System

Abstract

An object of a system according to an embodiment is to reduce the risk of a user suffering a fraud.SOLUTION: A system according to an embodiment includes a generation AI, a checker, and a warning unit. The generation AI analyzes a method of fraud using the generation AI. The checker checks for possible fraud just before the user actually makes a deposit. The alert component alerts the user based on the likelihood of fraud detected by the checker.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 have had the problem that it is difficult to completely eradicate investment fraud and other fraudulent methods, and there is still a risk that users will be defrauded.

[0005] The system according to the embodiment aims to reduce the risk of users being scammed. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, a checker, and a warning unit. The generation AI analyzes fraudulent methods using the generation AI. The checker checks for the possibility of fraud just before a user actually makes a deposit. The warning unit issues a warning to the user based on the possibility of fraud detected by the checker. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the risk of users being scammed. [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 investment fraud prevention system according to an embodiment of the present invention is a system for preventing investment fraud triggered by solicitations via social media. This system uses a generation AI to analyze fraudulent methods, check for the possibility of fraud just before a deposit is made, and issue a warning. This allows the investment fraud prevention system to prevent users from falling victim to fraud.

[0029] An investment fraud prevention system according to an embodiment includes a generation AI, a checker, and a warning unit. The generation AI analyzes fraudulent methods. For example, the generation AI uses deep learning to learn fraudulent methods and detect signs of fraud. The generation AI can also use natural language processing technology to analyze the content of social media advertisements and determine the possibility of fraud. The generation AI can also detect new fraudulent methods based on past fraud data. The checker checks for the possibility of fraud just before a user actually makes a deposit. For example, the checker analyzes the deposit destination information to determine whether it has been used for fraud in the past. The checker can also analyze the transaction content and determine the possibility of fraud if the advertisement promises excessively high profits or urges the user to make a quick deposit. The checker can also analyze the user's past transaction history to detect patterns similar to fraudulent methods. The warning unit issues a warning to the user based on the possibility of fraud detected by the checker. For example, the warning unit can issue a warning to the user using a pop-up notification. The warning unit can also issue a warning to the user using an email notification. The warning unit can also issue a warning to the user using a voice notification. This allows the investment fraud prevention system according to the embodiment to prevent users from falling victim to fraud. For example, users can avoid falling victim to fraud by checking for the possibility of fraud just before making a deposit and receiving a warning. Furthermore, users can deepen their knowledge of fraud by obtaining information about fraudulent methods.

[0030] The generation AI can analyze the content of social media ads and determine that they are likely to be fraudulent if they claim excessively high profits. For example, the generation AI can analyze the content of social media ads using deep learning and determine that they are likely to be fraudulent if they claim excessively high profits. The generation AI can also analyze the content of social media ads using natural language processing technology and determine that they are likely to be fraudulent if they claim excessively high profits. The generation AI can also analyze the content of social media ads based on past fraud data and determine that they are likely to be fraudulent if they claim excessively high profits. This allows users to prevent themselves from becoming victims of fraud by analyzing the content of social media ads and determining that they are likely to be fraudulent if they claim excessively high profits.

[0031] The generation AI can analyze the recipient's account information and issue a warning if it has been used for fraud in the past. For example, the generation AI can analyze the recipient's account information using deep learning and issue a warning if it has been used for fraud in the past. The generation AI can also analyze the recipient's account information based on past fraud data and issue a warning if it has been used for fraud in the past. The generation AI can also analyze the recipient's account information using natural language processing technology and issue a warning if it has been used for fraud in the past. In this way, by analyzing the recipient's account information and issuing a warning if it has been used for fraud in the past, users can prevent themselves from becoming victims of fraud.

[0032] The generation AI can analyze transaction details and determine that a transaction is likely to be fraudulent if it advertises excessively high profits or urges users to make a quick deposit. For example, the generation AI can analyze transaction details using deep learning and determine that a transaction is likely to be fraudulent if it advertises excessively high profits or urges users to make a quick deposit. The generation AI can also analyze transaction details using natural language processing technology and determine that a transaction is likely to be fraudulent if it advertises excessively high profits or urges users to make a quick deposit. The generation AI can also analyze transaction details based on past fraud data and determine that a transaction is likely to be fraudulent if it advertises excessively high profits or urges users to make a quick deposit. By analyzing transaction details and determining that a transaction is likely to be fraudulent if it advertises excessively high profits or urges users to make a quick deposit, users can prevent themselves from falling victim to fraud.

[0033] The generative AI can be equipped with an educational function that provides users with information about fraud methods and signs of fraud. For example, the generative AI can translate data about fraud methods into different languages ​​and build an international database of fraud methods. The generative AI can also share data about fraud methods with experts in different industries and fields to strengthen cross-domain fraud prevention measures. The generative AI can also use an emotion estimation function to generate educational content about fraud methods and provide it in a format that users can easily empathize with. By providing users with information about fraud methods and signs of fraud, users can deepen their knowledge of fraud and prevent themselves from becoming victims of fraud.

[0034] The generation AI can have the function of accepting feedback from users, learning fraudulent methods based on that information, and issuing warnings to other users. The generation AI can, for example, accept feedback from users, learn fraudulent methods based on that information, and issue warnings to other users. For example, if a user reports that a transaction may be fraudulent, the generation AI can learn fraudulent methods based on that information and issue warnings to other users. The generation AI can also learn fraudulent methods based on user feedback and improve its accuracy in detecting signs of fraud. The generation AI can also learn fraudulent methods based on user feedback and quickly detect new fraudulent methods. This makes it possible to prevent fraud by learning fraudulent methods based on user feedback and issuing warnings to other users.

[0035] Generative AI can analyze the latest news articles and social media posts about fraud methods in real time, allowing it to quickly detect new fraud methods. For example, generative AI can automatically collect the latest news articles about fraud methods and analyze them in real time. For example, it can extract articles about fraud from news sites and detect new fraud methods. Generative AI can also analyze social media posts to collect information about fraud methods in real time. For example, it can monitor posts on Twitter and Facebook to detect signs of fraud. Generative AI can also analyze posts on forums and message boards about fraud methods and quickly detect new fraud methods. This allows users to prevent themselves from becoming victims of fraud by analyzing the latest news articles and social media posts in real time and quickly detecting new fraud methods.

[0036] The generation AI can analyze a user's past transaction history, detect patterns similar to fraudulent methods, and issue a warning. For example, the generation AI can analyze a user's past transaction history using deep learning, detect patterns similar to fraudulent methods, and issue a warning. The generation AI can also identify signs of fraud based on past transaction data and issue a warning. The generation AI can also monitor a user's transaction history in real time, detect patterns similar to fraudulent methods, and issue a warning. In this way, by analyzing a user's past transaction history, detecting patterns similar to fraudulent methods, and issuing a warning, users can prevent themselves from becoming victims of fraud.

[0037] The generative AI can translate data related to fraud methods into different languages ​​and build an international database of fraud methods. For example, the generative AI can automatically translate data related to fraud methods and build an international database of fraud methods. For example, it can translate data into multiple languages, such as English, Japanese, and Chinese. The generative AI can also collect, translate, and integrate data on fraud methods in different languages. The generative AI can also translate data related to fraud methods in real time and build an international database of fraud methods. This allows users to prevent themselves from becoming victims of fraud by translating data related to fraud methods into different languages ​​and building an international database of fraud methods.

[0038] Generative AI can share data on fraud methods with experts from different industries and fields to strengthen cross-domain fraud prevention measures. For example, generative AI can share data on fraud methods with experts from different industries to strengthen cross-domain fraud prevention measures. For example, experts from the financial and IT industries can collaborate to combat fraud. Generative AI can also share data on fraud methods with experts from different fields to build a system for joint fraud prevention measures. Generative AI can also share data on fraud methods with experts from different industries and fields in real time to strengthen cross-domain fraud prevention measures. By sharing data on fraud methods with experts from different industries and fields and strengthening cross-domain fraud prevention measures, users can prevent themselves from becoming victims of fraud.

[0039] The checker can compare the information of the recipient of the deposit with a past fraud database and issue a detailed warning if there is a high possibility of fraud. For example, the checker builds a system that compares the information of the recipient of the deposit with a past fraud database. For example, it compares the account information of the recipient of the deposit with the database and determines the possibility of fraud. The checker can also analyze the information of the recipient of the deposit with a generative AI, compare it with a past fraud database, and issue a warning. The checker can also compare the information of the recipient of the deposit with a past fraud database in real time and issue a detailed warning if there is a high possibility of fraud. In this way, by comparing the information of the recipient of the deposit with a past fraud database and issuing a detailed warning if there is a high possibility of fraud, users can prevent themselves from becoming victims of fraud.

[0040] The checker can analyze a user's past deposit patterns, detect abnormal transactions, and issue a warning. For example, the checker can analyze a user's past deposit patterns using generative AI to detect abnormal transactions. For example, it can identify transactions of unusual amounts or frequencies. The checker can also build a system that analyzes a user's past deposit patterns and issues a warning if an abnormal transaction is detected. The checker can also analyze a user's past deposit patterns in real time, detect abnormal transactions, and issue a warning. This allows users to prevent fraud by analyzing a user's past deposit patterns, detecting abnormal transactions, and issuing a warning.

[0041] The checker can cooperate with different financial institutions and payment platforms to build an extensive anti-fraud network. The checker, for example, cooperates with different financial institutions to build an extensive anti-fraud network. For example, it cooperates with multiple banks to share fraud information. The checker can also cooperate with different payment platforms to build a system that strengthens anti-fraud measures. The checker can also cooperate with different financial institutions and payment platforms in real time to build an extensive anti-fraud network. By cooperating with different financial institutions and payment platforms to build an extensive anti-fraud network, users can prevent themselves from falling victim to fraud.

[0042] The checker can be equipped with a function that analyzes the content of a user's transactions and automatically suspends the transaction if an abnormal transaction is detected. For example, the checker can analyze the content of a user's transactions using a generation AI and automatically suspend the transaction if an abnormal transaction is detected. For example, it can detect transactions of abnormal amounts or frequencies. The checker can also build a system that analyzes the content of a user's transactions and automatically suspends the transaction if an abnormal transaction occurs. The checker can also be equipped with a function that analyzes the content of a user's transactions in real time and automatically suspends the transaction if an abnormal transaction is detected. This allows users to prevent fraud by analyzing the content of a user's transactions and automatically suspending the transaction if an abnormal transaction is detected.

[0043] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0044] The investment fraud prevention system may further include an anomaly detection unit that analyzes a user's transaction history and detects abnormal transaction patterns. For example, the anomaly detection unit may detect large transactions that differ from normal transaction patterns or transactions that are repeated within a short period of time. The anomaly detection unit may also learn past transaction patterns associated with fraud based on the user's transaction history and detect similar transactions. Furthermore, the anomaly detection unit may monitor transactions in real time and issue a warning if an abnormal transaction occurs. This allows users to prevent fraud before it occurs.

[0045] The investment fraud prevention system can further include a risk assessment unit that assesses the risk level of a transaction based on the user's transaction history. For example, the risk assessment unit can analyze past transaction data and identify high-risk transaction patterns. The risk assessment unit can also calculate a risk score based on transaction amount, frequency, and transaction partner information. Furthermore, the risk assessment unit can issue a detailed warning to the user if a high-risk transaction is detected. This allows the user to prevent fraud before it occurs.

[0046] The investment fraud prevention system may further include a reliability evaluation unit that evaluates the reliability of transactions based on the user's transaction history. For example, the reliability evaluation unit may analyze past transaction data and identify highly reliable transaction patterns. The reliability evaluation unit may also calculate a reliability score based on information about the trading partner and the content of the transaction. Furthermore, the reliability evaluation unit may issue a detailed warning to the user if an unreliable transaction is detected. This allows the user to prevent fraud before it occurs.

[0047] The investment fraud prevention system can further include a safety assessment unit that assesses the safety of transactions based on the user's transaction history. For example, the safety assessment unit can analyze past transaction data and identify highly secure transaction patterns. The safety assessment unit can also calculate a safety score based on information about the trading partner and the content of the transaction. Furthermore, the safety assessment unit can issue a detailed warning to the user if a low-security transaction is detected. This allows the user to prevent fraud before it occurs.

[0048] The investment fraud prevention system can further include a transparency evaluation unit that evaluates the transparency of transactions based on the user's transaction history. For example, the transparency evaluation unit analyzes past transaction data and identifies highly transparent transaction patterns. The transparency evaluation unit can also calculate a transparency score based on information about the trading partner and the content of the transaction. Furthermore, the transparency evaluation unit can issue a detailed warning to the user if a transaction with low transparency is detected. This allows the user to prevent fraud before it occurs.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The generative AI analyzes fraud methods. For example, the generative AI can use deep learning to learn fraud methods and detect signs of fraud. The generative AI can also use natural language processing technology to analyze the content of social media advertisements and determine whether they are fraudulent. Furthermore, the generative AI can detect new fraud methods based on past fraud data. Step 2: The checker checks for possible fraud just before the user actually makes a deposit. For example, the checker analyzes the deposit recipient's information to determine whether it has been used for fraud in the past. The checker can also analyze the transaction details to determine the possibility of fraud if the promise of excessively high profits or the message urges the user to make a quick deposit. Furthermore, the checker can analyze the user's past transaction history to detect patterns similar to fraudulent methods. Step 3: The warning unit issues a warning to the user based on the possible fraud detected by the checker. For example, the warning unit may issue a warning to the user using a pop-up notification. The warning unit may also issue a warning to the user using an email notification or a voice notification.

[0051] (Example 2) The investment fraud prevention system according to an embodiment of the present invention is a system for preventing investment fraud triggered by solicitations via social media. This system uses a generation AI to analyze fraudulent methods, check for the possibility of fraud just before a deposit is made, and issue a warning. This allows the investment fraud prevention system to prevent users from falling victim to fraud.

[0052] An investment fraud prevention system according to an embodiment includes a generation AI, a checker, and a warning unit. The generation AI analyzes fraudulent methods. For example, the generation AI uses deep learning to learn fraudulent methods and detect signs of fraud. The generation AI can also use natural language processing technology to analyze the content of social media advertisements and determine the possibility of fraud. The generation AI can also detect new fraudulent methods based on past fraud data. The checker checks for the possibility of fraud just before a user actually makes a deposit. For example, the checker analyzes the deposit destination information to determine whether it has been used for fraud in the past. The checker can also analyze the transaction content and determine the possibility of fraud if the advertisement promises excessively high profits or urges the user to make a quick deposit. The checker can also analyze the user's past transaction history to detect patterns similar to fraudulent methods. The warning unit issues a warning to the user based on the possibility of fraud detected by the checker. For example, the warning unit can issue a warning to the user using a pop-up notification. The warning unit can also issue a warning to the user using an email notification. The warning unit can also issue a warning to the user using a voice notification. This allows the investment fraud prevention system according to the embodiment to prevent users from falling victim to fraud. For example, users can avoid falling victim to fraud by checking for the possibility of fraud just before making a deposit and receiving a warning. Furthermore, users can deepen their knowledge of fraud by obtaining information about fraudulent methods.

[0053] The generation AI can analyze the content of social media ads and determine that they are likely to be fraudulent if they claim excessively high profits. For example, the generation AI can analyze the content of social media ads using deep learning and determine that they are likely to be fraudulent if they claim excessively high profits. The generation AI can also analyze the content of social media ads using natural language processing technology and determine that they are likely to be fraudulent if they claim excessively high profits. The generation AI can also analyze the content of social media ads based on past fraud data and determine that they are likely to be fraudulent if they claim excessively high profits. This allows users to prevent themselves from becoming victims of fraud by analyzing the content of social media ads and determining that they are likely to be fraudulent if they claim excessively high profits.

[0054] The generation AI can analyze the recipient's account information and issue a warning if it has been used for fraud in the past. For example, the generation AI can analyze the recipient's account information using deep learning and issue a warning if it has been used for fraud in the past. The generation AI can also analyze the recipient's account information based on past fraud data and issue a warning if it has been used for fraud in the past. The generation AI can also analyze the recipient's account information using natural language processing technology and issue a warning if it has been used for fraud in the past. In this way, by analyzing the recipient's account information and issuing a warning if it has been used for fraud in the past, users can prevent themselves from becoming victims of fraud.

[0055] The generation AI can analyze transaction details and determine that a transaction is likely to be fraudulent if it advertises excessively high profits or urges users to make a quick deposit. For example, the generation AI can analyze transaction details using deep learning and determine that a transaction is likely to be fraudulent if it advertises excessively high profits or urges users to make a quick deposit. The generation AI can also analyze transaction details using natural language processing technology and determine that a transaction is likely to be fraudulent if it advertises excessively high profits or urges users to make a quick deposit. The generation AI can also analyze transaction details based on past fraud data and determine that a transaction is likely to be fraudulent if it advertises excessively high profits or urges users to make a quick deposit. By analyzing transaction details and determining that a transaction is likely to be fraudulent if it advertises excessively high profits or urges users to make a quick deposit, users can prevent themselves from falling victim to fraud.

[0056] The generative AI can be equipped with an educational function that provides users with information about fraud methods and signs of fraud. For example, the generative AI can translate data about fraud methods into different languages ​​and build an international database of fraud methods. The generative AI can also share data about fraud methods with experts in different industries and fields to strengthen cross-domain fraud prevention measures. The generative AI can also use an emotion estimation function to generate educational content about fraud methods and provide it in a format that users can easily empathize with. By providing users with information about fraud methods and signs of fraud, users can deepen their knowledge of fraud and prevent themselves from becoming victims of fraud.

[0057] The generation AI can have the function of accepting feedback from users, learning fraudulent methods based on that information, and issuing warnings to other users. The generation AI can, for example, accept feedback from users, learn fraudulent methods based on that information, and issue warnings to other users. For example, if a user reports that a transaction may be fraudulent, the generation AI can learn fraudulent methods based on that information and issue warnings to other users. The generation AI can also learn fraudulent methods based on user feedback and improve its accuracy in detecting signs of fraud. The generation AI can also learn fraudulent methods based on user feedback and quickly detect new fraudulent methods. This makes it possible to prevent fraud by learning fraudulent methods based on user feedback and issuing warnings to other users.

[0058] Generative AI can analyze the latest news articles and social media posts about fraud methods in real time, allowing it to quickly detect new fraud methods. For example, generative AI can automatically collect the latest news articles about fraud methods and analyze them in real time. For example, it can extract articles about fraud from news sites and detect new fraud methods. Generative AI can also analyze social media posts to collect information about fraud methods in real time. For example, it can monitor posts on Twitter and Facebook to detect signs of fraud. Generative AI can also analyze posts on forums and message boards about fraud methods and quickly detect new fraud methods. This allows users to prevent themselves from becoming victims of fraud by analyzing the latest news articles and social media posts in real time and quickly detecting new fraud methods.

[0059] The generation AI can analyze a user's past transaction history, detect patterns similar to fraudulent methods, and issue a warning. For example, the generation AI can analyze a user's past transaction history using deep learning, detect patterns similar to fraudulent methods, and issue a warning. The generation AI can also identify signs of fraud based on past transaction data and issue a warning. The generation AI can also monitor a user's transaction history in real time, detect patterns similar to fraudulent methods, and issue a warning. In this way, by analyzing a user's past transaction history, detecting patterns similar to fraudulent methods, and issuing a warning, users can prevent themselves from becoming victims of fraud.

[0060] The generation AI can use the emotion estimation function to analyze the user's emotional reactions to fraudulent methods and prioritize warnings about methods that cause particular anxiety or fear. The generation AI, for example, uses the emotion estimation function to analyze the anxiety or fear that the user feels about fraudulent methods. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The generation AI can also prioritize warnings about fraudulent methods that cause particular anxiety or fear based on the user's emotional reactions. The generation AI can also analyze the user's emotions in real time and prioritize warnings about methods that cause particular anxiety or fear. In this way, by using the emotion estimation function to analyze the user's emotional reactions and prioritize warnings about methods that cause particular anxiety or fear, users can prevent themselves from becoming victims of fraud.

[0061] The generative AI can translate data related to fraud methods into different languages ​​and build an international database of fraud methods. For example, the generative AI can automatically translate data related to fraud methods and build an international database of fraud methods. For example, it can translate data into multiple languages, such as English, Japanese, and Chinese. The generative AI can also collect, translate, and integrate data on fraud methods in different languages. The generative AI can also translate data related to fraud methods in real time and build an international database of fraud methods. This allows users to prevent themselves from becoming victims of fraud by translating data related to fraud methods into different languages ​​and building an international database of fraud methods.

[0062] Generative AI can share data on fraud methods with experts from different industries and fields to strengthen cross-domain fraud prevention measures. For example, generative AI can share data on fraud methods with experts from different industries to strengthen cross-domain fraud prevention measures. For example, experts from the financial and IT industries can collaborate to combat fraud. Generative AI can also share data on fraud methods with experts from different fields to build a system for joint fraud prevention measures. Generative AI can also share data on fraud methods with experts from different industries and fields in real time to strengthen cross-domain fraud prevention measures. By sharing data on fraud methods with experts from different industries and fields and strengthening cross-domain fraud prevention measures, users can prevent themselves from becoming victims of fraud.

[0063] The generation AI can use the emotion estimation function to generate educational content about fraudulent methods and provide it in a format that users can easily empathize with emotionally. The generation AI, for example, uses the emotion estimation function to generate educational content about fraudulent methods. For example, it creates content in the form of a story that users can easily empathize with emotionally. The generation AI can also customize educational content about fraudulent methods based on the user's emotional reactions. The generation AI can also use the emotion estimation function to generate educational content about fraudulent methods in real time and provide it to users. In this way, by using the emotion estimation function to generate educational content about fraudulent methods and providing it in a format that users can easily empathize with emotionally, users can prevent themselves from becoming victims of fraud.

[0064] The checker can compare the information of the recipient of the deposit with a past fraud database and issue a detailed warning if there is a high possibility of fraud. For example, the checker builds a system that compares the information of the recipient of the deposit with a past fraud database. For example, it compares the account information of the recipient of the deposit with the database and determines the possibility of fraud. The checker can also analyze the information of the recipient of the deposit with a generative AI, compare it with a past fraud database, and issue a warning. The checker can also compare the information of the recipient of the deposit with a past fraud database in real time and issue a detailed warning if there is a high possibility of fraud. In this way, by comparing the information of the recipient of the deposit with a past fraud database and issuing a detailed warning if there is a high possibility of fraud, users can prevent themselves from becoming victims of fraud.

[0065] The checker can analyze a user's past deposit patterns, detect abnormal transactions, and issue a warning. For example, the checker can analyze a user's past deposit patterns using generative AI to detect abnormal transactions. For example, it can identify transactions of unusual amounts or frequencies. The checker can also build a system that analyzes a user's past deposit patterns and issues a warning if an abnormal transaction is detected. The checker can also analyze a user's past deposit patterns in real time, detect abnormal transactions, and issue a warning. This allows users to prevent fraud by analyzing a user's past deposit patterns, detecting abnormal transactions, and issuing a warning.

[0066] The checker can use the emotion estimation function to analyze the anxiety and doubt a user feels just before making a deposit and customize a warning based on that emotion. The checker, for example, uses the emotion estimation function to analyze the anxiety and doubt a user feels just before making a deposit. For example, the checker can analyze the user's facial expressions and voice and calculate an emotion score. The checker can also build a system that customizes a warning just before making a deposit based on the user's emotional response. The checker can also use the emotion estimation function to analyze the emotions a user feels just before making a deposit in real time and customize a warning based on that emotion. In this way, by using the emotion estimation function to analyze the anxiety and doubt a user feels just before making a deposit and customizing a warning based on that emotion, users can prevent themselves from becoming victims of fraud.

[0067] The checker can cooperate with different financial institutions and payment platforms to build an extensive anti-fraud network. The checker, for example, cooperates with different financial institutions to build an extensive anti-fraud network. For example, it cooperates with multiple banks to share fraud information. The checker can also cooperate with different payment platforms to build a system that strengthens anti-fraud measures. The checker can also cooperate with different financial institutions and payment platforms in real time to build an extensive anti-fraud network. By cooperating with different financial institutions and payment platforms to build an extensive anti-fraud network, users can prevent themselves from falling victim to fraud.

[0068] The checker can be equipped with a function that analyzes the content of a user's transactions and automatically suspends the transaction if an abnormal transaction is detected. For example, the checker can analyze the content of a user's transactions using a generation AI and automatically suspend the transaction if an abnormal transaction is detected. For example, it can detect transactions of abnormal amounts or frequencies. The checker can also build a system that analyzes the content of a user's transactions and automatically suspends the transaction if an abnormal transaction occurs. The checker can also be equipped with a function that analyzes the content of a user's transactions in real time and automatically suspends the transaction if an abnormal transaction is detected. This allows users to prevent fraud by analyzing the content of a user's transactions and automatically suspending the transaction if an abnormal transaction is detected.

[0069] The checker can use the emotion estimation function to monitor the user's emotional state in real time and prevent transactions when the user is in an emotionally unstable state. The checker, for example, uses the emotion estimation function to monitor the user's emotional state in real time. For example, the checker analyzes the user's facial expressions and voice and calculates an emotion score. The checker can also build a system that prevents transactions when the user is in an emotionally unstable state based on the user's emotional state. The checker can also use the emotion estimation function to monitor the user's emotional state in real time and prevent transactions when the user is in an emotionally unstable state. In this way, by using the emotion estimation function to monitor the user's emotional state in real time and preventing transactions when the user is in an emotionally unstable state, the user can prevent fraud before it occurs.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The investment fraud prevention system may further include an anomaly detection unit that analyzes a user's transaction history and detects abnormal transaction patterns. For example, the anomaly detection unit may detect large transactions that differ from normal transaction patterns or transactions that are repeated within a short period of time. The anomaly detection unit may also learn past transaction patterns associated with fraud based on the user's transaction history and detect similar transactions. Furthermore, the anomaly detection unit may monitor transactions in real time and issue a warning if an abnormal transaction occurs. This allows users to prevent fraud before it occurs.

[0072] The investment fraud prevention system may further include an emotion response unit that estimates the user's emotion and customizes the content of the warning based on the estimated emotion. For example, the emotion response unit issues a more detailed warning when the user feels anxious or suspicious. The emotion response unit may also issue a concise warning when the user feels secure. Furthermore, the emotion response unit may monitor the user's emotional state in real time and adjust the content of the warning according to changes in emotion. This allows the user to prevent fraud before it occurs.

[0073] The investment fraud prevention system can further include a risk assessment unit that assesses the risk level of a transaction based on the user's transaction history. For example, the risk assessment unit can analyze past transaction data and identify high-risk transaction patterns. The risk assessment unit can also calculate a risk score based on transaction amount, frequency, and transaction partner information. Furthermore, the risk assessment unit can issue a detailed warning to the user if a high-risk transaction is detected. This allows the user to prevent fraud before it occurs.

[0074] The investment fraud prevention system may further include an emotional risk assessment unit that estimates the user's emotions and assesses the risk level of a transaction based on the estimated emotions. For example, the emotional risk assessment unit may assess the risk level of a transaction as high if the user feels anxious or fearful. The emotional risk assessment unit may also assess the risk level of a transaction as low if the user feels a sense of security. Furthermore, the emotional risk assessment unit may monitor the user's emotional state in real time and adjust the risk level according to changes in emotions. This allows the user to prevent fraud before it occurs.

[0075] The investment fraud prevention system may further include a reliability evaluation unit that evaluates the reliability of transactions based on the user's transaction history. For example, the reliability evaluation unit may analyze past transaction data and identify highly reliable transaction patterns. The reliability evaluation unit may also calculate a reliability score based on information about the trading partner and the content of the transaction. Furthermore, the reliability evaluation unit may issue a detailed warning to the user if an unreliable transaction is detected. This allows the user to prevent fraud before it occurs.

[0076] The investment fraud prevention system may further include an emotion reliability evaluation unit that estimates the user's emotions and evaluates the reliability of the transaction based on the estimated emotions. For example, the emotion reliability evaluation unit may evaluate the reliability of the transaction as low if the user feels anxious or suspicious. The emotion reliability evaluation unit may also evaluate the reliability of the transaction as high if the user feels a sense of security. Furthermore, the emotion reliability evaluation unit may monitor the user's emotional state in real time and adjust the reliability according to changes in emotions. This allows the user to prevent fraud before it occurs.

[0077] The investment fraud prevention system can further include a safety assessment unit that assesses the safety of transactions based on the user's transaction history. For example, the safety assessment unit can analyze past transaction data and identify highly secure transaction patterns. The safety assessment unit can also calculate a safety score based on information about the trading partner and the content of the transaction. Furthermore, the safety assessment unit can issue a detailed warning to the user if a low-security transaction is detected. This allows the user to prevent fraud before it occurs.

[0078] The investment fraud prevention system may further include an emotional safety assessment unit that estimates the user's emotions and assesses the safety of the transaction based on the estimated emotions. For example, the emotional safety assessment unit may assess the safety of the transaction as low if the user feels anxious or fearful. The emotional safety assessment unit may also assess the safety of the transaction as high if the user feels secure. Furthermore, the emotional safety assessment unit may monitor the user's emotional state in real time and adjust safety according to changes in emotions. This allows users to prevent themselves from falling victim to fraud.

[0079] The investment fraud prevention system can further include a transparency evaluation unit that evaluates the transparency of transactions based on the user's transaction history. For example, the transparency evaluation unit analyzes past transaction data and identifies highly transparent transaction patterns. The transparency evaluation unit can also calculate a transparency score based on information about the trading partner and the content of the transaction. Furthermore, the transparency evaluation unit can issue a detailed warning to the user if a transaction with low transparency is detected. This allows the user to prevent fraud before it occurs.

[0080] The investment fraud prevention system may further include an emotion transparency evaluation unit that estimates the user's emotion and evaluates the transparency of the transaction based on the estimated emotion. For example, the emotion transparency evaluation unit may evaluate the transparency of the transaction as low if the user feels anxious or suspicious. The emotion transparency evaluation unit may also evaluate the transparency of the transaction as high if the user feels secure. Furthermore, the emotion transparency evaluation unit may monitor the user's emotional state in real time and adjust the transparency according to changes in emotion. This allows the user to prevent fraud before it occurs.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The generative AI analyzes fraud methods. For example, the generative AI can use deep learning to learn fraud methods and detect signs of fraud. The generative AI can also use natural language processing technology to analyze the content of social media advertisements and determine whether they are fraudulent. Furthermore, the generative AI can detect new fraud methods based on past fraud data. Step 2: The checker checks for possible fraud just before the user actually makes a deposit. For example, the checker analyzes the deposit recipient's information to determine whether it has been used for fraud in the past. The checker can also analyze the transaction details to determine the possibility of fraud if the promise of excessively high profits or the message urges the user to make a quick deposit. Furthermore, the checker can analyze the user's past transaction history to detect patterns similar to fraudulent methods. Step 3: The warning unit issues a warning to the user based on the possible fraud detected by the checker. For example, the warning unit may issue a warning to the user using a pop-up notification. The warning unit may also issue a warning to the user using an email notification or a voice notification.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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. Generative AI that analyzes fraud methods using generative AI, A checker that checks for possible fraud just before the user actually makes a deposit, and a warning unit that issues a warning to a user based on the possibility of fraud detected by the checker. A system characterized by:

2. The generated AI is Analyze the content of the SNS advertisement and determine that excessively high profits are likely to be fraudulent.

2. The system of claim 1.

3. The generated AI is Analyzes the account information of the recipient and issues a warning if the account has been used for the above fraud in the past.

2. The system of claim 1.

4. The generated AI is Analyze the transaction details and determine that there is a high possibility of fraud if excessively high profits are being advertised or if the content urges the user to make a quick deposit.

2. The system of claim 1.

5. The generated AI is An educational function is provided to provide the user with information about the fraud methods and signs of the fraud.

2. The system of claim 1.

6. The generated AI is The system has a function of receiving feedback from the user, learning the fraudulent methods based on the information, and issuing the warning to other users.

2. The system of claim 1.

7. The generated AI is Analyze the latest news articles and social media posts related to the fraud methods in real time to quickly detect new methods of the fraud.

2. The system of claim 1.

8. The generated AI is Analyzing the user's past transaction history, detecting patterns similar to the fraudulent methods, and issuing the warning.

2. The system of claim 1.

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