system

Abstract

システムを提供する。【解決手段】ネットワークを介して情報収集を行う手段と、収集された情報を解析して個人データの漏えいを検出する手段と、検出結果に基づいて自動的に通知を送信する手段と、漏えいが確認された情報に対して削除申請を行う手段と、検出と削除申請の全ての活動を記録する手段と、ユーザーに対してリアルタイムでの通知を提供するために、警告を保有装置に送信する手段と、漏えい元への通知を自動で管理者に対して送信する手段と、生成AIモデルを使用して情報の分類と解析を行う手段と、プロンプト文を生成して情報解析結果の報告を行う手段を含むシステム。

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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern digital society, there is a problem that personal information leakage frequently occurs, resulting in privacy infringement and economic damage. In particular, information leakage through the Internet or the dark web poses a risk that personal information may be misused without the user's awareness. Conventional methods have the problem that the user himself / herself has to manually check for information leakage, which is time-consuming and difficult to detect information leakage at an early stage.

Means for Solving the Problems

[0005] This invention provides a system that automatically collects information via a network and detects personal data breaches by analyzing the collected data. Based on the detection results, it automatically sends notifications to users and is configured to promptly request the deletion of any information confirmed to have been leaked. Furthermore, by recording all activities, it enables effective implementation of leak prevention and subsequent countermeasures. As a result, it is possible to detect information breaches early and without hassle, thereby strengthening the protection of users' personal information.

[0006] A "network" is a system in which multiple computers or devices are connected to communicate with one another.

[0007] "Information gathering methods" refer to processes or tools for automatically acquiring specific data or information.

[0008] "Information analysis methods" refer to the process of evaluating and analyzing collected data to extract information useful for a specific purpose.

[0009] "Personal data breach" refers to the unintentional or intentional disclosure of personal information to an unauthorized third party.

[0010] A "notification transmission method" is a system or process for sending messages to inform users of analysis results or warnings.

[0011] A "deletion request method" is a means of officially requesting the removal of leaked information from the internet.

[0012] An "activity recording system" is a process that stores all actions performed by a system as logs, allowing them to be referenced and audited later. [Brief explanation of the drawing]

[0013] [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] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

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

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

[0016] 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.

[0017] 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.

[0018] 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.

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

[0020] 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."

[0021] [First Embodiment]

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

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0028] 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.

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

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] As an embodiment of this invention, a software system operated over a network has been devised. Its specific operation is described below.

[0035] Information gathering:

[0036] The server runs a web crawler to traverse specific areas of the internet and the dark web. The crawler uses keywords such as the user's registered personal information, including their name, email address, and credit card information, to automatically collect pages containing relevant information.

[0037] Information analysis:

[0038] The collected data is analyzed by a server. This analysis uses natural language processing techniques to identify whether there is any leakage of personal data within the text. For example, it can detect if an email address is being used in an inappropriate context. Furthermore, machine learning algorithms can be used to assess the credibility and risk level of the data. This process is performed in real time, supporting rapid decision-making.

[0039] Notifications and deletion requests:

[0040] Based on the analysis results, if the server determines there is a risk, it will immediately notify the user's device with a warning. The notification will be sent via email, SMS, or a dedicated application to alert the user. Furthermore, the server will automatically submit a deletion request to the site administrator. This will be done using an email clearly stating the information to be deleted or a standardized deletion request form.

[0041] Activity log and report generation:

[0042] The server meticulously records all program processes and stores them as activity logs. These logs are used for future audits and to improve countermeasures. The server also periodically provides users with reports detailing the analysis and response results, allowing users to verify the level of protection their information receives.

[0043] This system is designed to quickly detect personal data breaches and provide users with a secure environment for using the internet. Furthermore, continuous monitoring and automated responses allow for effortless protection of personal information.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The server launches a web crawler according to a network schedule. This crawler searches for target URLs based on a pre-registered list of users' personal information and automatically collects pages from the internet and the dark web. This collects page data that may contain personal information.

[0047] Step 2:

[0048] The server analyzes the collected web page data. It utilizes natural language processing techniques to search for traces of personal information within the text of the page. During this process, keyword detection and pattern matching are used to immediately determine whether or not personal data has been leaked.

[0049] Step 3:

[0050] The server classifies the discovered information based on the analysis results and scores its risk level. Information with a particularly high risk of leakage is assigned a high score, prioritizing immediate response. This information is recorded in the log database and used for later analysis and reporting.

[0051] Step 4:

[0052] The server sends a notification to the user's device based on the risk score. The notification includes a summary of the discovered leaked information and recommended countermeasures, prompting the user to take immediate action.

[0053] Step 5:

[0054] The server automatically issues a takedown request to the site hosting the leaked information. The request includes details about the problem and is prepared in accordance with any necessary legal requirements. The server tracks the progress of the response after the request is submitted and records completion notifications.

[0055] Step 6:

[0056] The server comprehensively logs all activities, including collection, analysis, notification, and deletion requests. The server periodically generates reports for users based on these logs, providing transparency and reliability to the system.

[0057] (Example 1)

[0058] 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."

[0059] In today's digital society, data breaches are frequent, leading to increased security risks. Existing solutions lack real-time capabilities and automation, making it difficult to accurately understand how data breaches occur and the level of risk involved. Furthermore, there are limited means to respond quickly and effectively when a breach is detected. There is a need for efficient systems to address these challenges.

[0060] 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.

[0061] In this invention, the server includes means for collecting information via digital communication, means for analyzing the collected data to identify leaks of personal information, and means for automatically transmitting the results based on the analysis using communication means. This enables the identification of leaks of personal information in real time and allows for a rapid response.

[0062] "Digital communication" is a technology that uses computer networks to send and receive data.

[0063] "Means of collecting information" refers to methods and devices for acquiring and storing data, and in particular includes software such as web crawlers.

[0064] "Personal information" refers to information that can identify a specific individual, and includes names, email addresses, credit card information, and so on.

[0065] "Means of analyzing data to identify personal information leaks" refers to the process of using statistical or machine learning methods on collected data to determine whether or not a leak has occurred.

[0066] A "machine learning algorithm for assessing risk" refers to a computational model that quantifies risk based on collected data and measures the urgency of a data breach.

[0067] "Means of transmission using communication means" refers to the processes and technologies for transmitting information to other devices or people via the internet or telephone networks.

[0068] A "removal request" refers to an official request to remove information that has been inappropriately published.

[0069] "Means of recording actions" refers to techniques or procedures for saving operations and processes performed within a system as logs.

[0070] To implement this invention, a software system operating over a network is utilized. A server plays a central role in this system, coordinating each component to collect, analyze, notify, request deletions from, and record information.

[0071] First, the server launches a web crawler implemented in Python to collect information. This crawler traverses the internet and specific online areas, searching for keywords that have been pre-registered as the user's personal information. The data collected by the crawler is securely stored on the server using database software.

[0072] Next, a natural language processing library (e.g., SpaCy) is used to analyze the data collected by the server. This makes it possible to identify locations in the text where personal information has been leaked. Furthermore, a machine learning algorithm utilizing TENSORFLOW® is used to assess the level of risk based on the analysis results.

[0073] If a high risk is detected, the server automatically sends a warning to the user's device. This communication utilizes email and messaging services. Furthermore, based on the discovered leaked information, the server requests the relevant website administrator to remove the content. Standardized email and online forms are used for this purpose.

[0074] As an activity record, the server saves all program processing data to a log management system such as Elasticsearch®. This allows for future audits and improvements to response procedures.

[0075] As a concrete example, consider the response when a user detects that their email address has been inappropriately published. In this case, the server immediately sends a warning message to the user's device and sends a request to the administrator of the relevant website to remove the information. This allows the user to quickly understand the information leak and take appropriate action.

[0076] An example of a prompt message for a generating AI model is: "Please verify that my data is secure and issue a warning if there is a possibility of leakage. Also, please submit an appropriate deletion request."

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The server launches a web crawler to collect information over the internet. The crawler inputs the user's personal information (e.g., name, email address) as keywords and visits web pages containing relevant information based on those keywords. The resulting output is collected as HTML data and sent to the server.

[0080] Step 2:

[0081] The server parses the collected HTML data to extract personal information. A data analysis library is used for the analysis, taking the collected web page content as input. The BeautifulSoup library is used to apply patterns of personal information (e.g., regular expressions for email addresses) to extract the data. The output is a list of the extracted personal information.

[0082] Step 3:

[0083] The server stores the extracted personal information in a database and prepares it for analysis. During storage, a timestamp is added to the extracted data as input, and the data is stored in a database such as MongoDB. The output is a database containing a record of searchable personal information.

[0084] Step 4:

[0085] The server analyzes the data using natural language processing techniques. It processes a list of personal information stored as input, analyzes the text context using the SpaCy library, and assesses the potential for data leakage. The output consists of data items identified as being at risk of leakage and their contextual information.

[0086] Step 5:

[0087] The server uses a machine learning algorithm to evaluate the credibility and risk level of the data. The data analysis is performed using a TensorFlow model with the leak risk information identified in the previous step as input data. The output is an evaluation result where the risk level is expressed numerically.

[0088] Step 6:

[0089] The server sends a warning to the user's device based on the risk assessment results. It generates a warning message based on the assessment results, according to the level of urgency. The output is a notification sent to the user via email or SMS.

[0090] Step 7:

[0091] The server automatically initiates a removal request for any leaked information detected. The input includes details of the leaked information and contact information for the affected website. The output is an official request, including the removal request, sent to the site administrator.

[0092] Step 8:

[0093] The server meticulously records all processes and saves them as digital logs. The input includes activity logs for each step, which are stored using Elasticsearch. The output is an activity record available for later auditing and enhancement.

[0094] (Application Example 1)

[0095] 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."

[0096] In today's world, where personal data breaches occur on a daily basis, there is a need to quickly and accurately detect breaches and notify users. However, conventional methods have limitations in the accuracy of information analysis and the timing of notifications, making it difficult for users to respond quickly to personal data breaches. Furthermore, there is no system to automatically submit appropriate deletion requests to the source of the breach, making it difficult to prevent secondary damage to personal data.

[0097] 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.

[0098] In this invention, the server includes means for collecting information via a network, means for analyzing the collected information to detect personal data leaks, means for automatically sending notifications based on the detection results, means for automatically sending notifications to the administrator regarding the source of the leak, means for classifying and analyzing information using a generative AI model, and means for generating prompt messages to report the results of the information analysis. This enables the rapid identification of personal data leaks in real time, allowing users to take appropriate measures.

[0099] "Means of collecting information via a network" refers to technologies that use the internet or other communication networks to automatically acquire target information.

[0100] "Methods for analyzing collected information to detect personal data breaches" refer to processing technologies that scrutinize collected data and determine, in particular, whether personal information is being handled improperly.

[0101] "Means of automatically sending notifications based on detection results" refers to an automated system that immediately sends warnings and information to users when a data breach of personal information is confirmed.

[0102] "Methods for requesting the deletion of information that has been confirmed to have been leaked" refers to methods for automating the process of requesting relevant parties and administrators to delete the discovered leaked information.

[0103] "Means for recording all detection and deletion request activities" refers to technology that records and stores the entire process from data breach detection to countermeasures, and is used for future audits and improvements.

[0104] "Means of sending warnings to the device in order to provide users with real-time notifications" refers to a technology that prompts immediate action by sending a warning to the user's device as soon as a data breach occurs.

[0105] "A means of automatically sending notifications to administrators regarding the source of a data leak" refers to a system that automatically sends warning messages or requests for deletion to administrators who handle the data that caused the leak.

[0106] "Methods for classifying and analyzing information using generative AI models" refer to methods that utilize machine learning and AI technologies to efficiently analyze large amounts of information and evaluate risk levels and reliability.

[0107] "A means of generating prompt messages and reporting information analysis results" refers to a technology that automatically creates text to clearly communicate analysis results to users and stakeholders.

[0108] The system that realizes this application example is implemented by building an information security system centered on a server. The server first runs dedicated crawling software to collect data from a wide range of sources via the network. This crawler traverses specific areas of the public and dark web, and stores the collected data in a database on the server.

[0109] The collected data is analyzed by a natural language processing (NLP) system and generative AI models on the server. This analysis determines whether or not a personal data breach has occurred, and if detected, the risk level is promptly assessed.

[0110] Furthermore, the server sends a warning to the user's device based on the evaluation results. This warning is designed to be sent in real time to the user's smartphone or other personal devices. If a data breach is confirmed, the server automatically requests deletion from the administrator of the source of the breach. This ensures that users' personal information is protected without unnecessary time or effort.

[0111] As a concrete example, if a user's email address is being used on a fraudulent bulletin board, the system will immediately detect this fact, a notification will be displayed on the user's smartphone, and a request for removal will be automatically submitted to the bulletin board administrator. This system is built using Python and various AI technologies, employing the "requests" library for data collection and the "BeautifulSoup" library for information analysis.

[0112] The generative AI model is used to analyze and classify collected data, generating prompt messages to report the results of the information analysis. An example of such a prompt message is, "Please describe the process for identifying websites where user email addresses have been leaked and requesting their removal." This allows for the rapid management of information leaks and provides users with a secure internet environment.

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The server collects data from various sources on the network. The input consists of keywords and URLs configured in the crawler, and the output is the content of the web pages retrieved. The server uses the "requests" library to access specific pages on the internet and retrieve their HTML content. This process accumulates data that meets the collection criteria.

[0116] Step 2:

[0117] The server analyzes the collected HTML data. The input is the web page content obtained in step 1, and the output is structured data about personal information and its relevance. The server uses the "BeautifulSoup" library to analyze the HTML and employs a generative AI model to determine the potential for personal data leakage from the text on the page. This allows for the identification of the personal information.

[0118] Step 3:

[0119] The server assesses the risk level based on the analysis results and prepares appropriate alerts. The input is the detection results of personal information, and the output is alert data with risk assessment. Machine learning models are used to determine the credibility and risk level of the data with greater accuracy. The server then develops notification methods based on the urgency of the situation.

[0120] Step 4:

[0121] The server sends a warning notification to the user's device based on the evaluation results. The input is the risk-assessed alert data, and the output is the notification message sent to the user's device. The server sends notifications via SMS, email, or a dedicated app to alert the user in real time. This allows the user to quickly consider countermeasures.

[0122] Step 5:

[0123] The server automatically submits a deletion request to the administrator of the source of the data breach. The input consists of the leaked data and a deletion request template, while the output is a formal deletion request addressed to the administrator. The server uses a standardized deletion request form to send messages to the relevant administrators, enabling immediate and efficient countermeasures.

[0124] Step 6:

[0125] The server records all processing and notification results and stores them in a database for future use. Input is a log of all system activity, and output is a detailed activity history. The server analyzes these records to improve the system and detect malfunctions. This strengthens the data analysis and monitoring system.

[0126] 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.

[0127] In embodiments of this invention, a client application installed on the user's device and a server located remotely function in combination. This system, including the emotion engine, aims to detect personal data breaches and, based on the results, evaluate the user's emotions and provide optimized responses.

[0128] Information gathering and analysis:

[0129] The server runs a web crawler to collect dangerous web pages related to user information from the internet and the dark web. The data obtained through this collection process is analyzed on the server using natural language processing and machine learning techniques to identify personal data breaches. Keywords and patterns specified by the user are utilized during this analysis stage.

[0130] Recognition of emotions:

[0131] Based on the analysis results, the server uses an emotion engine to infer the user's emotional state from their past responses and current device usage. This emotion recognition is performed, for example, to assess how much stress or anxiety the user is experiencing in response to risk information.

[0132] Notifications and response coordination:

[0133] After the emotion engine determines the user's emotions, the server selects an appropriate notification message based on the result. For example, if the user is feeling highly anxious, an encouraging notification using gentle language will be sent. Furthermore, if the user is determined to be calm, specific next steps will be provided to encourage action.

[0134] Deletion requests and records:

[0135] The system automatically submits deletion requests for any data breaches detected by the server and tracks their progress. All activities are recorded as logs, including user sentiment data. These records are used as an analytical foundation for future system improvements and increased prediction accuracy.

[0136] This invention aims to significantly improve the user experience not only by detecting the leakage of personal information, but also by enabling flexible responses that respond to the user's emotions. This is a modern means of living a safe digital life while protecting privacy.

[0137] The following describes the processing flow.

[0138] Step 1:

[0139] The server receives personal information sent from the user's device and updates the list of keywords to be monitored. Based on this list, it defines the internet areas to crawl and sets the next execution schedule for the web crawler.

[0140] Step 2:

[0141] The server launches a web crawler, which then traverses the configured internet and dark web areas. The crawler retrieves information from relevant pages, forums, and databases based on a keyword list.

[0142] Step 3:

[0143] The acquired information is immediately analyzed on the server. Natural language processing technology is used to identify potential leaks of user personal information and confidential data contained within the page. Based on the analysis results, the risk potential of each piece of information is scored.

[0144] Step 4:

[0145] The server uses an emotion engine to recognize the user's emotional state. It considers the user's past response history and device usage patterns to infer their current emotional state (e.g., anxiety, stress, calmness).

[0146] Step 5:

[0147] The server integrates the analysis results and the emotion engine's determination to generate an appropriate notification for the user. This notification is tailored to the user's emotional state. For example, if the user is feeling anxious, a reassuring message will be sent.

[0148] Step 6:

[0149] The server automatically handles deletion requests based on notifications sent to users. These deletion requests are sent to each site where the leaked information was detected, and the progress is tracked and recorded until the action is completed.

[0150] Step 7:

[0151] All processing results are recorded as logs on the server. This includes information gathering, analysis details, sentiment recognition results, notification details, and the progress of deletion actions. This data will be used for future improvement work and report generation.

[0152] (Example 2)

[0153] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0154] In modern society, data leaks on the internet are frequent, and the leakage of personal information has become a serious problem. While conventional systems are specialized in leak detection, they do not consider appropriate responses that take into account the user's emotional state. As a result, the psychological burden on users caused by information leaks may increase. Therefore, there is a need for methods that appropriately assess the user's emotional state and alleviate that burden.

[0155] 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.

[0156] In this invention, the server includes means for collecting data via an information recording device, means for analyzing the collected data using a generative model operating on a computing device to detect personal information leaks, and means for using an engine to evaluate emotional states according to the detected analysis results. This enables not only a rapid response to personal information leaks but also a flexible response that is sensitive to the user's emotions.

[0157] An "information recording device" is a device that has the function of collecting data and storing it temporarily or permanently for later processing purposes.

[0158] A "generative model" is a mathematical model that uses artificial intelligence technology to learn patterns from large amounts of data and perform analysis and predictions.

[0159] A "processing unit" is a general term for the hardware and software used to perform data processing and calculations.

[0160] "Emotional state" refers to the user's psychological response or emotional state, and is information that the system determines.

[0161] An "engine" is a software or hardware component designed to perform a specific function.

[0162] "Communication" is the act or process of sending and receiving information from one party to another.

[0163] A "removal request" is a formal request made to a relevant service or platform to remove inappropriate or inaccurate data found on the internet.

[0164] This invention is realized through the collaboration of client software installed on the user's electronic device and a remote data processing device. Specific embodiments are described below.

[0165] The server utilizes information gathering devices to find user-related data from the internet and the dark web. Specifically, it operates multiple automated crawling tools built using programming languages ​​to collect dangerous information based on pre-configured keywords. These crawlers, connected to the computer network in real time, quickly and accurately search for relevant information from large databases.

[0166] After data collection, the computing unit on the server runs a generative AI model to analyze the collected information. This model uses advanced natural language processing techniques to scrutinize content that may indicate a personal data breach. For example, the generative AI model has the ability to detect personal data patterns such as names, addresses, and financial information, and also identifies risk levels according to the type of breach.

[0167] Furthermore, the server implements an emotion recognition engine to estimate emotional states. This engine analyzes the user's past device usage history and current activity to assess the psychological impact of a data breach on the user. Emotions such as stress and anxiety are quantified based on the analysis results, and priorities for response are set accordingly.

[0168] During the notification phase, the server considers the generated emotional data and sends the most relevant information to the user's device. For example, if the user is showing high levels of anxiety, a reassuring message is created and sent quickly to reduce the user's mental burden.

[0169] Furthermore, if a data breach is detected, the server automatically sends a deletion request and records the results sequentially. All activities are systematically stored as logs in the database, forming the basis for future analysis and optimization.

[0170] An example of a prompt message is: "Analyze web pages that pose a risk of personal information leakage, and based on the results, recognize user sentiment and consider appropriate actions."

[0171] This system aims to provide a modern solution that protects users' personal information while also considering their mental well-being. By implementing this system, users can enjoy a safe and secure internet environment.

[0172] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0173] Step 1:

[0174] The server collects information from the internet and the dark web. The input is a pre-configured keyword, and the server uses an automated crawling tool to search for data based on that keyword. The crawling tool collects data relevant to the keyword from the internet and sends it to the server. The output is a list of collected web pages and their associated information. Specifically, the server activates the crawler at regular intervals and scans the configured area.

[0175] Step 2:

[0176] The server analyzes the collected information. The input is the web page information obtained in Step 1. The server activates a generative AI model and performs the analysis. This analysis uses natural language processing to verify whether it contains any leaked personal information. The output is a list of potentially leaked data. Specifically, the server processes the data through the generative AI model and assigns labels according to their importance.

[0177] Step 3:

[0178] The server evaluates the user's emotional state. The inputs are the analysis results obtained in step 2 and the user's past device usage history. The server uses an emotion recognition engine to evaluate the user's emotional state, inferred from the analysis results. The output is numerical data representing the emotional evaluation result. Specific operations include comparing past stress response data with current device usage data.

[0179] Step 4:

[0180] The server generates a notification message and sends it to the terminal. The input is the sentiment evaluation result from step 3. Based on the evaluation result, the server selects a notification message with an appropriate tone and content and sends it to the user's terminal. The output is the message the user receives. Specifically, the server selects a message template based on sentiment data, customizes the message content, and sends it.

[0181] Step 5:

[0182] The server automatically submits a deletion request for the leaked information. The input is the leaked data identified by the analysis results in step 2. The server sends a deletion request to the relevant website or service. The output is log data indicating that the deletion request was sent. The specific operation includes a process of generating a standardized request format for the deletion request and sending it to the appropriate recipient.

[0183] This series of steps creates a system that provides peace of mind in terms of both protecting personal information and ensuring the mental health of users.

[0184] (Application Example 2)

[0185] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0186] In today's digital society, the leakage of personal data and the resulting anxiety and stress on users are major challenges. In particular, the user experience can be impaired when the response after a leak is detected does not adapt to the emotional state of individual users. As a result, users may not receive sufficient support in deciding on their course of action. To solve this problem, it is necessary not only to detect personal data leaks but also to provide appropriate notifications and support based on the user's emotional state.

[0187] 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.

[0188] In this invention, the server includes means for collecting information via a network, means for analyzing the collected information to detect personal data leaks, means for automatically sending notifications based on the detection results, means for inferring the user's emotional state and adjusting the notification content based on the inference, and means for providing security instructions based on the user's emotional state. This enables flexible responses to users according to their individual emotional states, allowing them to live their digital lives with peace of mind.

[0189] A "device that collects information via a network" is a device that acquires data from an external source using the internet or other communication networks.

[0190] A "device that analyzes collected information to detect personal data leaks" is a device that performs analytical processing on acquired data and identifies instances of unauthorized use or leakage of personal information.

[0191] A "device that automatically sends notifications based on detection results" is a device that automatically issues alerts and notifications based on information obtained as a result of analysis.

[0192] A "device for submitting deletion requests for leaked information" is a device that manages the procedures for requesting the deletion of identified leaked information from relevant parties.

[0193] A "device for recording all actions related to detection and deletion requests" is a device that tracks and maintains records of various processes performed within the system and their results.

[0194] A "device that infers the user's emotional state and adjusts notification content based on that inference" is a device that infers the user's emotional state from their behavioral data and analysis results, and generates an appropriate notification message according to that situation.

[0195] A "device that provides security instructions based on the user's emotional state" is a device that instructs or suggests the user on the most appropriate security measures according to their estimated emotional state.

[0196] To implement this invention, a client application is installed on the user's mobile device and configured to interact with a server. The server implements a web crawler to collect data via the internet or other communication networks. The collected information is analyzed on the server side using natural language processing and machine learning techniques. This analysis allows for the detection of personal data breaches.

[0197] The server is written in Python, and libraries such as spaCy and scikit-learn are used for data analysis. To infer the user's emotional state, it makes inferences based on past user responses and device usage, and sentiment analysis tools such as NLTK are used for this inference.

[0198] Based on the results of emotion recognition, the server generates notification content that is tailored to the user's current mental state. This notification may include encouraging words or specific action guidelines.

[0199] For example, if a user is feeling very anxious due to a fraudulent credit card transaction, the app will send a notification such as "Emergency Response Guide: Please contact your card company first. We are here to help if you need assistance," to provide reassurance.

[0200] The server also automatically submits deletion requests for identified leaked information and records the progress of these requests. This record serves as a foundation for future system improvements and increased prediction accuracy.

[0201] An example of a prompt using a generative AI model is, "Create a security notification that takes the user's emotional state into account: {emotional state}". In this way, flexible responses that take user emotions into account become possible, providing a better user experience.

[0202] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0203] Step 1:

[0204] The server collects data from the internet and the dark web using a web crawler. It takes a list of URLs as input and raw data of the associated web pages as output. This data is used in the next analysis step.

[0205] Step 2:

[0206] The server analyzes the collected raw data. It takes raw data as input and generates structured data using natural language processing techniques. spaCy is used for data analysis, identifying patterns indicating personal data breaches and obtaining analysis results as output. These results include the likelihood and risk level of a breach.

[0207] Step 3:

[0208] The server infers the user's emotional state based on the analysis results. It receives the analysis results and the user's past usage data as input, and uses the emotion analysis tool NLTK to evaluate the user's feelings. The output is an emotional state (e.g., anxious, calm).

[0209] Step 4:

[0210] The server generates notification content based on the user's emotional state. It uses the predicted emotional state as input and a generation AI model to create prompt messages. Specifically, it generates notifications that offer encouragement and suggest specific actions based on the user's state, and outputs a refined notification message.

[0211] Step 5:

[0212] The device delivers generated notifications to the user. It receives notification messages from the server as input and displays them on the user's screen in an appropriate format as output. The device displays these notifications and, if necessary, records the user's response.

[0213] Step 6:

[0214] The server automatically submits a deletion request for any leaked information detected. It uses the identification data of the leaked information as input to initiate the deletion request process. This generates a log recording the progress of the deletion request as output.

[0215] Step 7:

[0216] The server records all activities, accumulating foundational data for future improvements. It receives result data from each step as input and generates log files for system improvement as output. These log files also include user feedback and sentiment data, which contribute to future model improvements.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] 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.

[0223] 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).

[0224] 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.

[0225] 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.

[0226] 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).

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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".

[0233] As an embodiment of this invention, a software system operated over a network has been devised. Its specific operation is described below.

[0234] Information gathering:

[0235] The server runs a web crawler to traverse specific areas of the internet and the dark web. The crawler uses keywords such as the user's registered personal information, including their name, email address, and credit card information, to automatically collect pages containing relevant information.

[0236] Information analysis:

[0237] The collected data is analyzed by a server. This analysis uses natural language processing techniques to identify whether there is any leakage of personal data within the text. For example, it can detect if an email address is being used in an inappropriate context. Furthermore, machine learning algorithms can be used to assess the credibility and risk level of the data. This process is performed in real time, supporting rapid decision-making.

[0238] Notifications and deletion requests:

[0239] Based on the analysis results, if the server determines there is a risk, it will immediately notify the user's device with a warning. The notification will be sent via email, SMS, or a dedicated application to alert the user. Furthermore, the server will automatically submit a deletion request to the site administrator. This will be done using an email clearly stating the information to be deleted or a standardized deletion request form.

[0240] Activity log and report generation:

[0241] The server meticulously records all program processes and stores them as activity logs. These logs are used for future audits and to improve countermeasures. The server also periodically provides users with reports detailing the analysis and response results, allowing users to verify the level of protection their information receives.

[0242] This system is designed to quickly detect personal data breaches and provide users with a secure environment for using the internet. Furthermore, continuous monitoring and automated responses allow for effortless protection of personal information.

[0243] The following describes the processing flow.

[0244] Step 1:

[0245] The server launches a web crawler according to a network schedule. This crawler searches for target URLs based on a pre-registered list of users' personal information and automatically collects pages from the internet and the dark web. This collects page data that may contain personal information.

[0246] Step 2:

[0247] The server analyzes the collected web page data. It utilizes natural language processing techniques to search for traces of personal information within the text of the page. During this process, keyword detection and pattern matching are used to immediately determine whether or not personal data has been leaked.

[0248] Step 3:

[0249] The server classifies the discovered information based on the analysis results and scores its risk level. Information with a particularly high risk of leakage is assigned a high score, prioritizing immediate response. This information is recorded in the log database and used for later analysis and reporting.

[0250] Step 4:

[0251] The server sends a notification to the user's device based on the risk score. The notification includes a summary of the discovered leaked information and recommended countermeasures, prompting the user to take immediate action.

[0252] Step 5:

[0253] The server automatically issues a takedown request to the site hosting the leaked information. The request includes details about the problem and is prepared in accordance with any necessary legal requirements. The server tracks the progress of the response after the request is submitted and records completion notifications.

[0254] Step 6:

[0255] The server comprehensively logs all activities, including collection, analysis, notification, and deletion requests. The server periodically generates reports for users based on these logs, providing transparency and reliability to the system.

[0256] (Example 1)

[0257] 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".

[0258] In today's digital society, data breaches are frequent, leading to increased security risks. Existing solutions lack real-time capabilities and automation, making it difficult to accurately understand how data breaches occur and the level of risk involved. Furthermore, there are limited means to respond quickly and effectively when a breach is detected. There is a need for efficient systems to address these challenges.

[0259] 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.

[0260] In this invention, the server includes means for collecting information via digital communication, means for analyzing the collected data to identify leaks of personal information, and means for automatically transmitting the results based on the analysis using communication means. This enables the identification of leaks of personal information in real time and allows for a rapid response.

[0261] "Digital communication" is a technology that uses computer networks to send and receive data.

[0262] "Means of collecting information" refers to methods and devices for acquiring and storing data, and in particular includes software such as web crawlers.

[0263] "Personal information" refers to information that can identify a specific individual, and includes names, email addresses, credit card information, and so on.

[0264] "Means of analyzing data to identify personal information leaks" refers to the process of determining whether or not a leak has occurred using statistical or machine learning methods on collected data.

[0265] A "machine learning algorithm for assessing risk" refers to a computational model that quantifies risk based on collected data and measures the urgency of a data breach.

[0266] "Means of transmission using communication means" refers to the processes and technologies for transmitting information to other devices or people via the internet or telephone networks.

[0267] A "removal request" refers to an official request to remove information that has been inappropriately published.

[0268] "Means of recording actions" refers to techniques or procedures for saving operations and processes performed within a system as logs.

[0269] To implement this invention, a software system operating on a network is used. A server plays a central role in this system, coordinating each component to collect, analyze, notify, request deletions from, and record information.

[0270] First, the server launches a web crawler implemented in Python to collect information. This crawler traverses the internet and specific online areas, searching for keywords that have been pre-registered as the user's personal information. The data collected by the crawler is securely stored on the server using database software.

[0271] Next, a natural language processing library (e.g., SpaCy) is used to analyze the data collected by the server. This makes it possible to identify locations within the text where personal information has been leaked. Based on these analysis results, a machine learning algorithm utilizing TensorFlow is used to assess the level of risk.

[0272] If a high risk is detected, the server automatically sends a warning to the user's device. This communication utilizes email and messaging services. Furthermore, based on the discovered leaked information, the server requests the relevant website administrator to remove the content. Standardized email and online forms are used for this purpose.

[0273] As an activity record, the server saves all program processing data to a log management system such as Elasticsearch. This allows for future audits and improvements to response procedures.

[0274] As a concrete example, consider the response when a user detects that their email address has been inappropriately published. In this case, the server immediately sends a warning message to the user's device and sends a request to the administrator of the relevant website to remove the information. This allows the user to quickly understand the information leak and take appropriate action.

[0275] An example of a prompt message for a generating AI model is: "Please verify that my data is secure and issue a warning if there is a possibility of leakage. Also, please submit an appropriate deletion request."

[0276] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0277] Step 1:

[0278] The server starts a web crawler to collect information via the Internet. The crawler uses the user's personal information (e.g., name, email address) as keywords and visits web pages containing relevant information based on those keywords. As the output obtained, HTML-formatted data is collected and sent to the server.

[0279] Step 2:

[0280] The server analyzes the collected HTML data to extract personal information. A data analysis library is used for the analysis, taking in the content of the collected web pages as input. The BeautifulSoup library is used to apply patterns of personal information (e.g., regular expressions for email addresses) to extract the data. The output obtained is a list of the extracted personal information.

[0281] Step 3:

[0282] The server saves the extracted personal information in a database and prepares it for analysis. When saving, in addition to the extracted data as input, a timestamp is added and stored in something like MongoDB. As the output, searchable records of personal information are accumulated in the database.

[0283] Step 4:

[0284] The server analyzes the data using natural language processing technology. It processes the list of saved personal information, uses the SpaCy library to analyze the context of the text, and evaluates the possibility of information leakage. The output is the data items identified as having a risk of leakage and their context information.

[0285] Step 5:

[0286] The server evaluates the reliability and risk level of data using machine learning algorithms. Using the leakage risk information identified in the previous step as input data, it performs analysis with a model using TensorFlow. The output is an evaluation result in which the risk level is represented numerically.

[0287] Step 6:

[0288] Based on the risk assessment result, the server sends a warning to the user's terminal. Using the evaluation result as input, it generates a warning message according to the urgency. As output, a notification is sent to the user via email or SMS.

[0289] Step 7:

[0290] The server automatically executes a deletion application for the detected leakage information. Using the details of the leaked information and the contact information of the target website as input. The output is that an official request including the deletion application is sent to the site administrator.

[0291] Step 8:

[0292] The server records all processes in detail and saves them as a digital log. The input includes the activity logs of each step, and this is accumulated using Elasticsearch. The output is an activity record that can be used for later audits and enhancements.

[0293] (Application Example 1)

[0294] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0295] In today's world, where personal data breaches occur on a daily basis, there is a need to quickly and accurately detect breaches and notify users. However, conventional methods have limitations in the accuracy of information analysis and the timing of notifications, making it difficult for users to respond quickly to personal data breaches. Furthermore, there is no system to automatically submit appropriate deletion requests to the source of the breach, making it difficult to prevent secondary damage to personal data.

[0296] 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.

[0297] In this invention, the server includes means for collecting information via a network, means for analyzing the collected information to detect personal data leaks, means for automatically sending notifications based on the detection results, means for automatically sending notifications to the administrator regarding the source of the leak, means for classifying and analyzing information using a generative AI model, and means for generating prompt messages to report the results of the information analysis. This enables the rapid identification of personal data leaks in real time, allowing users to take appropriate measures.

[0298] "Means of collecting information via a network" refers to technologies that use the internet or other communication networks to automatically acquire target information.

[0299] "Methods for analyzing collected information to detect personal data breaches" refer to processing technologies that scrutinize collected data and determine, in particular, whether personal information is being handled improperly.

[0300] "Means of automatically sending notifications based on detection results" refers to an automated system that immediately sends warnings and information to users when a data breach of personal information is confirmed.

[0301] "Methods for requesting the deletion of information that has been confirmed to have been leaked" refers to methods for automating the process of requesting relevant parties and administrators to delete the discovered leaked information.

[0302] The means for "recording all activities of detection and deletion applications" is a technology that records and stores the entire process from data leakage detection to countermeasures, and is used for future audits and improvements.

[0303] The means for "sending warnings to a holding device to provide real-time notifications to users" is a technology that prompts immediate response by sending warnings to the devices in use by users immediately when leakage occurs.

[0304]

[0305] The means for "classifying and analyzing information using a generative AI model" is a method that efficiently analyzes a large amount of information using machine learning and AI technologies, and evaluates the risk level and reliability.

[0306] The means for "generating a prompt sentence to report the information analysis result" is a technology that automatically creates a sentence for clearly conveying the analysis result to users and related parties.

[0307] The system for realizing this application example is implemented by constructing an information security system centered on a server. The server first runs dedicated crawling software to collect data from a wide range of information sources via a network. This crawler tours specific areas of the public and dark web and accumulates the collected data in a database within the server.

[0308] The collected data is analyzed by a natural language processing (NLP) system and a generative AI model on the server. In this analysis, it is determined whether personal data leakage is confirmed, and if detected, the risk level is promptly evaluated.

[0309] ​Furthermore, the server sends a warning to the user's device based on the evaluation results. This warning is designed to be sent in real time to the user's smartphone or other personal devices. If a data breach is confirmed, the server automatically requests deletion from the administrator of the source of the breach. This ensures that users' personal information is protected without unnecessary time or effort.

[0310] As a concrete example, if a user's email address is being used on a fraudulent bulletin board, the system will immediately detect this fact, a notification will be displayed on the user's smartphone, and a request for removal will be automatically submitted to the bulletin board administrator. This system is built using Python and various AI technologies, employing the "requests" library for data collection and the "BeautifulSoup" library for information analysis.

[0311] The generative AI model is used to analyze and classify collected data, generating prompt messages to report the results of the information analysis. An example of such a prompt message is, "Please describe the process for identifying websites where user email addresses have been leaked and requesting their removal." This allows for the rapid management of information leaks and provides users with a secure internet environment.

[0312] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0313] Step 1:

[0314] The server collects data from various sources on the network. The input consists of keywords and URLs configured in the crawler, and the output is the content of the web pages retrieved. The server uses the "requests" library to access specific pages on the internet and retrieve their HTML content. This process accumulates data that meets the collection criteria.

[0315] Step 2:

[0316] The server analyzes the collected HTML data. The input is the web page content obtained in step 1, and the output is structured data about personal information and its relevance. The server uses the "BeautifulSoup" library to analyze the HTML and employs a generative AI model to determine the potential for personal data leakage from the text on the page. This allows for the identification of the personal information.

[0317] Step 3:

[0318] The server assesses the risk level based on the analysis results and prepares appropriate alerts. The input is the detection results of personal information, and the output is alert data with risk assessment. Machine learning models are used to determine the credibility and risk level of the data with greater accuracy. The server then develops notification methods based on the urgency of the situation.

[0319] Step 4:

[0320] The server sends a warning notification to the user's device based on the evaluation results. The input is the risk-assessed alert data, and the output is the notification message sent to the user's device. The server sends notifications via SMS, email, or a dedicated app to alert the user in real time. This allows the user to quickly consider countermeasures.

[0321] Step 5:

[0322] The server automatically submits a deletion request to the administrator of the source of the data breach. The input consists of the leaked data and a deletion request template, while the output is a formal deletion request addressed to the administrator. The server uses a standardized deletion request form to send messages to the relevant administrators, enabling immediate and efficient countermeasures.

[0323] Step 6:

[0324] The server records all processing and notification results and stores them in a database for future use. Input is a log of all system activity, and output is a detailed activity history. The server analyzes these records to improve the system and detect malfunctions. This strengthens the data analysis and monitoring system.

[0325] 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.

[0326] In embodiments of this invention, a client application installed on the user's device and a server located remotely function in combination. This system, including the emotion engine, aims to detect personal data breaches and, based on the results, evaluate the user's emotions and provide optimized responses.

[0327] Information gathering and analysis:

[0328] The server runs a web crawler to collect dangerous web pages related to user information from the internet and the dark web. The data obtained through this collection process is analyzed on the server using natural language processing and machine learning techniques to identify personal data breaches. Keywords and patterns specified by the user are utilized during this analysis stage.

[0329] Recognition of emotions:

[0330] Based on the analysis results, the server uses an emotion engine to infer the user's emotional state from their past responses and current device usage. This emotion recognition is performed, for example, to assess how much stress or anxiety the user is experiencing in response to risk information.

[0331] Notifications and response coordination:

[0332] After the emotion engine determines the user's emotions, the server selects an appropriate notification message based on the result. For example, if the user is feeling highly anxious, an encouraging notification using gentle language will be sent. Furthermore, if the user is determined to be calm, specific next steps will be provided to encourage action.

[0333] Deletion requests and records:

[0334] The system automatically submits deletion requests for any data breaches detected by the server and tracks their progress. All activities are recorded as logs, including user sentiment data. These records are used as an analytical foundation for future system improvements and increased prediction accuracy.

[0335] This invention aims to significantly improve the user experience not only by detecting the leakage of personal information, but also by enabling flexible responses that respond to the user's emotions. This is a modern means of living a safe digital life while protecting privacy.

[0336] The following describes the processing flow.

[0337] Step 1:

[0338] The server receives personal information sent from the user's device and updates the list of keywords to be monitored. Based on this list, it defines the internet areas to crawl and sets the next execution schedule for the web crawler.

[0339] Step 2:

[0340] The server launches a web crawler, which then traverses the configured internet and dark web areas. The crawler retrieves information from relevant pages, forums, and databases based on a keyword list.

[0341] Step 3:

[0342] The acquired information is immediately analyzed on the server. Natural language processing technology is used to identify potential leaks of user personal information and confidential data contained within the page. Based on the analysis results, the risk potential of each piece of information is scored.

[0343] Step 4:

[0344] The server uses an emotion engine to recognize the user's emotional state. It considers the user's past response history and device usage patterns to infer their current emotional state (e.g., anxiety, stress, calmness).

[0345] Step 5:

[0346] The server integrates the analysis results and the emotion engine's determination to generate an appropriate notification for the user. This notification is tailored to the user's emotional state. For example, if the user is feeling anxious, a reassuring message will be sent.

[0347] Step 6:

[0348] Based on notifications sent to users, the server automatically handles deletion requests. These deletion requests are sent to each site where the leaked information was detected, and the progress is tracked and recorded until the action is completed.

[0349] Step 7:

[0350] All processing results are recorded as logs on the server. This includes information gathering, analysis details, sentiment recognition results, notification details, and the progress of deletion actions. This data will be used for future improvement work and report generation.

[0351] (Example 2)

[0352] 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".

[0353] In modern society, data leaks on the internet are frequent, and the leakage of personal information has become a serious problem. While conventional systems are specialized in leak detection, they do not consider appropriate responses that take into account the user's emotional state. As a result, the psychological burden on users caused by information leaks may increase. Therefore, there is a need for methods that appropriately assess the user's emotional state and alleviate that burden.

[0354] 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.

[0355] In this invention, the server includes means for collecting data via an information recording device, means for analyzing the collected data using a generative model operating on a computing device to detect personal information leaks, and means for using an engine to evaluate emotional states according to the detected analysis results. This enables not only a rapid response to personal information leaks but also a flexible response that is sensitive to the user's emotions.

[0356] An "information recording device" is a device that has the function of collecting data and storing it temporarily or permanently for later processing purposes.

[0357] A "generative model" is a mathematical model that uses artificial intelligence technology to learn patterns from large amounts of data and perform analysis and predictions.

[0358] A "processing unit" is a general term for the hardware and software used to perform data processing and calculations.

[0359] "Emotional state" refers to the user's psychological response or emotional state, and is information that the system determines.

[0360] An "engine" is a software or hardware component designed to perform a specific function.

[0361] "Communication" is the act or process of sending and receiving information from one party to another.

[0362] A "removal request" is a formal request made to a relevant service or platform to remove inappropriate or inaccurate data found on the internet.

[0363] This invention is realized through the collaboration of client software installed on the user's electronic device and a remote data processing device. Specific embodiments are described below.

[0364] The server utilizes information gathering devices to find user-related data from the internet and the dark web. Specifically, it operates multiple automated crawling tools built using programming languages ​​to collect dangerous information based on pre-configured keywords. These crawlers, connected to the computer network in real time, quickly and accurately search for relevant information from large databases.

[0365] After data collection, the computing unit on the server runs a generative AI model to analyze the collected information. This model uses advanced natural language processing techniques to scrutinize content that may indicate a personal data breach. For example, the generative AI model has the ability to detect personal data patterns such as names, addresses, and financial information, and also identifies risk levels according to the type of breach.

[0366] Furthermore, the server implements an emotion recognition engine to estimate emotional states. This engine analyzes the user's past device usage history and current activity to assess the psychological impact of a data breach on the user. Emotions such as stress and anxiety are quantified based on the analysis results, and priorities for response are set accordingly.

[0367] During the notification phase, the server considers the generated emotional data and sends the most relevant information to the user's device. For example, if a user is showing high levels of anxiety, a reassuring message is created and sent quickly to alleviate the user's mental burden.

[0368] Furthermore, if a data breach is detected, the server automatically sends a deletion request and records the results sequentially. All activities are systematically stored as logs in the database, forming the basis for future analysis and optimization.

[0369] An example of a prompt message is: "Analyze web pages that pose a risk of personal information leakage, and based on the results, recognize user sentiment and consider appropriate actions."

[0370] This system aims to provide a modern solution that protects users' personal information while also considering their mental well-being. By implementing this system, users can enjoy a safe and secure internet environment.

[0371] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0372] Step 1:

[0373] The server collects information from the internet and the dark web. The input is a pre-configured keyword, and the server uses an automated crawling tool to search for data based on that keyword. The crawling tool collects data relevant to the keyword from the internet and sends it to the server. The output is a list of collected web pages and their associated information. Specifically, the server activates the crawler at regular intervals and scans the configured area.

[0374] Step 2:

[0375] The server analyzes the collected information. The input is the web page information obtained in Step 1. The server activates a generative AI model and performs the analysis. This analysis uses natural language processing to verify whether it contains any leaked personal information. The output is a list of potentially leaked data. Specifically, the server processes the data through the generative AI model and assigns labels according to their importance.

[0376] Step 3:

[0377] The server evaluates the user's emotional state. The inputs are the analysis results obtained in step 2 and the user's past device usage history. The server uses an emotion recognition engine to evaluate the user's emotional state, inferred from the analysis results. The output is numerical data representing the emotional evaluation result. Specific operations include comparing past stress response data with current device usage data.

[0378] Step 4:

[0379] The server generates a notification message and sends it to the terminal. The input is the sentiment evaluation result from step 3. Based on the evaluation result, the server selects a notification message with an appropriate tone and content and sends it to the user's terminal. The output is the message the user receives. Specifically, the server selects a message template based on sentiment data, customizes the message content, and sends it.

[0380] Step 5:

[0381] The server automatically submits a deletion request for the leaked information. The input is the leaked data identified by the analysis results in step 2. The server sends a deletion request to the relevant website or service. The output is log data indicating that the deletion request was sent. The specific operation includes a process of generating a standardized request format for the deletion request and sending it to the appropriate recipient.

[0382] This series of steps creates a system that provides peace of mind in terms of both protecting personal information and ensuring the mental health of users.

[0383] (Application Example 2)

[0384] 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."

[0385] In today's digital society, the leakage of personal data and the resulting anxiety and stress on users are major challenges. In particular, the user experience can be impaired when the response after a leak is detected does not adapt to the emotional state of individual users. As a result, users may not receive sufficient support in deciding on their course of action. To solve this problem, it is necessary not only to detect personal data leaks but also to provide appropriate notifications and support based on the user's emotional state.

[0386] 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.

[0387] In this invention, the server includes means for collecting information via a network, means for analyzing the collected information to detect personal data leaks, means for automatically sending notifications based on the detection results, means for inferring the user's emotional state and adjusting the notification content based on the inference, and means for providing security instructions based on the user's emotional state. This enables flexible responses to users according to their individual emotional states, allowing them to live their digital lives with peace of mind.

[0388] A "device that collects information via a network" is a device that acquires data from an external source using the internet or other communication networks.

[0389] A "device that analyzes collected information to detect personal data leaks" is a device that performs analytical processing on acquired data and identifies instances of unauthorized use or leakage of personal information.

[0390] A "device that automatically sends notifications based on detection results" is a device that automatically issues alerts and notifications based on information obtained as a result of analysis.

[0391] A "device for submitting deletion requests for leaked information" is a device that manages the procedures for requesting the deletion of identified leaked information from relevant parties.

[0392] A "device for recording all actions related to detection and deletion requests" is a device that tracks and maintains records of various processes performed within the system and their results.

[0393] A "device that infers the user's emotional state and adjusts notification content based on that inference" is a device that infers the user's emotional state from their behavioral data and analysis results, and generates an appropriate notification message according to that situation.

[0394] A "device that provides security instructions based on the user's emotional state" is a device that instructs or suggests the user on the most appropriate security measures according to their estimated emotional state.

[0395] To implement this invention, a client application is installed on the user's mobile device and configured to interact with a server. The server implements a web crawler to collect data via the internet or other communication networks. The collected information is analyzed on the server side using natural language processing and machine learning techniques. This analysis allows for the detection of personal data breaches.

[0396] The server is written in Python, and libraries such as spaCy and scikit-learn are used for data analysis. To infer the user's emotional state, it makes inferences based on past user responses and device usage, and sentiment analysis tools such as NLTK are used for this inference.

[0397] Based on the results of emotion recognition, the server generates notification content that is tailored to the user's current mental state. This notification may include encouraging words or specific action guidelines.

[0398] For example, if a user is feeling very anxious due to a fraudulent credit card transaction, the app will send a notification such as "Emergency Response Guide: Please contact your card company first. We are here to help if you need assistance," to provide reassurance.

[0399] The server also automatically submits deletion requests for identified leaked information and records the progress of these requests. This record serves as a foundation for future system improvements and increased prediction accuracy.

[0400] An example of a prompt using a generative AI model is, "Create a security notification that takes the user's emotional state into account: {emotional state}". In this way, flexible responses that take user emotions into account become possible, providing a better user experience.

[0401] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0402] Step 1:

[0403] The server collects data from the internet and the dark web using a web crawler. It takes a list of URLs as input and raw data of the associated web pages as output. This data is used in the next analysis step.

[0404] Step 2:

[0405] The server analyzes the collected raw data. It takes raw data as input and generates structured data using natural language processing techniques. spaCy is used for data analysis, identifying patterns indicating personal data breaches and obtaining analysis results as output. These results include the likelihood and risk level of a breach.

[0406] Step 3:

[0407] The server infers the user's emotional state based on the analysis results. It receives the analysis results and the user's past usage data as input, and uses the emotion analysis tool NLTK to evaluate the user's feelings. The output is an emotional state (e.g., anxious, calm).

[0408] Step 4:

[0409] The server generates notification content based on the user's emotional state. It uses the predicted emotional state as input and a generation AI model to create prompt messages. Specifically, it generates notifications that offer encouragement and suggest specific actions based on the user's state, and outputs a refined notification message.

[0410] Step 5:

[0411] The device delivers generated notifications to the user. It receives notification messages from the server as input and displays them on the user's screen in an appropriate format as output. The device displays these notifications and, if necessary, records the user's response.

[0412] Step 6:

[0413] The server automatically submits a deletion request for any leaked information detected. It uses the identification data of the leaked information as input to initiate the deletion request process. This generates a log recording the progress of the deletion request as output.

[0414] Step 7:

[0415] The server records all activities, accumulating foundational data for future improvements. It receives result data from each step as input and generates log files for system improvement as output. These log files also include user feedback and sentiment data, which contribute to future model improvements.

[0416] 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.

[0417] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0418] 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.

[0419] [Third Embodiment]

[0420] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0421] 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.

[0422] 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).

[0423] 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.

[0424] 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.

[0425] 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).

[0426] 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.

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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".

[0432] As an embodiment of this invention, a software system operated over a network has been devised. Its specific operation is described below.

[0433] Information gathering:

[0434] The server runs a web crawler to traverse specific areas of the internet and the dark web. The crawler uses keywords such as the user's registered personal information, including their name, email address, and credit card information, to automatically collect pages containing relevant information.

[0435] Information analysis:

[0436] The collected data is analyzed by a server. This analysis uses natural language processing techniques to identify whether there is any leakage of personal data within the text. For example, it can detect if an email address is being used in an inappropriate context. Furthermore, machine learning algorithms can be used to assess the credibility and risk level of the data. This process is performed in real time, supporting rapid decision-making.

[0437] Notifications and deletion requests:

[0438] Based on the analysis results, if the server determines there is a risk, it will immediately notify the user's device with a warning. The notification will be sent via email, SMS, or a dedicated application to alert the user. Furthermore, the server will automatically submit a deletion request to the site administrator. This will be done using an email clearly stating the information to be deleted or a standardized deletion request form.

[0439] Activity log and report generation:

[0440] The server meticulously records all program processes and stores them as activity logs. These logs are used for future audits and to improve countermeasures. The server also periodically provides users with reports detailing the analysis and response results, allowing users to verify the level of protection their information receives.

[0441] This system is designed to quickly detect personal data breaches and provide users with a secure environment for using the internet. Furthermore, continuous monitoring and automated responses allow for effortless protection of personal information.

[0442] The following describes the processing flow.

[0443] Step 1:

[0444] The server launches a web crawler according to a network schedule. This crawler searches for target URLs based on a pre-registered list of users' personal information and automatically collects pages from the internet and the dark web. This collects page data that may contain personal information.

[0445] Step 2:

[0446] The server analyzes the collected web page data. It utilizes natural language processing techniques to search for traces of personal information within the text of the page. During this process, keyword detection and pattern matching are used to immediately determine whether or not personal data has been leaked.

[0447] Step 3:

[0448] The server classifies the discovered information based on the analysis results and scores its risk level. Information with a particularly high risk of leakage is assigned a high score, prioritizing immediate response. This information is recorded in the log database and used for later analysis and reporting.

[0449] Step 4:

[0450] The server sends a notification to the user's device based on the risk score. The notification includes a summary of the discovered leaked information and recommended countermeasures, prompting the user to take immediate action.

[0451] Step 5:

[0452] The server automatically issues a takedown request to the site hosting the leaked information. The request includes details about the problem and is prepared in accordance with any necessary legal requirements. The server tracks the progress of the response after the request is submitted and records completion notifications.

[0453] Step 6:

[0454] The server comprehensively logs all activities, including collection, analysis, notification, and deletion requests. The server periodically generates reports for users based on these logs, providing transparency and reliability to the system.

[0455] (Example 1)

[0456] 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."

[0457] In today's digital society, data breaches are frequent, leading to increased security risks. Existing solutions lack real-time capabilities and automation, making it difficult to accurately understand how data breaches occur and the level of risk involved. Furthermore, there are limited means to respond quickly and effectively when a breach is detected. There is a need for efficient systems to address these challenges.

[0458] 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.

[0459] In this invention, the server includes means for collecting information via digital communication, means for analyzing the collected data to identify leaks of personal information, and means for automatically transmitting the results based on the analysis using communication means. This enables the identification of leaks of personal information in real time and allows for a rapid response.

[0460] "Digital communication" is a technology that uses computer networks to send and receive data.

[0461] "Means of collecting information" refers to methods and devices for acquiring and storing data, and in particular includes software such as web crawlers.

[0462] "Personal information" refers to information that can identify a specific individual, and includes names, email addresses, credit card information, and so on.

[0463] "Means of analyzing data to identify personal information leaks" refers to the process of determining whether or not a leak has occurred using statistical or machine learning methods on collected data.

[0464] A "machine learning algorithm for assessing risk" refers to a computational model that quantifies risk based on collected data and measures the urgency of a data breach.

[0465] "Means of transmission using communication means" refers to the processes and technologies for transmitting information to other devices or people via the internet or telephone networks.

[0466] A "removal request" refers to an official request to remove information that has been inappropriately published.

[0467] "Means of recording actions" refers to techniques or procedures for saving operations and processes performed within a system as logs.

[0468] To implement this invention, a software system operating on a network is used. A server plays a central role in this system, coordinating each component to collect, analyze, notify, request deletions from, and record information.

[0469] First, the server launches a web crawler implemented in Python to collect information. This crawler traverses the internet and specific online areas, searching for keywords that have been pre-registered as the user's personal information. The data collected by the crawler is securely stored on the server using database software.

[0470] Next, a natural language processing library (e.g., SpaCy) is used to analyze the data collected by the server. This makes it possible to identify locations within the text where personal information has been leaked. Based on these analysis results, a machine learning algorithm utilizing TensorFlow is used to assess the level of risk.

[0471] If a high risk is detected, the server automatically sends a warning to the user's device. This communication utilizes email and messaging services. Furthermore, based on the discovered leaked information, the server requests the relevant website administrator to remove the content. Standardized email and online forms are used for this purpose.

[0472] As an activity record, the server saves all program processing data to a log management system such as Elasticsearch. This allows for future audits and improvements to response procedures.

[0473] As a concrete example, consider the response when a user detects that their email address has been inappropriately published. In this case, the server immediately sends a warning message to the user's device and sends a request to the administrator of the relevant website to remove the information. This allows the user to quickly understand the information leak and take appropriate action.

[0474] An example of a prompt message for a generating AI model is: "Please verify that my data is secure and issue a warning if there is a possibility of leakage. Also, please submit an appropriate deletion request."

[0475] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0476] Step 1:

[0477] The server launches a web crawler to collect information over the internet. The crawler inputs the user's personal information (e.g., name, email address) as keywords and visits web pages containing relevant information based on those keywords. The resulting output is collected as HTML data and sent to the server.

[0478] Step 2:

[0479] The server parses the collected HTML data to extract personal information. A data analysis library is used for the analysis, taking the collected web page content as input. The BeautifulSoup library is used to apply patterns of personal information (e.g., regular expressions for email addresses) to extract the data. The output is a list of the extracted personal information.

[0480] Step 3:

[0481] The server stores the extracted personal information in a database and prepares it for analysis. During storage, a timestamp is added to the extracted data as input, and the data is stored in a database such as MongoDB. The output is a database containing a record of searchable personal information.

[0482] Step 4:

[0483] The server analyzes the data using natural language processing techniques. It processes a list of personal information stored as input, analyzes the text context using the SpaCy library, and assesses the potential for data leakage. The output consists of data items identified as being at risk of leakage and their contextual information.

[0484] Step 5:

[0485] The server uses a machine learning algorithm to evaluate the credibility and risk level of the data. The data analysis is performed using a TensorFlow model with the leak risk information identified in the previous step as input data. The output is an evaluation result where the risk level is expressed numerically.

[0486] Step 6:

[0487] The server sends a warning to the user's device based on the risk assessment results. It generates a warning message based on the assessment results, according to the level of urgency. The output is a notification sent to the user via email or SMS.

[0488] Step 7:

[0489] The server automatically initiates a removal request for any leaked information detected. The input includes details of the leaked information and contact information for the affected website. The output is an official request, including the removal request, sent to the site administrator.

[0490] Step 8:

[0491] The server meticulously records all processes and saves them as digital logs. The input includes activity logs for each step, which are stored using Elasticsearch. The output is an activity record available for later auditing and enhancement.

[0492] (Application Example 1)

[0493] 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."

[0494] In today's world, where personal data breaches occur on a daily basis, there is a need to quickly and accurately detect breaches and notify users. However, conventional methods have limitations in the accuracy of information analysis and the timing of notifications, making it difficult for users to respond quickly to personal data breaches. Furthermore, there is no system to automatically submit appropriate deletion requests to the source of the breach, making it difficult to prevent secondary damage to personal data.

[0495] 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.

[0496] In this invention, the server includes means for collecting information via a network, means for analyzing the collected information to detect personal data leaks, means for automatically sending notifications based on the detection results, means for automatically sending notifications to the administrator regarding the source of the leak, means for classifying and analyzing information using a generative AI model, and means for generating prompt messages to report the results of the information analysis. This enables the rapid identification of personal data leaks in real time, allowing users to take appropriate measures.

[0497] "Means of collecting information via a network" refers to technologies that use the internet or other communication networks to automatically acquire target information.

[0498] "Methods for analyzing collected information to detect personal data breaches" refer to processing technologies that scrutinize collected data and determine, in particular, whether personal information is being handled improperly.

[0499] "Means of automatically sending notifications based on detection results" refers to an automated system that immediately sends warnings and information to users when a data breach of personal information is confirmed.

[0500] "Methods for requesting the deletion of information that has been confirmed to have been leaked" refers to methods for automating the process of requesting relevant parties and administrators to delete the discovered leaked information.

[0501] "Means of recording all detection and deletion request activities" refers to technology that records and stores the entire process from the detection to the remediation of a data breach, and is used for future audits and improvements.

[0502] "Means of sending warnings to the user's device in order to provide real-time notifications to the user" refers to a technology that prompts immediate action by sending a warning to the user's device at the moment of a data breach.

[0503] "A means of automatically sending notifications to administrators regarding the source of a data leak" refers to a system that automatically sends warning messages or requests for deletion to administrators who handle the data that caused the leak.

[0504] "Methods for classifying and analyzing information using generative AI models" refer to methods that utilize machine learning and AI technologies to efficiently analyze large amounts of information and evaluate risk levels and reliability.

[0505] "A means of generating prompt messages and reporting information analysis results" refers to a technology that automatically creates text to clearly communicate analysis results to users and stakeholders.

[0506] The system that realizes this application example is implemented by building an information security system centered on a server. The server first runs dedicated crawling software to collect data from a wide range of sources via the network. This crawler traverses specific areas of the public and dark web, and stores the collected data in a database on the server.

[0507] The collected data is analyzed by a natural language processing (NLP) system and generative AI models on the server. This analysis determines whether or not a personal data breach has occurred, and if detected, the risk level is promptly assessed.

[0508] Furthermore, the server sends a warning to the user's device based on the evaluation results. This warning is designed to be sent in real time to the user's smartphone or other personal devices. If a data breach is confirmed, the server automatically requests deletion from the administrator of the source of the breach. This ensures that users' personal information is protected without unnecessary time or effort.

[0509] As a concrete example, if a user's email address is being used on a fraudulent bulletin board, the system will immediately detect this fact, a notification will be displayed on the user's smartphone, and a request for removal will be automatically submitted to the bulletin board administrator. This system is built using Python and various AI technologies, employing the "requests" library for data collection and the "BeautifulSoup" library for information analysis.

[0510] The generative AI model is used to analyze and classify collected data, generating prompt messages to report the results of the information analysis. An example of such a prompt message is, "Please describe the process for identifying websites where user email addresses have been leaked and requesting their removal." This allows for the rapid management of information leaks and provides users with a secure internet environment.

[0511] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0512] Step 1:

[0513] The server collects data from various sources on the network. The input consists of keywords and URLs configured in the crawler, and the output is the content of the web pages retrieved. The server uses the "requests" library to access specific pages on the internet and retrieve their HTML content. This process accumulates data that meets the collection criteria.

[0514] Step 2:

[0515] The server analyzes the collected HTML data. The input is the web page content obtained in step 1, and the output is structured data about personal information and its relevance. The server uses the "BeautifulSoup" library to analyze the HTML and employs a generative AI model to determine the potential for personal data leakage from the text on the page. This allows for the identification of the personal information.

[0516] Step 3:

[0517] The server assesses the risk level based on the analysis results and prepares appropriate alerts. The input is the detection results of personal information, and the output is alert data with risk assessment. Machine learning models are used to determine the credibility and risk level of the data with greater accuracy. The server then develops notification methods based on the urgency of the situation.

[0518] Step 4:

[0519] The server sends a warning notification to the user's device based on the evaluation results. The input is the risk-assessed alert data, and the output is the notification message sent to the user's device. The server sends notifications via SMS, email, or a dedicated app to alert the user in real time. This allows the user to quickly consider countermeasures.

[0520] Step 5:

[0521] The server automatically submits a deletion request to the administrator of the source of the data breach. The input consists of the leaked data and a deletion request template, while the output is a formal deletion request addressed to the administrator. The server uses a standardized deletion request form to send messages to the relevant administrators, enabling immediate and efficient countermeasures.

[0522] Step 6:

[0523] The server records all processing and notification results and stores them in a database for future use. Input is a log of all system activity, and output is a detailed activity history. The server analyzes these records to improve the system and detect malfunctions. This strengthens the data analysis and monitoring system.

[0524] 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.

[0525] In embodiments of this invention, a client application installed on the user's device and a server located remotely function in combination. This system, including the emotion engine, aims to detect personal data breaches and, based on the results, evaluate the user's emotions and provide optimized responses.

[0526] Information gathering and analysis:

[0527] The server runs a web crawler to collect dangerous web pages related to user information from the internet and the dark web. The data obtained through this collection process is analyzed on the server using natural language processing and machine learning techniques to identify personal data breaches. Keywords and patterns specified by the user are utilized during this analysis stage.

[0528] Recognition of emotions:

[0529] Based on the analysis results, the server uses an emotion engine to infer the user's emotional state from their past responses and current device usage. This emotion recognition is performed, for example, to assess how much stress or anxiety the user is experiencing in response to risk information.

[0530] Notifications and response coordination:

[0531] After the emotion engine determines the user's emotions, the server selects an appropriate notification message based on the result. For example, if the user is feeling highly anxious, an encouraging notification using gentle language will be sent. Furthermore, if the user is determined to be calm, specific next steps will be provided to encourage action.

[0532] Deletion requests and records:

[0533] The system automatically submits deletion requests for any data breaches detected by the server and tracks their progress. All activities are recorded as logs, including user sentiment data. These records are used as an analytical foundation for future system improvements and increased prediction accuracy.

[0534] This invention aims to significantly improve the user experience not only by detecting the leakage of personal information, but also by enabling flexible responses that respond to the user's emotions. This is a modern means of living a safe digital life while protecting privacy.

[0535] The following describes the processing flow.

[0536] Step 1:

[0537] The server receives personal information sent from the user's device and updates the list of keywords to be monitored. Based on this list, it defines the internet areas to crawl and sets the next execution schedule for the web crawler.

[0538] Step 2:

[0539] The server launches a web crawler, which then traverses the configured internet and dark web areas. The crawler retrieves information from relevant pages, forums, and databases based on a keyword list.

[0540] Step 3:

[0541] The acquired information is immediately analyzed on the server. Natural language processing technology is used to identify potential leaks of user personal information and confidential data contained within the page. Based on the analysis results, the risk potential of each piece of information is scored.

[0542] Step 4:

[0543] The server uses an emotion engine to recognize the user's emotional state. It considers the user's past response history and device usage patterns to infer their current emotional state (e.g., anxiety, stress, calmness).

[0544] Step 5:

[0545] The server integrates the analysis results and the emotion engine's determination to generate an appropriate notification for the user. This notification is tailored to the user's emotional state. For example, if the user is feeling anxious, a reassuring message will be sent.

[0546] Step 6:

[0547] Based on notifications sent to users, the server automatically handles deletion requests. These deletion requests are sent to each site where the leaked information was detected, and the progress is tracked and recorded until the action is completed.

[0548] Step 7:

[0549] All processing results are recorded as logs on the server. This includes information gathering, analysis details, sentiment recognition results, notification details, and the progress of deletion actions. This data will be used for future improvement work and report generation.

[0550] (Example 2)

[0551] 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."

[0552] In modern society, data leaks on the internet are frequent, and the leakage of personal information has become a serious problem. While conventional systems are specialized in leak detection, they do not consider appropriate responses that take into account the user's emotional state. As a result, the psychological burden on users caused by information leaks may increase. Therefore, there is a need for methods that appropriately assess the user's emotional state and alleviate that burden.

[0553] 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.

[0554] In this invention, the server includes means for collecting data via an information recording device, means for analyzing the collected data using a generative model operating on a computing device to detect personal information leaks, and means for using an engine to evaluate emotional states according to the detected analysis results. This enables not only a rapid response to personal information leaks but also a flexible response that is sensitive to the user's emotions.

[0555] An "information recording device" is a device that has the function of collecting data and storing it temporarily or permanently for later processing purposes.

[0556] A "generative model" is a mathematical model that uses artificial intelligence technology to learn patterns from large amounts of data and perform analysis and predictions.

[0557] A "processing unit" is a general term for the hardware and software used to perform data processing and calculations.

[0558] "Emotional state" refers to the user's psychological response or emotional state, and is information that the system determines.

[0559] An "engine" is a software or hardware component designed to perform a specific function.

[0560] "Communication" is the act or process of sending and receiving information from one party to another.

[0561] A "removal request" is a formal request made to a relevant service or platform to remove inappropriate or inaccurate data found on the internet.

[0562] This invention is realized through the collaboration of client software installed on the user's electronic device and a remote data processing device. Specific embodiments are described below.

[0563] The server utilizes information gathering devices to find user-related data from the internet and the dark web. Specifically, it operates multiple automated crawling tools built using programming languages ​​to collect dangerous information based on pre-configured keywords. These crawlers, connected to the computer network in real time, quickly and accurately search for relevant information from large databases.

[0564] After data collection, the computing unit on the server runs a generative AI model to analyze the collected information. This model uses advanced natural language processing techniques to scrutinize content that may indicate a personal data breach. For example, the generative AI model has the ability to detect personal data patterns such as names, addresses, and financial information, and also identifies risk levels according to the type of breach.

[0565] Furthermore, the server implements an emotion recognition engine to estimate emotional states. This engine analyzes the user's past device usage history and current activity to assess the psychological impact of a data breach on the user. Emotions such as stress and anxiety are quantified based on the analysis results, and priorities for response are set accordingly.

[0566] During the notification phase, the server considers the generated emotional data and sends the most relevant information to the user's device. For example, if a user is showing high levels of anxiety, a reassuring message is created and sent quickly to alleviate the user's mental burden.

[0567] Furthermore, if a data breach is detected, the server automatically sends a deletion request and records the results sequentially. All activities are systematically stored as logs in the database, forming the basis for future analysis and optimization.

[0568] An example of a prompt message is: "Analyze web pages that pose a risk of personal information leakage, and based on the results, recognize user sentiment and consider appropriate actions."

[0569] This system aims to provide a modern solution that protects users' personal information while also considering their mental well-being. By implementing this system, users can enjoy a safe and secure internet environment.

[0570] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0571] Step 1:

[0572] The server collects information from the internet and the dark web. The input is a pre-configured keyword, and the server uses an automated crawling tool to search for data based on that keyword. The crawling tool collects data relevant to the keyword from the internet and sends it to the server. The output is a list of collected web pages and their associated information. Specifically, the server activates the crawler at regular intervals and scans the configured area.

[0573] Step 2:

[0574] The server analyzes the collected information. The input is the web page information obtained in Step 1. The server activates a generative AI model and performs the analysis. This analysis uses natural language processing to verify whether it contains any leaked personal information. The output is a list of potentially leaked data. Specifically, the server processes the data through the generative AI model and assigns labels according to their importance.

[0575] Step 3:

[0576] The server evaluates the user's emotional state. The inputs are the analysis results obtained in step 2 and the user's past device usage history. The server uses an emotion recognition engine to evaluate the user's emotional state, inferred from the analysis results. The output is numerical data representing the emotional evaluation result. Specific operations include comparing past stress response data with current device usage data.

[0577] Step 4:

[0578] The server generates a notification message and sends it to the terminal. The input is the sentiment evaluation result from step 3. Based on the evaluation result, the server selects a notification message with an appropriate tone and content and sends it to the user's terminal. The output is the message the user receives. Specifically, the server selects a message template based on sentiment data, customizes the message content, and sends it.

[0579] Step 5:

[0580] The server automatically submits a deletion request for the leaked information. The input is the leaked data identified by the analysis results in step 2. The server sends a deletion request to the relevant website or service. The output is log data indicating that the deletion request was sent. The specific operation includes a process of generating a standardized request format for the deletion request and sending it to the appropriate recipient.

[0581] This series of steps creates a system that provides peace of mind in terms of both protecting personal information and ensuring the mental health of users.

[0582] (Application Example 2)

[0583] 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."

[0584] In today's digital society, the leakage of personal data and the resulting anxiety and stress on users are major challenges. In particular, the user experience can be impaired when the response after a leak is detected does not adapt to the emotional state of individual users. As a result, users may not receive sufficient support in deciding on their course of action. To solve this problem, it is necessary not only to detect personal data leaks but also to provide appropriate notifications and support based on the user's emotional state.

[0585] 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.

[0586] In this invention, the server includes means for collecting information via a network, means for analyzing the collected information to detect personal data leaks, means for automatically sending notifications based on the detection results, means for inferring the user's emotional state and adjusting the notification content based on the inference, and means for providing security instructions based on the user's emotional state. This enables flexible responses to users according to their individual emotional states, allowing them to live their digital lives with peace of mind.

[0587] A "device that collects information via a network" is a device that acquires data from an external source using the internet or other communication networks.

[0588] A "device that analyzes collected information to detect personal data leaks" is a device that performs analytical processing on acquired data and identifies instances of unauthorized use or leakage of personal information.

[0589] A "device that automatically sends notifications based on detection results" is a device that automatically issues alerts and notifications based on information obtained as a result of analysis.

[0590] A "device for submitting deletion requests for leaked information" is a device that manages the procedures for requesting the deletion of identified leaked information from relevant parties.

[0591] A "device for recording all actions related to detection and deletion requests" is a device that tracks and maintains records of various processes performed within the system and their results.

[0592] A "device that infers the user's emotional state and adjusts notification content based on that inference" is a device that infers the user's emotional state from their behavioral data and analysis results, and generates an appropriate notification message according to that situation.

[0593] A "device that provides security instructions based on the user's emotional state" is a device that instructs or suggests the user on the most appropriate security measures according to their estimated emotional state.

[0594] To implement this invention, a client application is installed on the user's mobile device and configured to interact with a server. The server implements a web crawler to collect data via the internet or other communication networks. The collected information is analyzed on the server side using natural language processing and machine learning techniques. This analysis allows for the detection of personal data breaches.

[0595] The server is written in Python, and libraries such as spaCy and scikit-learn are used for data analysis. To infer the user's emotional state, it makes inferences based on past user responses and device usage, and sentiment analysis tools such as NLTK are used for this inference.

[0596] Based on the results of emotion recognition, the server generates notification content that is tailored to the user's current mental state. This notification may include encouraging words or specific action guidelines.

[0597] For example, if a user is feeling very anxious due to a fraudulent credit card transaction, the app will send a notification such as "Emergency Response Guide: Please contact your card company first. We are here to help if you need assistance," to provide reassurance.

[0598] The server also automatically submits deletion requests for identified leaked information and records the progress of these requests. This record serves as a foundation for future system improvements and increased prediction accuracy.

[0599] An example of a prompt using a generative AI model is, "Create a security notification that takes the user's emotional state into account: {emotional state}". In this way, flexible responses that take user emotions into account become possible, providing a better user experience.

[0600] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0601] Step 1:

[0602] The server collects data from the internet and the dark web using a web crawler. It takes a list of URLs as input and raw data of the associated web pages as output. This data is used in the next analysis step.

[0603] Step 2:

[0604] The server analyzes the collected raw data. It takes raw data as input and generates structured data using natural language processing techniques. spaCy is used for data analysis, identifying patterns indicating personal data breaches and obtaining analysis results as output. These results include the likelihood and risk level of a breach.

[0605] Step 3:

[0606] The server infers the user's emotional state based on the analysis results. It receives the analysis results and the user's past usage data as input, and uses the emotion analysis tool NLTK to evaluate the user's feelings. The output is an emotional state (e.g., anxious, calm).

[0607] Step 4:

[0608] The server generates notification content based on the user's emotional state. It uses the predicted emotional state as input and a generation AI model to create prompt messages. Specifically, it generates notifications that offer encouragement and suggest specific actions based on the user's state, and outputs a refined notification message.

[0609] Step 5:

[0610] The device delivers generated notifications to the user. It receives notification messages from the server as input and displays them on the user's screen in an appropriate format as output. The device displays these notifications and, if necessary, records the user's response.

[0611] Step 6:

[0612] The server automatically submits a deletion request for any leaked information detected. It uses the identification data of the leaked information as input to initiate the deletion request process. This generates a log recording the progress of the deletion request as output.

[0613] Step 7:

[0614] The server records all activities, accumulating foundational data for future improvements. It receives result data from each step as input and generates log files for system improvement as output. These log files also include user feedback and sentiment data, which contribute to future model improvements.

[0615] 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.

[0616] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0617] 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.

[0618] [Fourth Embodiment]

[0619] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0620] 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.

[0621] 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).

[0622] 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.

[0623] 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.

[0624] 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).

[0625] 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.

[0626] 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.

[0627] 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.

[0628] 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.

[0629] 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.

[0630] 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.

[0631] 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".

[0632] As an embodiment of this invention, a software system operated over a network has been devised. Its specific operation is described below.

[0633] Information gathering:

[0634] The server runs a web crawler to traverse specific areas of the internet and the dark web. The crawler uses keywords such as the user's registered personal information, including their name, email address, and credit card information, to automatically collect pages containing relevant information.

[0635] Information analysis:

[0636] The collected data is analyzed by a server. This analysis uses natural language processing techniques to identify whether there is any leakage of personal data within the text. For example, it can detect if an email address is being used in an inappropriate context. Furthermore, machine learning algorithms can be used to assess the credibility and risk level of the data. This process is performed in real time, supporting rapid decision-making.

[0637] Notifications and deletion requests:

[0638] Based on the analysis results, if the server determines there is a risk, it will immediately notify the user's device with a warning. The notification will be sent via email, SMS, or a dedicated application to alert the user. Furthermore, the server will automatically submit a deletion request to the site administrator. This will be done using an email clearly stating the information to be deleted or a standardized deletion request form.

[0639] Activity log and report generation:

[0640] The server meticulously records all program processes and stores them as activity logs. These logs are used for future audits and to improve countermeasures. The server also periodically provides users with reports detailing the analysis and response results, allowing users to verify the level of protection their information receives.

[0641] This system is designed to quickly detect personal data breaches and provide users with a secure environment for using the internet. Furthermore, continuous monitoring and automated responses allow for effortless protection of personal information.

[0642] The following describes the processing flow.

[0643] Step 1:

[0644] The server launches a web crawler according to a network schedule. This crawler searches for target URLs based on a pre-registered list of users' personal information and automatically collects pages from the internet and the dark web. This collects page data that may contain personal information.

[0645] Step 2:

[0646] The server analyzes the collected web page data. It utilizes natural language processing techniques to search for traces of personal information within the text of the page. During this process, keyword detection and pattern matching are used to immediately determine whether or not personal data has been leaked.

[0647] Step 3:

[0648] The server classifies the discovered information based on the analysis results and scores its risk level. Information with a particularly high risk of leakage is assigned a high score, prioritizing immediate response. This information is recorded in the log database and used for later analysis and reporting.

[0649] Step 4:

[0650] The server sends a notification to the user's device based on the risk score. The notification includes a summary of the discovered leaked information and recommended countermeasures, prompting the user to take immediate action.

[0651] Step 5:

[0652] The server automatically issues a takedown request to the site hosting the leaked information. The request includes details about the problem and is prepared in accordance with any necessary legal requirements. The server tracks the progress of the response after the request is submitted and records completion notifications.

[0653] Step 6:

[0654] The server comprehensively logs all activities, including collection, analysis, notification, and deletion requests. The server periodically generates reports for users based on these logs, providing transparency and reliability to the system.

[0655] (Example 1)

[0656] 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".

[0657] In today's digital society, data breaches are frequent, leading to increased security risks. Existing solutions lack real-time capabilities and automation, making it difficult to accurately understand how data breaches occur and the level of risk involved. Furthermore, there are limited means to respond quickly and effectively when a breach is detected. There is a need for efficient systems to address these challenges.

[0658] 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.

[0659] In this invention, the server includes means for collecting information via digital communication, means for analyzing the collected data to identify leaks of personal information, and means for automatically transmitting the results based on the analysis using communication means. This enables the identification of leaks of personal information in real time and allows for a rapid response.

[0660] "Digital communication" is a technology that uses computer networks to send and receive data.

[0661] "Means of collecting information" refers to methods and devices for acquiring and storing data, and in particular includes software such as web crawlers.

[0662] "Personal information" refers to information that can identify a specific individual, and includes names, email addresses, credit card information, and so on.

[0663] "Means of analyzing data to identify personal information leaks" refers to the process of determining whether or not a leak has occurred using statistical or machine learning methods on collected data.

[0664] A "machine learning algorithm for assessing risk" refers to a computational model that quantifies risk based on collected data and measures the urgency of a data breach.

[0665] "Means of transmission using communication means" refers to the processes and technologies for transmitting information to other devices or people via the internet or telephone networks.

[0666] A "removal request" refers to an official request to remove information that has been inappropriately published.

[0667] "Means of recording actions" refers to techniques or procedures for saving operations and processes performed within a system as logs.

[0668] To implement this invention, a software system operating on a network is used. A server plays a central role in this system, coordinating each component to collect, analyze, notify, request deletions from, and record information.

[0669] First, the server launches a web crawler implemented in Python to collect information. This crawler traverses the internet and specific online areas, searching for keywords that have been pre-registered as the user's personal information. The data collected by the crawler is securely stored on the server using database software.

[0670] Next, a natural language processing library (e.g., SpaCy) is used to analyze the data collected by the server. This makes it possible to identify locations within the text where personal information has been leaked. Based on these analysis results, a machine learning algorithm utilizing TensorFlow is used to assess the level of risk.

[0671] If a high risk is detected, the server automatically sends a warning to the user's device. This communication utilizes email and messaging services. Furthermore, based on the discovered leaked information, the server requests the relevant website administrator to remove the content. Standardized email and online forms are used for this purpose.

[0672] As an activity record, the server saves all program processing data to a log management system such as Elasticsearch. This allows for future audits and improvements to response procedures.

[0673] As a concrete example, consider the response when a user detects that their email address has been inappropriately published. In this case, the server immediately sends a warning message to the user's device and sends a request to the administrator of the relevant website to remove the information. This allows the user to quickly understand the information leak and take appropriate action.

[0674] An example of a prompt message for a generating AI model is: "Please verify that my data is secure and issue a warning if there is a possibility of leakage. Also, please submit an appropriate deletion request."

[0675] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0676] Step 1:

[0677] The server launches a web crawler to collect information over the internet. The crawler inputs the user's personal information (e.g., name, email address) as keywords and visits web pages containing relevant information based on those keywords. The resulting output is collected as HTML data and sent to the server.

[0678] Step 2:

[0679] The server parses the collected HTML data to extract personal information. A data analysis library is used for the analysis, taking the collected web page content as input. The BeautifulSoup library is used to apply patterns of personal information (e.g., regular expressions for email addresses) to extract the data. The output is a list of the extracted personal information.

[0680] Step 3:

[0681] The server stores the extracted personal information in a database and prepares it for analysis. During storage, a timestamp is added to the extracted data as input, and the data is stored in a database such as MongoDB. The output is a database containing a record of searchable personal information.

[0682] Step 4:

[0683] The server analyzes the data using natural language processing techniques. It processes a list of personal information stored as input, analyzes the text context using the SpaCy library, and assesses the potential for data leakage. The output consists of data items identified as being at risk of leakage and their contextual information.

[0684] Step 5:

[0685] The server uses a machine learning algorithm to evaluate the credibility and risk level of the data. The data analysis is performed using a TensorFlow model with the leak risk information identified in the previous step as input data. The output is an evaluation result where the risk level is expressed numerically.

[0686] Step 6:

[0687] The server sends a warning to the user's device based on the risk assessment results. It generates a warning message based on the assessment results, according to the level of urgency. The output is a notification sent to the user via email or SMS.

[0688] Step 7:

[0689] The server automatically initiates a removal request for any leaked information detected. The input includes details of the leaked information and contact information for the affected website. The output is an official request, including the removal request, sent to the site administrator.

[0690] Step 8:

[0691] The server meticulously records all processes and saves them as digital logs. The input includes activity logs for each step, which are stored using Elasticsearch. The output is an activity record available for later auditing and enhancement.

[0692] (Application Example 1)

[0693] 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".

[0694] In today's world, where personal data breaches occur on a daily basis, there is a need to quickly and accurately detect breaches and notify users. However, conventional methods have limitations in the accuracy of information analysis and the timing of notifications, making it difficult for users to respond quickly to personal data breaches. Furthermore, there is no system to automatically submit appropriate deletion requests to the source of the breach, making it difficult to prevent secondary damage to personal data.

[0695] 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.

[0696] In this invention, the server includes means for collecting information via a network, means for analyzing the collected information to detect personal data leaks, means for automatically sending notifications based on the detection results, means for automatically sending notifications to the administrator regarding the source of the leak, means for classifying and analyzing information using a generative AI model, and means for generating prompt messages to report the results of the information analysis. This enables the rapid identification of personal data leaks in real time, allowing users to take appropriate measures.

[0697] "Means of collecting information via a network" refers to technologies that use the internet or other communication networks to automatically acquire target information.

[0698] "Methods for analyzing collected information to detect personal data breaches" refer to processing technologies that scrutinize collected data and determine, in particular, whether personal information is being handled improperly.

[0699] "Means of automatically sending notifications based on detection results" refers to an automated system that immediately sends warnings and information to users when a data breach of personal information is confirmed.

[0700] "Methods for requesting the deletion of information that has been confirmed to have been leaked" refers to methods for automating the process of requesting relevant parties and administrators to delete the discovered leaked information.

[0701] "Means of recording all detection and deletion request activities" refers to technology that records and stores the entire process from the detection to the remediation of a data breach, and is used for future audits and improvements.

[0702] "Means of sending warnings to the user's device in order to provide real-time notifications to the user" refers to a technology that prompts immediate action by sending a warning to the user's device at the moment of a data breach.

[0703] "A means of automatically sending notifications to administrators regarding the source of a data leak" refers to a system that automatically sends warning messages or requests for deletion to administrators who handle the data that caused the leak.

[0704] "Methods for classifying and analyzing information using generative AI models" refer to methods that utilize machine learning and AI technologies to efficiently analyze large amounts of information and evaluate risk levels and reliability.

[0705] "A means of generating prompt messages and reporting information analysis results" refers to a technology that automatically creates text to clearly communicate analysis results to users and stakeholders.

[0706] The system that realizes this application example is implemented by building an information security system centered on a server. The server first runs dedicated crawling software to collect data from a wide range of sources via the network. This crawler traverses specific areas of the public and dark web, and stores the collected data in a database on the server.

[0707] The collected data is analyzed by a natural language processing (NLP) system and generative AI models on the server. This analysis determines whether or not a personal data breach has occurred, and if detected, the risk level is promptly assessed.

[0708] Furthermore, the server sends a warning to the user's device based on the evaluation results. This warning is designed to be sent in real time to the user's smartphone or other personal devices. If a data breach is confirmed, the server automatically requests deletion from the administrator of the source of the breach. This ensures that users' personal information is protected without unnecessary time or effort.

[0709] As a concrete example, if a user's email address is being used on a fraudulent bulletin board, the system will immediately detect this fact, a notification will be displayed on the user's smartphone, and a request for removal will be automatically submitted to the bulletin board administrator. This system is built using Python and various AI technologies, employing the "requests" library for data collection and the "BeautifulSoup" library for information analysis.

[0710] The generative AI model is used to analyze and classify collected data, generating prompt messages to report the results of the information analysis. An example of such a prompt message is, "Please describe the process for identifying websites where user email addresses have been leaked and requesting their removal." This allows for the rapid management of information leaks and provides users with a secure internet environment.

[0711] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0712] Step 1:

[0713] The server collects data from various sources on the network. The input consists of keywords and URLs configured in the crawler, and the output is the content of the web pages retrieved. The server uses the "requests" library to access specific pages on the internet and retrieve their HTML content. This process accumulates data that meets the collection criteria.

[0714] Step 2:

[0715] The server analyzes the collected HTML data. The input is the web page content obtained in step 1, and the output is structured data about personal information and its relevance. The server uses the "BeautifulSoup" library to analyze the HTML and employs a generative AI model to determine the potential for personal data leakage from the text on the page. This allows for the identification of the personal information.

[0716] Step 3:

[0717] The server assesses the risk level based on the analysis results and prepares appropriate alerts. The input is the detection results of personal information, and the output is alert data with risk assessment. Machine learning models are used to determine the credibility and risk level of the data with greater accuracy. The server then develops notification methods based on the urgency of the situation.

[0718] Step 4:

[0719] The server sends a warning notification to the user's device based on the evaluation results. The input is the risk-assessed alert data, and the output is the notification message sent to the user's device. The server sends notifications via SMS, email, or a dedicated app to alert the user in real time. This allows the user to quickly consider countermeasures.

[0720] Step 5:

[0721] The server automatically submits a deletion request to the administrator of the source of the data breach. The input consists of the leaked data and a deletion request template, while the output is a formal deletion request addressed to the administrator. The server uses a standardized deletion request form to send messages to the relevant administrators, enabling immediate and efficient countermeasures.

[0722] Step 6:

[0723] The server records all processing and notification results and stores them in a database for future use. Input is a log of all system activity, and output is a detailed activity history. The server analyzes these records to improve the system and detect malfunctions. This strengthens the data analysis and monitoring system.

[0724] 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.

[0725] In embodiments of this invention, a client application installed on the user's device and a server located remotely function in combination. This system, including the emotion engine, aims to detect personal data breaches and, based on the results, evaluate the user's emotions and provide optimized responses.

[0726] Information gathering and analysis:

[0727] The server runs a web crawler to collect dangerous web pages related to user information from the internet and the dark web. The data obtained through this collection process is analyzed on the server using natural language processing and machine learning techniques to identify personal data breaches. Keywords and patterns specified by the user are utilized during this analysis stage.

[0728] Recognition of emotions:

[0729] Based on the analysis results, the server uses an emotion engine to infer the user's emotional state from their past responses and current device usage. This emotion recognition is performed, for example, to assess how much stress or anxiety the user is experiencing in response to risk information.

[0730] Notifications and response coordination:

[0731] After the emotion engine determines the user's emotions, the server selects an appropriate notification message based on the result. For example, if the user is feeling highly anxious, an encouraging notification using gentle language will be sent. Furthermore, if the user is determined to be calm, specific next steps will be provided to encourage action.

[0732] Deletion requests and records:

[0733] The system automatically submits deletion requests for any data breaches detected by the server and tracks their progress. All activities are recorded as logs, including user sentiment data. These records are used as an analytical foundation for future system improvements and increased prediction accuracy.

[0734] This invention aims to significantly improve the user experience not only by detecting the leakage of personal information, but also by enabling flexible responses that respond to the user's emotions. This is a modern means of living a safe digital life while protecting privacy.

[0735] The following describes the processing flow.

[0736] Step 1:

[0737] The server receives personal information sent from the user's device and updates the list of keywords to be monitored. Based on this list, it defines the internet areas to crawl and sets the next execution schedule for the web crawler.

[0738] Step 2:

[0739] The server launches a web crawler, which then traverses the configured internet and dark web areas. The crawler retrieves information from relevant pages, forums, and databases based on a keyword list.

[0740] Step 3:

[0741] The acquired information is immediately analyzed on the server. Natural language processing technology is used to identify potential leaks of user personal information and confidential data contained within the page. Based on the analysis results, the risk potential of each piece of information is scored.

[0742] Step 4:

[0743] The server uses an emotion engine to recognize the user's emotional state. It considers the user's past response history and device usage patterns to infer their current emotional state (e.g., anxiety, stress, calmness).

[0744] Step 5:

[0745] The server integrates the analysis results and the emotion engine's determination to generate an appropriate notification for the user. This notification is tailored to the user's emotional state. For example, if the user is feeling anxious, a reassuring message will be sent.

[0746] Step 6:

[0747] Based on notifications sent to users, the server automatically handles deletion requests. These deletion requests are sent to each site where the leaked information was detected, and the progress is tracked and recorded until the action is completed.

[0748] Step 7:

[0749] All processing results are recorded as logs on the server. This includes information gathering, analysis details, sentiment recognition results, notification details, and the progress of deletion actions. This data will be used for future improvement work and report generation.

[0750] (Example 2)

[0751] 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".

[0752] In modern society, data leaks on the internet are frequent, and the leakage of personal information has become a serious problem. While conventional systems are specialized in leak detection, they do not consider appropriate responses that take into account the user's emotional state. As a result, the psychological burden on users caused by information leaks may increase. Therefore, there is a need for methods that appropriately assess the user's emotional state and alleviate that burden.

[0753] 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.

[0754] In this invention, the server includes means for collecting data via an information recording device, means for analyzing the collected data using a generative model operating on a computing device to detect personal information leaks, and means for using an engine to evaluate emotional states according to the detected analysis results. This enables not only a rapid response to personal information leaks but also a flexible response that is sensitive to the user's emotions.

[0755] An "information recording device" is a device that has the function of collecting data and storing it temporarily or permanently for later processing purposes.

[0756] A "generative model" is a mathematical model that uses artificial intelligence technology to learn patterns from large amounts of data and perform analysis and predictions.

[0757] A "processing unit" is a general term for the hardware and software used to perform data processing and calculations.

[0758] "Emotional state" refers to the user's psychological response or emotional state, and is information that the system determines.

[0759] An "engine" is a software or hardware component designed to perform a specific function.

[0760] "Communication" is the act or process of sending and receiving information from one party to another.

[0761] A "removal request" is a formal request made to a relevant service or platform to remove inappropriate or inaccurate data found on the internet.

[0762] This invention is realized through the collaboration of client software installed on the user's electronic device and a remote data processing device. Specific embodiments are described below.

[0763] The server utilizes information gathering devices to find user-related data from the internet and the dark web. Specifically, it operates multiple automated crawling tools built using programming languages ​​to collect dangerous information based on pre-configured keywords. These crawlers, connected to the computer network in real time, quickly and accurately search for relevant information from large databases.

[0764] After data collection, the computing unit on the server runs a generative AI model to analyze the collected information. This model uses advanced natural language processing techniques to scrutinize content that may indicate a personal data breach. For example, the generative AI model has the ability to detect personal data patterns such as names, addresses, and financial information, and also identifies risk levels according to the type of breach.

[0765] Furthermore, the server implements an emotion recognition engine to estimate emotional states. This engine analyzes the user's past device usage history and current activity to assess the psychological impact of a data breach on the user. Emotions such as stress and anxiety are quantified based on the analysis results, and priorities for response are set accordingly.

[0766] During the notification phase, the server considers the generated emotional data and sends the most relevant information to the user's device. For example, if a user is showing high levels of anxiety, a reassuring message is created and sent quickly to alleviate the user's mental burden.

[0767] Furthermore, if a data breach is detected, the server automatically sends a deletion request and records the results sequentially. All activities are systematically stored as logs in the database, forming the basis for future analysis and optimization.

[0768] An example of a prompt message is: "Analyze web pages that pose a risk of personal information leakage, and based on the results, recognize user sentiment and consider appropriate actions."

[0769] This system aims to provide a modern solution that protects users' personal information while also considering their mental well-being. By implementing this system, users can enjoy a safe and secure internet environment.

[0770] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0771] Step 1:

[0772] The server collects information from the internet and the dark web. The input is a pre-configured keyword, and the server uses an automated crawling tool to search for data based on that keyword. The crawling tool collects data relevant to the keyword from the internet and sends it to the server. The output is a list of collected web pages and their associated information. Specifically, the server activates the crawler at regular intervals and scans the configured area.

[0773] Step 2:

[0774] The server analyzes the collected information. The input is the web page information obtained in Step 1. The server activates a generative AI model and performs the analysis. This analysis uses natural language processing to verify whether it contains any leaked personal information. The output is a list of potentially leaked data. Specifically, the server processes the data through the generative AI model and assigns labels according to their importance.

[0775] Step 3:

[0776] The server evaluates the user's emotional state. The inputs are the analysis results obtained in step 2 and the user's past device usage history. The server uses an emotion recognition engine to evaluate the user's emotional state, inferred from the analysis results. The output is numerical data representing the emotional evaluation result. Specific operations include comparing past stress response data with current device usage data.

[0777] Step 4:

[0778] The server generates a notification message and sends it to the terminal. The input is the sentiment evaluation result from step 3. Based on the evaluation result, the server selects a notification message with an appropriate tone and content and sends it to the user's terminal. The output is the message the user receives. Specifically, the server selects a message template based on sentiment data, customizes the message content, and sends it.

[0779] Step 5:

[0780] The server automatically submits a deletion request for the leaked information. The input is the leaked data identified by the analysis results in step 2. The server sends a deletion request to the relevant website or service. The output is log data indicating that the deletion request was sent. The specific operation includes a process of generating a standardized request format for the deletion request and sending it to the appropriate recipient.

[0781] This series of steps creates a system that provides peace of mind in terms of both protecting personal information and ensuring the mental health of users.

[0782] (Application Example 2)

[0783] 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".

[0784] In today's digital society, the leakage of personal data and the resulting anxiety and stress on users are major challenges. In particular, the user experience can be impaired when the response after a leak is detected does not adapt to the emotional state of individual users. As a result, users may not receive sufficient support in deciding on their course of action. To solve this problem, it is necessary not only to detect personal data leaks but also to provide appropriate notifications and support based on the user's emotional state.

[0785] 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.

[0786] In this invention, the server includes means for collecting information via a network, means for analyzing the collected information to detect personal data leaks, means for automatically sending notifications based on the detection results, means for inferring the user's emotional state and adjusting the notification content based on the inference, and means for providing security instructions based on the user's emotional state. This enables flexible responses to users according to their individual emotional states, allowing them to live their digital lives with peace of mind.

[0787] A "device that collects information via a network" is a device that acquires data from an external source using the internet or other communication networks.

[0788] A "device that analyzes collected information to detect personal data leaks" is a device that performs analytical processing on acquired data and identifies instances of unauthorized use or leakage of personal information.

[0789] A "device that automatically sends notifications based on detection results" is a device that automatically issues alerts and notifications based on information obtained as a result of analysis.

[0790] A "device for submitting deletion requests for leaked information" is a device that manages the procedures for requesting the deletion of identified leaked information from relevant parties.

[0791] A "device for recording all actions related to detection and deletion requests" is a device that tracks and maintains records of various processes performed within the system and their results.

[0792] A "device that infers the user's emotional state and adjusts notification content based on that inference" is a device that infers the user's emotional state from their behavioral data and analysis results, and generates an appropriate notification message according to that situation.

[0793] A "device that provides security instructions based on the user's emotional state" is a device that instructs or suggests the user on the most appropriate security measures according to their estimated emotional state.

[0794] To implement this invention, a client application is installed on the user's mobile device and configured to interact with a server. The server implements a web crawler to collect data via the internet or other communication networks. The collected information is analyzed on the server side using natural language processing and machine learning techniques. This analysis allows for the detection of personal data breaches.

[0795] The server is written in Python, and libraries such as spaCy and scikit-learn are used for data analysis. To infer the user's emotional state, it makes inferences based on past user responses and device usage, and sentiment analysis tools such as NLTK are used for this inference.

[0796] Based on the results of emotion recognition, the server generates notification content that is tailored to the user's current mental state. This notification may include encouraging words or specific action guidelines.

[0797] For example, if a user is feeling very anxious due to a fraudulent credit card transaction, the app will send a notification such as "Emergency Response Guide: Please contact your card company first. We are here to help if you need assistance," to provide reassurance.

[0798] The server also automatically submits deletion requests for identified leaked information and records the progress of these requests. This record serves as a foundation for future system improvements and increased prediction accuracy.

[0799] An example of a prompt using a generative AI model is, "Create a security notification that takes the user's emotional state into account: {emotional state}". In this way, flexible responses that take user emotions into account become possible, providing a better user experience.

[0800] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0801] Step 1:

[0802] The server collects data from the internet and the dark web using a web crawler. It takes a list of URLs as input and raw data of the associated web pages as output. This data is used in the next analysis step.

[0803] Step 2:

[0804] The server analyzes the collected raw data. It takes raw data as input and generates structured data using natural language processing techniques. spaCy is used for data analysis, identifying patterns indicating personal data breaches and obtaining analysis results as output. These results include the likelihood and risk level of a breach.

[0805] Step 3:

[0806] The server infers the user's emotional state based on the analysis results. It receives the analysis results and the user's past usage data as input, and uses the emotion analysis tool NLTK to evaluate the user's feelings. The output is an emotional state (e.g., anxious, calm).

[0807] Step 4:

[0808] The server generates notification content based on the user's emotional state. It uses the predicted emotional state as input and a generation AI model to create prompt messages. Specifically, it generates notifications that offer encouragement and suggest specific actions based on the user's state, and outputs a refined notification message.

[0809] Step 5:

[0810] The device delivers generated notifications to the user. It receives notification messages from the server as input and displays them on the user's screen in an appropriate format as output. The device displays these notifications and, if necessary, records the user's response.

[0811] Step 6:

[0812] The server automatically submits a deletion request for any leaked information detected. It uses the identification data of the leaked information as input to initiate the deletion request process. This generates a log recording the progress of the deletion request as output.

[0813] Step 7:

[0814] The server records all activities, accumulating foundational data for future improvements. It receives result data from each step as input and generates log files for system improvement as output. These log files also include user feedback and sentiment data, which contribute to future model improvements.

[0815] 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.

[0816] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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."

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] The following is further disclosed regarding the embodiments described above.

[0837] (Claim 1)

[0838] Means of collecting information via a network,

[0839] A means of analyzing collected information to detect the leakage of personal data,

[0840] A means of automatically sending notifications based on the detection results,

[0841] A means of requesting the deletion of information that has been confirmed to have been leaked,

[0842] A system that includes means of recording all detection and deletion request activities.

[0843] (Claim 2)

[0844] The system according to claim 1, comprising means for using predefined keywords or patterns to monitor for the leakage of personal data.

[0845] (Claim 3)

[0846] The system according to claim 1, comprising means for evaluating the risk level of detected leaked information and setting the urgency of an alert based on the evaluation result.

[0847] "Example 1"

[0848] (Claim 1)

[0849] Means of collecting information via digital communication,

[0850] A means of analyzing the collected data to identify the leakage of personal information,

[0851] A means for automatically transmitting the results based on the analysis using a communication means,

[0852] Means for issuing a removal request for information that has been identified as having been leaked,

[0853] Means for recording all operations of detection and removal requests,

[0854] A system that includes means of using machine learning algorithms to evaluate the credibility and risk level of data.

[0855] (Claim 2)

[0856] The system according to claim 1, comprising means for using pre-set recognition words or patterns and means for analyzing specific elements within a text using natural language processing technology, in order to monitor for the leakage of personal information.

[0857] (Claim 3)

[0858] The system according to claim 1, comprising means for setting warning priorities based on the risk level of the evaluated leaked information and sending notifications to the user.

[0859] "Application Example 1"

[0860] (Claim 1)

[0861] Means of collecting information via a network,

[0862] A means of analyzing collected information to detect the leakage of personal data,

[0863] A means of automatically sending notifications based on the detection results,

[0864] A means of requesting the deletion of information that has been confirmed to have been leaked,

[0865] A means of recording all detection and deletion request activities,

[0866] In order to provide real-time notifications to users, means for transmitting warnings to the device,

[0867] A means of automatically sending a notification to the administrator regarding the source of the leak,

[0868] A means of classifying and analyzing information using a generative AI model,

[0869] A system that includes means for generating prompt statements and reporting the results of information analysis.

[0870] (Claim 2)

[0871] The system according to claim 1, comprising means for using predefined keywords or patterns to monitor for the leakage of personal data.

[0872] (Claim 3)

[0873] The system according to claim 1, comprising means for evaluating the risk level of detected leaked information and setting the urgency of an alert based on the evaluation result.

[0874] "Example 2 of combining an emotion engine"

[0875] (Claim 1)

[0876] Means for collecting data via an information recording device,

[0877] A means for detecting the leakage of personal information by analyzing data collected using a generative model that operates on a computing device,

[0878] A means of using an engine that evaluates emotional state according to the detected analysis results,

[0879] A means for transmitting appropriate communication to a terminal device based on the evaluation results,

[0880] A means of automatically requesting the deletion of confirmed leaked information,

[0881] A system that includes means for recording all of these processing activities and emotional data.

[0882] (Claim 2)

[0883] The system according to claim 1, comprising means for using pre-set words or forms to monitor for the leakage of personal information.

[0884] (Claim 3)

[0885] The system according to claim 1, comprising means for evaluating the level of response to detected leaked information and setting the urgency of the communication content based on the evaluation result.

[0886] "Application example 2 when combining with an emotional engine"

[0887] (Claim 1)

[0888] A device that collects information via a network,

[0889] A device that analyzes collected information to detect the leakage of personal data,

[0890] A device that automatically sends notifications based on detection results,

[0891] A device for submitting deletion requests for information that has been confirmed to have been leaked,

[0892] A device that records all actions related to detection and deletion requests,

[0893] A device that infers the user's emotional state and adjusts the notification content based on that inference,

[0894] A device that provides security instructions based on the user's emotional state,

[0895] A system that includes this.

[0896] (Claim 2)

[0897] The system according to claim 1, comprising a device that uses a predefined identifier or pattern to monitor for the leakage of personal data.

[0898] (Claim 3)

[0899] The system according to claim 1, comprising a device for evaluating the risk level of detected leaked information and setting an emergency level based on that evaluation. [Explanation of symbols]

[0900] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of collecting information via a network, A means of analyzing collected information to detect the leakage of personal data, A means of automatically sending notifications based on the detection results, A means of requesting the deletion of information that has been confirmed to have been leaked, A means of recording all detection and deletion request activities, In order to provide real-time notifications to users, means for transmitting warnings to the device, A means of automatically sending a notification to the administrator regarding the source of the leak, A means of classifying and analyzing information using a generative AI model, A system that includes means for generating prompt statements and reporting the results of information analysis.

2. The system according to claim 1, comprising means for using predefined keywords or patterns to monitor for the leakage of personal data.

3. The system according to claim 1, comprising means for evaluating the risk level of detected leaked information and setting the urgency of an alert based on the evaluation result.

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