 Step 7: 【0648】 This system allows users to review warnings and, if necessary, click on the provided links to contact support. The user's chosen action is sent as feedback to the server via the terminal for future system improvements. 【0649】 Step 8: 【0650】 The server aggregates user feedback and interaction data, which is used to improve the accuracy of the AI ​​analysis module and emotion engine. Through continuous learning, the system becomes more effective over time. 【0651】 (Example 2) 【0652】 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". 【0653】 In recent years, much of the information provided through information and communication networks contains potential risks. However, a problem exists in that users often struggle to receive appropriate warnings tailored to their individual emotional states and circumstances regarding this risk information. In particular, uniform warning messages are ineffective in prompting users to take appropriate action, and their effectiveness in improving the user experience and providing protection is limited. 【0654】 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. 【0655】 In this invention, the server includes means for collecting data via an information and communication network, means for analyzing the collected data and evaluating potential risks, means for recognizing the user's emotional state in real time based on the analysis results and optimally adjusting warnings, and means for notifying the user of the generated warning message. This enables flexible and effective risk warnings tailored to each user's emotional state, improving the user experience and providing more effective protection against fraudulent information. 【0656】 An "information and communication network" is a communication infrastructure that interconnects computers, servers, and other devices for sending and receiving data. 【0657】 "Means of data collection" refers to the functionality of hardware and software that retrieve information from network sources based on specific conditions. 【0658】 "Means of analyzing data and assessing potential risks" refers to the technologies that support the process of processing collected information and identifying and quantifying the risks and problems contained in that information. 【0659】 "A means of recognizing the user's emotional state in real time and optimally adjusting warnings" refers to a technology that instantly determines the user's current psychological state and dynamically changes the content and expression of warnings according to that state. 【0660】 "Means of notifying users of warning messages" refers to methods or devices for presenting users with risk information, enabling them to take immediate action. 【0661】 This invention comprises a system that provides users with necessary information via an information and communication network. The system primarily consists of a server, a terminal, and a user. The roles and technical characteristics of each component are described below. 【0662】 First, the server collects data from multiple sources via the internet. Specifically, it collects publicly available information from social networking services and job posting sites, filters this information based on specific keywords, and stores it in a database. Servers are often implemented using programming languages ​​such as Python or JavaScript. 【0663】 Next, the terminal is responsible for receiving data sent from the server. The received data is analyzed by an AI analysis module using natural language processing technology within the terminal. This AI analysis module uses libraries such as TensorFlow and PyTorch. This module processes the received information to assess potential risks and performs risk scoring. 【0664】 Furthermore, the device is equipped with an emotion engine that recognizes the user's emotional state in real time based on the user's input information. The emotion engine analyzes the user's facial expressions and voice using the camera and microphone, and updates the emotion model as needed. 【0665】 By combining analysis results with the emotion engine's judgment, the device generates individually customized warning messages for each user. For example, if the emotion engine determines that the user is feeling down, it adds reassuring phrases and support information in addition to the usual warning message. Furthermore, even when a high-priority risk is recognized, the strength of the warning is adjusted according to the user's emotions. 【0666】 For example, suppose a user is viewing a suspicious job advertisement, and the device detects a risk in the advertisement information, while the emotion engine simultaneously recognizes that the user is showing signs of anxiety. In this case, the device will directly display a message on the screen saying, "This information is risky. If you are concerned, please use this link for support," encouraging the user to proceed with confidence. 【0667】 An example of a prompt message is: "Based on data collected from social media, generate appropriate warning messages for each user's current emotional state. If the user is feeling down, include additional information to provide reassurance." 【0668】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0669】 Step 1: 【0670】 The server collects data publicly available from social networking services (SNS) and job search websites via information and communication networks. The input is text data from the internet, which the server obtains using scraping techniques. The collected data is filtered based on specific keywords and stored in a database as output. Specifically, an automated script using a Python library runs periodically to collect the necessary data. 【0671】 Step 2: 【0672】 The terminal receives filtered data sent from the server. The input is the data collected in step 1. The terminal's AI analysis module analyzes this data using natural language processing techniques, performing evaluations based on word frequency and sentiment analysis models. The output is a potential risk score, and the analyzed data is obtained as numerical information indicating the level of risk. Specifically, text analysis and scoring are performed using NLTK and spaCy. 【0673】 Step 3: 【0674】 The device utilizes an emotion engine to recognize the user's emotional state in real time. Input consists of the user's facial expressions and voice, acquired through the device's camera and microphone. The emotion engine analyzes the acquired data to determine the user's emotions. Output is a tag or score indicating the user's emotional state. Specifically, a TensorFlow model analyzes the user's facial expressions and voice data to determine their emotional state in real time. 【0675】 Step 4: 【0676】 The device generates a warning message based on the analyzed risk score and the user's emotional state. The inputs are the risk score obtained in step 2 and the emotional state in step 3. The device uses a generation AI model to create a warning message in response to the prompt, and the message includes customized wording based on the user's situation. The output is the warning message displayed to the user. Specifically, the warning message is dynamically generated in response to the prompt and visually presented on the screen. 【0677】 Step 5: 【0678】 The user reviews the warning message displayed on the device and clicks on links to request additional information or support as needed. The input is the warning message generated in step 4. The user's action connects them to a support desk. The output is the user's action and the subsequent support provided. Specifically, a web browser opens displaying relevant information, and if support is needed, more detailed guidance is provided. 【0679】 (Application Example 2) 【0680】 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". 【0681】 Conventional warning systems struggle to appropriately recognize and address the individual emotional state of each user, resulting in warnings that are not optimal for the user. This can lead to inaccurate warnings, potentially exacerbating user anxiety and negatively impacting the user experience. 【0682】 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. 【0683】 In this invention, the server includes means for collecting data via an information and communication network, means for analyzing the collected data and evaluating potential risks, and means for recognizing the user's emotional state and adjusting the content and intensity of warnings based on that emotional state. This enables the provision of personalized warnings tailored to the user, improving the user experience and providing information in an appropriate format. 【0684】 An "information and communication network" is a technological foundation that enables the transmission and reception of data, and is a system that uses the internet to exchange information between multiple devices. 【0685】 "Means of data collection" refers to the function of acquiring various data from external sources via information and communication networks and making it available for use within the system. 【0686】 "Potential risks" refer to unseen dangers or disadvantages that users may encounter in information or situations they might come into contact with. 【0687】 "Means of analysis" refers to the process technology of analyzing collected data and interpreting its content and meaning. 【0688】 "Means of notifying users of warnings" refers to a function that sends messages or alerts to users based on analysis results to draw their attention. 【0689】 "Means for recognizing a user's emotional state" refers to a function that determines the user's emotional state in real time based on their interactions and input information. 【0690】 "Means of adjusting the content and intensity of warnings based on emotional state" refers to the process of changing the way warnings are delivered and the depth of their content, taking into account the recognized emotions of the user. 【0691】 This invention relates to the realization of a system that collects data via an information and communication network and provides warnings that take user sentiment into consideration based on the analysis results. The server collects data via the internet and obtains information from social networking services and job search websites. The collected data is analyzed by an analysis module using natural language processing technology, and potential risks are evaluated. 【0692】 The device incorporates an AI analysis module that scores potential risks based on information transmitted from the analysis module, and an emotion engine that processes the user's facial expressions and input information in real time. The emotion engine recognizes the user's emotional state and uses that information to adjust the content and intensity of warnings. For example, if the device detects that the user is anxious, it will change the normal warning message to reassuring wording and add a link to a support center to help alleviate the user's anxiety. 【0693】 For example, if a user expresses concern after accessing fraudulent part-time job information, the device will assess the risk of that information and display a message such as, "This information is risky. If you are concerned, please seek support via this link." In this way, flexible risk warnings tailored to the user's emotional state become possible, resulting in more effective protection. 【0694】 An example of a prompt message for a generative AI model would be: "I feel uneasy about a certain website. Please assess the risks and display reassuring support information." 【0695】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0696】 Step 1: 【0697】 The server collects data from social networking services (SNS) and job search websites via information and communication networks. It uses access information to external data sources as input and outputs collected text data. This collection process utilizes APIs and web crawling technologies to periodically retrieve relevant information. 【0698】 Step 2: 【0699】 The terminal receives text data sent from the server and performs analysis using natural language processing technology. The input is the collected text data, and the output is a score indicating the analyzed topic and risk level. Specifically, the text is tokenized, and relationships and risk factors are extracted using big data analysis algorithms. 【0700】 Step 3: 【0701】 The emotion engine built into the device processes the user's facial expressions and input data in real time to recognize their emotional state. Input is user interaction data (camera footage, keyboard input, etc.), and output is the recognized emotional state. A machine learning model is used for emotion recognition, extracting emotional characteristics from the data. 【0702】 Step 4: 【0703】 The device combines the analyzed risk score with the user's emotional state to generate the optimal warning message. The input is the analyzed risk score and the user's emotional state, and the output is the adjusted warning message. In this step, a generative AI model is used to create a customized message based on the prompt text. 【0704】 Step 5: 【0705】 The device displays appropriate warning messages to the user and, if necessary, provides links to support services. The input is the warning message, and the output is the information displayed on the user's screen. Throughout this process, information is provided in a visually clear and easy-to-understand format via the user interface. 【0706】 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. 【0707】 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. 【0708】 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. 【0709】 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. 【0710】 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. 【0711】 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. 【0712】 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. 【0713】 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. 【0714】 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." 【0715】 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. 【0716】 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. 【0717】 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. 【0718】 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. 【0719】 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. 【0720】 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. 【0721】 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. 【0722】 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. 【0723】 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. 【0724】 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. 【0725】 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. 【0726】 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 as being incorporated by reference. 【0727】 The following is further disclosed regarding the embodiments described above. 【0728】 (Claim 1) 【0729】 Means for collecting data via information and communication networks, 【0730】 A means of analyzing collected data and assessing potential risks, 【0731】 A means of notifying the user of a warning based on the analysis results, 【0732】 A system that includes this. 【0733】 (Claim 2) 【0734】 The system according to claim 1, wherein the analysis means analyzes the data using natural language processing technology. 【0735】 (Claim 3) 【0736】 The system according to claim 1, wherein the warning notification means includes a link to a consultation service corresponding to the notification. 【0737】 "Example 1" 【0738】 (Claim 1) 【0739】 An information processing device provides a means for collecting data via an information and communication network, 【0740】 A means of analyzing collected data, scoring the risk level of posted content using machine learning algorithms, and evaluating potential risks, 【0741】 A means to notify users of warnings based on the analysis results and provide a link to a consultation service to ensure safety, 【0742】 A means of sharing data with other information processing devices and strengthening the secure network, 【0743】 A system that includes this. 【0744】 (Claim 2) 【0745】 The system according to claim 1, wherein the analysis means analyzes text information using natural language processing technology. 【0746】 (Claim 3) 【0747】 The system according to claim 1, wherein the warning notification means visually displays a warning message for information that is of high risk. 【0748】 "Application Example 1" 【0749】 (Claim 1) 【0750】 Means of acquiring data via information and communication networks, 【0751】 A means of processing the acquired information and assessing potential risks, 【0752】 A means of displaying a warning to the user based on the processing results, 【0753】 A means to detect the content being viewed at the time of a warning and display it immediately, 【0754】 A system that includes this. 【0755】 (Claim 2) 【0756】 The system according to claim 1, wherein the analysis means analyzes information using a machine learning model. 【0757】 (Claim 3) 【0758】 The system according to claim 1, wherein the warning notification means includes a connection to an advisory desk corresponding to the notification. 【0759】 "Example 2 of combining an emotion engine" 【0760】 (Claim 1) 【0761】 Means for collecting data via information and communication networks, 【0762】 A means of analyzing collected data and assessing potential risks, 【0763】 Based on the analysis results, a means to recognize the user's emotional state in real time and optimally adjust warnings, 【0764】 A means of notifying the user of the generated warning message, 【0765】 A system that includes this. 【0766】 (Claim 2) 【0767】 The system according to claim 1, wherein the analysis means has the function of analyzing data using natural language processing technology and recognizing the user's emotional state. 【0768】 (Claim 3) 【0769】 The system according to claim 1, wherein the warning notification means includes a customized message according to the user's emotional state and a link to a consultation service that corresponds to the notification. 【0770】 "Application example 2 when combining with an emotional engine" 【0771】 (Claim 1) 【0772】 Means for collecting data via information and communication networks, 【0773】 A means of analyzing collected data and assessing potential risks, 【0774】 A means of notifying the user of a warning based on the analysis results, 【0775】 A means of recognizing the user's emotional state, 【0776】 A means of adjusting the content and intensity of the warning based on that emotional state, 【0777】 A system that includes this. 【0778】 (Claim 2) 【0779】 The system according to claim 1, wherein the analysis means analyzes data using natural language processing technology and evaluates the user's emotional state in real time. 【0780】 (Claim 3) 【0781】 The system according to claim 1, wherein the warning notification means includes a link to support information corresponding to the notification. [Explanation of symbols] 【0782】 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

[Claim 1] Means for collecting data via information and communication networks, A means of analyzing collected data and assessing potential risks, A means of notifying the user of a warning based on the analysis results, A system that includes this. [Claim 2] The system according to claim 1, wherein the analysis means analyzes the data using natural language processing technology. [Claim 3] The system according to claim 1, wherein the warning notification means includes a link to a consultation service corresponding to the notification.

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