System

Abstract

An object of the system according to the embodiment is to immediately respond to unauthorized use or leakage of personal information and to promptly respond to a question or consultation about security from a user.SOLUTION: A system includes a monitoring unit, a notification unit, a countermeasure unit, and a response unit. The monitoring unit constantly monitors the account and the settlement history of the user. The notification unit immediately notifies when an abnormal access or transaction is detected by the monitoring unit. The countermeasure unit takes a countermeasure against the abnormality notified by the notification unit. The response part corresponds to a question or consultation about security from the user 24 hours 365 days.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not provide sufficient immediate responses to fraudulent use or leaks of personal information, or prompt responses to security-related questions or inquiries from users, and there is room for improvement.

[0005] The system according to the embodiment aims to immediately respond to fraudulent use and leaks of personal information, and to quickly respond to security-related questions and inquiries from users. [Means for solving the problem]

[0006] The system according to the embodiment comprises a monitoring unit, a notification unit, a countermeasure unit, and a response unit. The monitoring unit constantly monitors users' accounts and payment histories. The notification unit immediately notifies the user if the monitoring unit detects any abnormal access or transactions. The countermeasure unit takes measures against the abnormality notified by the notification unit. The response unit responds to security-related questions and inquiries from users 24 hours a day, 365 days a year. [Effects of the Invention]

[0007] The system according to the embodiment can immediately respond to fraudulent use and leaks of personal information, and can quickly respond to security-related questions and inquiries from users. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The security support system according to an embodiment of the present invention constantly monitors users' accounts and payment histories, and the AI ​​generator detects any abnormal access or transactions, immediately notifies the user, and takes countermeasures. This allows the security support system to protect users' accounts and payment histories and quickly respond when abnormal access or transactions are detected.

[0029] A security support system according to an embodiment includes a monitoring unit, a notification unit, a countermeasure unit, and a response unit. The monitoring unit constantly monitors a user's account and payment history. For example, the monitoring unit monitors the user's account information and payment history in real time to detect abnormal access or transactions. The monitoring unit can also learn the user's purchasing patterns and detect abnormal transactions. For example, the monitoring unit analyzes the user's past purchasing history to detect transactions that deviate from normal purchasing patterns. The notification unit immediately notifies the user when the monitoring unit detects abnormal access or transactions. For example, the notification unit sends a notification to the user such as, "Unauthorized access has been detected. Your account has been temporarily locked. Please confirm." Furthermore, when the notification unit detects an abnormality, it can propose optimal countermeasures based on the user's past behavioral history. For example, the notification unit makes proposals based on countermeasures taken when similar abnormalities occurred in the past. The countermeasure unit takes countermeasures in response to the abnormality notified by the notification unit. For example, the countermeasure unit automatically takes countermeasures such as locking the account or suspending transactions. The countermeasures unit can also work with the user's financial institution to immediately lock the account when an abnormality is detected. For example, the countermeasures unit automatically locks the account if a fraudulent transaction is detected. The response unit responds to security-related questions and inquiries from users 24 hours a day, 365 days a year. For example, if a user asks, "What should I do if my PayPay account is fraudulently used?", the response unit responds by saying, "First of all, don't worry. PayPay generally provides full compensation in the event of fraudulent use, but the following conditions must be met." The response unit can also learn the user's past question history to provide faster and more accurate answers. For example, if the same question has been asked in the past, the response unit can respond quickly based on the answer. This allows the security support system according to the embodiment to protect the user's account and payment history and respond quickly when abnormal access or transactions are detected. For example, the output unit provides the user with a notification of the detected abnormality and the results of countermeasures. Notifications can be sent via email, SMS, in-app notifications, etc.The results of the measures can be displayed to the user through web and mobile applications.

[0030] The monitoring unit not only learns users' purchasing patterns and detects transactions, but also predicts future fraudulent activity and issues warnings in advance. For example, the monitoring unit uses a generation AI to analyze users' past purchasing history and learn normal purchasing patterns. For example, it detects abnormal transactions based on transactions made on specific days of the week or during specific times of the day. The monitoring unit also uses a generation AI to predict future fraudulent activity based on users' purchasing patterns and issue warnings in advance. For example, it issues a warning if the purchase frequency of a specific product suddenly increases. The monitoring unit also uses a generation AI to learn users' purchasing patterns and predict abnormal transactions before they occur and issue a warning. For example, it issues a warning if a transaction deviates from the normal purchasing pattern. This makes it possible to strengthen security by learning users' purchasing patterns and predicting future fraudulent activity and issuing warnings in advance.

[0031] The monitoring unit can monitor a user's social media activity and issue a warning if there is a possibility that account information has been leaked. For example, the generation AI analyzes the user's social media activity and issues a warning if there is a possibility that account information has been leaked. For example, the monitoring unit detects posts in which the user's personal information is publicly disclosed. The monitoring unit also monitors the user's social media activity and issues a warning if there is a possibility that account information has been leaked. For example, the monitoring unit issues a warning if the user's account information is shared by other users. The monitoring unit also monitors the user's social media activity and issues a warning if there is a possibility that account information has been leaked. For example, the monitoring unit issues a warning if the user's account information is being used fraudulently. This makes it possible to strengthen security by monitoring a user's social media activity and issuing a warning if there is a possibility that account information has been leaked.

[0032] The monitoring unit can monitor the user's location information and issue a warning if a transaction is made outside of the user's normal range of activity. For example, the generation AI monitors the user's physical location information and issues a warning if a transaction is made outside of the user's normal range of activity. For example, a warning is issued if a transaction is made in an area the user does not normally visit. The monitoring unit also analyzes the user's location information and issues a warning if a transaction is made outside of the user's normal range of activity. For example, a warning is issued if a transaction made while the user is traveling is determined to be abnormal. The monitoring unit also monitors the user's location information and issues a warning if a transaction is made outside of the user's normal range of activity. For example, a warning is issued if a large transaction is made in an area the user does not normally visit. This makes it possible to strengthen security by monitoring the user's physical location information and issuing a warning if a transaction is made outside of the user's normal range of activity.

[0033] The monitoring unit can monitor the user's device information and issue a warning if there is access from an unknown device. For example, the generation AI in the monitoring unit monitors the user's device information and issues a warning if there is access from an unknown device. For example, a warning is issued if access is detected from a device that the user does not normally use. The monitoring unit also analyzes the user's device information and issues a warning if there is access from an unknown device. For example, a warning is issued if the first access from a new device is determined to be abnormal. The monitoring unit also monitors the user's device information and issues a warning if there is access from an unknown device. For example, a warning is issued if a large transaction is made from a device that the user does not normally use. In this way, security can be strengthened by monitoring the user's device information and issuing a warning if there is access from an unknown device.

[0034] When an anomaly is detected, the notification unit can propose the optimal countermeasure based on the user's past behavioral history. For example, when the generation AI detects an anomaly, the notification unit analyzes the user's past behavioral history and proposes the optimal countermeasure. For example, the proposal is made based on countermeasures taken when a similar anomaly occurred in the past. In addition, when the generation AI detects an anomaly, the notification unit proposes the optimal countermeasure based on the user's past behavioral history. For example, it analyzes past transaction history and proposes appropriate countermeasures for abnormal transactions. In addition, when the generation AI detects an anomaly, the notification unit proposes the optimal countermeasure based on the user's past behavioral history. For example, it proposes the optimal countermeasure based on the response history when an anomaly was detected in the past. As a result, when an anomaly is detected, the optimal countermeasure is proposed based on the user's past behavioral history, enabling a quick and appropriate response.

[0035] The notification unit can automatically update the user's contact information when an anomaly is detected, enabling a prompt response. For example, the notification unit automatically updates the user's contact information when the generation AI detects an anomaly, enabling a prompt response. For example, it sends a notification based on the user's latest contact information. The notification unit also automatically updates the user's contact information when the generation AI detects an anomaly, enabling a prompt response. For example, it automatically updates the user's contact information if it has changed. The notification unit also automatically updates the user's contact information when the generation AI detects an anomaly, enabling a prompt response. For example, it automatically corrects the user's contact information if it is inaccurate. This makes it possible to automatically update the user's contact information when an anomaly is detected, enabling a prompt response, thereby strengthening security.

[0036] The notification unit can send a notification to the user's family or trusted friends and request support when an abnormality is detected. For example, when the generation AI detects an abnormality, the notification unit sends a notification to the user's family or trusted friends and request support. For example, it sends a notification to a person the user should contact in an emergency. Furthermore, when the generation AI detects an abnormality, the notification unit sends a notification to the user's family or trusted friends and request support. For example, it sends a message to a contact specified by the user notifying them of an abnormality. Furthermore, when the generation AI detects an abnormality, the notification unit sends a notification to the user's family or trusted friends and request support. For example, it sends a notification to a person registered by the user as an emergency contact notifying them of an abnormality. This enables a rapid response by sending a notification to the user's family or trusted friends and requesting support when an abnormality is detected.

[0037] The notification unit can work with the user's financial institution when an anomaly is detected and immediately lock the account. For example, when the generation AI detects an anomaly, the notification unit works with the user's financial institution and immediately locks the account. For example, if a fraudulent transaction is detected, the account is automatically locked. The notification unit also works with the user's financial institution when the generation AI detects an anomaly and immediately locks the account. For example, if abnormal access is detected, the account is temporarily locked. The notification unit also works with the user's financial institution when the generation AI detects an anomaly and immediately locks the account. For example, the account is locked if the user reports a fraudulent transaction. This makes it possible to strengthen security by working with the user's financial institution when an anomaly is detected and immediately locking the account.

[0038] The response unit learns the user's question history and can provide faster and more accurate answers. For example, the generation AI in the response unit analyzes the user's past question history and provides faster and more accurate answers to similar questions. For example, if the same question has been asked in the past, the response unit provides a faster response based on that answer. The response unit also learns the user's past question history and provides appropriate answers to related questions. For example, it provides related information based on the content of the past question. The response unit also learns the user's past question history and provides faster and more accurate answers. For example, it understands the user's tendencies based on the past question history and generates appropriate answers. This allows the system to learn the user's past question history and provide faster and more accurate answers, improving user convenience.

[0039] The response unit can analyze the content of the user's question and provide related security news and trend information. In the response unit, for example, the generation AI analyzes the content of the user's question and provides related security news and trend information. For example, information on the latest security threats is provided. In addition, the response unit can analyze the content of the user's question and provide related security news and trend information. For example, information on recent security incidents is provided. In addition, the response unit can analyze the content of the user's question and provide related security news and trend information. For example, information on the latest trends in security measures is provided. In this way, the user's knowledge can be improved by analyzing the content of the user's question and providing related security news and trend information.

[0040] The response unit can share the content of the user's question with other users and provide a community-based answer. For example, the generation AI can share the content of the user's question with other users and provide a community-based answer. For example, the response unit can collect answers from users who have experienced the same problem. The response unit can also share the content of the user's question with other users and provide a community-based answer. For example, the response unit can collect advice from security experts and other users. The response unit can also share the content of the user's question with other users and provide a community-based answer. For example, the response unit can collect answers through forums and discussion boards. This can help the user solve their problem by sharing the content of the user's question with other users and providing a community-based answer.

[0041] The response unit can analyze the content of the user's question and provide a relevant video tutorial or guide. For example, the generation AI in the response unit analyzes the content of the user's question and provides a relevant video tutorial or guide. For example, a video explaining the procedure for security settings is provided. The response unit can also analyze the content of the user's question and provide a relevant video tutorial or guide. For example, a video explaining how to prevent phishing scams is provided. The response unit can also analyze the content of the user's question and provide a relevant video tutorial or guide. For example, a video explaining how to set up two-step authentication is provided. In this way, the content of the user's question can be analyzed and a relevant video tutorial or guide can be provided to help the user solve their problem.

[0042] The notification unit can automatically update the user's contact information when an anomaly is detected, enabling a prompt response. For example, the notification unit automatically updates the user's contact information when the generation AI detects an anomaly, enabling a prompt response. For example, it sends a notification based on the user's latest contact information. The notification unit also automatically updates the user's contact information when the generation AI detects an anomaly, enabling a prompt response. For example, it automatically updates the user's contact information if it has changed. The notification unit also automatically updates the user's contact information when the generation AI detects an anomaly, enabling a prompt response. For example, it automatically corrects the user's contact information if it is inaccurate. This makes it possible to automatically update the user's contact information when an anomaly is detected, enabling a prompt response, thereby strengthening security.

[0043] The notification unit can send a notification to the user's family or trusted friends and request support when an abnormality is detected. For example, when the generation AI detects an abnormality, the notification unit sends a notification to the user's family or trusted friends and request support. For example, it sends a notification to a person the user should contact in an emergency. Furthermore, when the generation AI detects an abnormality, the notification unit sends a notification to the user's family or trusted friends and request support. For example, it sends a message to a contact specified by the user notifying them of an abnormality. Furthermore, when the generation AI detects an abnormality, the notification unit sends a notification to the user's family or trusted friends and request support. For example, it sends a notification to a person registered by the user as an emergency contact notifying them of an abnormality. This enables a rapid response by sending a notification to the user's family or trusted friends and requesting support when an abnormality is detected.

[0044] The notification unit can work with the user's financial institution when an anomaly is detected and immediately lock the account. For example, when the generation AI detects an anomaly, the notification unit works with the user's financial institution and immediately locks the account. For example, if a fraudulent transaction is detected, the account is automatically locked. The notification unit also works with the user's financial institution when the generation AI detects an anomaly and immediately locks the account. For example, if abnormal access is detected, the account is temporarily locked. The notification unit also works with the user's financial institution when the generation AI detects an anomaly and immediately locks the account. For example, the account is locked if the user reports a fraudulent transaction. This makes it possible to strengthen security by working with the user's financial institution when an anomaly is detected and immediately locking the account.

[0045] The response unit learns the user's question history and can provide faster and more accurate answers. For example, the generation AI in the response unit analyzes the user's past question history and provides faster and more accurate answers to similar questions. For example, if the same question has been asked in the past, the response unit provides a faster response based on that answer. The response unit also learns the user's past question history and provides appropriate answers to related questions. For example, it provides related information based on the content of the past question. The response unit also learns the user's past question history and provides faster and more accurate answers. For example, it understands the user's tendencies based on the past question history and generates appropriate answers. This allows the system to learn the user's past question history and provide faster and more accurate answers, improving user convenience.

[0046] The response unit can analyze the content of the user's question and provide related security news and trend information. In the response unit, for example, the generation AI analyzes the content of the user's question and provides related security news and trend information. For example, information on the latest security threats is provided. In addition, the response unit can analyze the content of the user's question and provide related security news and trend information. For example, information on recent security incidents is provided. In addition, the response unit can analyze the content of the user's question and provide related security news and trend information. For example, information on the latest trends in security measures is provided. In this way, the user's knowledge can be improved by analyzing the content of the user's question and providing related security news and trend information.

[0047] The response unit can share the content of the user's question with other users and provide a community-based answer. For example, the generation AI can share the content of the user's question with other users and provide a community-based answer. For example, the response unit can collect answers from users who have experienced the same problem. The response unit can also share the content of the user's question with other users and provide a community-based answer. For example, the response unit can collect advice from security experts and other users. The response unit can also share the content of the user's question with other users and provide a community-based answer. For example, the response unit can collect answers through forums and discussion boards. This can help the user solve their problem by sharing the content of the user's question with other users and providing a community-based answer.

[0048] The response unit can analyze the content of the user's question and provide a relevant video tutorial or guide. For example, the generation AI in the response unit analyzes the content of the user's question and provides a relevant video tutorial or guide. For example, a video explaining the procedure for security settings is provided. The response unit can also analyze the content of the user's question and provide a relevant video tutorial or guide. For example, a video explaining how to prevent phishing scams is provided. The response unit can also analyze the content of the user's question and provide a relevant video tutorial or guide. For example, a video explaining how to set up two-step authentication is provided. In this way, the content of the user's question can be analyzed and a relevant video tutorial or guide can be provided to help the user solve their problem.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The security support system can also monitor the user's health data and issue a warning if an abnormal health condition is detected. For example, a warning can be issued if the user's heart rate or blood pressure fluctuates suddenly. It can also monitor the user's sleep patterns and issue a warning if abnormally insufficient sleep continues. It can also monitor the user's exercise volume and issue a warning if abnormally insufficient or excessive exercise is detected. This makes it possible to monitor the user's health condition and respond quickly if an abnormality is detected.

[0051] The security support system can also predict future purchasing trends based on the user's purchasing history and make optimal purchasing suggestions to the user. For example, if a user tends to frequently purchase a particular product during a particular season, suggestions will be made when that season approaches. The system can also analyze the user's past purchasing history and suggest related new products. Furthermore, it can predict price fluctuations for specific products based on the user's purchasing patterns and suggest the optimal timing for purchase. This can improve the user's purchasing experience.

[0052] The security support system can also monitor a user's social media activity and provide customized content based on the user's interests. For example, if a user shows interest in a particular topic, it can provide news and articles related to that topic. It can also analyze the user's social media activity and suggest related events and communities. It can also suggest new accounts and pages that may be of interest to the user based on the user's social media activity. This can improve the user's social media experience.

[0053] The security support system can also suggest optimal routes based on the user's location information. For example, when a user heads to a specific destination, it can suggest the shortest route or a route that avoids traffic congestion. It can also suggest nearby restaurants and cafes based on the user's location information. It can also suggest nearby tourist spots and events based on the user's location information. This can improve the user's travel experience.

[0054] The security support system can also suggest optimal device settings based on the user's device information. For example, when a user uses a new device, it can suggest optimal security settings. It can also suggest settings to improve device performance based on the user's device information. It can also suggest settings to extend the device's battery life based on the user's device information. This can improve the user's device usage experience.

[0055] The security support system can also predict future behavior based on the user's past behavior history and make optimal suggestions. For example, if a user tends to behave in a specific way during a specific time period, it can make suggestions related to that time period. It can also analyze the user's past behavior history and suggest new related actions. It can also suggest specific events or activities based on the user's behavior patterns. This makes it possible to predict user behavior and make optimal suggestions, thereby improving the user experience.

[0056] The security support system can also automatically contact users in an emergency based on their contact information. For example, if a user is in an emergency, a notification can be automatically sent to designated contacts. The system can also suggest the best response to an emergency based on the user's contact information. Furthermore, the system can provide necessary information in an emergency based on the user's contact information. This makes it possible to respond quickly and appropriately in an emergency based on the user's contact information.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The monitoring unit constantly monitors the user's account and payment history. For example, the monitoring unit monitors the user's account information and payment history in real time to detect abnormal access or transactions. The monitoring unit can also learn the user's purchasing patterns and detect abnormal transactions. For example, the monitoring unit analyzes the user's past purchasing history and detects transactions that deviate from normal purchasing patterns. Step 2: The notification unit immediately notifies the user when the monitoring unit detects any abnormal access or transactions. For example, the notification unit may send a message to the user saying, "Unauthorized access has been detected. Your account has been temporarily locked. Please confirm." In addition, when an abnormality is detected, the notification unit may suggest optimal countermeasures based on the user's past behavioral history. For example, the notification unit may make suggestions based on countermeasures taken when similar abnormalities occurred in the past. Step 3: The countermeasures department takes measures against the anomaly notified by the notification department. For example, the countermeasures department may automatically take measures such as locking the account or suspending transactions. In addition, the countermeasures department can also work with the user's financial institution when an anomaly is detected and immediately lock the account. For example, the countermeasures department may automatically lock the account if a fraudulent transaction is detected. Step 4: The response unit responds to security-related questions and inquiries from users 24 hours a day, 365 days a year. For example, if a user asks, "What should I do if my PayPay account is fraudulently used?", the response unit will respond by saying, "First of all, don't worry. PayPay will provide full compensation in principle in the event of fraudulent use, but the following conditions must be met." The response unit can also learn from the user's past question history to provide faster and more accurate answers. For example, if the same question has been asked in the past, the response unit will respond quickly based on the answers given.

[0059] (Example 2) The security support system according to an embodiment of the present invention constantly monitors users' accounts and payment histories, and the AI ​​generator detects any abnormal access or transactions, immediately notifies the user, and takes countermeasures. This allows the security support system to protect users' accounts and payment histories and quickly respond when abnormal access or transactions are detected.

[0060] A security support system according to an embodiment includes a monitoring unit, a notification unit, a countermeasure unit, and a response unit. The monitoring unit constantly monitors a user's account and payment history. For example, the monitoring unit monitors the user's account information and payment history in real time to detect abnormal access or transactions. The monitoring unit can also learn the user's purchasing patterns and detect abnormal transactions. For example, the monitoring unit analyzes the user's past purchasing history to detect transactions that deviate from normal purchasing patterns. The notification unit immediately notifies the user when the monitoring unit detects abnormal access or transactions. For example, the notification unit sends a notification to the user such as, "Unauthorized access has been detected. Your account has been temporarily locked. Please confirm." Furthermore, when the notification unit detects an abnormality, it can propose optimal countermeasures based on the user's past behavioral history. For example, the notification unit makes proposals based on countermeasures taken when similar abnormalities occurred in the past. The countermeasure unit takes countermeasures in response to the abnormality notified by the notification unit. For example, the countermeasure unit automatically takes countermeasures such as locking the account or suspending transactions. The countermeasures unit can also work with the user's financial institution to immediately lock the account when an abnormality is detected. For example, the countermeasures unit automatically locks the account if a fraudulent transaction is detected. The response unit responds to security-related questions and inquiries from users 24 hours a day, 365 days a year. For example, if a user asks, "What should I do if my PayPay account is fraudulently used?", the response unit responds by saying, "First of all, don't worry. PayPay generally provides full compensation in the event of fraudulent use, but the following conditions must be met." The response unit can also learn the user's past question history to provide faster and more accurate answers. For example, if the same question has been asked in the past, the response unit can respond quickly based on the answer. This allows the security support system according to the embodiment to protect the user's account and payment history and respond quickly when abnormal access or transactions are detected. For example, the output unit provides the user with a notification of the detected abnormality and the results of countermeasures. Notifications can be sent via email, SMS, in-app notifications, etc.The results of the measures can be displayed to the user through web and mobile applications.

[0061] The monitoring unit not only learns users' purchasing patterns and detects transactions, but also predicts future fraudulent activity and issues warnings in advance. For example, the monitoring unit uses a generation AI to analyze users' past purchasing history and learn normal purchasing patterns. For example, it detects abnormal transactions based on transactions made on specific days of the week or during specific times of the day. The monitoring unit also uses a generation AI to predict future fraudulent activity based on users' purchasing patterns and issue warnings in advance. For example, it issues a warning if the purchase frequency of a specific product suddenly increases. The monitoring unit also uses a generation AI to learn users' purchasing patterns and predict abnormal transactions before they occur and issue a warning. For example, it issues a warning if a transaction deviates from the normal purchasing pattern. This makes it possible to strengthen security by learning users' purchasing patterns and predicting future fraudulent activity and issuing warnings in advance.

[0062] The monitoring unit can monitor a user's social media activity and issue a warning if there is a possibility that account information has been leaked. For example, the generation AI analyzes the user's social media activity and issues a warning if there is a possibility that account information has been leaked. For example, the monitoring unit detects posts in which the user's personal information is publicly disclosed. The monitoring unit also monitors the user's social media activity and issues a warning if there is a possibility that account information has been leaked. For example, the monitoring unit issues a warning if the user's account information is shared by other users. The monitoring unit also monitors the user's social media activity and issues a warning if there is a possibility that account information has been leaked. For example, the monitoring unit issues a warning if the user's account information is being used fraudulently. This makes it possible to strengthen security by monitoring a user's social media activity and issuing a warning if there is a possibility that account information has been leaked.

[0063] The monitoring unit can use the emotion estimation function to analyze the user's emotion during a transaction and detect the possibility of fraud if there is an abnormal emotional fluctuation. The monitoring unit, for example, uses the emotion estimation function to analyze the user's emotion during a transaction in real time and detect the possibility of fraud if there is an abnormal emotional fluctuation. For example, it issues a warning if the user feels anxious or nervous during a transaction. The monitoring unit also uses the emotion estimation function to analyze the user's emotion during a transaction and detect the possibility of fraud if there is an abnormal emotional fluctuation. For example, it issues a warning if an emotional state different from that during a normal transaction is detected. The monitoring unit also uses the emotion estimation function to analyze the user's emotion during a transaction and detect the possibility of fraud if there is an abnormal emotional fluctuation. For example, it issues a warning if the user feels anger or sadness during a transaction. In this way, security can be strengthened by analyzing the user's emotion during a transaction and detecting the possibility of fraud if there is an abnormal emotional fluctuation.

[0064] The monitoring unit can monitor the user's location information and issue a warning if a transaction is made outside of the user's normal range of activity. For example, the generation AI monitors the user's physical location information and issues a warning if a transaction is made outside of the user's normal range of activity. For example, a warning is issued if a transaction is made in an area the user does not normally visit. The monitoring unit also analyzes the user's location information and issues a warning if a transaction is made outside of the user's normal range of activity. For example, a warning is issued if a transaction made while the user is traveling is determined to be abnormal. The monitoring unit also monitors the user's location information and issues a warning if a transaction is made outside of the user's normal range of activity. For example, a warning is issued if a large transaction is made in an area the user does not normally visit. This makes it possible to strengthen security by monitoring the user's physical location information and issuing a warning if a transaction is made outside of the user's normal range of activity.

[0065] The monitoring unit can monitor the user's device information and issue a warning if there is access from an unknown device. For example, the generation AI in the monitoring unit monitors the user's device information and issues a warning if there is access from an unknown device. For example, a warning is issued if access is detected from a device that the user does not normally use. The monitoring unit also analyzes the user's device information and issues a warning if there is access from an unknown device. For example, a warning is issued if the first access from a new device is determined to be abnormal. The monitoring unit also monitors the user's device information and issues a warning if there is access from an unknown device. For example, a warning is issued if a large transaction is made from a device that the user does not normally use. In this way, security can be strengthened by monitoring the user's device information and issuing a warning if there is access from an unknown device.

[0066] The monitoring unit can use the emotion estimation function to analyze the emotion of a user when conducting a transaction in real time, and suspend the transaction if an abnormal emotion is detected. The monitoring unit, for example, uses the emotion estimation function to analyze the emotion of a user when conducting a transaction in real time, and suspend the transaction if an abnormal emotion is detected. For example, the monitoring unit suspends the transaction if the user is feeling strong anxiety during the transaction. The monitoring unit also uses the emotion estimation function to analyze the emotion of a user when conducting a transaction, and suspends the transaction if an abnormal emotion is detected. For example, the monitoring unit suspends the transaction if an emotional state different from that during normal transactions is detected. The monitoring unit also uses the emotion estimation function to analyze the emotion of a user when conducting a transaction in real time, and suspends the transaction if an abnormal emotion is detected. For example, the monitoring unit suspends the transaction if the user is feeling strong anger or sadness during the transaction. In this way, security can be strengthened by analyzing the emotion of a user when conducting a transaction in real time, and suspending the transaction if an abnormal emotion is detected.

[0067] When an anomaly is detected, the notification unit can propose the optimal countermeasure based on the user's past behavioral history. For example, when the generation AI detects an anomaly, the notification unit analyzes the user's past behavioral history and proposes the optimal countermeasure. For example, the proposal is made based on countermeasures taken when a similar anomaly occurred in the past. In addition, when the generation AI detects an anomaly, the notification unit proposes the optimal countermeasure based on the user's past behavioral history. For example, it analyzes past transaction history and proposes appropriate countermeasures for abnormal transactions. In addition, when the generation AI detects an anomaly, the notification unit proposes the optimal countermeasure based on the user's past behavioral history. For example, it proposes the optimal countermeasure based on the response history when an anomaly was detected in the past. As a result, when an anomaly is detected, the optimal countermeasure is proposed based on the user's past behavioral history, enabling a quick and appropriate response.

[0068] The notification unit can automatically update the user's contact information when an anomaly is detected, enabling a prompt response. For example, the notification unit automatically updates the user's contact information when the generation AI detects an anomaly, enabling a prompt response. For example, it sends a notification based on the user's latest contact information. The notification unit also automatically updates the user's contact information when the generation AI detects an anomaly, enabling a prompt response. For example, it automatically updates the user's contact information if it has changed. The notification unit also automatically updates the user's contact information when the generation AI detects an anomaly, enabling a prompt response. For example, it automatically corrects the user's contact information if it is inaccurate. This makes it possible to automatically update the user's contact information when an anomaly is detected, enabling a prompt response, thereby strengthening security.

[0069] The notification unit can use the emotion estimation function to analyze the user's emotional state when an abnormality is detected and send a customized notification for reducing stress. For example, the notification unit can use the emotion estimation function to analyze the user's emotional state in real time when an abnormality is detected and send a customized notification for reducing stress. For example, if the user is feeling anxious, the notification unit can send a reassuring message. The notification unit can also use the emotion estimation function to analyze the user's emotional state when an abnormality is detected and send a customized notification for reducing stress. For example, if the user is feeling angry, the notification unit can send a message urging the user to stay calm. The notification unit can also use the emotion estimation function to analyze the user's emotional state when an abnormality is detected and send a customized notification for reducing stress. For example, if the user is feeling sad, the notification unit can send an encouraging message. In this way, by analyzing the user's emotional state when an abnormality is detected and sending a customized notification for reducing stress, the user's sense of security can be increased.

[0070] The notification unit can send a notification to the user's family or trusted friends and request support when an abnormality is detected. For example, when the generation AI detects an abnormality, the notification unit sends a notification to the user's family or trusted friends and request support. For example, it sends a notification to a person the user should contact in an emergency. Furthermore, when the generation AI detects an abnormality, the notification unit sends a notification to the user's family or trusted friends and request support. For example, it sends a message to a contact specified by the user notifying them of an abnormality. Furthermore, when the generation AI detects an abnormality, the notification unit sends a notification to the user's family or trusted friends and request support. For example, it sends a notification to a person registered by the user as an emergency contact notifying them of an abnormality. This enables a rapid response by sending a notification to the user's family or trusted friends and requesting support when an abnormality is detected.

[0071] The notification unit can work with the user's financial institution when an anomaly is detected and immediately lock the account. For example, when the generation AI detects an anomaly, the notification unit works with the user's financial institution and immediately locks the account. For example, if a fraudulent transaction is detected, the account is automatically locked. The notification unit also works with the user's financial institution when the generation AI detects an anomaly and immediately locks the account. For example, if abnormal access is detected, the account is temporarily locked. The notification unit also works with the user's financial institution when the generation AI detects an anomaly and immediately locks the account. For example, the account is locked if the user reports a fraudulent transaction. This makes it possible to strengthen security by working with the user's financial institution when an anomaly is detected and immediately locking the account.

[0072] The notification unit can use the emotion estimation function to analyze the user's emotion in real time when an anomaly is detected and propose optimal countermeasures. For example, the notification unit uses the emotion estimation function to analyze the user's emotion in real time when an anomaly is detected and propose optimal countermeasures. For example, if the user is feeling anxious, the notification unit proposes countermeasures to reassure the user. The notification unit also uses the emotion estimation function to analyze the user's emotion when an anomaly is detected and propose optimal countermeasures. For example, if the user is feeling angry, the notification unit proposes countermeasures to encourage the user to stay calm. The notification unit also uses the emotion estimation function to analyze the user's emotion when an anomaly is detected and propose optimal countermeasures. For example, if the user is feeling sad, the notification unit proposes countermeasures to encourage the user. In this way, by analyzing the user's emotion in real time when an anomaly is detected and proposing optimal countermeasures, a quick and appropriate response is possible.

[0073] The response unit learns the user's question history and can provide faster and more accurate answers. For example, the generation AI in the response unit analyzes the user's past question history and provides faster and more accurate answers to similar questions. For example, if the same question has been asked in the past, the response unit provides a faster response based on that answer. The response unit also learns the user's past question history and provides appropriate answers to related questions. For example, it provides related information based on the content of the past question. The response unit also learns the user's past question history and provides faster and more accurate answers. For example, it understands the user's tendencies based on the past question history and generates appropriate answers. This allows the system to learn the user's past question history and provide faster and more accurate answers, improving user convenience.

[0074] The response unit can analyze the content of the user's question and provide related security news and trend information. In the response unit, for example, the generation AI analyzes the content of the user's question and provides related security news and trend information. For example, information on the latest security threats is provided. In addition, the response unit can analyze the content of the user's question and provide related security news and trend information. For example, information on recent security incidents is provided. In addition, the response unit can analyze the content of the user's question and provide related security news and trend information. For example, information on the latest trends in security measures is provided. In this way, the user's knowledge can be improved by analyzing the content of the user's question and providing related security news and trend information.

[0075] The response unit can use the emotion estimation function to analyze the emotion of the user when asking a question and provide a customized answer according to the emotion. For example, the response unit uses the emotion estimation function to analyze the emotion of the user when asking a question in real time and provide a customized answer according to the emotion. For example, if the user is feeling anxious, it provides a reassuring answer. The response unit also uses the emotion estimation function to analyze the emotion of the user when asking a question and provide a customized answer according to the emotion. For example, if the user is feeling angry, it provides an answer encouraging the user to stay calm. The response unit also uses the emotion estimation function to analyze the emotion of the user when asking a question and provide a customized answer according to the emotion. For example, if the user is feeling sad, it provides an encouraging answer. In this way, by analyzing the emotion of the user when asking a question and providing a customized answer according to the emotion, it is possible to improve user satisfaction.

[0076] The response unit can share the content of the user's question with other users and provide a community-based answer. For example, the generation AI can share the content of the user's question with other users and provide a community-based answer. For example, the response unit can collect answers from users who have experienced the same problem. The response unit can also share the content of the user's question with other users and provide a community-based answer. For example, the response unit can collect advice from security experts and other users. The response unit can also share the content of the user's question with other users and provide a community-based answer. For example, the response unit can collect answers through forums and discussion boards. This can help the user solve their problem by sharing the content of the user's question with other users and providing a community-based answer.

[0077] The response unit can analyze the content of the user's question and provide a relevant video tutorial or guide. For example, the generation AI in the response unit analyzes the content of the user's question and provides a relevant video tutorial or guide. For example, a video explaining the procedure for security settings is provided. The response unit can also analyze the content of the user's question and provide a relevant video tutorial or guide. For example, a video explaining how to prevent phishing scams is provided. The response unit can also analyze the content of the user's question and provide a relevant video tutorial or guide. For example, a video explaining how to set up two-step authentication is provided. In this way, the content of the user's question can be analyzed and a relevant video tutorial or guide can be provided to help the user solve their problem.

[0078] The response unit can use the emotion estimation function to analyze the user's emotion when asking a question in real time and provide support according to the emotion. The response unit, for example, uses the emotion estimation function to analyze the user's emotion when asking a question in real time and provide support according to the emotion. For example, if the user is feeling anxious, it provides support that reassures the user. The response unit also uses the emotion estimation function to analyze the user's emotion when asking a question and provide support according to the emotion. For example, if the user is feeling angry, it provides support that encourages the user to stay calm. The response unit also uses the emotion estimation function to analyze the user's emotion when asking a question in real time and provide support according to the emotion. For example, if the user is feeling sad, it provides support that encourages the user. In this way, by analyzing the user's emotion when asking a question in real time and providing support according to the emotion, it is possible to improve user satisfaction.

[0079] The notification unit can automatically update the user's contact information when an anomaly is detected, enabling a prompt response. For example, the notification unit automatically updates the user's contact information when the generation AI detects an anomaly, enabling a prompt response. For example, it sends a notification based on the user's latest contact information. The notification unit also automatically updates the user's contact information when the generation AI detects an anomaly, enabling a prompt response. For example, it automatically updates the user's contact information if it has changed. The notification unit also automatically updates the user's contact information when the generation AI detects an anomaly, enabling a prompt response. For example, it automatically corrects the user's contact information if it is inaccurate. This makes it possible to automatically update the user's contact information when an anomaly is detected, enabling a prompt response, thereby strengthening security.

[0080] The notification unit can use the emotion estimation function to analyze the user's emotional state when an abnormality is detected and send a customized notification for reducing stress. For example, the notification unit can use the emotion estimation function to analyze the user's emotional state in real time when an abnormality is detected and send a customized notification for reducing stress. For example, if the user is feeling anxious, the notification unit can send a reassuring message. The notification unit can also use the emotion estimation function to analyze the user's emotional state when an abnormality is detected and send a customized notification for reducing stress. For example, if the user is feeling angry, the notification unit can send a message urging the user to stay calm. The notification unit can also use the emotion estimation function to analyze the user's emotional state when an abnormality is detected and send a customized notification for reducing stress. For example, if the user is feeling sad, the notification unit can send an encouraging message. In this way, by analyzing the user's emotional state when an abnormality is detected and sending a customized notification for reducing stress, the user's sense of security can be increased.

[0081] The notification unit can send a notification to the user's family or trusted friends and request support when an abnormality is detected. For example, when the generation AI detects an abnormality, the notification unit sends a notification to the user's family or trusted friends and request support. For example, it sends a notification to a person the user should contact in an emergency. Furthermore, when the generation AI detects an abnormality, the notification unit sends a notification to the user's family or trusted friends and request support. For example, it sends a message to a contact specified by the user notifying them of an abnormality. Furthermore, when the generation AI detects an abnormality, the notification unit sends a notification to the user's family or trusted friends and request support. For example, it sends a notification to a person registered by the user as an emergency contact notifying them of an abnormality. This enables a rapid response by sending a notification to the user's family or trusted friends and requesting support when an abnormality is detected.

[0082] The notification unit can work with the user's financial institution when an anomaly is detected and immediately lock the account. For example, when the generation AI detects an anomaly, the notification unit works with the user's financial institution and immediately locks the account. For example, if a fraudulent transaction is detected, the account is automatically locked. The notification unit also works with the user's financial institution when the generation AI detects an anomaly and immediately locks the account. For example, if abnormal access is detected, the account is temporarily locked. The notification unit also works with the user's financial institution when the generation AI detects an anomaly and immediately locks the account. For example, the account is locked if the user reports a fraudulent transaction. This makes it possible to strengthen security by working with the user's financial institution when an anomaly is detected and immediately locking the account.

[0083] The notification unit can use the emotion estimation function to analyze the user's emotion in real time when an anomaly is detected and propose optimal countermeasures. For example, the notification unit uses the emotion estimation function to analyze the user's emotion in real time when an anomaly is detected and propose optimal countermeasures. For example, if the user is feeling anxious, the notification unit proposes countermeasures to reassure the user. The notification unit also uses the emotion estimation function to analyze the user's emotion when an anomaly is detected and propose optimal countermeasures. For example, if the user is feeling angry, the notification unit proposes countermeasures to encourage the user to stay calm. The notification unit also uses the emotion estimation function to analyze the user's emotion when an anomaly is detected and propose optimal countermeasures. For example, if the user is feeling sad, the notification unit proposes countermeasures to encourage the user. In this way, by analyzing the user's emotion in real time when an anomaly is detected and proposing optimal countermeasures, a quick and appropriate response is possible.

[0084] The response unit learns the user's question history and can provide faster and more accurate answers. For example, the generation AI in the response unit analyzes the user's past question history and provides faster and more accurate answers to similar questions. For example, if the same question has been asked in the past, the response unit provides a faster response based on that answer. The response unit also learns the user's past question history and provides appropriate answers to related questions. For example, it provides related information based on the content of the past question. The response unit also learns the user's past question history and provides faster and more accurate answers. For example, it understands the user's tendencies based on the past question history and generates appropriate answers. This allows the system to learn the user's past question history and provide faster and more accurate answers, improving user convenience.

[0085] The response unit can analyze the content of the user's question and provide related security news and trend information. In the response unit, for example, the generation AI analyzes the content of the user's question and provides related security news and trend information. For example, information on the latest security threats is provided. In addition, the response unit can analyze the content of the user's question and provide related security news and trend information. For example, information on recent security incidents is provided. In addition, the response unit can analyze the content of the user's question and provide related security news and trend information. For example, information on the latest trends in security measures is provided. In this way, the user's knowledge can be improved by analyzing the content of the user's question and providing related security news and trend information.

[0086] The response unit can use the emotion estimation function to analyze the emotion of the user when asking a question and provide a customized answer according to the emotion. For example, the response unit uses the emotion estimation function to analyze the emotion of the user when asking a question in real time and provide a customized answer according to the emotion. For example, if the user is feeling anxious, it provides a reassuring answer. The response unit also uses the emotion estimation function to analyze the emotion of the user when asking a question and provide a customized answer according to the emotion. For example, if the user is feeling angry, it provides an answer encouraging the user to stay calm. The response unit also uses the emotion estimation function to analyze the emotion of the user when asking a question and provide a customized answer according to the emotion. For example, if the user is feeling sad, it provides an encouraging answer. In this way, by analyzing the emotion of the user when asking a question and providing a customized answer according to the emotion, it is possible to improve user satisfaction.

[0087] The response unit can share the content of the user's question with other users and provide a community-based answer. For example, the generation AI can share the content of the user's question with other users and provide a community-based answer. For example, the response unit can collect answers from users who have experienced the same problem. The response unit can also share the content of the user's question with other users and provide a community-based answer. For example, the response unit can collect advice from security experts and other users. The response unit can also share the content of the user's question with other users and provide a community-based answer. For example, the response unit can collect answers through forums and discussion boards. This can help the user solve their problem by sharing the content of the user's question with other users and providing a community-based answer.

[0088] The response unit can analyze the content of the user's question and provide a relevant video tutorial or guide. For example, the generation AI in the response unit analyzes the content of the user's question and provides a relevant video tutorial or guide. For example, a video explaining the procedure for security settings is provided. The response unit can also analyze the content of the user's question and provide a relevant video tutorial or guide. For example, a video explaining how to prevent phishing scams is provided. The response unit can also analyze the content of the user's question and provide a relevant video tutorial or guide. For example, a video explaining how to set up two-step authentication is provided. In this way, the content of the user's question can be analyzed and a relevant video tutorial or guide can be provided to help the user solve their problem.

[0089] The response unit can use the emotion estimation function to analyze the user's emotion when asking a question in real time and provide support according to the emotion. The response unit, for example, uses the emotion estimation function to analyze the user's emotion when asking a question in real time and provide support according to the emotion. For example, if the user is feeling anxious, it provides support that reassures the user. The response unit also uses the emotion estimation function to analyze the user's emotion when asking a question and provide support according to the emotion. For example, if the user is feeling angry, it provides support that encourages the user to stay calm. The response unit also uses the emotion estimation function to analyze the user's emotion when asking a question in real time and provide support according to the emotion. For example, if the user is feeling sad, it provides support that encourages the user. In this way, by analyzing the user's emotion when asking a question in real time and providing support according to the emotion, it is possible to improve user satisfaction.

[0090] The response unit can use the emotion estimation function to analyze the user's emotion when asking a question in real time and provide support according to the emotion. The response unit, for example, uses the emotion estimation function to analyze the user's emotion when asking a question in real time and provide support according to the emotion. For example, if the user is feeling anxious, it provides support that reassures the user. The response unit also uses the emotion estimation function to analyze the user's emotion when asking a question and provide support according to the emotion. For example, if the user is feeling angry, it provides support that encourages the user to stay calm. The response unit also uses the emotion estimation function to analyze the user's emotion when asking a question in real time and provide support according to the emotion. For example, if the user is feeling sad, it provides support that encourages the user. In this way, by analyzing the user's emotion when asking a question in real time and providing support according to the emotion, it is possible to improve user satisfaction.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The security support system can also monitor the user's health data and issue a warning if an abnormal health condition is detected. For example, a warning can be issued if the user's heart rate or blood pressure fluctuates suddenly. It can also monitor the user's sleep patterns and issue a warning if abnormally insufficient sleep continues. It can also monitor the user's exercise volume and issue a warning if abnormally insufficient or excessive exercise is detected. This makes it possible to monitor the user's health condition and respond quickly if an abnormality is detected.

[0093] The security support system can also predict future purchasing trends based on the user's purchasing history and make optimal purchasing suggestions to the user. For example, if a user tends to frequently purchase a particular product during a particular season, suggestions will be made when that season approaches. The system can also analyze the user's past purchasing history and suggest related new products. Furthermore, it can predict price fluctuations for specific products based on the user's purchasing patterns and suggest the optimal timing for purchase. This can improve the user's purchasing experience.

[0094] The security support system can also monitor a user's social media activity and provide customized content based on the user's interests. For example, if a user shows interest in a particular topic, it can provide news and articles related to that topic. It can also analyze the user's social media activity and suggest related events and communities. It can also suggest new accounts and pages that may be of interest to the user based on the user's social media activity. This can improve the user's social media experience.

[0095] The security support system can also analyze the user's emotions during a transaction and provide customized advertisements according to the emotions. For example, if the user feels joy during a transaction, advertisements for products and services that match that emotion can be provided. Also, if the user feels anxiety during a transaction, advertisements for products and services that will alleviate that anxiety can be provided. Furthermore, if the user feels excitement during a transaction, advertisements for products and services that will further increase that excitement can be provided. In this way, by providing advertisements that match the user's emotions, the effectiveness of advertising can be increased.

[0096] The security support system can also suggest optimal routes based on the user's location information. For example, when a user heads to a specific destination, it can suggest the shortest route or a route that avoids traffic congestion. It can also suggest nearby restaurants and cafes based on the user's location information. It can also suggest nearby tourist spots and events based on the user's location information. This can improve the user's travel experience.

[0097] The security support system can also suggest optimal device settings based on the user's device information. For example, when a user uses a new device, it can suggest optimal security settings. It can also suggest settings to improve device performance based on the user's device information. It can also suggest settings to extend the device's battery life based on the user's device information. This can improve the user's device usage experience.

[0098] The security support system can also analyze the user's emotions during trading and provide customized feedback according to the emotions. For example, if the user feels joy during trading, it can provide positive feedback to further enhance that emotion. Also, if the user feels anxiety during trading, it can provide reassuring feedback to reduce that anxiety. Furthermore, if the user feels excited during trading, it can provide exciting feedback to further enhance that excitement. In this way, the user experience can be improved by providing feedback according to the user's emotions.

[0099] The security support system can also predict future behavior based on the user's past behavior history and make optimal suggestions. For example, if a user tends to behave in a specific way during a specific time period, it can make suggestions related to that time period. It can also analyze the user's past behavior history and suggest new related actions. It can also suggest specific events or activities based on the user's behavior patterns. This makes it possible to predict user behavior and make optimal suggestions, thereby improving the user experience.

[0100] The security support system can also automatically contact users in an emergency based on their contact information. For example, if a user is in an emergency, a notification can be automatically sent to designated contacts. The system can also suggest the best response to an emergency based on the user's contact information. Furthermore, the system can provide necessary information in an emergency based on the user's contact information. This makes it possible to respond quickly and appropriately in an emergency based on the user's contact information.

[0101] The security support system can also analyze the user's emotional state and provide customized support according to the emotion. For example, if the user feels anxious during a transaction, support to alleviate that anxiety can be provided. If the user feels angry during a transaction, support to calm the anger can be provided. Furthermore, if the user feels sad during a transaction, support to alleviate the sadness can be provided. In this way, by providing support according to the user's emotional state, the user's sense of security can be increased.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The monitoring unit constantly monitors the user's account and payment history. For example, the monitoring unit monitors the user's account information and payment history in real time to detect abnormal access or transactions. The monitoring unit can also learn the user's purchasing patterns and detect abnormal transactions. For example, the monitoring unit analyzes the user's past purchasing history and detects transactions that deviate from normal purchasing patterns. Step 2: The notification unit immediately notifies the user when the monitoring unit detects any abnormal access or transactions. For example, the notification unit may send a message to the user saying, "Unauthorized access has been detected. Your account has been temporarily locked. Please confirm." In addition, when an abnormality is detected, the notification unit may suggest optimal countermeasures based on the user's past behavioral history. For example, the notification unit may make suggestions based on countermeasures taken when similar abnormalities occurred in the past. Step 3: The countermeasures department takes measures against the anomaly notified by the notification department. For example, the countermeasures department may automatically take measures such as locking the account or suspending transactions. In addition, the countermeasures department can also work with the user's financial institution when an anomaly is detected and immediately lock the account. For example, the countermeasures department may automatically lock the account if a fraudulent transaction is detected. Step 4: The response unit responds to security-related questions and inquiries from users 24 hours a day, 365 days a year. For example, if a user asks, "What should I do if my PayPay account is fraudulently used?", the response unit will respond by saying, "First of all, don't worry. PayPay will provide full compensation in principle in the event of fraudulent use, but the following conditions must be met." The response unit can also learn from the user's past question history to provide faster and more accurate answers. For example, if the same question has been asked in the past, the response unit will respond quickly based on the answers given.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0161] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0162] 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.

[0163] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0165] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0166] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A monitoring unit that constantly monitors users' accounts and payment history; a notification unit that immediately notifies when an abnormal access or transaction is detected by the monitoring unit; a countermeasure unit that takes measures against the abnormality notified by the notification unit; A response unit that responds to security-related questions and inquiries from users 24 hours a day, 365 days a year. A system characterized by:

2. The monitoring unit Learn the user's purchasing patterns and not only detect the transaction but also predict future fraudulent activity and issue advance warnings.

2. The system of claim 1.

3. The monitoring unit Monitor the user's social media activity and issue alerts if account information may have been compromised.

2. The system of claim 1.

4. The monitoring unit Analyzing the user's emotions during the transaction and detecting the possibility of fraud when there are abnormal emotional fluctuations 2. The system of claim 1.

5. The monitoring unit Monitor the user's location information and issue an alert if the transaction occurs outside of the user's normal range of movement.

2. The system of claim 1.

6. The monitoring unit Monitors the user's device information and issues a warning if the access is made from an unknown device.

2. The system of claim 1.

Images