system

Abstract

We provide the system. [Solution] Means of collecting information, A means of analyzing collected information to assess the risk of personal information leakage, A means of notifying the user of a warning based on the evaluation results, A system that includes means for providing appropriate responses to warnings.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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】 Smartphone users are at risk of personal information leakage due to an increase in nuisance calls, messages from unknown sources, and phishing fraud. Also, since effective security measures against these problems are generally difficult, there is a need to prevent personal information leakage and create an environment in which users can communicate safely and comfortably. 【Means for Solving the Problems】 【0005】 This invention utilizes information gathering methods to obtain the latest risk information from highly reliable sources. Furthermore, it uses AI to analyze this information and evaluate the risk of personal information leakage in real time. Based on the evaluation results, it provides a system that notifies users of warnings and suggests specific countermeasures. This prevents the occurrence of nuisance calls and malicious messages, thereby realizing a safe communication environment for users. 【0006】 "Means of collecting information" refers to functions for acquiring risk information from external organizations, user communication history, contact information, etc., and for storing or updating necessary data. 【0007】 "Means of analyzing and evaluating the risk of personal information leakage" refers to a function that uses generated AI and other analytical technologies based on collected information to detect and evaluate situations and signs that may lead to the leakage of personal information. 【0008】 "Means of notifying users of warnings based on evaluation results" refers to a function that communicates the analyzed risk assessment results to users in real time and sends warning messages as needed. 【0009】 "Means of providing countermeasures" refers to functions that, in response to detected risks, provide users with specific countermeasures and procedures for changing settings, thereby encouraging appropriate action. [Brief explanation of the drawing] 【0010】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [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] 【0011】 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. 【0012】 First, let's explain the terminology used in the following explanation. 【0013】 In the following embodiments, the 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), and the like. 【0014】 In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0015】 In the following embodiments, the 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, and the like. 【0016】 In the following embodiments, the labeled communication I / F (Interface) is an interface that includes 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), and the like. 【0017】 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." 【0018】 [First Embodiment] 【0019】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0020】 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. 【0021】 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). 【0022】 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. 【0023】 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. 【0024】 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. 【0025】 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. 【0026】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0027】 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. 【0028】 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. 【0029】 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. 【0030】 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". 【0031】 This invention provides a personal information protection system that operates on mobile devices such as smartphones. This system collects and analyzes information related to user communications to assess the risk of personal information leakage and issues real-time warnings based on the results. Specific examples are shown below. 【0032】 The server obtains information about dangerous sites and vendors from reliable external sources. This information is regularly added to the system's database and updated to address the latest threats. 【0033】 The device records received messages, call history, and contact information based on the user's permission. The device queries the database for updates from the server and evaluates the current communication content based on newly acquired risk information. 【0034】 The analyzed information is used to assess the risk of personal information leakage using machine learning models. For example, if a message suspected of being a phishing scam is received, natural language processing technology is used to analyze its content and determine whether it is likely to be a scam. 【0035】 Users can receive notifications from the system and see specific steps to take if a risk is detected. The notifications include specific advice such as, "This message may be a scam. Do not click the link," or "This phone number is likely spam. Do you want to block it?" 【0036】 This reduces the risk of users becoming victims of unauthorized access or data breaches due to their own actions. The system also continuously supports user safety by sending suggestions to improve security settings on devices based on newly discovered threat information. 【0037】 The following describes the processing flow. 【0038】 Step 1: 【0039】 The server obtains information about dangerous websites and vendors from reliable external sources and updates the system's database. 【0040】 Step 2: 【0041】 The device, with the user's permission, locally records received messages, call history, and contact information. 【0042】 Step 3: 【0043】 The terminal queries the server based on the recorded data to confirm any newly acquired risk information. 【0044】 Step 4: 【0045】 The system uses information received by the device to analyze messages and call content, employing machine learning models and natural language processing techniques. 【0046】 Step 5: 【0047】 If the analysis detects a risk of phishing scams or spam calls, the device will display a warning notification to the user. 【0048】 Step 6: 【0049】 The user checks the notification and follows the specific actions suggested (e.g., delete the message, block the phone number). 【0050】 Step 7: 【0051】 The server generates suggestions for improving security settings based on newly discovered threat information and sends them to the terminal. 【0052】 Step 8: 【0053】 The device displays suggested configuration improvements to the user and asks them whether they want to make the suggested changes. 【0054】 Step 9: 【0055】 Users can review their device settings and update them as needed to enhance the protection of their personal information. 【0056】 (Example 1) 【0057】 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." 【0058】 In recent years, the leakage of personal information has become a social problem, and the risk is particularly high in communications via mobile devices. In this situation, users need appropriate means to protect their information. However, conventional technologies have been problematic because they do not adequately perform real-time risk assessment or rapid response. 【0059】 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. 【0060】 In this invention, the server includes a device for collecting information, a device for analyzing the collected information and evaluating the risk of personal information leakage, and a device for notifying the user based on the evaluation results. This enables the user to recognize potential risks in real time and respond quickly and appropriately. 【0061】 A "device for collecting information" is a device that, with the user's permission, collects and securely manages data such as received messages, call history, and contact information. 【0062】 A "device for analyzing and evaluating the risk of personal information leakage" is a device that uses machine learning models and natural language processing technology to quantify the risk of personal information leakage based on collected information. 【0063】 A "device that notifies users based on evaluation results" is a device that, based on analysis results, sends warning messages to users regarding communications suspected of being fraudulent, providing real-time alerts. 【0064】 A "device that updates the database based on information from a trusted external source" is a device that obtains the latest information on phishing sites and malicious operators and keeps the database constantly updated. 【0065】 A "device for identifying potential threats" is a device that analyzes communication content to detect potentially harmful actions at an early stage. 【0066】 "Methods using generative AI technology" refer to processing methods that utilize artificial intelligence technology to analyze the content of messages and calls in an advanced manner and determine whether or not there is a threat. 【0067】 This personal information protection system is implemented as an application that runs on mobile devices and is designed to reduce the risk of personal information leakage by monitoring users' daily communications in real time. 【0068】 The server regularly collects information about dangerous sites and fraudulent operators from trusted external sources and updates the system's database with this information. This server possesses high-performance data processing capabilities and uses a database management system to quickly and accurately organize the information. 【0069】 The device operates on mobile operating systems such as Android® and iOS, and collects messages, call history, and contact information with the user's permission. The collected data is processed using generative AI models that utilize natural language processing technology, and the risk of personal information leakage is assessed. For example, if an email or SMS showing typical signs of a phishing scam is detected, its content is analyzed in depth, and if the likelihood of fraud is high, a warning is issued. 【0070】 Users receive warning notifications based on the results of analysis performed on their devices. These notifications include specific advice such as, "This message may be a scam. Do not click the link." This allows users to use their devices with peace of mind and mitigate the risk of unauthorized access and data breaches. 【0071】 For example, if a user receives a suspicious email, the device analyzes it and immediately notifies the user, along with sending a notification indicating that the email is likely a phishing attempt. The system continues to protect users in this way. 【0072】 An example of a prompt message to the generating AI model is: "We have analyzed your incoming email. The following email may be a phishing scam. We suggest the following steps. Do not click on the link." In this way, the system ensures the maximum security of the user's communications. 【0073】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0074】 Step 1: 【0075】 The server collects threat information from trusted external sources and updates its database. In this process, new threat information obtained from external sources is supplied as input, and that data is added to the server's database. As a result, the database always maintains the most up-to-date threat information. 【0076】 Step 2: 【0077】 The device collects received messages, call history, and contact information based on the user's permission. This input data is securely managed by the device's application and prepared for the next analysis step. As output, this data is stored in a format that allows for later analysis. 【0078】 Step 3: 【0079】 The terminal compares collected user data with the server and database to assess potential risks. Input includes user communication data recorded on the terminal and risk information obtained from the server. Using this data, natural language processing and machine learning techniques are employed to calculate the likelihood of personal information leakage, resulting in an output that identifies high-risk communications. 【0080】 Step 4: 【0081】 The device issues a real-time warning notification based on the results analyzed in the previous step. The risk information evaluated in step 3 is used as input. Based on this data, the device outputs an alert such as "This message may be a scam," and displays it to the user. 【0082】 Step 5: 【0083】 The user receives a warning notification from their device and checks the instructions for action. Based on this, the user is required to take specific actions, such as not clicking on links that are suspected of being fraudulent. As an output, the user takes safe action. 【0084】 Step 6: 【0085】 The server sends suggestions to the device for improving security settings based on newly discovered threat intelligence from the model. These suggestions are based on feedback from the generating AI model and produce output that enhances the security of the user's device. 【0086】 (Application Example 1) 【0087】 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." 【0088】 With the proliferation of smart devices, the risk of personal information leaks and unauthorized access is increasing. Users need to respond quickly and appropriately to threats lurking in the applications they use daily and the networks they connect to. However, it is difficult for individuals to fairly assess these risks and take appropriate measures, which may compromise the safe and secure use of devices. 【0089】 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. 【0090】 In this invention, the server includes means for obtaining risk information from a reliable external organization and updating the database, means for analyzing the collected information and evaluating the risk of personal information leakage, means for scanning access rights and detecting excessive permission requests, and means for monitoring communications in real time and detecting suspicious activity. This enables users to quickly recognize the risk of personal information leakage and to use the device safely and securely. 【0091】 "Means of collecting information" refers to the function of acquiring user and communication data from smart devices and servers. 【0092】 "Means of risk assessment" refers to the process of analyzing collected data and determining the potential for personal information leakage contained within it. 【0093】 A "means of notifying warnings" refers to an alert function that informs users when a risk is detected and prompts them to take safety measures. 【0094】 "Methods for scanning access rights to detect excessive permission requests" refers to a function that investigates the access rights requested by applications installed on smart devices and identifies cases where more permissions than necessary are being requested. 【0095】 "Means for monitoring communications in real time and detecting suspicious activity" refers to a function that observes a device's network traffic in real time and identifies unusual or suspicious behavior or data communications. 【0096】 "Means of obtaining threat information from reliable external organizations and updating the database" refers to the process of obtaining the latest threat information from public or specialized organizations and adding it to the database within the system. 【0097】 To implement this invention, it is necessary to install a personal information protection system on a mobile device such as a smartphone. It mainly consists of the following elements. 【0098】 Server Role 【0099】 The server collects the latest and most reliable threat information from public institutions and trusted external organizations, updating its database accordingly. This ensures that threat information is always up-to-date, providing a high level of user safety. The server transmits this information to terminals and uses it as a basis for real-time assessments. 【0100】 Device functions 【0101】 The device includes software that analyzes the risk of personal information leakage based on the acquired data. As accessories, machine learning libraries (e.g., TENSORFLOW® and PyTorch) and natural language processing frameworks (e.g., spaCy and NLTK) are used to automatically detect suspicious messages and excessive access requests. Furthermore, it monitors communications in real time and immediately warns the user if suspicious activity is detected. 【0102】 User actions 【0103】 Users can receive notifications from their devices to proactively understand the risk of personal information leakage and confirm the necessary countermeasures. For example, if a social networking app requests access rights beyond what is normally required for its functions, the user will be notified of the risk and how to address it. Furthermore, if a malicious phishing site is detected while connected to public Wi-Fi, a warning will be sent, preventing access. 【0104】 Example prompts for generative AI models 【0105】 "Please explain how to provide real-time warnings if a user's personal information is potentially being accessed illegally." 【0106】 This system is a highly effective means of protecting personal information, based on industry-standard technologies for enhancing smartphone security. 【0107】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0108】 Step 1: 【0109】 The server periodically obtains threat intelligence from trusted external sources. The input consists of threat intelligence data provided via external APIs and data streams. This information is stored in the server's database, and data processing is performed to maintain its up-to-date state. The output is updated threat intelligence data. 【0110】 Step 2: 【0111】 The terminal receives updates from the server and uses this information to scan the access permissions of installed applications on the user's device. The input is the access permission data for all applications installed on the terminal. An over-permission detection algorithm is used as the data calculation to identify applications making excessive requests. The output is a list of applications that are requesting excessive access permissions. 【0112】 Step 3: 【0113】 The device monitors the user's communications in real time and analyzes received messages and network traffic. The input consists of received message data and network packet data. Machine learning models are used for analysis to detect suspicious patterns (e.g., phishing scams). The output is a list of detected suspicious communications. 【0114】 Step 4: 【0115】 The user receives notifications from their device and checks for warnings regarding excessive privileges or suspicious communications. The input is warning data regarding excessive privileges or suspicious communications. When notifying the user, a generative AI model is used to generate a prompt explaining the risks in natural language. The output is the warning message sent to the user. 【0116】 Step 5: 【0117】 The user takes necessary actions based on the warning. The input is the warning message sent. For example, the user might take specific actions such as deleting an application with excessive privileges or blocking suspicious communication sources. The output is the status of the improved security settings. 【0118】 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. 【0119】 This invention provides a system equipped with an emotion engine that collects and analyzes information related to user communications to assess the risk of personal information leakage and adjust warnings based on the results. This system recognizes the user's emotional state, issues warnings at the appropriate time and in the appropriate manner, and further suggests countermeasures. Specific examples are shown below. 【0120】 The server obtains the latest security risks from reliable external sources and regularly updates the system's database. Furthermore, the server accumulates data on security trends to enhance communication security. 【0121】 The device records received messages and call history with the user's permission and analyzes them in real time. It retrieves the latest risk information from the server and assesses the risk of personal information leakage. The device has an emotion engine built in that recognizes the user's emotional state from their voice and input. 【0122】 The emotion engine can detect the user's stress and anxiety levels and adjust the timing and method of warnings accordingly. For example, if the user is stressed, the warning will be gentler and detailed coping steps will be displayed in an easy-to-understand manner. 【0123】 Users can receive notifications from their devices and take appropriate actions that take their emotional state into consideration. For example, they might receive advice such as, "Treat potentially phishing messages with caution and do not open suspicious links." Furthermore, the suggested actions are adjusted according to the user's stress level, allowing them to proceed with confidence. 【0124】 Thus, the present invention enables the provision of information tailored to the user's emotional state, supporting the efficient and secure protection of personal information. The system continuously updates data and strives for further improvements in security settings to maintain a secure communication environment for users. 【0125】 The following describes the processing flow. 【0126】 Step 1: 【0127】 The server retrieves information on dangerous websites and vendors from reliable external sources and updates the system's database. This update is performed regularly to ensure that the latest danger information is always reflected. 【0128】 Step 2: 【0129】 The device, with the user's permission, locally records received messages and call history. This data will be used later for risk analysis. 【0130】 Step 3: 【0131】 The device activates an emotion engine that analyzes the user's emotional state from their voice tone and input text. The analysis results are used to determine how and when to issue warnings. 【0132】 Step 4: 【0133】 The system compares the communication content recorded by the terminal with a database of risk information provided by the server to assess the risk of personal information leakage. This process utilizes machine learning models to calculate the likelihood of risk. 【0134】 Step 5: 【0135】 If the risk assessment determines that a warning is necessary, the device will generate a warning message that takes the user's emotional state into consideration. If high stress levels are detected, the warning will be gentle and detailed. 【0136】 Step 6: 【0137】 The user receives a warning from their device and reviews the suggested course of action. The user then takes steps to mitigate the risk by following the instructions, such as deleting the message or blocking the relevant number. 【0138】 Step 7: 【0139】 The server uses newly discovered threat intelligence and user feedback to further improve the database. This will increase the accuracy of future risk assessments and improve the effectiveness of sentiment recognition. 【0140】 Step 8: 【0141】 The device will notify the user of updated security settings and improvements, prompting them to change settings as needed. Users will then review their security measures and strengthen the protection of their personal information. 【0142】 (Example 2) 【0143】 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." 【0144】 In modern society, while internet-based communication is becoming increasingly important, the risks of personal information leaks and the threat of cyber fraud are also on the rise. Therefore, it is necessary to effectively assess the risk of information leaks while considering the emotional state of users and to issue warnings at the appropriate time. Furthermore, it is required to offer flexible coping methods that are tailored to the emotional state of users so that they do not experience excessive stress. 【0145】 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. 【0146】 In this invention, the server includes means for collecting information, means for analyzing the collected information to evaluate the risk of personal information leakage, and means for notifying the user of a warning based on the evaluation results. This allows for timely warnings while considering the user's emotional state, mitigating the risk of personal information leakage, and enabling the user to deal with the situation without feeling stressed. 【0147】 "Means of collecting information" refers to the procedures or processes for obtaining risk information from external organizations and collecting data. 【0148】 "Means of assessing the risk of personal information leakage" refers to procedures or processes for analyzing collected information and determining the likelihood of personal information being misused. 【0149】 "Means of notifying users of warnings" refers to procedures or processes for issuing warnings to users and urging them to pay attention based on the results of risk assessments. 【0150】 "Means of providing appropriate countermeasures" refers to procedures or processes for providing users with specific countermeasures or instructions regarding the risks they have identified. 【0151】 "Means of recognizing emotional states and adjusting the timing and method of warnings" refers to procedures or processes for detecting a user's emotional state and changing the intensity and presentation method of warnings accordingly. 【0152】 "Means for analyzing the content of calls and messages to detect signs of fraudulent activity" refers to procedures or processes for analyzing the content of received calls and messages to identify fraudulent elements or potential fraudulent activity. 【0153】 This system effectively collects and analyzes information related to user communications and assesses the risk of personal information leakage. A specific implementation is shown below. 【0154】 The server regularly acquires reliable risk information from external organizations. This acquisition utilizes the external organizations' APIs and incorporates the latest data in an automated manner. The acquired information is stored in a database on the server, maintaining its up-to-date status at all times. The server also serves as a platform for analyzing the data and extracting general risk information trends. Data mining tools and risk information classification algorithms are used for this analysis. 【0155】 The device records received messages and call history with the user's permission. This data is analyzed in real time on the device using natural language processing tools. For example, signal processing software and speech recognition software are used to convert the content of voice calls into text, which is then analyzed. The device has a built-in emotion engine that recognizes the user's emotional state from their voice and input text. The emotion engine has the function of measuring and quantifying the user's stress and anxiety. 【0156】 Users receive notifications from their devices based on the risk assessment results. For example, if an email they receive may contain a phishing scam, they will receive a specific warning such as, "This message requires caution. Please double-check before opening the link," allowing them to address the risk with peace of mind. 【0157】 As a concrete example, here is an example of a prompt message for a generative AI model: "Please create an example of a notification that would appropriately alert a user if a message in their inbox is potentially a phishing scam. Please also tell us how to adjust this notification if the user is experiencing stress." 【0158】 This system works in conjunction with servers, terminals, and users to provide users with a secure information and communication environment. It enables real-time data analysis and tailors notifications to user sentiment, ensuring that users can protect their personal information without experiencing stress. 【0159】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0160】 Step 1: 【0161】 The server obtains risk information from external organizations. The input is risk information obtained through the external organization's API in exchange for necessary authorization information. The server retrieves this information using the HTTP protocol and parses the data in JSON format. The output is a list of the parsed risk information. This data is stored in a database and used for future analysis. 【0162】 Step 2: 【0163】 The device collects received messages and call history with the user's permission. The input consists of messages and call history stored on the user's device. The device uses natural language processing software to transcribe and analyze this data. The output is the transcribed message and call content, converted into a parsable format. This data is temporarily stored on the device's storage. 【0164】 Step 3: 【0165】 The terminal analyzes the collected communication data in real time. The input consists of the text-based communication data obtained in step 2 and risk information acquired from the server. The terminal analyzes this data using natural language processing tools and comparison algorithms to detect signs of phishing and fraud. The output is the risk assessment result, showing the risk level for each communication. 【0166】 Step 4: 【0167】 The emotion engine built into the device analyzes the user's emotional state from their voice and text input. The input consists of the user's actual voice data and text input. The emotion engine analyzes this and outputs the emotional state as a numerical value. Specifically, stress and anxiety levels are quantified and generated as data. 【0168】 Step 5: 【0169】 The device generates a warning to the user based on the risk assessment results and the output of the emotion engine. The input is the results of steps 3 and 4. The device adjusts the way the warning is expressed according to the user's emotional state and creates a notification. The output is the adjusted warning message. For example, it selects a direct warning for a relaxed user and a calmer tone of explanation for a stressed user. 【0170】 Step 6: 【0171】 The user receives a tailored warning notification from their device. The input is the warning message generated in step 5. The user takes appropriate action based on this message. The output is the action taken by the user, requiring them to properly handle the risk by following the instructions in the message. For example, it may include specific instructions such as "Please check carefully before opening the link." 【0172】 (Application Example 2) 【0173】 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 device 14 will be referred to as the "terminal." 【0174】 In modern communications, the risk of personal information leaks is increasing. Furthermore, because warnings and response methods are uniform, appropriate responses that take into account the emotional state of users are not being implemented. Therefore, there is a need for a system that can more accurately assess the risk of information leaks and adjust responses according to the psychological state of the user. 【0175】 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. 【0176】 In this invention, the server includes means for collecting information, means for analyzing the collected information to evaluate the risk of personal information leakage, and means for analyzing voice and text data to determine the user's emotional state. This makes it possible to notify the user of a warning tailored to their psychological state based on the evaluation results, and to adjust and present appropriate countermeasures for the warning based on the user's emotional state. 【0177】 "Means of collecting information" refers to a system for efficiently collecting user communication data and external risk information. 【0178】 "Means for assessing the risk of personal information leakage" refers to a function that analyzes collected information and calculates the potential risk of data leakage. 【0179】 "A means of notifying users of warnings tailored to their psychological state" refers to a function that uses the results of emotion analysis to individually adjust the content and method of warnings before notification. 【0180】 "Means for analyzing voice and text data to determine a user's emotional state" refers to a system that reads emotions from a user's voice and text and clarifies that state. 【0181】 "Means for adjusting and presenting appropriate responses to warnings" refers to a function that takes into account the user's emotional state and presents individually optimized responses. 【0182】 "Means of obtaining risk information from highly reliable external organizations and updating the information collection" refers to a system for obtaining the latest risk information from certified external sources and maintaining it at all times. 【0183】 "Means for detecting indicators of fraudulent activity" refers to a function that analyzes communication content to find patterns and signs that indicate the possibility of fraud. 【0184】 The system for realizing this invention collects and analyzes information, assesses the risk of personal information leakage, and provides warnings and countermeasures that correspond to the user's emotional state. 【0185】 The server regularly acquires reliable risk information from external organizations and updates its database. This process ensures that the risk information is up-to-date and improves the accuracy of risk assessments. Furthermore, the server accumulates information trends and automatically learns new methods to enhance communication security. 【0186】 The device analyzes incoming messages and call history in real time, with the user's permission. In this process, it uses the Google® Cloud Speech-to-Text API for speech analysis and the Affectiva API for emotional state analysis to determine the user's emotional state by analyzing voice and text data. Based on this, the device assesses and notifies the user of the risk of personal information leakage. For example, if a particular message shows signs of a phishing scam, it will display advice to handle the message with caution. 【0187】 Users receive notifications from their devices and choose the most appropriate response based on the situation. For example, a relaxed user is provided with gentle warnings and intuitive instructions. The processing steps are also adjusted according to the user's stress level. 【0188】 The processing in this system is characterized by the fact that the program optimizes warnings based on the user's emotional state and presents solutions tailored to individual needs. 【0189】 For example, if a user receives a potentially phishing email while traveling, the sentiment engine detects that the user is relaxed and warns them with neutral language such as, "Do you recognize this email? We recommend you check before opening the link." 【0190】 An example of a prompt message might be: "Analyze the user's emotional state in real time and determine the communication risk. If the user is particularly anxious, create a gentle warning." 【0191】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0192】 Step 1: 【0193】 The server obtains the latest threat information from reliable external organizations. This information is stored in a database on the server and continuously updated. This ensures that the system always operates based on the most up-to-date threat information. It receives threat information from external organizations as input and obtains an updated database as output. 【0194】 Step 2: 【0195】 The device collects data on received messages and call history in real time, with the user's permission. The user's communication data serves as input, which is stored on the device as raw data for analysis. 【0196】 Step 3: 【0197】 The device converts collected communication data into text format using the Google Cloud Speech-to-Text API and analyzes the user's emotional state using the Affectiva API. The analysis results output data based on the user's emotional state and communication content. Input is audio and text data, and output is the analyzed emotional state. 【0198】 Step 4: 【0199】 The device assesses the risk of personal information leakage based on the analysis results and generates an appropriate warning message based on the assessment results. Collected risk information is also taken into account in the risk assessment. The inputs are emotional states and risk information, and the output is the generated warning message. 【0200】 Step 5: 【0201】 The user receives the generated warning message on their device and takes appropriate action based on it. The warning is tailored to the user's emotional state and includes appropriate action instructions. As output, the user receives an easy-to-understand operation guide and countermeasures. 【0202】 Step 6: 【0203】 After the entire system has finished operating, the server uses the collected data to analyze trends in new hazard information and updates the knowledge base to prepare for future risk assessments. This will allow for more accurate assessments in the next cycle. The input is the overall operation result data, and the output is the updated knowledge base. 【0204】 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. 【0205】 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. 【0206】 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. 【0207】 [Second Embodiment] 【0208】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0209】 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. 【0210】 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). 【0211】 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. 【0212】 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. 【0213】 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). 【0214】 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. 【0215】 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. 【0216】 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. 【0217】 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. 【0218】 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. 【0219】 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". 【0220】 This invention provides a personal information protection system that operates on mobile devices such as smartphones. This system collects and analyzes information related to user communications to assess the risk of personal information leakage and issues real-time warnings based on the results. Specific examples are shown below. 【0221】 The server obtains information about dangerous sites and vendors from reliable external sources. This information is regularly added to the system's database and updated to address the latest threats. 【0222】 The device records received messages, call history, and contact information based on the user's permission. The device queries the database for updates from the server and evaluates the current communication content based on newly acquired risk information. 【0223】 The analyzed information is used to assess the risk of personal information leakage using machine learning models. For example, if a message suspected of being a phishing scam is received, natural language processing technology is used to analyze its content and determine whether it is likely to be a scam. 【0224】 Users can receive notifications from the system and see specific steps to take if a risk is detected. The notifications include specific advice such as, "This message may be a scam. Do not click the link," or "This phone number is likely spam. Do you want to block it?" 【0225】 This reduces the risk of users becoming victims of unauthorized access or data breaches due to their own actions. The system also continuously supports user safety by sending suggestions to improve security settings on devices based on newly discovered threat information. 【0226】 The following describes the processing flow. 【0227】 Step 1: 【0228】 The server obtains information about dangerous websites and vendors from reliable external sources and updates the system's database. 【0229】 Step 2: 【0230】 The device, with the user's permission, locally records received messages, call history, and contact information. 【0231】 Step 3: 【0232】 The terminal queries the server based on the recorded data to confirm any newly acquired risk information. 【0233】 Step 4: 【0234】 The system uses information received by the device to analyze messages and call content, employing machine learning models and natural language processing techniques. 【0235】 Step 5: 【0236】 If the analysis detects a risk of phishing scams or spam calls, the device will display a warning notification to the user. 【0237】 Step 6: 【0238】 The user checks the notification and follows the specific actions suggested (e.g., delete the message, block the phone number). 【0239】 Step 7: 【0240】 The server generates suggestions for improving security settings based on newly discovered threat information and sends them to the terminal. 【0241】 Step 8: 【0242】 The device displays suggested configuration improvements to the user and asks them whether they want to make the suggested changes. 【0243】 Step 9: 【0244】 Users can review their device settings and update them as needed to enhance the protection of their personal information. 【0245】 (Example 1) 【0246】 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." 【0247】 In recent years, the leakage of personal information has become a social problem, and the risk is particularly high in communications via mobile devices. In this situation, users need appropriate means to protect their information. However, conventional technologies have been problematic because they do not adequately perform real-time risk assessment or rapid response. 【0248】 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. 【0249】 In this invention, the server includes a device for collecting information, a device for analyzing the collected information and evaluating the risk of personal information leakage, and a device for notifying the user based on the evaluation results. This enables the user to recognize potential risks in real time and respond quickly and appropriately. 【0250】 A "device for collecting information" is a device that, with the user's permission, collects and securely manages data such as received messages, call history, and contact information. 【0251】 A "device for analyzing and evaluating the risk of personal information leakage" is a device that uses machine learning models and natural language processing technology to quantify the risk of personal information leakage based on collected information. 【0252】 A "device that notifies users based on evaluation results" is a device that, based on analysis results, sends warning messages to users regarding communications suspected of being fraudulent, providing real-time alerts. 【0253】 A "device that updates the database based on information from a trusted external source" is a device that obtains the latest information on phishing sites and malicious operators and keeps the database constantly updated. 【0254】 A "device for identifying potential threats" is a device that analyzes communication content to detect potentially harmful actions at an early stage. 【0255】 "Methods using generative AI technology" refer to processing methods that utilize artificial intelligence technology to analyze the content of messages and calls in an advanced manner and determine whether or not there is a threat. 【0256】 This personal information protection system is implemented as an application that runs on mobile devices and is designed to reduce the risk of personal information leakage by monitoring users' daily communications in real time. 【0257】 The server regularly collects information about dangerous sites and fraudulent operators from trusted external sources and updates the system's database with this information. This server possesses high-performance data processing capabilities and uses a database management system to quickly and accurately organize the information. 【0258】 The device operates on mobile operating systems such as Android and iOS and collects messages, call history, and contact information with the user's permission. The collected data is processed using generative AI models that utilize natural language processing technology to assess the risk of personal information leakage. For example, if an email or SMS showing typical signs of a phishing scam is detected, its content is analyzed in depth, and if the likelihood of fraud is high, a warning is issued. 【0259】 Users receive warning notifications based on the results of analysis performed on their devices. These notifications include specific advice such as, "This message may be a scam. Do not click the link." This allows users to use their devices with peace of mind and mitigate the risk of unauthorized access and data breaches. 【0260】 For example, if a user receives a suspicious email, the device analyzes it and immediately notifies the user, along with sending a notification indicating that the email is likely a phishing attempt. The system continues to protect users in this way. 【0261】 An example of a prompt message to the generating AI model is: "We have analyzed your incoming email. The following email may be a phishing scam. We suggest the following steps. Do not click on the link." In this way, the system ensures the maximum security of the user's communications. 【0262】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0263】 Step 1: 【0264】 The server collects threat information from trusted external sources and updates its database. In this process, new threat information obtained from external sources is supplied as input, and that data is added to the server's database. As a result, the database always maintains the most up-to-date threat information. 【0265】 Step 2: 【0266】 The device collects received messages, call history, and contact information based on the user's permission. This input data is securely managed by the device's application and prepared for the next analysis step. As output, this data is stored in a format that allows for later analysis. 【0267】 Step 3: 【0268】 The terminal compares collected user data with the server and database to assess potential risks. Input includes user communication data recorded on the terminal and risk information obtained from the server. Using this data, natural language processing and machine learning techniques are employed to calculate the likelihood of personal information leakage, resulting in an output that identifies high-risk communications. 【0269】 Step 4: 【0270】 The device issues a real-time warning notification based on the results analyzed in the previous step. The risk information evaluated in step 3 is used as input. Based on this data, the device outputs an alert such as "This message may be a scam," and displays it to the user. 【0271】 Step 5: 【0272】 The user receives a warning notification from their device and checks the instructions for action. Based on this, the user is required to take specific actions, such as not clicking on links that are suspected of being fraudulent. As an output, the user takes safe action. 【0273】 Step 6: 【0274】 The server sends suggestions to the device for improving security settings based on newly discovered threat intelligence from the model. These suggestions are based on feedback from the generating AI model and produce output that enhances the security of the user's device. 【0275】 (Application Example 1) 【0276】 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." 【0277】 With the proliferation of smart devices, the risk of personal information leaks and unauthorized access is increasing. Users need to respond quickly and appropriately to threats lurking in the applications they use daily and the networks they connect to. However, it is difficult for individuals to fairly assess these risks and take appropriate measures, which may compromise the safe and secure use of devices. 【0278】 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. 【0279】 In this invention, the server includes means for obtaining risk information from a reliable external organization and updating the database, means for analyzing the collected information and evaluating the risk of personal information leakage, means for scanning access rights and detecting excessive permission requests, and means for monitoring communications in real time and detecting suspicious activity. This enables users to quickly recognize the risk of personal information leakage and to use the device safely and securely. 【0280】 "Means of collecting information" refers to the function of acquiring user and communication data from smart devices and servers. 【0281】 "Means of risk assessment" refers to the process of analyzing collected data and determining the potential for personal information leakage contained within it. 【0282】 A "means of notifying warnings" refers to an alert function that informs users when a risk is detected and prompts them to take safety measures. 【0283】 "Methods for scanning access rights to detect excessive permission requests" refers to a function that investigates the access rights requested by applications installed on smart devices and identifies cases where more permissions than necessary are being requested. 【0284】 "Means for monitoring communication in real time and detecting suspicious activities" refers to the function of observing the network traffic of a device in real time and detecting suspicious operations or data communications that are different from normal ones. 【0285】 "Means for obtaining threat information from reliable external institutions and updating the database" refers to the process of obtaining the latest threat information from public institutions or specialized institutions and adding it to the database within the system. 【0286】 To implement this invention, it is necessary to install a personal information protection system on a mobile terminal such as a smartphone. It is mainly composed of the following elements. 【0287】 Role of the server 【0288】 The server collects the latest and reliable threat information from public institutions or trustworthy external institutions and updates the database. This enables the threat information to always be up-to-date and highly ensures the safety of users. The server transmits this information to the terminal and uses it as a criterion for real-time evaluation. 【0289】 Function of the terminal 【0290】 The terminal includes software for analyzing the risk of personal information leakage based on the acquired data. As accessories, machine learning libraries (such as TensorFlow or PyTorch) and natural language processing frameworks (such as spaCy or NLTK) are used to automatically determine suspicious messages or excessive requests for access rights. Furthermore, when monitoring communication in real time and detecting suspicious activities, a warning is immediately issued to the user. 【0291】 User operations 【0292】 Users can receive notifications from their devices to proactively understand the risk of personal information leakage and confirm the necessary countermeasures. For example, if a social networking app requests access rights beyond what is normally required for its functions, the user will be notified of the risk and how to address it. Furthermore, if a malicious phishing site is detected while connected to public Wi-Fi, a warning will be sent, preventing access. 【0293】 Example prompts for generative AI models 【0294】 "Please explain how to provide real-time warnings if a user's personal information is potentially being accessed illegally." 【0295】 This system is a highly effective means of protecting personal information, based on industry-standard technologies for enhancing smartphone security. 【0296】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0297】 Step 1: 【0298】 The server periodically obtains threat intelligence from trusted external sources. The input consists of threat intelligence data provided via external APIs and data streams. This information is stored in the server's database, and data processing is performed to maintain its up-to-date state. The output is updated threat intelligence data. 【0299】 Step 2: 【0300】 The terminal receives updates from the server and uses this information to scan the access permissions of installed applications on the user's device. The input is the access permission data for all applications installed on the terminal. An over-permission detection algorithm is used as the data calculation to identify applications making excessive requests. The output is a list of applications that are requesting excessive access permissions. 【0301】 Step 3: 【0302】 The device monitors the user's communications in real time and analyzes received messages and network traffic. The input consists of received message data and network packet data. Machine learning models are used for analysis to detect suspicious patterns (e.g., phishing scams). The output is a list of detected suspicious communications. 【0303】 Step 4: 【0304】 The user receives notifications from their device and checks for warnings regarding excessive privileges or suspicious communications. The input is warning data regarding excessive privileges or suspicious communications. When notifying the user, a generative AI model is used to generate a prompt explaining the risks in natural language. The output is the warning message sent to the user. 【0305】 Step 5: 【0306】 The user takes necessary actions based on the warning. The input is the warning message sent. For example, the user might take specific actions such as deleting an application with excessive privileges or blocking suspicious communication sources. The output is the status of the improved security settings. 【0307】 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. 【0308】 The present invention provides a system equipped with an emotion engine that evaluates the risk of personal information leakage and adjusts warnings based on the results by collecting and analyzing information related to user communication. This system recognizes the user's emotional state, gives warnings at appropriate times and in appropriate ways, and further presents coping methods. Specific examples are shown below. 【0309】 The server obtains the latest danger information from a highly reliable external agency and periodically updates the system's database. The server is also accumulating trends in danger information to enhance communication security. 【0310】 The terminal records received messages and call histories with the user's permission and performs real-time analysis. It obtains the latest danger information from the server and evaluates the risk of personal information leakage. An emotion engine is incorporated in the terminal to recognize the emotional state from the user's voice and input content. 【0311】 The emotion engine can detect the user's stress and anxiety and adjusts the timing and method of warnings based on this. For example, when the user is in a stressed state, the warning is given more gently and detailed coping procedures are displayed clearly. 【0312】 The user can receive notifications from the terminal and take appropriate actions considering their emotional state. For example, receive advice such as "Handle messages that may be phishing carefully and do not open suspicious links." Furthermore, the presented coping methods are adjusted according to the user's stress state, allowing the user to proceed with operations with peace of mind. 【0313】 In this way, the present invention enables information provision according to the user's emotional state, efficiently and securely supports the protection of personal information. The system continuously updates data and aims for further improvement of security settings to maintain a safe communication environment for the user. 【0314】 The following describes the processing flow. 【0315】 Step 1: 【0316】 The server retrieves information on dangerous websites and vendors from reliable external sources and updates the system's database. This update is performed regularly to ensure that the latest danger information is always reflected. 【0317】 Step 2: 【0318】 The device, with the user's permission, locally records received messages and call history. This data will be used later for risk analysis. 【0319】 Step 3: 【0320】 The device activates an emotion engine that analyzes the user's emotional state from their voice tone and input text. The analysis results are used to determine how and when to issue warnings. 【0321】 Step 4: 【0322】 The system compares the communication content recorded by the terminal with a database of risk information provided by the server to assess the risk of personal information leakage. This process utilizes machine learning models to calculate the likelihood of risk. 【0323】 Step 5: 【0324】 If the risk assessment determines that a warning is necessary, the device will generate a warning message that takes the user's emotional state into consideration. If high stress levels are detected, the warning will be gentle and detailed. 【0325】 Step 6: 【0326】 The user receives a warning from their device and reviews the suggested course of action. The user then takes steps to mitigate the risk by following the instructions, such as deleting the message or blocking the relevant number. 【0327】 Step 7: 【0328】 The server uses newly discovered threat intelligence and user feedback to further improve the database. This will increase the accuracy of future risk assessments and improve the effectiveness of sentiment recognition. 【0329】 Step 8: 【0330】 The device will notify the user of updated security settings and improvements, prompting them to change settings as needed. Users will then review their security measures and strengthen the protection of their personal information. 【0331】 (Example 2) 【0332】 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 glasses 214 will be referred to as the "terminal". 【0333】 In modern society, while internet-based communication is becoming increasingly important, the risks of personal information leaks and the threat of cyber fraud are also on the rise. Therefore, it is necessary to effectively assess the risk of information leaks while considering the emotional state of users and to issue warnings at the appropriate time. Furthermore, it is required to offer flexible coping methods that are tailored to the emotional state of users so that they do not experience excessive stress. 【0334】 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. 【0335】 In this invention, the server includes means for collecting information, means for analyzing the collected information to evaluate the risk of personal information leakage, and means for notifying the user of a warning based on the evaluation results. This allows for timely warnings while considering the user's emotional state, mitigating the risk of personal information leakage, and enabling the user to deal with the situation without feeling stressed. 【0336】 "Means of collecting information" refers to the procedures or processes for obtaining risk information from external organizations and collecting data. 【0337】 "Means of assessing the risk of personal information leakage" refers to procedures or processes for analyzing collected information and determining the likelihood of personal information being misused. 【0338】 "Means of notifying users of warnings" refers to procedures or processes for issuing warnings to users and urging them to pay attention based on the results of risk assessments. 【0339】 "Means of providing appropriate countermeasures" refers to procedures or processes for providing users with specific countermeasures or instructions regarding the risks they have identified. 【0340】 "Means of recognizing emotional states and adjusting the timing and method of warnings" refers to procedures or processes for detecting a user's emotional state and changing the intensity and presentation method of warnings accordingly. 【0341】 "Means for analyzing the content of calls and messages to detect signs of fraudulent activity" refers to procedures or processes for analyzing the content of received calls and messages to identify fraudulent elements or potential fraudulent activity. 【0342】 This system effectively collects and analyzes information related to user communications and assesses the risk of personal information leakage. A specific implementation is shown below. 【0343】 The server regularly acquires reliable risk information from external organizations. This acquisition utilizes the external organizations' APIs and incorporates the latest data in an automated manner. The acquired information is stored in a database on the server, maintaining its up-to-date status at all times. The server also serves as a platform for analyzing the data and extracting general risk information trends. Data mining tools and risk information classification algorithms are used for this analysis. 【0344】 The device records received messages and call history with the user's permission. This data is analyzed in real time on the device using natural language processing tools. For example, signal processing software and speech recognition software are used to convert the content of voice calls into text, which is then analyzed. The device has a built-in emotion engine that recognizes the user's emotional state from their voice and input text. The emotion engine has the function of measuring and quantifying the user's stress and anxiety. 【0345】 Users receive notifications from their devices based on the risk assessment results. For example, if an email they receive may contain a phishing scam, they will receive a specific warning such as, "This message requires caution. Please double-check before opening the link," allowing them to address the risk with peace of mind. 【0346】 As a concrete example, here is an example of a prompt message for a generative AI model: "Please create an example of a notification that would appropriately alert a user if a message in their inbox is potentially a phishing scam. Please also tell us how to adjust this notification if the user is experiencing stress." 【0347】 This system works in conjunction with servers, terminals, and users to provide users with a secure information and communication environment. It enables real-time data analysis and tailors notifications to user sentiment, ensuring that users can protect their personal information without experiencing stress. 【0348】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0349】 Step 1: 【0350】 The server obtains risk information from external organizations. The input is risk information obtained through the external organization's API in exchange for necessary authorization information. The server retrieves this information using the HTTP protocol and parses the data in JSON format. The output is a list of the parsed risk information. This data is stored in a database and used for future analysis. 【0351】 Step 2: 【0352】 The device collects received messages and call history with the user's permission. The input consists of messages and call history stored on the user's device. The device uses natural language processing software to transcribe and analyze this data. The output is the transcribed message and call content, converted into a parsable format. This data is temporarily stored on the device's storage. 【0353】 Step 3: 【0354】 The terminal analyzes the collected communication data in real time. The input consists of the text-based communication data obtained in step 2 and risk information acquired from the server. The terminal analyzes this data using natural language processing tools and comparison algorithms to detect signs of phishing and fraud. The output is the risk assessment result, showing the risk level for each communication. 【0355】 Step 4: 【0356】 The emotion engine built into the device analyzes the user's emotional state from their voice and text input. The input consists of the user's actual voice data and text input. The emotion engine analyzes this and outputs the emotional state as a numerical value. Specifically, stress and anxiety levels are quantified and generated as data. 【0357】 Step 5: 【0358】 The device generates a warning to the user based on the risk assessment results and the output of the emotion engine. The input is the results of steps 3 and 4. The device adjusts the way the warning is expressed according to the user's emotional state and creates a notification. The output is the adjusted warning message. For example, it selects a direct warning for a relaxed user and a calmer tone of explanation for a stressed user. 【0359】 Step 6: 【0360】 The user receives a tailored warning notification from their device. The input is the warning message generated in step 5. The user takes appropriate action based on this message. The output is the action taken by the user, requiring them to properly handle the risk by following the instructions in the message. For example, it may include specific instructions such as "Please check carefully before opening the link." 【0361】 (Application Example 2) 【0362】 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." 【0363】 In modern communications, the risk of personal information leaks is increasing. Furthermore, because warnings and response methods are uniform, appropriate responses that take into account the emotional state of users are not being implemented. Therefore, there is a need for a system that can more accurately assess the risk of information leaks and adjust responses according to the psychological state of the user. 【0364】 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. 【0365】 In this invention, the server includes means for collecting information, means for analyzing the collected information to evaluate the risk of personal information leakage, and means for analyzing voice and text data to determine the user's emotional state. This makes it possible to notify the user of a warning tailored to their psychological state based on the evaluation results, and to adjust and present appropriate countermeasures for the warning based on the user's emotional state. 【0366】 "Means of collecting information" refers to a system for efficiently collecting user communication data and external risk information. 【0367】 "Means for assessing the risk of personal information leakage" refers to a function that analyzes collected information and calculates the potential risk of data leakage. 【0368】 "A means of notifying users of warnings tailored to their psychological state" refers to a function that uses the results of emotion analysis to individually adjust the content and method of warnings before notification. 【0369】 "Means for analyzing voice and text data to determine a user's emotional state" refers to a system that reads emotions from a user's voice and text and clarifies that state. 【0370】 "Means for adjusting and presenting appropriate responses to warnings" refers to a function that takes into account the user's emotional state and presents individually optimized responses. 【0371】 "Means of obtaining risk information from highly reliable external organizations and updating the information collection" refers to a system for obtaining the latest risk information from certified external sources and maintaining it at all times. 【0372】 "Means for detecting indicators of fraudulent activity" refers to a function that analyzes communication content to find patterns and signs that indicate the possibility of fraud. 【0373】 The system for realizing this invention collects and analyzes information, assesses the risk of personal information leakage, and provides warnings and countermeasures that correspond to the user's emotional state. 【0374】 The server regularly acquires reliable risk information from external organizations and updates its database. This process ensures that the risk information is up-to-date and improves the accuracy of risk assessments. Furthermore, the server accumulates information trends and automatically learns new methods to enhance communication security. 【0375】 The device analyzes incoming messages and call history in real time, with the user's permission. This process uses the Google Cloud Speech-to-Text API for speech analysis and the Affectiva API for emotional state analysis to determine the user's emotional state by analyzing voice and text data. Based on this, the device assesses and notifies the user of the risk of personal information leakage. For example, if a particular message shows signs of a phishing scam, it will display advice to handle the message with caution. 【0376】 Users receive notifications from their devices and choose the most appropriate response based on the situation. For example, a relaxed user is provided with gentle warnings and intuitive instructions. The processing steps are also adjusted according to the user's stress level. 【0377】 The processing in this system is characterized by the fact that the program optimizes warnings based on the user's emotional state and presents solutions tailored to individual needs. 【0378】 For example, if a user receives a potentially phishing email while traveling, the sentiment engine detects that the user is relaxed and warns them with neutral language such as, "Do you recognize this email? We recommend you check before opening the link." 【0379】 An example of a prompt message might be: "Analyze the user's emotional state in real time and determine the communication risk. If the user is particularly anxious, create a gentle warning." 【0380】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0381】 Step 1: 【0382】 The server obtains the latest threat information from reliable external organizations. This information is stored in a database on the server and continuously updated. This ensures that the system always operates based on the most up-to-date threat information. It receives threat information from external organizations as input and obtains an updated database as output. 【0383】 Step 2: 【0384】 The device collects data on received messages and call history in real time, with the user's permission. The user's communication data serves as input, which is stored on the device as raw data for analysis. 【0385】 Step 3: 【0386】 The device converts collected communication data into text format using the Google Cloud Speech-to-Text API and analyzes the user's emotional state using the Affectiva API. The analysis results output data based on the user's emotional state and communication content. Input is audio and text data, and output is the analyzed emotional state. 【0387】 Step 4: 【0388】 The device assesses the risk of personal information leakage based on the analysis results and generates an appropriate warning message based on the assessment results. Collected risk information is also taken into account in the risk assessment. The inputs are emotional states and risk information, and the output is the generated warning message. 【0389】 Step 5: 【0390】 The user receives the generated warning message on their device and takes appropriate action based on it. The warning is tailored to the user's emotional state and includes appropriate action instructions. As output, the user receives an easy-to-understand operation guide and countermeasures. 【0391】 Step 6: 【0392】 After the entire system has finished operating, the server uses the collected data to analyze trends in new hazard information and updates the knowledge base to prepare for future risk assessments. This will allow for more accurate assessments in the next cycle. The input is the overall operation result data, and the output is the updated knowledge base. 【0393】 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. 【0394】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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. 【0395】 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. 【0396】 [Third Embodiment] 【0397】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0398】 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. 【0399】 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). 【0400】 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. 【0401】 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. 【0402】 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). 【0403】 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. 【0404】 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. 【0405】 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. 【0406】 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. 【0407】 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. 【0408】 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". 【0409】 This invention provides a personal information protection system that operates on mobile devices such as smartphones. This system collects and analyzes information related to user communications to assess the risk of personal information leakage and issues real-time warnings based on the results. Specific examples are shown below. 【0410】 The server obtains information about dangerous sites and vendors from reliable external sources. This information is regularly added to the system's database and updated to address the latest threats. 【0411】 The device records received messages, call history, and contact information based on the user's permission. The device queries the database for updates from the server and evaluates the current communication content based on newly acquired risk information. 【0412】 The analyzed information is used to assess the risk of personal information leakage using machine learning models. For example, if a message suspected of being a phishing scam is received, natural language processing technology is used to analyze its content and determine whether it is likely to be a scam. 【0413】 Users can receive notifications from the system and see specific steps to take if a risk is detected. The notifications include specific advice such as, "This message may be a scam. Do not click the link," or "This phone number is likely spam. Do you want to block it?" 【0414】 This reduces the risk of users becoming victims of unauthorized access or data breaches due to their own actions. The system also continuously supports user safety by sending suggestions to improve security settings on devices based on newly discovered threat information. 【0415】 The following describes the processing flow. 【0416】 Step 1: 【0417】 The server obtains information about dangerous websites and vendors from reliable external sources and updates the system's database. 【0418】 Step 2: 【0419】 The device, with the user's permission, locally records received messages, call history, and contact information. 【0420】 Step 3: 【0421】 The terminal queries the server based on the recorded data to confirm any newly acquired risk information. 【0422】 Step 4: 【0423】 The system uses information received by the device to analyze messages and call content, employing machine learning models and natural language processing techniques. 【0424】 Step 5: 【0425】 If the analysis detects a risk of phishing scams or spam calls, the device will display a warning notification to the user. 【0426】 Step 6: 【0427】 The user checks the notification and follows the specific actions suggested (e.g., delete the message, block the phone number). 【0428】 Step 7: 【0429】 The server generates suggestions for improving security settings based on newly discovered threat information and sends them to the terminal. 【0430】 Step 8: 【0431】 The device displays suggested configuration improvements to the user and asks them whether they want to make the suggested changes. 【0432】 Step 9: 【0433】 Users can review their device settings and update them as needed to enhance the protection of their personal information. 【0434】 (Example 1) 【0435】 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." 【0436】 In recent years, the leakage of personal information has become a social problem, and the risk is particularly high in communications via mobile devices. In this situation, users need appropriate means to protect their information. However, conventional technologies have been problematic because they do not adequately perform real-time risk assessment or rapid response. 【0437】 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. 【0438】 In this invention, the server includes a device for collecting information, a device for analyzing the collected information and evaluating the risk of personal information leakage, and a device for notifying the user based on the evaluation results. This enables the user to recognize potential risks in real time and respond quickly and appropriately. 【0439】 A "device for collecting information" is a device that, with the user's permission, collects and securely manages data such as received messages, call history, and contact information. 【0440】 A "device for analyzing and evaluating the risk of personal information leakage" is a device that uses machine learning models and natural language processing technology to quantify the risk of personal information leakage based on collected information. 【0441】 A "device that notifies users based on evaluation results" is a device that, based on analysis results, sends warning messages to users regarding communications suspected of being fraudulent, providing real-time alerts. 【0442】 A "device that updates the database based on information from a trusted external source" is a device that obtains the latest information on phishing sites and malicious operators and keeps the database constantly updated. 【0443】 A "device for identifying potential threats" is a device that analyzes communication content to detect potentially harmful actions at an early stage. 【0444】 "Methods using generative AI technology" refer to processing methods that utilize artificial intelligence technology to analyze the content of messages and calls in an advanced manner and determine whether or not there is a threat. 【0445】 This personal information protection system is implemented as an application that runs on mobile devices and is designed to reduce the risk of personal information leakage by monitoring users' daily communications in real time. 【0446】 The server regularly collects information about dangerous sites and fraudulent operators from trusted external sources and updates the system's database with this information. This server possesses high-performance data processing capabilities and uses a database management system to quickly and accurately organize the information. 【0447】 The device operates on mobile operating systems such as Android and iOS and collects messages, call history, and contact information with the user's permission. The collected data is processed using generative AI models that utilize natural language processing technology to assess the risk of personal information leakage. For example, if an email or SMS showing typical signs of a phishing scam is detected, its content is analyzed in depth, and if the likelihood of fraud is high, a warning is issued. 【0448】 Users receive warning notifications based on the results of analysis performed on their devices. These notifications include specific advice such as, "This message may be a scam. Do not click the link." This allows users to use their devices with peace of mind and mitigate the risk of unauthorized access and data breaches. 【0449】 For example, if a user receives a suspicious email, the device analyzes it and immediately notifies the user, along with sending a notification indicating that the email is likely a phishing attempt. The system continues to protect users in this way. 【0450】 An example of a prompt message to the generating AI model is: "We have analyzed your incoming email. The following email may be a phishing scam. We suggest the following steps. Do not click on the link." In this way, the system ensures the maximum security of the user's communications. 【0451】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0452】 Step 1: 【0453】 The server collects threat information from trusted external sources and updates its database. In this process, new threat information obtained from external sources is supplied as input, and that data is added to the server's database. As a result, the database always maintains the most up-to-date threat information. 【0454】 Step 2: 【0455】 The device collects received messages, call history, and contact information based on the user's permission. This input data is securely managed by the device's application and prepared for the next analysis step. As output, this data is stored in a format that allows for later analysis. 【0456】 Step 3: 【0457】 The terminal compares collected user data with the server and database to assess potential risks. Input includes user communication data recorded on the terminal and risk information obtained from the server. Using this data, natural language processing and machine learning techniques are employed to calculate the likelihood of personal information leakage, resulting in an output that identifies high-risk communications. 【0458】 Step 4: 【0459】 The device issues a real-time warning notification based on the results analyzed in the previous step. The risk information evaluated in step 3 is used as input. Based on this data, the device outputs an alert such as "This message may be a scam," and displays it to the user. 【0460】 Step 5: 【0461】 The user receives a warning notification from their device and checks the instructions for action. Based on this, the user is required to take specific actions, such as not clicking on links that are suspected of being fraudulent. As an output, the user takes safe action. 【0462】 Step 6: 【0463】 The server sends suggestions to the device for improving security settings based on newly discovered threat intelligence from the model. These suggestions are based on feedback from the generating AI model and produce output that enhances the security of the user's device. 【0464】 (Application Example 1) 【0465】 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." 【0466】 With the proliferation of smart devices, the risk of personal information leaks and unauthorized access is increasing. Users need to respond quickly and appropriately to threats lurking in the applications they use daily and the networks they connect to. However, it is difficult for individuals to fairly assess these risks and take appropriate measures, which may compromise the safe and secure use of devices. 【0467】 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. 【0468】 In this invention, the server includes means for obtaining risk information from a reliable external organization and updating the database, means for analyzing the collected information and evaluating the risk of personal information leakage, means for scanning access rights and detecting excessive permission requests, and means for monitoring communications in real time and detecting suspicious activity. This enables users to quickly recognize the risk of personal information leakage and to use the device safely and securely. 【0469】 "Means of collecting information" refers to the function of acquiring user and communication data from smart devices and servers. 【0470】 "Means of risk assessment" refers to the process of analyzing collected data and determining the potential for personal information leakage contained within it. 【0471】 A "means of notifying warnings" refers to an alert function that informs users when a risk is detected and prompts them to take safety measures. 【0472】 "Methods for scanning access rights to detect excessive permission requests" refers to a function that investigates the access rights requested by applications installed on smart devices and identifies cases where more permissions than necessary are being requested. 【0473】 "Means for monitoring communications in real time and detecting suspicious activity" refers to a function that observes a device's network traffic in real time and identifies unusual or suspicious behavior or data communications. 【0474】 "Means of obtaining threat information from reliable external organizations and updating the database" refers to the process of obtaining the latest threat information from public or specialized organizations and adding it to the database within the system. 【0475】 To implement this invention, it is necessary to install a personal information protection system on a mobile device such as a smartphone. It mainly consists of the following elements. 【0476】 Server Role 【0477】 The server collects the latest and most reliable threat information from public institutions and trusted external organizations, updating its database accordingly. This ensures that threat information is always up-to-date, providing a high level of user safety. The server transmits this information to terminals and uses it as a basis for real-time assessments. 【0478】 Device functions 【0479】 The device includes software that analyzes the risk of personal information leakage based on the acquired data. As accessories, machine learning libraries (e.g., TensorFlow and PyTorch) and natural language processing frameworks (e.g., spaCy and NLTK) are used to automatically detect suspicious messages and excessive access requests. Furthermore, it monitors communications in real time and immediately warns the user if suspicious activity is detected. 【0480】 User actions 【0481】 Users can receive notifications from their devices to proactively understand the risk of personal information leakage and confirm the necessary countermeasures. For example, if a social networking app requests access rights beyond what is normally required for its functions, the user will be notified of the risk and how to address it. Furthermore, if a malicious phishing site is detected while connected to public Wi-Fi, a warning will be sent, preventing access. 【0482】 Example prompts for generative AI models 【0483】 "Please explain how to provide real-time warnings if a user's personal information is potentially being accessed illegally." 【0484】 This system is a highly effective means of protecting personal information, based on industry-standard technologies for enhancing smartphone security. 【0485】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0486】 Step 1: 【0487】 The server periodically obtains threat intelligence from trusted external sources. The input consists of threat intelligence data provided via external APIs and data streams. This information is stored in the server's database, and data processing is performed to maintain its up-to-date state. The output is updated threat intelligence data. 【0488】 Step 2: 【0489】 The terminal receives updates from the server and uses this information to scan the access permissions of installed applications on the user's device. The input is the access permission data for all applications installed on the terminal. An over-permission detection algorithm is used as the data calculation to identify applications making excessive requests. The output is a list of applications that are requesting excessive access permissions. 【0490】 Step 3: 【0491】 The device monitors the user's communications in real time and analyzes received messages and network traffic. The input consists of received message data and network packet data. Machine learning models are used for analysis to detect suspicious patterns (e.g., phishing scams). The output is a list of detected suspicious communications. 【0492】 Step 4: 【0493】 The user receives notifications from their device and checks for warnings regarding excessive privileges or suspicious communications. The input is warning data regarding excessive privileges or suspicious communications. When notifying the user, a generative AI model is used to generate a prompt explaining the risks in natural language. The output is the warning message sent to the user. 【0494】 Step 5: 【0495】 The user takes necessary actions based on the warning. The input is the warning message sent. For example, the user might take specific actions such as deleting an application with excessive privileges or blocking suspicious communication sources. The output is the status of the improved security settings. 【0496】 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. 【0497】 This invention provides a system equipped with an emotion engine that collects and analyzes information related to user communications to assess the risk of personal information leakage and adjust warnings based on the results. This system recognizes the user's emotional state, issues warnings at the appropriate time and in the appropriate manner, and further suggests countermeasures. Specific examples are shown below. 【0498】 The server obtains the latest security risks from reliable external sources and regularly updates the system's database. Furthermore, the server accumulates data on security trends to enhance communication security. 【0499】 The device records received messages and call history with the user's permission and analyzes them in real time. It retrieves the latest risk information from the server and assesses the risk of personal information leakage. The device has an emotion engine built in that recognizes the user's emotional state from their voice and input. 【0500】 The emotion engine can detect the user's stress and anxiety levels and adjust the timing and method of warnings accordingly. For example, if the user is stressed, the warning will be gentler and detailed coping steps will be displayed in an easy-to-understand manner. 【0501】 Users can receive notifications from their devices and take appropriate actions that take their emotional state into consideration. For example, they might receive advice such as, "Treat potentially phishing messages with caution and do not open suspicious links." Furthermore, the suggested actions are adjusted according to the user's stress level, allowing them to proceed with confidence. 【0502】 Thus, the present invention enables the provision of information tailored to the user's emotional state, supporting the efficient and secure protection of personal information. The system continuously updates data and strives for further improvements in security settings to maintain a secure communication environment for users. 【0503】 The following describes the processing flow. 【0504】 Step 1: 【0505】 The server retrieves information on dangerous websites and vendors from reliable external sources and updates the system's database. This update is performed regularly to ensure that the latest danger information is always reflected. 【0506】 Step 2: 【0507】 The device, with the user's permission, locally records received messages and call history. This data will be used later for risk analysis. 【0508】 Step 3: 【0509】 The device activates an emotion engine that analyzes the user's emotional state from their voice tone and input text. The analysis results are used to determine how and when to issue warnings. 【0510】 Step 4: 【0511】 The system compares the communication content recorded by the terminal with a database of risk information provided by the server to assess the risk of personal information leakage. This process utilizes machine learning models to calculate the likelihood of risk. 【0512】 Step 5: 【0513】 If the risk assessment determines that a warning is necessary, the device will generate a warning message that takes the user's emotional state into consideration. If high stress levels are detected, the warning will be gentle and detailed. 【0514】 Step 6: 【0515】 The user receives a warning from their device and reviews the suggested course of action. The user then takes steps to mitigate the risk by following the instructions, such as deleting the message or blocking the relevant number. 【0516】 Step 7: 【0517】 The server uses newly discovered threat intelligence and user feedback to further improve the database. This will increase the accuracy of future risk assessments and improve the effectiveness of sentiment recognition. 【0518】 Step 8: 【0519】 The device will notify the user of updated security settings and improvements, prompting them to change settings as needed. Users will then review their security measures and strengthen the protection of their personal information. 【0520】 (Example 2) 【0521】 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." 【0522】 In modern society, while internet-based communication is becoming increasingly important, the risks of personal information leaks and the threat of cyber fraud are also on the rise. Therefore, it is necessary to effectively assess the risk of information leaks while considering the emotional state of users and to issue warnings at the appropriate time. Furthermore, it is required to offer flexible coping methods that are tailored to the emotional state of users so that they do not experience excessive stress. 【0523】 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. 【0524】 In this invention, the server includes means for collecting information, means for analyzing the collected information to evaluate the risk of personal information leakage, and means for notifying the user of a warning based on the evaluation results. This allows for timely warnings while considering the user's emotional state, mitigating the risk of personal information leakage, and enabling the user to deal with the situation without feeling stressed. 【0525】 "Means of collecting information" refers to the procedures or processes for obtaining risk information from external organizations and collecting data. 【0526】 "Means of assessing the risk of personal information leakage" refers to procedures or processes for analyzing collected information and determining the likelihood of personal information being misused. 【0527】 "Means of notifying users of warnings" refers to procedures or processes for issuing warnings to users and urging them to pay attention based on the results of risk assessments. 【0528】 "Means of providing appropriate countermeasures" refers to procedures or processes for providing users with specific countermeasures or instructions regarding the risks they have identified. 【0529】 "Means of recognizing emotional states and adjusting the timing and method of warnings" refers to procedures or processes for detecting a user's emotional state and changing the intensity and presentation method of warnings accordingly. 【0530】 "Means for analyzing the content of calls and messages to detect signs of fraudulent activity" refers to procedures or processes for analyzing the content of received calls and messages to identify fraudulent elements or potential fraudulent activity. 【0531】 This system effectively collects and analyzes information related to user communications and assesses the risk of personal information leakage. A specific implementation is shown below. 【0532】 The server regularly acquires reliable risk information from external organizations. This acquisition utilizes the external organizations' APIs and incorporates the latest data in an automated manner. The acquired information is stored in a database on the server, maintaining its up-to-date status at all times. The server also serves as a platform for analyzing the data and extracting general risk information trends. Data mining tools and risk information classification algorithms are used for this analysis. 【0533】 The device records received messages and call history with the user's permission. This data is analyzed in real time on the device using natural language processing tools. For example, signal processing software and speech recognition software are used to convert the content of voice calls into text, which is then analyzed. The device has a built-in emotion engine that recognizes the user's emotional state from their voice and input text. The emotion engine has the function of measuring and quantifying the user's stress and anxiety. 【0534】 Users receive notifications from their devices based on the risk assessment results. For example, if an email they receive may contain a phishing scam, they will receive a specific warning such as, "This message requires caution. Please double-check before opening the link," allowing them to address the risk with peace of mind. 【0535】 As a concrete example, here is an example of a prompt message for a generative AI model: "Please create an example of a notification that would appropriately alert a user if a message in their inbox is potentially a phishing scam. Please also tell us how to adjust this notification if the user is experiencing stress." 【0536】 This system works in conjunction with servers, terminals, and users to provide users with a secure information and communication environment. It enables real-time data analysis and tailors notifications to user sentiment, ensuring that users can protect their personal information without experiencing stress. 【0537】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0538】 Step 1: 【0539】 The server obtains risk information from external organizations. The input is risk information obtained through the external organization's API in exchange for necessary authorization information. The server retrieves this information using the HTTP protocol and parses the data in JSON format. The output is a list of the parsed risk information. This data is stored in a database and used for future analysis. 【0540】 Step 2: 【0541】 The device collects received messages and call history with the user's permission. The input consists of messages and call history stored on the user's device. The device uses natural language processing software to transcribe and analyze this data. The output is the transcribed message and call content, converted into a parsable format. This data is temporarily stored on the device's storage. 【0542】 Step 3: 【0543】 The terminal analyzes the collected communication data in real time. The input consists of the text-based communication data obtained in step 2 and risk information acquired from the server. The terminal analyzes this data using natural language processing tools and comparison algorithms to detect signs of phishing and fraud. The output is the risk assessment result, showing the risk level for each communication. 【0544】 Step 4: 【0545】 The emotion engine built into the device analyzes the user's emotional state from their voice and text input. The input consists of the user's actual voice data and text input. The emotion engine analyzes this and outputs the emotional state as a numerical value. Specifically, stress and anxiety levels are quantified and generated as data. 【0546】 Step 5: 【0547】 The device generates a warning to the user based on the risk assessment results and the output of the emotion engine. The input is the results of steps 3 and 4. The device adjusts the way the warning is expressed according to the user's emotional state and creates a notification. The output is the adjusted warning message. For example, it selects a direct warning for a relaxed user and a calmer tone of explanation for a stressed user. 【0548】 Step 6: 【0549】 The user receives a tailored warning notification from their device. The input is the warning message generated in step 5. The user takes appropriate action based on this message. The output is the action taken by the user, requiring them to properly handle the risk by following the instructions in the message. For example, it may include specific instructions such as "Please check carefully before opening the link." 【0550】 (Application Example 2) 【0551】 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." 【0552】 In modern communications, the risk of personal information leaks is increasing. Furthermore, because warnings and response methods are uniform, appropriate responses that take into account the emotional state of users are not being implemented. Therefore, there is a need for a system that can more accurately assess the risk of information leaks and adjust responses according to the psychological state of the user. 【0553】 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. 【0554】 In this invention, the server includes means for collecting information, means for analyzing the collected information to evaluate the risk of personal information leakage, and means for analyzing voice and text data to determine the user's emotional state. This makes it possible to notify the user of a warning tailored to their psychological state based on the evaluation results, and to adjust and present appropriate countermeasures for the warning based on the user's emotional state. 【0555】 "Means of collecting information" refers to a system for efficiently collecting user communication data and external risk information. 【0556】 "Means for assessing the risk of personal information leakage" refers to a function that analyzes collected information and calculates the potential risk of data leakage. 【0557】 "A means of notifying users of warnings tailored to their psychological state" refers to a function that uses the results of emotion analysis to individually adjust the content and method of warnings before notification. 【0558】 "Means for analyzing voice and text data to determine a user's emotional state" refers to a system that reads emotions from a user's voice and text and clarifies that state. 【0559】 "Means for adjusting and presenting appropriate responses to warnings" refers to a function that takes into account the user's emotional state and presents individually optimized responses. 【0560】 "Means of obtaining risk information from highly reliable external organizations and updating the information collection" refers to a system for obtaining the latest risk information from certified external sources and maintaining it at all times. 【0561】 "Means for detecting indicators of fraudulent activity" refers to a function that analyzes communication content to find patterns and signs that indicate the possibility of fraud. 【0562】 The system for realizing this invention collects and analyzes information, assesses the risk of personal information leakage, and provides warnings and countermeasures that correspond to the user's emotional state. 【0563】 The server regularly acquires reliable risk information from external organizations and updates its database. This process ensures that the risk information is up-to-date and improves the accuracy of risk assessments. Furthermore, the server accumulates information trends and automatically learns new methods to enhance communication security. 【0564】 The device analyzes incoming messages and call history in real time, with the user's permission. This process uses the Google Cloud Speech-to-Text API for speech analysis and the Affectiva API for emotional state analysis to determine the user's emotional state by analyzing voice and text data. Based on this, the device assesses and notifies the user of the risk of personal information leakage. For example, if a particular message shows signs of a phishing scam, it will display advice to handle the message with caution. 【0565】 Users receive notifications from their devices and choose the most appropriate response based on the situation. For example, a relaxed user is provided with gentle warnings and intuitive instructions. The processing steps are also adjusted according to the user's stress level. 【0566】 The processing in this system is characterized by the fact that the program optimizes warnings based on the user's emotional state and presents solutions tailored to individual needs. 【0567】 For example, if a user receives a potentially phishing email while traveling, the sentiment engine detects that the user is relaxed and warns them with neutral language such as, "Do you recognize this email? We recommend you check before opening the link." 【0568】 An example of a prompt message might be: "Analyze the user's emotional state in real time and determine the communication risk. If the user is particularly anxious, create a gentle warning." 【0569】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0570】 Step 1: 【0571】 The server obtains the latest threat information from reliable external organizations. This information is stored in a database on the server and continuously updated. This ensures that the system always operates based on the most up-to-date threat information. It receives threat information from external organizations as input and obtains an updated database as output. 【0572】 Step 2: 【0573】 The device collects data on received messages and call history in real time, with the user's permission. The user's communication data serves as input, which is stored on the device as raw data for analysis. 【0574】 Step 3: 【0575】 The device converts collected communication data into text format using the Google Cloud Speech-to-Text API and analyzes the user's emotional state using the Affectiva API. The analysis results output data based on the user's emotional state and communication content. Input is audio and text data, and output is the analyzed emotional state. 【0576】 Step 4: 【0577】 The device assesses the risk of personal information leakage based on the analysis results and generates an appropriate warning message based on the assessment results. Collected risk information is also taken into account in the risk assessment. The inputs are emotional states and risk information, and the output is the generated warning message. 【0578】 Step 5: 【0579】 The user receives the generated warning message on their device and takes appropriate action based on it. The warning is tailored to the user's emotional state and includes appropriate action instructions. As output, the user receives an easy-to-understand operation guide and countermeasures. 【0580】 Step 6: 【0581】 After the entire system has finished operating, the server uses the collected data to analyze trends in new hazard information and updates the knowledge base to prepare for future risk assessments. This will allow for more accurate assessments in the next cycle. The input is the overall operation result data, and the output is the updated knowledge base. 【0582】 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. 【0583】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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. 【0584】 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. 【0585】 [Fourth Embodiment] 【0586】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0587】 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. 【0588】 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). 【0589】 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. 【0590】 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. 【0591】 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). 【0592】 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. 【0593】 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. 【0594】 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. 【0595】 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. 【0596】 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. 【0597】 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. 【0598】 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". 【0599】 This invention provides a personal information protection system that operates on mobile devices such as smartphones. This system collects and analyzes information related to user communications to assess the risk of personal information leakage and issues real-time warnings based on the results. Specific examples are shown below. 【0600】 The server obtains information about dangerous sites and vendors from reliable external sources. This information is regularly added to the system's database and updated to address the latest threats. 【0601】 The device records received messages, call history, and contact information based on the user's permission. The device queries the database for updates from the server and evaluates the current communication content based on newly acquired risk information. 【0602】 The analyzed information is used to assess the risk of personal information leakage using machine learning models. For example, if a message suspected of being a phishing scam is received, natural language processing technology is used to analyze its content and determine whether it is likely to be a scam. 【0603】 Users can receive notifications from the system and see specific steps to take if a risk is detected. The notifications include specific advice such as, "This message may be a scam. Do not click the link," or "This phone number is likely spam. Do you want to block it?" 【0604】 This reduces the risk of users becoming victims of unauthorized access or data breaches due to their own actions. The system also continuously supports user safety by sending suggestions to improve security settings on devices based on newly discovered threat information. 【0605】 The following describes the processing flow. 【0606】 Step 1: 【0607】 The server obtains information about dangerous websites and vendors from reliable external sources and updates the system's database. 【0608】 Step 2: 【0609】 The device, with the user's permission, locally records received messages, call history, and contact information. 【0610】 Step 3: 【0611】 The terminal queries the server based on the recorded data to confirm any newly acquired risk information. 【0612】 Step 4: 【0613】 The system uses information received by the device to analyze messages and call content, employing machine learning models and natural language processing techniques. 【0614】 Step 5: 【0615】 If the analysis detects a risk of phishing scams or spam calls, the device will display a warning notification to the user. 【0616】 Step 6: 【0617】 The user checks the notification and follows the specific actions suggested (e.g., delete the message, block the phone number). 【0618】 Step 7: 【0619】 The server generates suggestions for improving security settings based on newly discovered threat information and sends them to the terminal. 【0620】 Step 8: 【0621】 The device displays suggested configuration improvements to the user and asks them whether they want to make the suggested changes. 【0622】 Step 9: 【0623】 Users can review their device settings and update them as needed to enhance the protection of their personal information. 【0624】 (Example 1) 【0625】 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". 【0626】 In recent years, the leakage of personal information has become a social problem, and the risk is particularly high in communications via mobile devices. In this situation, users need appropriate means to protect their information. However, conventional technologies have been problematic because they do not adequately perform real-time risk assessment or rapid response. 【0627】 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. 【0628】 In this invention, the server includes a device for collecting information, a device for analyzing the collected information and evaluating the risk of personal information leakage, and a device for notifying the user based on the evaluation results. This enables the user to recognize potential risks in real time and respond quickly and appropriately. 【0629】 A "device for collecting information" is a device that, with the user's permission, collects and securely manages data such as received messages, call history, and contact information. 【0630】 A "device for analyzing and evaluating the risk of personal information leakage" is a device that uses machine learning models and natural language processing technology to quantify the risk of personal information leakage based on collected information. 【0631】 A "device that notifies users based on evaluation results" is a device that, based on analysis results, sends warning messages to users regarding communications suspected of being fraudulent, providing real-time alerts. 【0632】 A "device that updates the database based on information from a trusted external source" is a device that obtains the latest information on phishing sites and malicious operators and keeps the database constantly updated. 【0633】 A "device for identifying potential threats" is a device that analyzes communication content to detect potentially harmful actions at an early stage. 【0634】 "Methods using generative AI technology" refer to processing methods that utilize artificial intelligence technology to analyze the content of messages and calls in an advanced manner and determine whether or not there is a threat. 【0635】 This personal information protection system is implemented as an application that runs on mobile devices and is designed to reduce the risk of personal information leakage by monitoring users' daily communications in real time. 【0636】 The server regularly collects information about dangerous sites and fraudulent operators from trusted external sources and updates the system's database with this information. This server possesses high-performance data processing capabilities and uses a database management system to quickly and accurately organize the information. 【0637】 The device operates on mobile operating systems such as Android and iOS and collects messages, call history, and contact information with the user's permission. The collected data is processed using generative AI models that utilize natural language processing technology to assess the risk of personal information leakage. For example, if an email or SMS showing typical signs of a phishing scam is detected, its content is analyzed in depth, and if the likelihood of fraud is high, a warning is issued. 【0638】 Users receive warning notifications based on the results of analysis performed on their devices. These notifications include specific advice such as, "This message may be a scam. Do not click the link." This allows users to use their devices with peace of mind and mitigate the risk of unauthorized access and data breaches. 【0639】 For example, if a user receives a suspicious email, the device analyzes it and immediately notifies the user, along with sending a notification indicating that the email is likely a phishing attempt. The system continues to protect users in this way. 【0640】 An example of a prompt message to the generating AI model is: "We have analyzed your incoming email. The following email may be a phishing scam. We suggest the following steps. Do not click on the link." In this way, the system ensures the maximum security of the user's communications. 【0641】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0642】 Step 1: 【0643】 The server collects threat information from trusted external sources and updates its database. In this process, new threat information obtained from external sources is supplied as input, and that data is added to the server's database. As a result, the database always maintains the most up-to-date threat information. 【0644】 Step 2: 【0645】 The device collects received messages, call history, and contact information based on the user's permission. This input data is securely managed by the device's application and prepared for the next analysis step. As output, this data is stored in a format that allows for later analysis. 【0646】 Step 3: 【0647】 The terminal compares collected user data with the server and database to assess potential risks. Input includes user communication data recorded on the terminal and risk information obtained from the server. Using this data, natural language processing and machine learning techniques are employed to calculate the likelihood of personal information leakage, resulting in an output that identifies high-risk communications. 【0648】 Step 4: 【0649】 The device issues a real-time warning notification based on the results analyzed in the previous step. The risk information evaluated in step 3 is used as input. Based on this data, the device outputs an alert such as "This message may be a scam," and displays it to the user. 【0650】 Step 5: 【0651】 The user receives a warning notification from their device and checks the instructions for action. Based on this, the user is required to take specific actions, such as not clicking on links that are suspected of being fraudulent. As an output, the user takes safe action. 【0652】 Step 6: 【0653】 The server sends suggestions to the device for improving security settings based on newly discovered threat intelligence from the model. These suggestions are based on feedback from the generating AI model and produce output that enhances the security of the user's device. 【0654】 (Application Example 1) 【0655】 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". 【0656】 With the proliferation of smart devices, the risk of personal information leaks and unauthorized access is increasing. Users need to respond quickly and appropriately to threats lurking in the applications they use daily and the networks they connect to. However, it is difficult for individuals to fairly assess these risks and take appropriate measures, which may compromise the safe and secure use of devices. 【0657】 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. 【0658】 In this invention, the server includes means for obtaining risk information from a reliable external organization and updating the database, means for analyzing the collected information and evaluating the risk of personal information leakage, means for scanning access rights and detecting excessive permission requests, and means for monitoring communications in real time and detecting suspicious activity. This enables users to quickly recognize the risk of personal information leakage and to use the device safely and securely. 【0659】 "Means of collecting information" refers to the function of acquiring user and communication data from smart devices and servers. 【0660】 "Means of risk assessment" refers to the process of analyzing collected data and determining the potential for personal information leakage contained within it. 【0661】 A "means of notifying warnings" refers to an alert function that informs users when a risk is detected and prompts them to take safety measures. 【0662】 "Methods for scanning access rights to detect excessive permission requests" refers to a function that investigates the access rights requested by applications installed on smart devices and identifies cases where more permissions than necessary are being requested. 【0663】 "Means for monitoring communications in real time and detecting suspicious activity" refers to a function that observes a device's network traffic in real time and identifies unusual or suspicious behavior or data communications. 【0664】 "Means of obtaining threat information from reliable external organizations and updating the database" refers to the process of obtaining the latest threat information from public or specialized organizations and adding it to the database within the system. 【0665】 To implement this invention, it is necessary to install a personal information protection system on a mobile device such as a smartphone. It mainly consists of the following elements. 【0666】 Server Role 【0667】 The server collects the latest and most reliable threat information from public institutions and trusted external organizations, updating its database accordingly. This ensures that threat information is always up-to-date, providing a high level of user safety. The server transmits this information to terminals and uses it as a basis for real-time assessments. 【0668】 Device functions 【0669】 The device includes software that analyzes the risk of personal information leakage based on the acquired data. As accessories, machine learning libraries (e.g., TensorFlow and PyTorch) and natural language processing frameworks (e.g., spaCy and NLTK) are used to automatically detect suspicious messages and excessive access requests. Furthermore, it monitors communications in real time and immediately warns the user if suspicious activity is detected. 【0670】 User actions 【0671】 Users can receive notifications from their devices to proactively understand the risk of personal information leakage and confirm the necessary countermeasures. For example, if a social networking app requests access rights beyond what is normally required for its functions, the user will be notified of the risk and how to address it. Furthermore, if a malicious phishing site is detected while connected to public Wi-Fi, a warning will be sent, preventing access. 【0672】 Example prompts for generative AI models 【0673】 "Please explain how to provide real-time warnings if a user's personal information is potentially being accessed illegally." 【0674】 This system is a highly effective means of protecting personal information, based on industry-standard technologies for enhancing smartphone security. 【0675】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0676】 Step 1: 【0677】 The server periodically obtains threat intelligence from trusted external sources. The input consists of threat intelligence data provided via external APIs and data streams. This information is stored in the server's database, and data processing is performed to maintain its up-to-date state. The output is updated threat intelligence data. 【0678】 Step 2: 【0679】 The terminal receives updates from the server and uses this information to scan the access permissions of installed applications on the user's device. The input is the access permission data for all applications installed on the terminal. An over-permission detection algorithm is used as the data calculation to identify applications making excessive requests. The output is a list of applications that are requesting excessive access permissions. 【0680】 Step 3: 【0681】 The device monitors the user's communications in real time and analyzes received messages and network traffic. The input consists of received message data and network packet data. Machine learning models are used for analysis to detect suspicious patterns (e.g., phishing scams). The output is a list of detected suspicious communications. 【0682】 Step 4: 【0683】 The user receives notifications from their device and checks for warnings regarding excessive privileges or suspicious communications. The input is warning data regarding excessive privileges or suspicious communications. When notifying the user, a generative AI model is used to generate a prompt explaining the risks in natural language. The output is the warning message sent to the user. 【0684】 Step 5: 【0685】 The user takes necessary actions based on the warning. The input is the warning message sent. For example, the user might take specific actions such as deleting an application with excessive privileges or blocking suspicious communication sources. The output is the status of the improved security settings. 【0686】 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. 【0687】 This invention provides a system equipped with an emotion engine that collects and analyzes information related to user communications to assess the risk of personal information leakage and adjust warnings based on the results. This system recognizes the user's emotional state, issues warnings at the appropriate time and in the appropriate manner, and further suggests countermeasures. Specific examples are shown below. 【0688】 The server obtains the latest security risks from reliable external sources and regularly updates the system's database. Furthermore, the server accumulates data on security trends to enhance communication security. 【0689】 The device records received messages and call history with the user's permission and analyzes them in real time. It retrieves the latest risk information from the server and assesses the risk of personal information leakage. The device has an emotion engine built in that recognizes the user's emotional state from their voice and input. 【0690】 The emotion engine can detect the user's stress and anxiety levels and adjust the timing and method of warnings accordingly. For example, if the user is stressed, the warning will be gentler and detailed coping steps will be displayed in an easy-to-understand manner. 【0691】 Users can receive notifications from their devices and take appropriate actions that take their emotional state into consideration. For example, they might receive advice such as, "Treat potentially phishing messages with caution and do not open suspicious links." Furthermore, the suggested actions are adjusted according to the user's stress level, allowing them to proceed with confidence. 【0692】 Thus, the present invention enables the provision of information tailored to the user's emotional state, supporting the efficient and secure protection of personal information. The system continuously updates data and strives for further improvements in security settings to maintain a secure communication environment for users. 【0693】 The following describes the processing flow. 【0694】 Step 1: 【0695】 The server retrieves information on dangerous websites and vendors from reliable external sources and updates the system's database. This update is performed regularly to ensure that the latest danger information is always reflected. 【0696】 Step 2: 【0697】 The device, with the user's permission, locally records received messages and call history. This data will be used later for risk analysis. 【0698】 Step 3: 【0699】 The device activates an emotion engine that analyzes the user's emotional state from their voice tone and input text. The analysis results are used to determine how and when to issue warnings. 【0700】 Step 4: 【0701】 The system compares the communication content recorded by the terminal with a database of risk information provided by the server to assess the risk of personal information leakage. This process utilizes machine learning models to calculate the likelihood of risk. 【0702】 Step 5: 【0703】 If the risk assessment determines that a warning is necessary, the device will generate a warning message that takes the user's emotional state into consideration. If high stress levels are detected, the warning will be gentle and detailed. 【0704】 Step 6: 【0705】 The user receives a warning from their device and reviews the suggested course of action. The user then takes steps to mitigate the risk by following the instructions, such as deleting the message or blocking the relevant number. 【0706】 Step 7: 【0707】 The server uses newly discovered threat intelligence and user feedback to further improve the database. This will increase the accuracy of future risk assessments and improve the effectiveness of sentiment recognition. 【0708】 Step 8: 【0709】 The device will notify the user of updated security settings and improvements, prompting them to change settings as needed. Users will then review their security measures and strengthen the protection of their personal information. 【0710】 (Example 2) 【0711】 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". 【0712】 In modern society, while internet-based communication is becoming increasingly important, the risks of personal information leaks and the threat of cyber fraud are also on the rise. Therefore, it is necessary to effectively assess the risk of information leaks while considering the emotional state of users and to issue warnings at the appropriate time. Furthermore, it is required to offer flexible coping methods that are tailored to the emotional state of users so that they do not experience excessive stress. 【0713】 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. 【0714】 In this invention, the server includes means for collecting information, means for analyzing the collected information to evaluate the risk of personal information leakage, and means for notifying the user of a warning based on the evaluation results. This allows for timely warnings while considering the user's emotional state, mitigating the risk of personal information leakage, and enabling the user to deal with the situation without feeling stressed. 【0715】 "Means of collecting information" refers to the procedures or processes for obtaining risk information from external organizations and collecting data. 【0716】 "Means of assessing the risk of personal information leakage" refers to procedures or processes for analyzing collected information and determining the likelihood of personal information being misused. 【0717】 "Means of notifying users of warnings" refers to procedures or processes for issuing warnings to users and urging them to pay attention based on the results of risk assessments. 【0718】 "Means of providing appropriate countermeasures" refers to procedures or processes for providing users with specific countermeasures or instructions regarding the risks they have identified. 【0719】 "Means of recognizing emotional states and adjusting the timing and method of warnings" refers to procedures or processes for detecting a user's emotional state and changing the intensity and presentation method of warnings accordingly. 【0720】 "Means for analyzing the content of calls and messages to detect signs of fraudulent activity" refers to procedures or processes for analyzing the content of received calls and messages to identify fraudulent elements or potential fraudulent activity. 【0721】 This system effectively collects and analyzes information related to user communications and assesses the risk of personal information leakage. A specific implementation is shown below. 【0722】 The server regularly acquires reliable risk information from external organizations. This acquisition utilizes the external organizations' APIs and incorporates the latest data in an automated manner. The acquired information is stored in a database on the server, maintaining its up-to-date status at all times. The server also serves as a platform for analyzing the data and extracting general risk information trends. Data mining tools and risk information classification algorithms are used for this analysis. 【0723】 The device records received messages and call history with the user's permission. This data is analyzed in real time on the device using natural language processing tools. For example, signal processing software and speech recognition software are used to convert the content of voice calls into text, which is then analyzed. The device has a built-in emotion engine that recognizes the user's emotional state from their voice and input text. The emotion engine has the function of measuring and quantifying the user's stress and anxiety. 【0724】 Users receive notifications from their devices based on the risk assessment results. For example, if an email they receive may contain a phishing scam, they will receive a specific warning such as, "This message requires caution. Please double-check before opening the link," allowing them to address the risk with peace of mind. 【0725】 As a concrete example, here is an example of a prompt message for a generative AI model: "Please create an example of a notification that would appropriately alert a user if a message in their inbox is potentially a phishing scam. Please also tell us how to adjust this notification if the user is experiencing stress." 【0726】 This system works in conjunction with servers, terminals, and users to provide users with a secure information and communication environment. It enables real-time data analysis and tailors notifications to user sentiment, ensuring that users can protect their personal information without experiencing stress. 【0727】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0728】 Step 1: 【0729】 The server obtains risk information from external organizations. The input is risk information obtained through the external organization's API in exchange for necessary authorization information. The server retrieves this information using the HTTP protocol and parses the data in JSON format. The output is a list of the parsed risk information. This data is stored in a database and used for future analysis. 【0730】 Step 2: 【0731】 The device collects received messages and call history with the user's permission. The input consists of messages and call history stored on the user's device. The device uses natural language processing software to transcribe and analyze this data. The output is the transcribed message and call content, converted into a parsable format. This data is temporarily stored on the device's storage. 【0732】 Step 3: 【0733】 The terminal analyzes the collected communication data in real time. The input consists of the text-based communication data obtained in step 2 and risk information acquired from the server. The terminal analyzes this data using natural language processing tools and comparison algorithms to detect signs of phishing and fraud. The output is the risk assessment result, showing the risk level for each communication. 【0734】 Step 4: 【0735】 The emotion engine built into the device analyzes the user's emotional state from their voice and text input. The input consists of the user's actual voice data and text input. The emotion engine analyzes this and outputs the emotional state as a numerical value. Specifically, stress and anxiety levels are quantified and generated as data. 【0736】 Step 5: 【0737】 The device generates a warning to the user based on the risk assessment results and the output of the emotion engine. The input is the results of steps 3 and 4. The device adjusts the way the warning is expressed according to the user's emotional state and creates a notification. The output is the adjusted warning message. For example, it selects a direct warning for a relaxed user and a calmer tone of explanation for a stressed user. 【0738】 Step 6: 【0739】 The user receives a tailored warning notification from their device. The input is the warning message generated in step 5. The user takes appropriate action based on this message. The output is the action taken by the user, requiring them to properly handle the risk by following the instructions in the message. For example, it may include specific instructions such as "Please check carefully before opening the link." 【0740】 (Application Example 2) 【0741】 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". 【0742】 In modern communications, the risk of personal information leaks is increasing. Furthermore, because warnings and response methods are uniform, appropriate responses that take into account the emotional state of users are not being implemented. Therefore, there is a need for a system that can more accurately assess the risk of information leaks and adjust responses according to the psychological state of the user. 【0743】 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. 【0744】 In this invention, the server includes means for collecting information, means for analyzing the collected information to evaluate the risk of personal information leakage, and means for analyzing voice and text data to determine the user's emotional state. This makes it possible to notify the user of a warning tailored to their psychological state based on the evaluation results, and to adjust and present appropriate countermeasures for the warning based on the user's emotional state. 【0745】 "Means of collecting information" refers to a system for efficiently collecting user communication data and external risk information. 【0746】 "Means for assessing the risk of personal information leakage" refers to a function that analyzes collected information and calculates the potential risk of data leakage. 【0747】 "A means of notifying users of warnings tailored to their psychological state" refers to a function that uses the results of emotion analysis to individually adjust the content and method of warnings before notification. 【0748】 "Means for analyzing voice and text data to determine a user's emotional state" refers to a system that reads emotions from a user's voice and text and clarifies that state. 【0749】 "Means for adjusting and presenting appropriate responses to warnings" refers to a function that takes into account the user's emotional state and presents individually optimized responses. 【0750】 "Means of obtaining risk information from highly reliable external organizations and updating the information collection" refers to a system for obtaining the latest risk information from certified external sources and maintaining it at all times. 【0751】 "Means for detecting indicators of fraudulent activity" refers to a function that analyzes communication content to find patterns and signs that indicate the possibility of fraud. 【0752】 The system for realizing this invention collects and analyzes information, assesses the risk of personal information leakage, and provides warnings and countermeasures that correspond to the user's emotional state. 【0753】 The server regularly acquires reliable risk information from external organizations and updates its database. This process ensures that the risk information is up-to-date and improves the accuracy of risk assessments. Furthermore, the server accumulates information trends and automatically learns new methods to enhance communication security. 【0754】 The device analyzes incoming messages and call history in real time, with the user's permission. This process uses the Google Cloud Speech-to-Text API for speech analysis and the Affectiva API for emotional state analysis to determine the user's emotional state by analyzing voice and text data. Based on this, the device assesses and notifies the user of the risk of personal information leakage. For example, if a particular message shows signs of a phishing scam, it will display advice to handle the message with caution. 【0755】 Users receive notifications from their devices and choose the most appropriate response based on the situation. For example, a relaxed user is provided with gentle warnings and intuitive instructions. The processing steps are also adjusted according to the user's stress level. 【0756】 The processing in this system is characterized by the fact that the program optimizes warnings based on the user's emotional state and presents solutions tailored to individual needs. 【0757】 For example, if a user receives a potentially phishing email while traveling, the sentiment engine detects that the user is relaxed and warns them with neutral language such as, "Do you recognize this email? We recommend you check before opening the link." 【0758】 An example of a prompt message might be: "Analyze the user's emotional state in real time and determine the communication risk. If the user is particularly anxious, create a gentle warning." 【0759】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0760】 Step 1: 【0761】 The server obtains the latest threat information from reliable external organizations. This information is stored in a database on the server and continuously updated. This ensures that the system always operates based on the most up-to-date threat information. It receives threat information from external organizations as input and obtains an updated database as output. 【0762】 Step 2: 【0763】 The device collects data on received messages and call history in real time, with the user's permission. The user's communication data serves as input, which is stored on the device as raw data for analysis. 【0764】 Step 3: 【0765】 The device converts collected communication data into text format using the Google Cloud Speech-to-Text API and analyzes the user's emotional state using the Affectiva API. The analysis results output data based on the user's emotional state and communication content. Input is audio and text data, and output is the analyzed emotional state. 【0766】 Step 4: 【0767】 The device assesses the risk of personal information leakage based on the analysis results and generates an appropriate warning message based on the assessment results. Collected risk information is also taken into account in the risk assessment. The inputs are emotional states and risk information, and the output is the generated warning message. 【0768】 Step 5: 【0769】 The user receives the generated warning message on their device and takes appropriate action based on it. The warning is tailored to the user's emotional state and includes appropriate action instructions. As output, the user receives an easy-to-understand operation guide and countermeasures. 【0770】 Step 6: 【0771】 After the entire system has finished operating, the server uses the collected data to analyze trends in new hazard information and updates the knowledge base to prepare for future risk assessments. This will allow for more accurate assessments in the next cycle. The input is the overall operation result data, and the output is the updated knowledge base. 【0772】 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. 【0773】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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. 【0774】 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. 【0775】 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. 【0776】 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. 【0777】 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. 【0778】 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. 【0779】 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. 【0780】 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." 【0781】 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. 【0782】 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. 【0783】 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. 【0784】 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. 【0785】 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. 【0786】 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. 【0787】 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. 【0788】 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. 【0789】 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. 【0790】 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. 【0791】 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. 【0792】 All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference. 【0793】 The following is further disclosed regarding the embodiments described above. 【0794】 (Claim 1) 【0795】 Means of collecting information, 【0796】 A means of analyzing collected information to assess the risk of personal information leakage, 【0797】 A means of notifying the user of a warning based on the evaluation results, 【0798】 A system that includes means for providing appropriate responses to warnings. 【0799】 (Claim 2) 【0800】 The system according to claim 1, comprising means for obtaining risk information from a reliable external organization and updating the database. 【0801】 (Claim 3) 【0802】 The system according to claim 1, comprising means for analyzing the content of calls and messages and detecting signs of fraudulent activity. 【0803】 "Example 1" 【0804】 (Claim 1) 【0805】 A device for collecting information, 【0806】 A device that analyzes collected information to assess the risk of personal information leakage, 【0807】 A device that notifies the user based on the evaluation results, 【0808】 A device that presents appropriate means of responding to notifications, 【0809】 A device that uses machine learning models to analyze communication content and identify potential threats, 【0810】 A system that includes a device for updating a database based on information from a trusted external source. 【0811】 (Claim 2) 【0812】 The system according to claim 1, comprising means for evaluating dangerous communications in real time and issuing warnings. 【0813】 (Claim 3) 【0814】 The system according to claim 1, comprising means for using generative AI technology when analyzing user communications. 【0815】 "Application Example 1" 【0816】 (Claim 1) 【0817】 Means of collecting information, 【0818】 A means of analyzing collected information to assess the risk of personal information leakage, 【0819】 A means of notifying users of warnings based on evaluation results, 【0820】 A means of scanning access rights to detect excessive permission requests, 【0821】 A means of monitoring communications in real time and detecting suspicious activity, 【0822】 A system that includes means for providing appropriate responses to warnings. 【0823】 (Claim 2) 【0824】 The system according to claim 1, comprising means for obtaining risk information from a reliable external organization and updating the database. 【0825】 (Claim 3) 【0826】 The system according to claim 1, comprising means for analyzing the content of calls and messages and detecting signs of fraudulent activity. 【0827】 "Example 2 of combining an emotion engine" 【0828】 (Claim 1) 【0829】 Means of collecting information, 【0830】 A means of analyzing collected information to assess the risk of personal information leakage, 【0831】 A means of notifying the user of a warning based on the evaluation results, 【0832】 A means of providing appropriate responses to warnings, 【0833】 A means of recognizing the user's emotional state and adjusting the timing and method of warnings, 【0834】 A method for analyzing received messages and call history to assess risk, 【0835】 A system that includes this. 【0836】 (Claim 2) 【0837】 The system according to claim 1, comprising means for obtaining risk information from a reliable external organization and updating the database. 【0838】 (Claim 3) 【0839】 The system according to claim 1, comprising means for analyzing the content of calls and messages and detecting signs of fraudulent activity. 【0840】 "Application example 2 when combining with an emotional engine" 【0841】 (Claim 1) 【0842】 Means of collecting information, 【0843】 A means of analyzing the collected information to assess the risk of personal information leakage, 【0844】 A means of notifying the user of a warning based on the evaluation results, tailored to their psychological state, 【0845】 A means of adjusting and presenting appropriate responses to warnings based on the user's emotional state, 【0846】 A system that includes means for analyzing voice and text data to determine the user's emotional state. 【0847】 (Claim 2) 【0848】 The system according to claim 1, comprising means for obtaining risk information from a highly reliable external organization and updating the information collection. 【0849】 (Claim 3) 【0850】 The system according to claim 1, comprising means for analyzing the content of calls and messages to detect indicators of fraudulent activity, and further adjusting warnings based on the user's emotional state. [Explanation of symbols] 【0851】 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 of collecting information, A means of analyzing collected information to assess the risk of personal information leakage, A means of notifying the user of a warning based on the evaluation results, A system that includes means for providing appropriate responses to warnings. [Claim 2] The system according to claim 1, comprising means for obtaining risk information from a highly reliable external organization and updating the database. [Claim 3] The system according to claim 1, comprising means for analyzing the content of calls and messages and detecting signs of fraudulent activity.

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