System

Abstract

An object of a system according to an embodiment is to analyze transaction data, detect a transaction having a possibility of fraud, and warn a user.SOLUTION: A system includes a transaction data collection part, an analysis part, and a warning part. The transaction data collection unit collects transaction data. The analysis unit analyzes the transaction data collected by the transaction data collection unit and detects a transaction that may be a fraud. The alert unit issues an alert message to the user based on the potentially fraudulent transaction detected by the analysis unit.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 of not being able to quickly and accurately detect potentially fraudulent transactions based on transaction data and warn users accordingly.

[0005] The system according to the embodiment aims to analyze transaction data, detect potentially fraudulent transactions, and warn users. [Means for solving the problem]

[0006] The system according to the embodiment includes a transaction data collection unit, an analysis unit, and a warning unit. The transaction data collection unit collects transaction data. The analysis unit analyzes the transaction data collected by the transaction data collection unit to detect potentially fraudulent transactions. The warning unit issues a warning message to a user based on the potentially fraudulent transactions detected by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze transaction data, detect potentially fraudulent transactions, and warn users. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 fraud detection system according to an embodiment of the present invention automatically collects user transaction data, analyzes it using a generation AI, detects potentially fraudulent transactions, and issues a warning to the user. This allows the fraud detection system to protect user assets and enable rapid response.

[0029] A fraud detection system according to an embodiment includes a transaction data collection unit, an analysis unit, and a warning unit. The transaction data collection unit collects user transaction data. For example, the transaction data collection unit collects bank transaction data, credit card transaction data, and online shopping transaction data. The transaction data collection unit can also collect user transaction data in real time. For example, the transaction data is obtained through an API. The analysis unit analyzes the transaction data collected by the transaction data collection unit to detect potentially fraudulent transactions. For example, the generation AI analyzes the transaction data using a machine learning algorithm to detect abnormal transaction patterns. The generation AI can also perform rule-based analysis to identify potentially fraudulent transactions. For example, the generation AI analyzes information such as transaction amount, frequency, and customer to detect abnormal patterns. The warning unit issues a warning message to the user based on the potentially fraudulent transaction detected by the analysis unit. For example, the warning unit sends a notification to the user's smartphone to notify them that a potentially fraudulent transaction has occurred. The warning unit can also send a warning message to the user's email address. For example, the warning unit sends an email containing detailed information about the potentially fraudulent transaction. As a result, the fraud detection system according to the embodiment can protect the user's assets and enable a prompt response, for example, by receiving a warning message, the user can quickly check the transaction and take necessary measures.

[0030] When analyzing transaction data, the analysis unit also takes into account the user's past consumption behavior or lifestyle data, enabling more accurate anomaly detection. In the analysis unit, for example, the generation AI collects the user's past consumption behavior data and learns normal consumption patterns. For example, it analyzes data on stores and services the user regularly uses to detect abnormal transactions. In addition, the analysis unit uses the generation AI to detect anomalies based on the user's lifestyle data. For example, it determines that transactions in an area different from the user's usual living area are abnormal. In addition, the analysis unit uses the generation AI to integrate the user's past consumption behavior and lifestyle data and detect abnormal transaction patterns with high accuracy. For example, it determines that transactions that differ significantly from the user's normal consumption pattern are abnormal. In this way, by taking the user's past consumption behavior and lifestyle data into account, the accuracy of anomaly detection is improved.

[0031] When analyzing transaction data, the analysis unit can detect abnormal transactions based on the geographical information of the transactions. For example, the generation AI analyzes the geographical information included in the transaction data and determines that transactions in locations different from the user's usual transaction area are abnormal. For example, it detects large withdrawals in areas that the user does not normally visit. The analysis unit also learns the user's usual transaction area and determines that transactions outside that area are abnormal. For example, it identifies transactions made while the user is traveling and issues a warning as an abnormal transaction. The analysis unit also detects abnormal transaction patterns based on the geographical information of the transactions. For example, it determines that transactions outside the user's usual living area are abnormal and issues a warning. In this way, by taking the geographical information of the transactions into consideration, the accuracy of detecting abnormal transactions is improved.

[0032] The transaction data collection unit can detect abnormal transaction patterns, including the user's social media activity or online shopping history. In the transaction data collection unit, for example, the generation AI collects the user's social media activity data and learns normal behavioral patterns. For example, it analyzes the user's posting content and activity time to detect abnormal transactions. In addition, the transaction data collection unit detects abnormal transaction patterns based on the user's online shopping history. For example, it determines large transactions that differ from normal purchasing patterns as abnormal. In addition, the generation AI integrates the user's social media activity and online shopping history to detect abnormal transaction patterns with high accuracy. For example, it determines transactions that differ significantly from normal behavioral patterns as abnormal. In this way, by taking the user's social media activity and online shopping history into consideration, the accuracy of detecting abnormal transaction patterns is improved.

[0033] The transaction data collection unit also integrates data from other financial institutions, allowing it to detect anomalies from a broader perspective. For example, the generation AI in the transaction data collection unit collects transaction data from other financial institutions and learns the user's overall transaction patterns. For example, it detects abnormal fund transfers between multiple accounts. The generation AI in the transaction data collection unit also detects abnormal transaction patterns based on data from other financial institutions. For example, it determines that large withdrawals from different financial institutions over the same period are abnormal. The generation AI in the transaction data collection unit also integrates data from other financial institutions and detects abnormal transaction patterns with high accuracy. For example, it analyzes transaction data from multiple financial institutions and detects abnormal transactions. In this way, by integrating data from other financial institutions, the accuracy of anomaly detection is improved.

[0034] When analyzing transaction data, the analysis unit also takes into account the time of day or day of the week of the transaction, and can determine that transactions that occur at times that differ from normal transaction patterns are abnormal. For example, the analysis unit uses the generation AI to analyze information about the time of day and day of the week contained in the transaction data and determine that transactions that occur at times that differ from normal transaction patterns are abnormal. For example, it detects large withdrawals of amounts late at night or on holidays. The analysis unit also learns the user's normal trading hours and determines that transactions that occur outside of those hours are abnormal. For example, it detects transactions that occur at times that differ from daytime transactions on weekdays. The analysis unit also detects abnormal transaction patterns based on the generation AI's time of day and day of the week. For example, it determines that transactions that occur at times that differ significantly from normal trading hours are abnormal and issues an alert. In this way, by taking the time of day and day of the week of the transaction into account, the accuracy of detecting abnormal transactions is improved.

[0035] When analyzing transaction data, the analysis unit can determine that transactions with counterparties with low credit ratings are abnormal based on the credit information of the counterparties. For example, the generation AI analyzes the credit information of counterparties included in the transaction data and determines that transactions with counterparties with low credit ratings are abnormal. For example, it detects large transactions with counterparties with low credit scores. The analysis unit also detects abnormal transactions based on the credit information of the user's transaction counterparties by the generation AI. For example, it determines that frequent transactions with counterparties with low credit ratings are abnormal. The analysis unit also detects abnormal transaction patterns based on the credit information of the transaction counterparties by the generation AI. For example, it determines that transactions with counterparties with low credit scores are abnormal and issues a warning. In this way, the accuracy of detecting abnormal transactions is improved by taking into account the credit information of the transaction counterparties.

[0036] The analysis unit can detect similar patterns based on the user's past fraud victim history. For example, the analysis unit uses the generation AI to analyze the user's past fraud victim history and detect similar patterns. For example, it determines transactions that are similar to transaction patterns that resulted in past fraud victimization as abnormal. The analysis unit also uses the generation AI to detect abnormal transactions based on the user's past fraud victimization history. For example, it particularly carefully monitors transactions with business partners that have previously been fraud victimizations. The analysis unit also builds a system in which the generation AI references the user's past fraud victimization history and detects similar patterns. For example, it determines transactions that match past fraud victimization patterns as abnormal. This improves the accuracy of detecting similar fraud patterns by referencing the user's past fraud victimization history.

[0037] The analysis unit can detect abnormal transaction patterns by comparing with the transaction data of other users. In the analysis unit, for example, the generation AI collects transaction data of other users and detects abnormal transaction patterns. For example, it detects abnormal transactions by comparing with other users of the same financial institution. In addition, the analysis unit detects abnormal transaction patterns by the generation AI based on the transaction data of other users. For example, it detects abnormal transactions by comparing with other users in the same region. In addition, the analysis unit integrates the transaction data of other users and detects abnormal transaction patterns with high accuracy. For example, it detects abnormal transactions by comparing with other users who trade with the same trading partner. As a result, by comparing with the transaction data of other users, the accuracy of detecting abnormal transaction patterns is improved.

[0038] When the warning unit detects a transaction that may be fraudulent, it can not only send a warning message to the user but also suggest specific countermeasures. For example, when the generation AI detects a transaction that may be fraudulent, the warning unit will suggest specific countermeasures to the user along with the warning message. For example, it will provide instructions on how to suspend the transaction or how to contact a financial institution. The warning unit will also include specific countermeasures when sending a warning message to the user. For example, it will provide instructions on how to cancel a transaction that may be fraudulent or how to report it to the police. The warning unit will also build a system that, when the generation AI detects a transaction that may be fraudulent, will suggest specific countermeasures to the user along with the warning message. For example, it will provide instructions on how to suspend the transaction or how to contact a financial institution. This allows for a quick response by suggesting specific countermeasures to the user.

[0039] When the warning unit detects a transaction that may be fraudulent, it can select the most appropriate warning method based on the user's past response history. For example, when the generation AI detects a transaction that may be fraudulent, the warning unit refers to the user's past response history and selects the most appropriate warning method. For example, it prioritizes the use of warning methods that have been effective in the past. The warning unit also selects the most appropriate warning method based on the user's past response history. For example, it reuses warning methods that have been quickly used in the past. The warning unit also builds a system where, when the generation AI detects a transaction that may be fraudulent, it refers to the user's past response history and selects the most appropriate warning method. For example, it prioritizes the use of warning methods that have been effective in the past. In this way, the most appropriate warning method can be selected by referring to the user's past response history.

[0040] The warning unit can also issue a warning to the user's family or a trusted third party if it detects a transaction that may be fraudulent. For example, the warning unit builds a system that issues a warning to the user's family and a trusted third party if the generation AI detects a transaction that may be fraudulent. For example, it sends a notification to contacts registered by the user in advance. The warning unit also encourages a prompt response by issuing a warning to the user's family and a trusted third party. For example, it enables family and a third party to respond even if the user does not notice a transaction that may be fraudulent. The warning unit also builds a system that issues a warning to the user's family and a trusted third party if the generation AI detects a transaction that may be fraudulent. For example, it sends a notification to contacts registered by the user in advance. This enables a prompt response by issuing a warning to the user's family and a trusted third party.

[0041] The warning unit can also issue a warning to the user's smart device if it detects a transaction that may be fraudulent. For example, the warning unit builds a system that issues a warning to the user's smart device if the generation AI detects a transaction that may be fraudulent. For example, the warning unit sends a notification to a smartwatch to issue a warning to the user. The warning unit also encourages a prompt response by issuing a warning through the user's smart device. For example, the warning unit issues a voice warning through a smart speaker. The warning unit also builds a system that issues a warning to the user's smart device if the generation AI detects a transaction that may be fraudulent. For example, the warning unit sends a notification to a smartwatch to issue a warning to the user. This enables a prompt response by issuing a warning to the user's smart device as well.

[0042] If the analysis unit detects an abnormal transaction, it not only suspends the transaction but also provides detailed information about the transaction to the user, thereby supporting rapid decision-making. For example, if the generation AI detects an abnormal transaction, the analysis unit suspends the transaction and provides detailed information about the transaction to the user. For example, it displays information such as the transaction amount, the trading partner, and the date and time of the transaction. The analysis unit also supports rapid decision-making by providing detailed information about the transaction to the user. For example, it checks the content of the transaction and, if there are no problems, guides the user through the steps to resume the transaction. The analysis unit also builds a system in which, if the generation AI detects an abnormal transaction, it suspends the transaction and provides detailed information about the transaction to the user. For example, it displays information such as the transaction amount, the trading partner, and the date and time of the transaction. This allows the user to make rapid decision-making by providing detailed information about the transaction.

[0043] If the analysis unit detects an abnormal transaction, it not only suspends the transaction but also compares it with the user's past transaction history to evaluate the degree of abnormality. For example, if the generation AI detects an abnormal transaction, the analysis unit suspends the transaction and compares it with the user's past transaction history to evaluate the degree of abnormality. For example, it identifies an abnormal transaction by comparing it with past transaction patterns. The analysis unit also evaluates the degree of abnormality based on the user's past transaction history. For example, it determines that a transaction that differs significantly from past transaction patterns is abnormal. The analysis unit also builds a system where, if the generation AI detects an abnormal transaction, it suspends the transaction and compares it with the user's past transaction history to evaluate the degree of abnormality. For example, it identifies an abnormal transaction by comparing it with past transaction patterns. This makes it possible to evaluate the degree of abnormality by comparing it with the user's past transaction history.

[0044] If the analysis unit detects an abnormal transaction, it can not only suspend the transaction but also issue a warning to the user's other financial accounts. For example, if the generation AI detects an abnormal transaction, the analysis unit builds a system that suspends the transaction and issues a warning to the user's other financial accounts. For example, if a user has multiple accounts, it issues a warning to all of the accounts. The analysis unit also issues a warning to the user's other financial accounts, encouraging a prompt response. For example, if an abnormal transaction is also being made in other accounts, it suspends all of the accounts. The analysis unit also builds a system that suspends the transaction and issues a warning to the user's other financial accounts if the generation AI detects an abnormal transaction. For example, if a user has multiple accounts, it issues a warning to all of the accounts. This enables a prompt response by issuing a warning to the user's other financial accounts.

[0045] If the analysis unit detects an abnormal transaction, it can not only suspend the transaction but also notify the user's credit information agency. For example, if the generation AI detects an abnormal transaction, the analysis unit builds a system that suspends the transaction and also notifies the user's credit information agency. For example, it provides information about the abnormal transaction to the credit information agency. The analysis unit also notifies the user's credit information agency, encouraging a prompt response. For example, if the abnormal transaction affects the credit information, it notifies the credit information agency. The analysis unit also builds a system that suspends the transaction and also notifies the user's credit information agency if the generation AI detects an abnormal transaction. For example, it provides information about the abnormal transaction to the credit information agency. This enables a prompt response by notifying the user's credit information agency.

[0046] When learning new fraud patterns, the analysis unit also integrates information from other financial institutions or security agencies, allowing it to learn a wider range of fraud patterns. In the analysis unit, for example, the generation AI collects information from other financial institutions and security agencies to learn new fraud patterns. For example, it learns fraud methods that have occurred at other financial institutions and detects anomalous transactions. The analysis unit also uses information from other financial institutions and security agencies to allow the generation AI to learn new fraud patterns. For example, it learns the latest fraud methods and detects anomalous transactions. The analysis unit also builds a system in which the generation AI integrates information from other financial institutions and security agencies to learn a wider range of fraud patterns. For example, it learns fraud methods that have occurred at other financial institutions and detects anomalous transactions. In this way, by integrating information from other financial institutions and security agencies, it is possible to learn a wider range of fraud patterns.

[0047] When learning a new fraud pattern, the analysis unit can reanalyze the user's past transaction data and reevaluate anomalies based on the new fraud pattern. For example, when the generation AI learns a new fraud pattern, the analysis unit reanalyzes the user's past transaction data and reevaluates anomalies based on the new fraud pattern. For example, the analysis unit reanalyzes the past transaction data and detects anomalies based on the new fraud pattern. The analysis unit also allows the generation AI to learn a new fraud pattern based on the user's past transaction data. For example, the analysis unit reanalyzes the past transaction data and detects anomalies based on the new fraud pattern. The analysis unit also builds a system in which, when the generation AI learns a new fraud pattern, the analysis unit reanalyzes the user's past transaction data and reevaluates anomalies based on the new fraud pattern. For example, the analysis unit reanalyzes the past transaction data and detects anomalies based on the new fraud pattern. In this way, anomalies based on the new fraud pattern can be reevaluated by reanalyzing the user's past transaction data.

[0048] When learning new fraud patterns, the analysis unit also learns fraud patterns from different industries, making it possible to integrate fraud techniques from different industries. For example, the analysis unit has the generation AI learn fraud patterns from different industries and integrate fraud techniques from different industries. For example, it learns fraud patterns from not only the financial industry, but also e-commerce and insurance industries. The analysis unit also has the generation AI learn new fraud patterns based on fraud patterns from different industries. For example, it integrates fraud techniques from different industries and detects abnormal transactions. The analysis unit also builds a system where the generation AI learns fraud patterns from different industries and integrates fraud techniques from different industries. For example, it learns fraud patterns from not only the financial industry, but also e-commerce and insurance industries. This makes it possible to integrate fraud techniques from different industries by learning fraud patterns from different industries.

[0049] When learning new fraud patterns, the analysis unit also takes into account the user's social media activity or online shopping history, allowing it to learn more multifaceted fraud patterns. In the analysis unit, for example, the generation AI collects the user's social media activity data and learns new fraud patterns. For example, it analyzes the content of the user's posts and the time of activity to identify fraud patterns. In addition, the analysis unit allows the generation AI to learn new fraud patterns based on the user's online shopping history. For example, it learns transactions that differ from normal purchasing patterns as fraud patterns. In addition, the analysis unit builds a system in which the generation AI integrates the user's social media activity and online shopping history to learn more multifaceted fraud patterns. For example, it learns transactions that differ significantly from normal behavior patterns as fraud patterns. In this way, by taking into account the user's social media activity and online shopping history, it is possible to learn more multifaceted fraud patterns.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] When analyzing a user's transaction data, the analysis unit also takes the user's health data into consideration and is able to detect abnormal transactions. For example, it monitors transactions particularly carefully during periods when the user's health condition is deteriorating. The analysis unit also uses the generation AI to detect abnormal transactions based on the user's health data. For example, it determines that transactions involving large amounts during periods when the user's health condition is deteriorating are abnormal. The analysis unit also integrates the user's health data and detects abnormal transaction patterns with high accuracy. For example, it determines that transactions during periods when the user's health condition is deteriorating are abnormal. In this way, by taking the user's health data into consideration, the accuracy of detecting abnormal transactions is improved.

[0052] When analyzing user transaction data, the analysis unit can detect abnormal transactions based on the user's hobbies and interests. For example, it will determine an abnormality if the user purchases a product or service that they do not normally purchase. The analysis unit also uses the generation AI to detect abnormal transactions based on the user's hobbies and interests. For example, it will determine an abnormality if the user purchases a product or service that differs from their usual purchasing pattern. The analysis unit also integrates the user's hobbies and interests to detect abnormal transaction patterns with high accuracy. For example, it will determine an abnormality if the user purchases a product or service that they do not normally purchase. In this way, by taking the user's hobbies and interests into consideration, the accuracy of detecting abnormal transactions is improved.

[0053] When analyzing a user's transaction data, the analysis unit can detect abnormal transactions based on the user's occupation and place of employment. For example, it may determine that a transaction of a large amount made during the user's working hours is abnormal. The analysis unit also uses the generation AI to detect abnormal transactions based on the user's occupation and place of employment. For example, it may determine that a transaction made outside of normal working hours is abnormal. The analysis unit also integrates the user's occupation and place of employment to detect abnormal transaction patterns with high accuracy. For example, it may determine that a transaction of a large amount made during working hours is abnormal. This improves the accuracy of detecting abnormal transactions by taking the user's occupation and place of employment into consideration.

[0054] When analyzing a user's transaction data, the analysis unit can detect abnormal transactions based on the user's family structure and living environment. For example, it will determine that an abnormality exists if the user's family uses a store or service that they do not normally use. The analysis unit also uses the generation AI to detect abnormal transactions based on the user's family structure and living environment. For example, it will determine that a transaction in an area that differs from the user's normal living environment is abnormal. The analysis unit also integrates the user's family structure and living environment to detect abnormal transaction patterns with high accuracy. For example, it will determine that an abnormality exists if the user uses a store or service that they do not normally use. This improves the accuracy of detecting abnormal transactions by taking the user's family structure and living environment into consideration.

[0055] When analyzing a user's transaction data, the analysis unit can detect abnormal transactions based on the user's travel history and business trip history. For example, it will determine that a large transaction in an area the user does not normally visit is abnormal. The analysis unit also uses the generation AI to detect abnormal transactions based on the user's travel history and business trip history. For example, it will determine that a transaction in an area different from the user's usual travel destination is abnormal. The analysis unit also integrates the user's travel history and business trip history to detect abnormal transaction patterns with high accuracy. For example, it will determine that a large transaction in an area the user does not normally visit is abnormal. This improves the accuracy of detecting abnormal transactions by taking the user's travel history and business trip history into consideration.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The transaction data collection unit collects user transaction data. For example, it collects bank transaction data, credit card transaction data, and online shopping transaction data. The transaction data collection unit can also collect user transaction data in real time. For example, it obtains transaction data through an API. Step 2: The analysis unit analyzes the transaction data collected by the transaction data collection unit to detect potentially fraudulent transactions. For example, the generation AI uses a machine learning algorithm to analyze the transaction data and detect abnormal transaction patterns. The generation AI can also perform rule-based analysis to identify potentially fraudulent transactions. For example, the generation AI analyzes information such as transaction amount, frequency, and counterparty to detect abnormal patterns. Step 3: The warning unit issues a warning message to the user based on the potentially fraudulent transaction detected by the analysis unit. For example, the warning unit may send a notification to the user's smartphone to inform the user that a potentially fraudulent transaction has occurred. The warning unit may also send a warning message to the user's email address. For example, the warning unit may send an email containing detailed information about the potentially fraudulent transaction.

[0058] (Example 2) The fraud detection system according to an embodiment of the present invention automatically collects user transaction data, analyzes it using a generation AI, detects potentially fraudulent transactions, and issues a warning to the user. This allows the fraud detection system to protect user assets and enable rapid response.

[0059] A fraud detection system according to an embodiment includes a transaction data collection unit, an analysis unit, and a warning unit. The transaction data collection unit collects user transaction data. For example, the transaction data collection unit collects bank transaction data, credit card transaction data, and online shopping transaction data. The transaction data collection unit can also collect user transaction data in real time. For example, the transaction data is obtained through an API. The analysis unit analyzes the transaction data collected by the transaction data collection unit to detect potentially fraudulent transactions. For example, the generation AI analyzes the transaction data using a machine learning algorithm to detect abnormal transaction patterns. The generation AI can also perform rule-based analysis to identify potentially fraudulent transactions. For example, the generation AI analyzes information such as transaction amount, frequency, and customer to detect abnormal patterns. The warning unit issues a warning message to the user based on the potentially fraudulent transaction detected by the analysis unit. For example, the warning unit sends a notification to the user's smartphone to notify them that a potentially fraudulent transaction has occurred. The warning unit can also send a warning message to the user's email address. For example, the warning unit sends an email containing detailed information about the potentially fraudulent transaction. As a result, the fraud detection system according to the embodiment can protect the user's assets and enable a prompt response, for example, by receiving a warning message, the user can quickly check the transaction and take necessary measures.

[0060] When analyzing transaction data, the analysis unit also takes into account the user's past consumption behavior or lifestyle data, enabling more accurate anomaly detection. In the analysis unit, for example, the generation AI collects the user's past consumption behavior data and learns normal consumption patterns. For example, it analyzes data on stores and services the user regularly uses to detect abnormal transactions. In addition, the analysis unit uses the generation AI to detect anomalies based on the user's lifestyle data. For example, it determines that transactions in an area different from the user's usual living area are abnormal. In addition, the analysis unit uses the generation AI to integrate the user's past consumption behavior and lifestyle data and detect abnormal transaction patterns with high accuracy. For example, it determines that transactions that differ significantly from the user's normal consumption pattern are abnormal. In this way, by taking the user's past consumption behavior and lifestyle data into account, the accuracy of anomaly detection is improved.

[0061] When analyzing transaction data, the analysis unit can detect abnormal transactions based on the geographical information of the transactions. For example, the generation AI analyzes the geographical information included in the transaction data and determines that transactions in locations different from the user's usual transaction area are abnormal. For example, it detects large withdrawals in areas that the user does not normally visit. The analysis unit also learns the user's usual transaction area and determines that transactions outside that area are abnormal. For example, it identifies transactions made while the user is traveling and issues a warning as an abnormal transaction. The analysis unit also detects abnormal transaction patterns based on the geographical information of the transactions. For example, it determines that transactions outside the user's usual living area are abnormal and issues a warning. In this way, by taking the geographical information of the transactions into consideration, the accuracy of detecting abnormal transactions is improved.

[0062] The analysis unit can use the emotion estimation function to analyze the user's emotional state and monitor transactions particularly carefully during periods of heightened stress or anxiety. The analysis unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and monitor transactions particularly carefully during periods of heightened stress or anxiety. For example, the analysis unit analyzes the user's facial expressions and voice and calculates an emotion score. The analysis unit also uses the generation AI to detect abnormal transactions based on the user's emotional state. For example, the analysis unit particularly carefully monitors transactions during periods with high emotion scores and detects abnormal transactions. The analysis unit also uses the emotion estimation function to build a system that analyzes the user's emotional state and monitors transactions particularly carefully during periods of heightened stress or anxiety. For example, the analysis unit determines that transactions during periods with high emotion scores are abnormal. This improves the accuracy of detecting abnormal transactions by taking the user's emotional state into consideration.

[0063] The transaction data collection unit can detect abnormal transaction patterns, including the user's social media activity or online shopping history. In the transaction data collection unit, for example, the generation AI collects the user's social media activity data and learns normal behavioral patterns. For example, it analyzes the user's posting content and activity time to detect abnormal transactions. In addition, the transaction data collection unit detects abnormal transaction patterns based on the user's online shopping history. For example, it determines large transactions that differ from normal purchasing patterns as abnormal. In addition, the generation AI integrates the user's social media activity and online shopping history to detect abnormal transaction patterns with high accuracy. For example, it determines transactions that differ significantly from normal behavioral patterns as abnormal. In this way, by taking the user's social media activity and online shopping history into consideration, the accuracy of detecting abnormal transaction patterns is improved.

[0064] The transaction data collection unit also integrates data from other financial institutions, allowing it to detect anomalies from a broader perspective. For example, the generation AI in the transaction data collection unit collects transaction data from other financial institutions and learns the user's overall transaction patterns. For example, it detects abnormal fund transfers between multiple accounts. The generation AI in the transaction data collection unit also detects abnormal transaction patterns based on data from other financial institutions. For example, it determines that large withdrawals from different financial institutions over the same period are abnormal. The generation AI in the transaction data collection unit also integrates data from other financial institutions and detects abnormal transaction patterns with high accuracy. For example, it analyzes transaction data from multiple financial institutions and detects abnormal transactions. In this way, by integrating data from other financial institutions, the accuracy of anomaly detection is improved.

[0065] The analysis unit can use the emotion estimation function to analyze the user's emotions when making a transaction in real time and issue a warning if an abnormal emotional state is detected. The analysis unit, for example, uses the emotion estimation function to analyze the user's emotions when making a transaction in real time and issue a warning if an abnormal emotional state is detected. For example, the analysis unit analyzes the user's facial expressions and voice and calculates an emotion score. The analysis unit also uses the generation AI to detect abnormal transactions based on the user's emotional state. For example, the analysis unit particularly carefully monitors transactions when the emotion score is high and detects abnormal transactions. The analysis unit also uses the emotion estimation function to build a system that analyzes the user's emotions when making a transaction in real time and issues a warning if an abnormal emotional state is detected. For example, it determines that transactions when the emotion score is high are abnormal. In this way, by analyzing the user's emotional state in real time, the accuracy of detecting abnormal transactions is improved.

[0066] When analyzing transaction data, the analysis unit also takes into account the time of day or day of the week of the transaction, and can determine that transactions that occur at times that differ from normal transaction patterns are abnormal. For example, the analysis unit uses the generation AI to analyze information about the time of day and day of the week contained in the transaction data and determine that transactions that occur at times that differ from normal transaction patterns are abnormal. For example, it detects large withdrawals of amounts late at night or on holidays. The analysis unit also learns the user's normal trading hours and determines that transactions that occur outside of those hours are abnormal. For example, it detects transactions that occur at times that differ from daytime transactions on weekdays. The analysis unit also detects abnormal transaction patterns based on the generation AI's time of day and day of the week. For example, it determines that transactions that occur at times that differ significantly from normal trading hours are abnormal and issues an alert. In this way, by taking the time of day and day of the week of the transaction into account, the accuracy of detecting abnormal transactions is improved.

[0067] When analyzing transaction data, the analysis unit can determine that transactions with counterparties with low credit ratings are abnormal based on the credit information of the counterparties. For example, the generation AI analyzes the credit information of counterparties included in the transaction data and determines that transactions with counterparties with low credit ratings are abnormal. For example, it detects large transactions with counterparties with low credit scores. The analysis unit also detects abnormal transactions based on the credit information of the user's transaction counterparties by the generation AI. For example, it determines that frequent transactions with counterparties with low credit ratings are abnormal. The analysis unit also detects abnormal transaction patterns based on the credit information of the transaction counterparties by the generation AI. For example, it determines that transactions with counterparties with low credit scores are abnormal and issues a warning. In this way, the accuracy of detecting abnormal transactions is improved by taking into account the credit information of the transaction counterparties.

[0068] The analysis unit can use the emotion estimation function to analyze the user's emotional state and monitor transactions during emotionally unstable periods with particular care. The analysis unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and monitor transactions during emotionally unstable periods with particular care. For example, the analysis unit analyzes the user's facial expressions and voice and calculates an emotion score. The analysis unit also uses the generation AI to detect abnormal transactions based on the user's emotional state. For example, the analysis unit particularly carefully monitors transactions during periods with high emotion scores and detects abnormal transactions. The analysis unit also uses the emotion estimation function to build a system that analyzes the user's emotional state and particularly carefully monitors transactions during emotionally unstable periods. For example, the analysis unit determines that transactions during periods with high emotion scores are abnormal. This improves the accuracy of detecting abnormal transactions by taking the user's emotional state into consideration.

[0069] The analysis unit can detect similar patterns based on the user's past fraud victim history. For example, the analysis unit uses the generation AI to analyze the user's past fraud victim history and detect similar patterns. For example, it determines transactions that are similar to transaction patterns that resulted in past fraud victimization as abnormal. The analysis unit also uses the generation AI to detect abnormal transactions based on the user's past fraud victimization history. For example, it particularly carefully monitors transactions with business partners that have previously been fraud victimizations. The analysis unit also builds a system in which the generation AI references the user's past fraud victimization history and detects similar patterns. For example, it determines transactions that match past fraud victimization patterns as abnormal. This improves the accuracy of detecting similar fraud patterns by referencing the user's past fraud victimization history.

[0070] The analysis unit can detect abnormal transaction patterns by comparing with the transaction data of other users. In the analysis unit, for example, the generation AI collects transaction data of other users and detects abnormal transaction patterns. For example, it detects abnormal transactions by comparing with other users of the same financial institution. In addition, the analysis unit detects abnormal transaction patterns by the generation AI based on the transaction data of other users. For example, it detects abnormal transactions by comparing with other users in the same region. In addition, the analysis unit integrates the transaction data of other users and detects abnormal transaction patterns with high accuracy. For example, it detects abnormal transactions by comparing with other users who trade with the same trading partner. As a result, by comparing with the transaction data of other users, the accuracy of detecting abnormal transaction patterns is improved.

[0071] The analysis unit can use the emotion estimation function to analyze the user's emotions when making a transaction in real time and issue a warning if an abnormal emotional state is detected. The analysis unit, for example, uses the emotion estimation function to analyze the user's emotions when making a transaction in real time and issue a warning if an abnormal emotional state is detected. For example, the analysis unit analyzes the user's facial expressions and voice and calculates an emotion score. The analysis unit also uses the generation AI to detect abnormal transactions based on the user's emotional state. For example, the analysis unit particularly carefully monitors transactions when the emotion score is high and detects abnormal transactions. The analysis unit also uses the emotion estimation function to build a system that analyzes the user's emotions when making a transaction in real time and issues a warning if an abnormal emotional state is detected. For example, it determines that transactions when the emotion score is high are abnormal. In this way, by analyzing the user's emotional state in real time, the accuracy of detecting abnormal transactions is improved.

[0072] When the warning unit detects a transaction that may be fraudulent, it can not only send a warning message to the user but also suggest specific countermeasures. For example, when the generation AI detects a transaction that may be fraudulent, the warning unit will suggest specific countermeasures to the user along with the warning message. For example, it will provide instructions on how to suspend the transaction or how to contact a financial institution. The warning unit will also include specific countermeasures when sending a warning message to the user. For example, it will provide instructions on how to cancel a transaction that may be fraudulent or how to report it to the police. The warning unit will also build a system that, when the generation AI detects a transaction that may be fraudulent, will suggest specific countermeasures to the user along with the warning message. For example, it will provide instructions on how to suspend the transaction or how to contact a financial institution. This allows for a quick response by suggesting specific countermeasures to the user.

[0073] When the warning unit detects a transaction that may be fraudulent, it can select the most appropriate warning method based on the user's past response history. For example, when the generation AI detects a transaction that may be fraudulent, the warning unit refers to the user's past response history and selects the most appropriate warning method. For example, it prioritizes the use of warning methods that have been effective in the past. The warning unit also selects the most appropriate warning method based on the user's past response history. For example, it reuses warning methods that have been quickly used in the past. The warning unit also builds a system where, when the generation AI detects a transaction that may be fraudulent, it refers to the user's past response history and selects the most appropriate warning method. For example, it prioritizes the use of warning methods that have been effective in the past. In this way, the most appropriate warning method can be selected by referring to the user's past response history.

[0074] The warning unit uses the emotion estimation function to generate a warning message according to the user's emotional state, enabling more effective warnings. The warning unit, for example, uses the emotion estimation function to generate a warning message according to the user's emotional state. For example, if the user is feeling stressed, it sends a message urging the user to respond calmly. The warning unit also uses a generation AI to generate an effective warning message based on the user's emotional state. For example, if the user is feeling anxious, it sends a message that reassures the user. The warning unit also uses the emotion estimation function to build a system that generates a warning message according to the user's emotional state. For example, if the user is feeling stressed, it sends a message urging the user to respond calmly. This enables more effective warnings by generating a warning message according to the user's emotional state.

[0075] The warning unit can also issue a warning to the user's family or a trusted third party if it detects a transaction that may be fraudulent. For example, the warning unit builds a system that issues a warning to the user's family and a trusted third party if the generation AI detects a transaction that may be fraudulent. For example, it sends a notification to contacts registered by the user in advance. The warning unit also encourages a prompt response by issuing a warning to the user's family and a trusted third party. For example, it enables family and a third party to respond even if the user does not notice a transaction that may be fraudulent. The warning unit also builds a system that issues a warning to the user's family and a trusted third party if the generation AI detects a transaction that may be fraudulent. For example, it sends a notification to contacts registered by the user in advance. This enables a prompt response by issuing a warning to the user's family and a trusted third party.

[0076] The warning unit can also issue a warning to the user's smart device if it detects a transaction that may be fraudulent. For example, the warning unit builds a system that issues a warning to the user's smart device if the generation AI detects a transaction that may be fraudulent. For example, the warning unit sends a notification to a smartwatch to issue a warning to the user. The warning unit also encourages a prompt response by issuing a warning through the user's smart device. For example, the warning unit issues a voice warning through a smart speaker. The warning unit also builds a system that issues a warning to the user's smart device if the generation AI detects a transaction that may be fraudulent. For example, the warning unit sends a notification to a smartwatch to issue a warning to the user. This enables a prompt response by issuing a warning to the user's smart device as well.

[0077] The warning unit uses the emotion estimation function to generate a warning message according to the user's emotional state, enabling more effective warnings. The warning unit, for example, uses the emotion estimation function to generate a warning message according to the user's emotional state. For example, if the user is feeling stressed, it sends a message urging the user to respond calmly. The warning unit also uses a generation AI to generate an effective warning message based on the user's emotional state. For example, if the user is feeling anxious, it sends a message that reassures the user. The warning unit also uses the emotion estimation function to build a system that generates a warning message according to the user's emotional state. For example, if the user is feeling stressed, it sends a message urging the user to respond calmly. This enables more effective warnings by generating a warning message according to the user's emotional state.

[0078] If the analysis unit detects an abnormal transaction, it not only suspends the transaction but also provides detailed information about the transaction to the user, thereby supporting rapid decision-making. For example, if the generation AI detects an abnormal transaction, the analysis unit suspends the transaction and provides detailed information about the transaction to the user. For example, it displays information such as the transaction amount, the trading partner, and the date and time of the transaction. The analysis unit also supports rapid decision-making by providing detailed information about the transaction to the user. For example, it checks the content of the transaction and, if there are no problems, guides the user through the steps to resume the transaction. The analysis unit also builds a system in which, if the generation AI detects an abnormal transaction, it suspends the transaction and provides detailed information about the transaction to the user. For example, it displays information such as the transaction amount, the trading partner, and the date and time of the transaction. This allows the user to make rapid decision-making by providing detailed information about the transaction.

[0079] If the analysis unit detects an abnormal transaction, it not only suspends the transaction but also compares it with the user's past transaction history to evaluate the degree of abnormality. For example, if the generation AI detects an abnormal transaction, the analysis unit suspends the transaction and compares it with the user's past transaction history to evaluate the degree of abnormality. For example, it identifies an abnormal transaction by comparing it with past transaction patterns. The analysis unit also evaluates the degree of abnormality based on the user's past transaction history. For example, it determines that a transaction that differs significantly from past transaction patterns is abnormal. The analysis unit also builds a system where, if the generation AI detects an abnormal transaction, it suspends the transaction and compares it with the user's past transaction history to evaluate the degree of abnormality. For example, it identifies an abnormal transaction by comparing it with past transaction patterns. This makes it possible to evaluate the degree of abnormality by comparing it with the user's past transaction history.

[0080] The analysis unit uses the emotion estimation function to adjust the timing of trading halts according to the user's emotional state, thereby reducing stress. The analysis unit, for example, uses the emotion estimation function to adjust the timing of trading halts according to the user's emotional state. For example, if the user is feeling stressed, the timing of trading halts is flexibly adjusted. The analysis unit also uses the generation AI to adjust the timing of trading halts based on the user's emotional state. For example, the timing of trading halts is adjusted so that the user can confirm trading in a calm state. The analysis unit also uses the emotion estimation function to build a system that adjusts the timing of trading halts according to the user's emotional state. For example, if the user is feeling stressed, the timing of trading halts is flexibly adjusted. In this way, stress can be reduced by adjusting the timing of trading halts according to the user's emotional state.

[0081] If the analysis unit detects an abnormal transaction, it can not only suspend the transaction but also issue a warning to the user's other financial accounts. For example, if the generation AI detects an abnormal transaction, the analysis unit builds a system that suspends the transaction and issues a warning to the user's other financial accounts. For example, if a user has multiple accounts, it issues a warning to all of the accounts. The analysis unit also issues a warning to the user's other financial accounts, encouraging a prompt response. For example, if an abnormal transaction is also being made in other accounts, it suspends all of the accounts. The analysis unit also builds a system that suspends the transaction and issues a warning to the user's other financial accounts if the generation AI detects an abnormal transaction. For example, if a user has multiple accounts, it issues a warning to all of the accounts. This enables a prompt response by issuing a warning to the user's other financial accounts.

[0082] If the analysis unit detects an abnormal transaction, it can not only suspend the transaction but also notify the user's credit information agency. For example, if the generation AI detects an abnormal transaction, the analysis unit builds a system that suspends the transaction and also notifies the user's credit information agency. For example, it provides information about the abnormal transaction to the credit information agency. The analysis unit also notifies the user's credit information agency, encouraging a prompt response. For example, if the abnormal transaction affects the credit information, it notifies the credit information agency. The analysis unit also builds a system that suspends the transaction and also notifies the user's credit information agency if the generation AI detects an abnormal transaction. For example, it provides information about the abnormal transaction to the credit information agency. This enables a prompt response by notifying the user's credit information agency.

[0083] The analysis unit uses the emotion estimation function to adjust the timing of trading halts according to the user's emotional state, thereby reducing stress. The analysis unit, for example, uses the emotion estimation function to adjust the timing of trading halts according to the user's emotional state. For example, if the user is feeling stressed, the timing of trading halts is flexibly adjusted. The analysis unit also uses the generation AI to adjust the timing of trading halts based on the user's emotional state. For example, the timing of trading halts is adjusted so that the user can confirm trading in a calm state. The analysis unit also uses the emotion estimation function to build a system that adjusts the timing of trading halts according to the user's emotional state. For example, if the user is feeling stressed, the timing of trading halts is flexibly adjusted. In this way, stress can be reduced by adjusting the timing of trading halts according to the user's emotional state.

[0084] When learning new fraud patterns, the analysis unit also integrates information from other financial institutions or security agencies, allowing it to learn a wider range of fraud patterns. In the analysis unit, for example, the generation AI collects information from other financial institutions and security agencies to learn new fraud patterns. For example, it learns fraud methods that have occurred at other financial institutions and detects anomalous transactions. The analysis unit also uses information from other financial institutions and security agencies to allow the generation AI to learn new fraud patterns. For example, it learns the latest fraud methods and detects anomalous transactions. The analysis unit also builds a system in which the generation AI integrates information from other financial institutions and security agencies to learn a wider range of fraud patterns. For example, it learns fraud methods that have occurred at other financial institutions and detects anomalous transactions. In this way, by integrating information from other financial institutions and security agencies, it is possible to learn a wider range of fraud patterns.

[0085] When learning a new fraud pattern, the analysis unit can reanalyze the user's past transaction data and reevaluate anomalies based on the new fraud pattern. For example, when the generation AI learns a new fraud pattern, the analysis unit reanalyzes the user's past transaction data and reevaluates anomalies based on the new fraud pattern. For example, the analysis unit reanalyzes the past transaction data and detects anomalies based on the new fraud pattern. The analysis unit also allows the generation AI to learn a new fraud pattern based on the user's past transaction data. For example, the analysis unit reanalyzes the past transaction data and detects anomalies based on the new fraud pattern. The analysis unit also builds a system in which, when the generation AI learns a new fraud pattern, the analysis unit reanalyzes the user's past transaction data and reevaluates anomalies based on the new fraud pattern. For example, the analysis unit reanalyzes the past transaction data and detects anomalies based on the new fraud pattern. In this way, anomalies based on the new fraud pattern can be reevaluated by reanalyzing the user's past transaction data.

[0086] The analysis unit uses the emotion estimation function to learn fraud patterns based on the user's emotional state and can identify fraud patterns occurring during emotionally unstable periods. The analysis unit, for example, uses the emotion estimation function to learn fraud patterns based on the user's emotional state. For example, it analyzes transaction data from periods when the user is feeling stressed and identifies fraud patterns. The analysis unit also has the generation AI learn fraud patterns based on the user's emotional state. For example, it analyzes transaction data from periods when the emotion score is high and identifies fraud patterns. The analysis unit also uses the emotion estimation function to build a system that learns fraud patterns based on the user's emotional state. For example, it analyzes transaction data from periods when the user is feeling stressed and identifies fraud patterns. In this way, by learning fraud patterns based on the user's emotional state, it is possible to identify fraud patterns occurring during emotionally unstable periods.

[0087] When learning new fraud patterns, the analysis unit also learns fraud patterns from different industries, making it possible to integrate fraud techniques from different industries. For example, the analysis unit has the generation AI learn fraud patterns from different industries and integrate fraud techniques from different industries. For example, it learns fraud patterns from not only the financial industry, but also e-commerce and insurance industries. The analysis unit also has the generation AI learn new fraud patterns based on fraud patterns from different industries. For example, it integrates fraud techniques from different industries and detects abnormal transactions. The analysis unit also builds a system where the generation AI learns fraud patterns from different industries and integrates fraud techniques from different industries. For example, it learns fraud patterns from not only the financial industry, but also e-commerce and insurance industries. This makes it possible to integrate fraud techniques from different industries by learning fraud patterns from different industries.

[0088] When learning new fraud patterns, the analysis unit also takes into account the user's social media activity or online shopping history, allowing it to learn more multifaceted fraud patterns. In the analysis unit, for example, the generation AI collects the user's social media activity data and learns new fraud patterns. For example, it analyzes the content of the user's posts and the time of activity to identify fraud patterns. In addition, the analysis unit allows the generation AI to learn new fraud patterns based on the user's online shopping history. For example, it learns transactions that differ from normal purchasing patterns as fraud patterns. In addition, the analysis unit builds a system in which the generation AI integrates the user's social media activity and online shopping history to learn more multifaceted fraud patterns. For example, it learns transactions that differ significantly from normal behavior patterns as fraud patterns. In this way, by taking into account the user's social media activity and online shopping history, it is possible to learn more multifaceted fraud patterns.

[0089] The analysis unit uses the emotion estimation function to learn fraud patterns based on the user's emotional state and can identify fraud patterns occurring during emotionally unstable periods. The analysis unit, for example, uses the emotion estimation function to learn fraud patterns based on the user's emotional state. For example, it analyzes transaction data from periods when the user is feeling stressed and identifies fraud patterns. The analysis unit also has the generation AI learn fraud patterns based on the user's emotional state. For example, it analyzes transaction data from periods when the emotion score is high and identifies fraud patterns. The analysis unit also uses the emotion estimation function to build a system that learns fraud patterns based on the user's emotional state. For example, it analyzes transaction data from periods when the user is feeling stressed and identifies fraud patterns. In this way, by learning fraud patterns based on the user's emotional state, it is possible to identify fraud patterns occurring during emotionally unstable periods.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] When analyzing a user's transaction data, the analysis unit also takes the user's health data into consideration and is able to detect abnormal transactions. For example, it monitors transactions particularly carefully during periods when the user's health condition is deteriorating. The analysis unit also uses the generation AI to detect abnormal transactions based on the user's health data. For example, it determines that transactions involving large amounts during periods when the user's health condition is deteriorating are abnormal. The analysis unit also integrates the user's health data and detects abnormal transaction patterns with high accuracy. For example, it determines that transactions during periods when the user's health condition is deteriorating are abnormal. In this way, by taking the user's health data into consideration, the accuracy of detecting abnormal transactions is improved.

[0092] When analyzing user transaction data, the analysis unit can detect abnormal transactions based on the user's hobbies and interests. For example, it will determine an abnormality if the user purchases a product or service that they do not normally purchase. The analysis unit also uses the generation AI to detect abnormal transactions based on the user's hobbies and interests. For example, it will determine an abnormality if the user purchases a product or service that differs from their usual purchasing pattern. The analysis unit also integrates the user's hobbies and interests to detect abnormal transaction patterns with high accuracy. For example, it will determine an abnormality if the user purchases a product or service that they do not normally purchase. In this way, by taking the user's hobbies and interests into consideration, the accuracy of detecting abnormal transactions is improved.

[0093] When analyzing a user's transaction data, the analysis unit can detect abnormal transactions based on the user's occupation and place of employment. For example, it may determine that a transaction of a large amount made during the user's working hours is abnormal. The analysis unit also uses the generation AI to detect abnormal transactions based on the user's occupation and place of employment. For example, it may determine that a transaction made outside of normal working hours is abnormal. The analysis unit also integrates the user's occupation and place of employment to detect abnormal transaction patterns with high accuracy. For example, it may determine that a transaction of a large amount made during working hours is abnormal. This improves the accuracy of detecting abnormal transactions by taking the user's occupation and place of employment into consideration.

[0094] When analyzing a user's transaction data, the analysis unit can detect abnormal transactions based on the user's family structure and living environment. For example, it will determine that an abnormality exists if the user's family uses a store or service that they do not normally use. The analysis unit also uses the generation AI to detect abnormal transactions based on the user's family structure and living environment. For example, it will determine that a transaction in an area that differs from the user's normal living environment is abnormal. The analysis unit also integrates the user's family structure and living environment to detect abnormal transaction patterns with high accuracy. For example, it will determine that an abnormality exists if the user uses a store or service that they do not normally use. This improves the accuracy of detecting abnormal transactions by taking the user's family structure and living environment into consideration.

[0095] When analyzing a user's transaction data, the analysis unit can detect abnormal transactions based on the user's travel history and business trip history. For example, it will determine that a large transaction in an area the user does not normally visit is abnormal. The analysis unit also uses the generation AI to detect abnormal transactions based on the user's travel history and business trip history. For example, it will determine that a transaction in an area different from the user's usual travel destination is abnormal. The analysis unit also integrates the user's travel history and business trip history to detect abnormal transaction patterns with high accuracy. For example, it will determine that a large transaction in an area the user does not normally visit is abnormal. This improves the accuracy of detecting abnormal transactions by taking the user's travel history and business trip history into consideration.

[0096] The analysis unit uses the emotion estimation function to analyze the user's emotional state and can particularly carefully monitor transactions during emotionally unstable periods. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The analysis unit also uses the generation AI to detect abnormal transactions based on the user's emotional state. For example, it particularly carefully monitors transactions during periods with high emotion scores and detects abnormal transactions. The analysis unit also uses the emotion estimation function to build a system that analyzes the user's emotional state and particularly carefully monitors transactions during emotionally unstable periods. For example, it determines transactions during periods with high emotion scores as abnormal. This improves the accuracy of detecting abnormal transactions by taking the user's emotional state into consideration.

[0097] The analysis unit uses the emotion estimation function to analyze the user's emotions when making transactions in real time and can issue a warning if an abnormal emotional state is detected. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The analysis unit also uses the generation AI to detect abnormal transactions based on the user's emotional state. For example, it particularly carefully monitors transactions when the emotion score is high and detects abnormal transactions. The analysis unit also uses the emotion estimation function to analyze the user's emotions when making transactions in real time and builds a system that issues a warning if an abnormal emotional state is detected. For example, it determines that transactions when the emotion score is high are abnormal. This improves the accuracy of detecting abnormal transactions by analyzing the user's emotional state in real time.

[0098] The analysis unit uses the emotion estimation function to learn fraud patterns based on the user's emotional state and can identify fraud patterns during emotionally unstable periods. For example, the analysis unit analyzes transaction data from periods when the user is feeling stressed and identifies fraud patterns. The analysis unit also uses the generation AI to learn fraud patterns based on the user's emotional state. For example, the analysis unit analyzes transaction data from periods when the emotion score is high and identifies fraud patterns. The analysis unit also uses the emotion estimation function to build a system that learns fraud patterns based on the user's emotional state. For example, the analysis unit analyzes transaction data from periods when the user is feeling stressed and identifies fraud patterns. In this way, by learning fraud patterns based on the user's emotional state, it is possible to identify fraud patterns during emotionally unstable periods.

[0099] The analysis unit uses the emotion estimation function to adjust the timing of trading halts according to the user's emotional state, thereby reducing stress. For example, if the user is feeling stressed, the timing of trading halts is flexibly adjusted. The analysis unit also uses the generation AI to adjust the timing of trading halts based on the user's emotional state. For example, the timing of trading halts is adjusted so that the user can confirm trading in a calm state. The analysis unit also uses the emotion estimation function to build a system that adjusts the timing of trading halts according to the user's emotional state. For example, if the user is feeling stressed, the timing of trading halts is flexibly adjusted. This makes it possible to reduce stress by adjusting the timing of trading halts according to the user's emotional state.

[0100] The analysis unit uses the emotion estimation function to generate warning messages according to the user's emotional state, enabling more effective warnings. For example, if the user is feeling stressed, a message urging the user to respond calmly is sent. The analysis unit also uses the generation AI to generate effective warning messages based on the user's emotional state. For example, if the user is feeling anxious, a message that reassures the user is sent. The analysis unit also uses the emotion estimation function to build a system that generates warning messages according to the user's emotional state. For example, if the user is feeling stressed, a message urging the user to respond calmly is sent. This allows for more effective warnings by generating warning messages according to the user's emotional state.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The transaction data collection unit collects user transaction data. For example, it collects bank transaction data, credit card transaction data, and online shopping transaction data. The transaction data collection unit can also collect user transaction data in real time. For example, it obtains transaction data through an API. Step 2: The analysis unit analyzes the transaction data collected by the transaction data collection unit to detect potentially fraudulent transactions. For example, the generation AI uses a machine learning algorithm to analyze the transaction data and detect abnormal transaction patterns. The generation AI can also perform rule-based analysis to identify potentially fraudulent transactions. For example, the generation AI analyzes information such as transaction amount, frequency, and counterparty to detect abnormal patterns. Step 3: The warning unit issues a warning message to the user based on the potentially fraudulent transaction detected by the analysis unit. For example, the warning unit may send a notification to the user's smartphone to inform the user that a potentially fraudulent transaction has occurred. The warning unit may also send a warning message to the user's email address. For example, the warning unit may send an email containing detailed information about the potentially fraudulent transaction.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a transaction data collection unit that collects transaction data; an analysis unit that analyzes the transaction data collected by the transaction data collection unit and detects transactions that may be fraudulent; a warning unit that issues a warning message to a user based on the possibly fraudulent transaction detected by the analysis unit. A system characterized by:

2. The transaction data collection unit Detecting unusual transaction patterns, including the user's social media activity or online shopping history 2. The system of claim 1.

3. The analysis unit When analyzing the transaction data, the time period or day of the week of the transaction is also taken into consideration, and transactions occurring during times that differ from normal transaction patterns are determined to be abnormal.

2. The system of claim 1.

4. The warning unit If a transaction that may be fraudulent is detected, not only the warning message but also specific countermeasures are proposed to the user.

2. The system of claim 1.

5. The analysis unit Analyzing the user's emotional state and monitoring transactions particularly closely during periods of heightened stress or anxiety 2. The system of claim 1.

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