System

Abstract

To provide a system for detecting the illegal use of a cash card in real time, and for quickly facilitating countermeasures to the illegal use of the cash card.SOLUTION: A system includes a monitoring unit, a detection unit, and a notification unit. The monitoring unit monitors the usage status of the cash card using the generated AI. The detection unit analyzes the transaction data and the use history monitored by the monitoring unit and detects an abnormal transaction. The notification unit notifies the user of the abnormal transaction and the unauthorized access detected by the detection 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 technology has had the problem of making it difficult to detect fraudulent use of cash cards in real time and respond quickly.

[0005] The system according to the embodiment aims to detect fraudulent use of cash cards in real time and respond promptly. [Means for solving the problem]

[0006] The system according to the embodiment includes a monitoring unit, a detection unit, and a notification unit. The monitoring unit monitors the usage of the cash card using a generation AI. The detection unit analyzes the transaction data and usage history monitored by the monitoring unit and detects abnormal transactions. The notification unit notifies the user of abnormal transactions and unauthorized access detected by the detection unit. [Effects of the Invention]

[0007] The system according to the embodiment can detect fraudulent use of cash cards in real time and respond quickly. [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 cash card fraud detection tool according to an embodiment of the present invention is a system that monitors the usage of cash cards, and the generated AI detects abnormal transactions or unauthorized access and notifies the user. As a result, the cash card fraud detection tool can effectively detect cash card fraud and protect the user's assets.

[0029] A cash card fraud detection tool according to an embodiment includes a monitoring unit, a detection unit, and a notification unit. The monitoring unit monitors the usage of a cash card using a generation AI. For example, the monitoring unit monitors the transaction data and usage history of the cash card in real time. The monitoring unit can also learn patterns, such as the area, time of day, and type of transaction, in which the cash card is usually used, and detect abnormal transactions based on the patterns. For example, the monitoring unit learns patterns, such as the area, time of day, and type of transaction, in which the cash card is usually used, and detects abnormal transactions based on the patterns. The detection unit analyzes the transaction data and usage history monitored by the monitoring unit to detect abnormal transactions. For example, the detection unit detects large withdrawals in areas not usually used, consecutive transactions in a short period of time, etc. The detection unit can also detect unauthorized access to the cash card. For example, the detection unit detects cases in which the cash card's PIN code is entered incorrectly multiple times in a short period of time, or access from a device not usually used. The notification unit notifies the user of abnormal transactions and unauthorized access detected by the detection unit. For example, if an abnormal transaction is detected, the notification unit sends a notification to the user's smartphone and requests confirmation of the transaction. Furthermore, if unauthorized access is detected, the notification unit issues a warning to the user and prompts the user to take necessary measures. This allows the cash card fraud detection tool according to the embodiment to prevent cash card fraud. For example, if an abnormal transaction is detected, the tool immediately suspends use of the cash card and notifies the user, thereby minimizing damage. Furthermore, if unauthorized access is detected, the tool issues a warning to the user, allowing for prompt measures to be taken.

[0030] The monitoring unit can learn user behavior patterns and detect abnormal behavior. For example, the generation AI in the monitoring unit learns the user's cash card usage patterns and identifies normal transaction times and locations. For example, it learns the locations and transaction times of the ATMs the user usually uses and detects abnormal transactions based on that. The generation AI in the monitoring unit also analyzes the user's transaction history and learns the normal transaction amounts and frequency. For example, it detects abnormal large transactions or consecutive transactions in a short period of time based on the amount and frequency of the user's usual transactions. The generation AI in the monitoring unit also learns the user's behavior patterns and detects abnormal behavior. For example, it detects transactions in areas the user does not usually use or transactions outside of normal trading hours. In this way, by learning the user's behavior patterns and detecting abnormal behavior, it is possible to prevent cash card fraud.

[0031] The monitoring unit can detect abnormalities by also using the user's biometric information. For example, the monitoring unit uses fingerprint authentication in addition to the generation AI when the cash card is used to check the user's biometric information. For example, it detects an abnormality if the fingerprint authentication does not match. The monitoring unit also uses facial authentication in addition to the generation AI when the cash card is used to check the user's facial information. For example, it detects an abnormality if the facial authentication does not match. The monitoring unit also uses biometric information in addition to the generation AI when the cash card is used to detect abnormalities. For example, it detects an abnormality if the fingerprint or facial authentication does not match and notifies the user. In this way, by detecting abnormalities by also using the user's biometric information, it is possible to prevent cash card fraud.

[0032] The monitoring unit can detect abnormalities using the location information of the user's smartphone. For example, the generation AI checks the location information of the user's smartphone when the cash card is used and detects an abnormality. For example, an abnormality is detected when the smartphone's location information does not match the transaction location. The monitoring unit also monitors the cash card usage status using the user's smartphone location information. For example, an abnormality is detected when the smartphone is not at the usual transaction location. The monitoring unit also monitors the cash card usage status based on the generation AI's smartphone location information. For example, an abnormality is detected when the smartphone is moved outside of usual transaction hours. In this way, cash card fraud can be prevented by detecting abnormalities using the user's smartphone's location information.

[0033] The monitoring unit can analyze a user's social media activity and detect abnormal transactions. For example, the generation AI analyzes a user's social media activity and monitors cash card usage. For example, an abnormality is detected when the content of a social media post does not match the content of a transaction. The monitoring unit also monitors cash card usage based on the user's social media activity. For example, an abnormality is detected when the location information on social media does not match the location of a transaction. The monitoring unit also monitors cash card usage based on the generation AI analyzing social media activity. For example, an abnormality is detected when the time of a social media post does not match the time of a transaction. In this way, cash card fraud can be prevented by analyzing a user's social media activity and detecting abnormal transactions.

[0034] The detection unit can analyze detailed transaction information and detect abnormalities. In the detection unit, for example, the generation AI analyzes detailed transaction information and detects abnormal transactions. For example, it detects transactions involving high-priced items that are not normally purchased or transactions at suspicious stores. In addition, the detection unit uses the generation AI to detect abnormal transactions based on detailed transaction information. For example, it detects transactions at stores that are not normally used or transactions involving abnormally high prices. In addition, the detection unit uses the generation AI to analyze detailed transaction information and detect abnormal transactions. For example, it detects transactions involving items that are not normally purchased or transactions involving abnormally high prices. In this way, by analyzing detailed transaction information and detecting abnormalities, it is possible to prevent cash card fraud.

[0035] The detection unit can detect anomalies by comparing with past transaction history. In the detection unit, for example, the generation AI analyzes past transaction history and detects abnormal transactions. For example, it detects abnormal transactions by comparing with transactions made in the past. In addition, the detection unit detects abnormal transactions by the generation AI based on past transaction history. For example, it detects transactions that do not match past transaction patterns. In addition, the detection unit detects abnormal transactions by the generation AI analyzing past transaction history. For example, it detects abnormal transactions by comparing with past transaction history. In this way, by detecting anomalies by comparing with past transaction history, it is possible to prevent cash card fraud.

[0036] The detection unit can detect anomalies by analyzing the time or location of a transaction. In the detection unit, for example, the generation AI analyzes the time of a transaction and detects abnormal transactions. For example, it detects transactions outside of normal trading hours. In the detection unit, the generation AI also analyzes the location of a transaction and detects abnormal transactions. For example, it detects transactions in areas that are not normally used. In the detection unit, the generation AI also detects abnormal transactions based on the time and location of a transaction. For example, it detects transactions outside of normal trading hours or in areas that are not normally used. In this way, by analyzing the time and location of a transaction and detecting anomalies, it is possible to prevent cash card fraud.

[0037] The detection unit can detect abnormalities by analyzing the IP address and device information of the access source. For example, the generation AI in the detection unit analyzes the IP address of the access source and detects abnormal access. For example, it detects access from an IP address that is not normally used. The detection unit also analyzes the device information of the access source and detects abnormal access. For example, it detects access from an IP address that is not normally used. The detection unit also detects abnormal access by the generation AI based on the IP address and device information of the access source. For example, it detects access from an IP address or device that is not normally used. In this way, by analyzing the IP address and device information of the access source and detecting abnormalities, it is possible to prevent cash card fraud.

[0038] The detection unit can detect anomalies by analyzing access frequency and patterns. In the detection unit, for example, the generation AI analyzes access frequency and detects abnormal access. For example, it detects continuous access in a short period of time. In the detection unit, the generation AI analyzes access patterns and detects abnormal access. For example, it detects access that does not match normal access patterns. In the detection unit, the generation AI detects abnormal access based on access frequency and patterns. For example, it detects continuous access in a short period of time or access that does not match normal access patterns. In this way, by analyzing access frequency and patterns and detecting anomalies, it is possible to prevent cash card fraud.

[0039] The detection unit can detect anomalies by analyzing the geographical information of the access source. In the detection unit, for example, the generation AI analyzes the geographical information of the access source and detects anomalous access. For example, it detects access from areas that are not normally used. The detection unit also detects anomalous access based on the geographical information of the access source by the generation AI. For example, it detects access from outside the normal trading area. The detection unit also detects anomalous access by analyzing the geographical information of the access source by the generation AI. For example, it detects access from countries or areas that are not normally used. In this way, by analyzing the geographical information of the access source and detecting anomalies, it is possible to prevent cash card fraud.

[0040] The detection unit can detect abnormalities by analyzing the operation history of the accessing device. For example, the generation AI in the detection unit analyzes the operation history of the accessing device and detects abnormal access. For example, it detects behavior that does not match normal operation patterns. The detection unit also detects abnormal access based on the operation history of the accessing device by the generation AI. For example, it detects behavior outside of normal usage hours. The detection unit also detects abnormal access by analyzing the operation history of the accessing device by the generation AI. For example, it detects behavior that does not match normal operation patterns or abnormal behavior. In this way, by analyzing the operation history of the accessing device and detecting abnormalities, it is possible to prevent cash card fraud.

[0041] The notification unit can immediately suspend the use of the cash card when fraudulent activity is detected and notify the user. For example, when the generation AI detects fraudulent activity, the notification unit immediately suspends the use of the cash card and notifies the user. For example, the notification unit suspends the use of the cash card when an abnormal transaction is detected. Furthermore, when the generation AI detects fraudulent activity, the notification unit temporarily suspends the use of the cash card and notifies the user. For example, the notification unit suspends the use of the cash card when unauthorized access is detected. Furthermore, when the generation AI detects fraudulent activity, the notification unit immediately suspends the use of the cash card and notifies the user. For example, the notification unit suspends the use of the cash card when an abnormal transaction or unauthorized access is detected. In this way, by immediately suspending the use of the cash card and notifying the user when fraudulent activity is detected, cash card fraud can be prevented.

[0042] The notification unit can temporarily freeze the user's bank account when fraudulent activity is detected, thereby minimizing damage. For example, when the generation AI detects fraudulent activity, the notification unit temporarily freezes the user's bank account, thereby minimizing damage. For example, it freezes the bank account when an abnormal transaction is detected. The notification unit can also temporarily freeze the user's bank account when the generation AI detects fraudulent activity, thereby preventing damage. For example, it freezes the bank account when unauthorized access is detected. The notification unit can also temporarily freeze the user's bank account when the generation AI detects fraudulent activity, thereby minimizing damage. For example, it freezes the bank account when an abnormal transaction or unauthorized access is detected. This makes it possible to temporarily freeze the user's bank account when fraudulent activity is detected, thereby minimizing damage and preventing cash card fraud.

[0043] The notification unit can send a notification to the user's family or trusted contacts when fraudulent activity is detected, and request their cooperation. For example, when the generation AI detects fraudulent activity, the notification unit sends a notification to the user's family or trusted contacts and requests their cooperation. For example, a notification is sent when an abnormal transaction is detected. In addition, when the generation AI detects fraudulent activity, the notification unit sends a notification to the user's family or trusted contacts and requests their cooperation. For example, a notification is sent when unauthorized access is detected. In addition, when the generation AI detects fraudulent activity, the notification unit sends a notification to the user's family or trusted contacts and requests their cooperation. For example, a notification is sent when an abnormal transaction or unauthorized access is detected. In this way, by sending a notification to the user's family or trusted contacts and requesting their cooperation when fraudulent activity is detected, cash card fraud can be prevented.

[0044] The notification unit can immediately send a notification to the user's smartphone when it detects an abnormal transaction or unauthorized access. For example, the notification unit immediately sends a notification to the user's smartphone when the generation AI detects an abnormal transaction. For example, it sends a notification when an abnormal transaction is detected. Furthermore, the notification unit immediately sends a notification to the user's smartphone when the generation AI detects unauthorized access. For example, it sends a notification when unauthorized access is detected. Furthermore, the notification unit immediately sends a notification to the user's smartphone when the generation AI detects an abnormal transaction or unauthorized access. For example, it sends a notification when an abnormal transaction or unauthorized access is detected. In this way, by immediately sending a notification to the user's smartphone when an abnormal transaction or unauthorized access is detected, cash card fraud can be prevented.

[0045] The notification unit can send a detailed notification to the user's email address when an abnormal transaction or unauthorized access is detected. For example, the notification unit sends a detailed notification to the user's email address when the generation AI detects an abnormal transaction. For example, a detailed notification is sent when an abnormal transaction is detected. Furthermore, the notification unit sends a detailed notification to the user's email address when the generation AI detects unauthorized access. For example, a detailed notification is sent when unauthorized access is detected. Furthermore, the notification unit sends a detailed notification to the user's email address when the generation AI detects an abnormal transaction or unauthorized access. For example, a detailed notification is sent when an abnormal transaction or unauthorized access is detected. In this way, by sending a detailed notification to the user's email address when an abnormal transaction or unauthorized access is detected, cash card fraud can be prevented.

[0046] The notification unit can send a notification to the user's SNS account when it detects an abnormal transaction or unauthorized access. The notification unit, for example, sends a notification to the user's SNS account when the generation AI detects an abnormal transaction. For example, it sends a notification to the SNS account when an abnormal transaction is detected. Furthermore, the notification unit sends a notification to the user's SNS account when the generation AI detects unauthorized access. For example, it sends a notification to the SNS account when unauthorized access is detected. Furthermore, the notification unit sends a notification to the user's SNS account when the generation AI detects an abnormal transaction or unauthorized access. For example, it sends a notification to the SNS account when an abnormal transaction or unauthorized access is detected. In this way, by sending a notification to the user's SNS account when an abnormal transaction or unauthorized access is detected, it is possible to prevent cash card fraud.

[0047] The notification unit can send a notification to the user's family or trusted contacts when an abnormal transaction or unauthorized access is detected. For example, the notification unit sends a notification to the user's family or trusted contacts when the generation AI detects an abnormal transaction. For example, it sends a notification to family members when an abnormal transaction is detected. Furthermore, the notification unit sends a notification to the user's family or trusted contacts when the generation AI detects unauthorized access. For example, it sends a notification to family members when unauthorized access is detected. Furthermore, the notification unit sends a notification to the user's family or trusted contacts when the generation AI detects an abnormal transaction or unauthorized access. For example, it sends a notification to family members when an abnormal transaction or unauthorized access is detected. In this way, by sending a notification to the user's family or trusted contacts when an abnormal transaction or unauthorized access is detected, cash card fraud can be prevented.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The cash card fraud detection tool can further include a purchase history analysis unit that analyzes the user's purchase history. The purchase history analysis unit analyzes the user's past purchase history and learns normal purchasing patterns. For example, it can learn the types of products and services the user usually purchases, their purchase frequency, and purchase amounts, and based on this, detect abnormal purchasing behavior. For example, it can detect transactions at high-priced items that the user does not normally purchase or at suspicious stores. The purchase history analysis unit also detects abnormal purchasing behavior based on the user's purchase history. For example, it can detect transactions at stores that the user does not normally use or at abnormally high prices. In this way, cash card fraud can be prevented by analyzing the user's purchase history and detecting abnormal purchasing behavior.

[0050] The cash card fraud detection tool may further include a health data analysis unit that analyzes the user's health data. The health data analysis unit analyzes the user's health data to detect abnormal health conditions. For example, it can analyze data such as the user's heart rate, blood pressure, and body temperature to detect abnormal fluctuations. For example, it detects abnormalities when the heart rate rises suddenly or when the blood pressure is abnormally high. The health data analysis unit also detects abnormal health conditions based on the user's health data. For example, it detects data that does not match the user's normal health condition. In this way, cash card fraud can be prevented by analyzing the user's health data and detecting abnormal health conditions.

[0051] The cash card fraud detection tool can further include a voice analysis unit that analyzes the user's voice data. The voice analysis unit analyzes the user's voice data and detects abnormal voice patterns. For example, it can analyze the tone, rhythm, volume, etc. of the user's voice to detect abnormal fluctuations. For example, it detects an abnormality when the voice tone changes suddenly or when the volume is abnormally high. The voice analysis unit also detects abnormal voice patterns based on the user's voice data. For example, it detects data that does not match normal voice patterns. In this way, by analyzing the user's voice data and detecting abnormal voice patterns, cash card fraud can be prevented.

[0052] The cash card fraud detection tool can further include a behavior history analysis unit that analyzes the user's behavior history. The behavior history analysis unit analyzes the user's behavior history and detects abnormal behavior patterns. For example, it can analyze the user's movement history and the history of services used to detect abnormal behavior patterns. For example, it can detect behavior in areas that the user does not normally use or abnormally frequent use of services. The behavior history analysis unit also detects abnormal behavior patterns based on the user's behavior history. For example, it can detect data that does not match normal behavior patterns. In this way, cash card fraud can be prevented by analyzing the user's behavior history and detecting abnormal behavior patterns.

[0053] The cash card fraud detection tool may further include a biometric information analysis unit that analyzes the user's biometric information. The biometric information analysis unit analyzes the user's biometric information and detects abnormal biometric reactions. For example, it can analyze the user's fingerprint or facial authentication data to detect abnormal biometric reactions. For example, an abnormality is detected when the fingerprint authentication does not match or when the facial authentication does not match. The biometric information analysis unit also detects abnormal biometric reactions based on the user's biometric information. For example, it detects data that does not match normal biometric information. In this way, cash card fraud can be prevented by analyzing the user's biometric information and detecting abnormal biometric reactions.

[0054] The cash card fraud detection tool can further include a social media analysis unit that analyzes the user's social media activity. The social media analysis unit analyzes the user's social media activity and detects abnormal activity. For example, it can detect abnormal activity by analyzing the content of the user's posts, location information, activity times, etc. For example, it can detect posts that do not match normal activity patterns or posts that are posted abnormally frequently. The social media analysis unit also detects abnormal activity based on the user's social media activity. For example, it can detect data that does not match normal activity patterns. In this way, cash card fraud can be prevented by analyzing the user's social media activity and detecting abnormal activity.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The monitoring unit uses the generation AI to monitor the usage of the cash card. For example, the monitoring unit monitors the cash card's transaction data and usage history in real time. The monitoring unit can also learn patterns such as the area, time of day, and type of transaction in which the cash card is usually used, and use this information to detect abnormal transactions. Step 2: The detection unit analyzes the transaction data and usage history monitored by the monitoring unit to detect abnormal transactions. For example, the detection unit detects large withdrawals in areas not normally used, or successive transactions in a short period of time. The detection unit can also detect unauthorized access to cash cards. For example, it can detect when the cash card PIN code is entered incorrectly multiple times in a short period of time, or when access is made from a device not normally used. Step 3: The notification unit notifies the user of any abnormal transactions or unauthorized access detected by the detection unit. For example, if the notification unit detects an abnormal transaction, it sends a notification to the user's smartphone, requesting confirmation of the transaction. Also, if unauthorized access is detected, it issues a warning to the user, urging them to take necessary measures.

[0057] (Example 2) The cash card fraud detection tool according to an embodiment of the present invention is a system that monitors the usage of cash cards, and the generated AI detects abnormal transactions or unauthorized access and notifies the user. As a result, the cash card fraud detection tool can effectively detect cash card fraud and protect the user's assets.

[0058] A cash card fraud detection tool according to an embodiment includes a monitoring unit, a detection unit, and a notification unit. The monitoring unit monitors the usage of a cash card using a generation AI. For example, the monitoring unit monitors the transaction data and usage history of the cash card in real time. The monitoring unit can also learn patterns, such as the area, time of day, and type of transaction, in which the cash card is usually used, and detect abnormal transactions based on the patterns. For example, the monitoring unit learns patterns, such as the area, time of day, and type of transaction, in which the cash card is usually used, and detects abnormal transactions based on the patterns. The detection unit analyzes the transaction data and usage history monitored by the monitoring unit to detect abnormal transactions. For example, the detection unit detects large withdrawals in areas not usually used, consecutive transactions in a short period of time, etc. The detection unit can also detect unauthorized access to the cash card. For example, the detection unit detects cases in which the cash card's PIN code is entered incorrectly multiple times in a short period of time, or access from a device not usually used. The notification unit notifies the user of abnormal transactions and unauthorized access detected by the detection unit. For example, if an abnormal transaction is detected, the notification unit sends a notification to the user's smartphone and requests confirmation of the transaction. Furthermore, if unauthorized access is detected, the notification unit issues a warning to the user and prompts the user to take necessary measures. This allows the cash card fraud detection tool according to the embodiment to prevent cash card fraud. For example, if an abnormal transaction is detected, the tool immediately suspends use of the cash card and notifies the user, thereby minimizing damage. Furthermore, if unauthorized access is detected, the tool issues a warning to the user, allowing for prompt measures to be taken.

[0059] The monitoring unit can learn user behavior patterns and detect abnormal behavior. For example, the generation AI in the monitoring unit learns the user's cash card usage patterns and identifies normal transaction times and locations. For example, it learns the locations and transaction times of the ATMs the user usually uses and detects abnormal transactions based on that. The generation AI in the monitoring unit also analyzes the user's transaction history and learns the normal transaction amounts and frequency. For example, it detects abnormal large transactions or consecutive transactions in a short period of time based on the amount and frequency of the user's usual transactions. The generation AI in the monitoring unit also learns the user's behavior patterns and detects abnormal behavior. For example, it detects transactions in areas the user does not usually use or transactions outside of normal trading hours. In this way, by learning the user's behavior patterns and detecting abnormal behavior, it is possible to prevent cash card fraud.

[0060] The monitoring unit can detect abnormalities by also using the user's biometric information. For example, the monitoring unit uses fingerprint authentication in addition to the generation AI when the cash card is used to check the user's biometric information. For example, it detects an abnormality if the fingerprint authentication does not match. The monitoring unit also uses facial authentication in addition to the generation AI when the cash card is used to check the user's facial information. For example, it detects an abnormality if the facial authentication does not match. The monitoring unit also uses biometric information in addition to the generation AI when the cash card is used to detect abnormalities. For example, it detects an abnormality if the fingerprint or facial authentication does not match and notifies the user. In this way, by detecting abnormalities by also using the user's biometric information, it is possible to prevent cash card fraud.

[0061] The monitoring unit uses the emotion estimation function to monitor the user's emotional state and can detect abnormal transactions when stress or anxiety increases. For example, the generation AI in the monitoring unit monitors the user's emotional state when using a cash card and detects abnormal transactions when stress or anxiety increases. For example, the monitoring unit analyzes the user's facial expressions and voice and calculates an emotion score. The monitoring unit also uses the emotion estimation function to monitor the user's emotional state in real time and detects abnormal transactions. For example, it issues a warning when stress or anxiety increases. The monitoring unit also uses the generation AI to monitor the user's emotional state and detects abnormal transactions. For example, it detects an abnormality when the emotion score is high and notifies the user. In this way, by monitoring the user's emotional state and detecting abnormal transactions when stress or anxiety increases, cash card fraud can be prevented.

[0062] The monitoring unit can detect abnormalities using the location information of the user's smartphone. For example, the generation AI checks the location information of the user's smartphone when the cash card is used and detects an abnormality. For example, an abnormality is detected when the smartphone's location information does not match the transaction location. The monitoring unit also monitors the cash card usage status using the user's smartphone location information. For example, an abnormality is detected when the smartphone is not at the usual transaction location. The monitoring unit also monitors the cash card usage status based on the generation AI's smartphone location information. For example, an abnormality is detected when the smartphone is moved outside of usual transaction hours. In this way, cash card fraud can be prevented by detecting abnormalities using the user's smartphone's location information.

[0063] The monitoring unit can analyze a user's social media activity and detect abnormal transactions. For example, the generation AI analyzes a user's social media activity and monitors cash card usage. For example, an abnormality is detected when the content of a social media post does not match the content of a transaction. The monitoring unit also monitors cash card usage based on the user's social media activity. For example, an abnormality is detected when the location information on social media does not match the location of a transaction. The monitoring unit also monitors cash card usage based on the generation AI analyzing social media activity. For example, an abnormality is detected when the time of a social media post does not match the time of a transaction. In this way, cash card fraud can be prevented by analyzing a user's social media activity and detecting abnormal transactions.

[0064] The monitoring unit uses the emotion estimation function to analyze the emotions of users when they use their cash cards in real time and can detect abnormal transactions. For example, the monitoring unit uses the emotion estimation function to analyze the emotions of users when they use their cash cards in real time and detect abnormal transactions. For example, it detects an abnormality when the emotion score is high. The monitoring unit also uses the generation AI to analyze the user's emotional state when they use the cash card and detects abnormal transactions. For example, it issues a warning when stress or anxiety increases. The monitoring unit also uses the emotion estimation function to monitor the user's emotional state in real time and detects abnormal transactions. For example, it detects an abnormality when the emotion score is high and notifies the user. In this way, by analyzing the emotions of users when they use their cash cards in real time and detecting abnormal transactions, it is possible to prevent cash card fraud.

[0065] The detection unit can analyze detailed transaction information and detect abnormalities. In the detection unit, for example, the generation AI analyzes detailed transaction information and detects abnormal transactions. For example, it detects transactions involving high-priced items that are not normally purchased or transactions at suspicious stores. In addition, the detection unit uses the generation AI to detect abnormal transactions based on detailed transaction information. For example, it detects transactions at stores that are not normally used or transactions involving abnormally high prices. In addition, the detection unit uses the generation AI to analyze detailed transaction information and detect abnormal transactions. For example, it detects transactions involving items that are not normally purchased or transactions involving abnormally high prices. In this way, by analyzing detailed transaction information and detecting abnormalities, it is possible to prevent cash card fraud.

[0066] The detection unit can detect anomalies by comparing with past transaction history. In the detection unit, for example, the generation AI analyzes past transaction history and detects abnormal transactions. For example, it detects abnormal transactions by comparing with transactions made in the past. In addition, the detection unit detects abnormal transactions by the generation AI based on past transaction history. For example, it detects transactions that do not match past transaction patterns. In addition, the detection unit detects abnormal transactions by the generation AI analyzing past transaction history. For example, it detects abnormal transactions by comparing with past transaction history. In this way, by detecting anomalies by comparing with past transaction history, it is possible to prevent cash card fraud.

[0067] The detection unit can use the emotion estimation function to analyze the user's emotional state and issue a warning when an abnormal transaction is performed. The detection unit, for example, uses the emotion estimation function to analyze the user's emotional state and issue a warning when an abnormal transaction is performed. For example, the detection unit issues a warning when the emotion score is high. The detection unit also uses the generation AI to analyze the user's emotional state and issue a warning when an abnormal transaction is performed. For example, the detection unit issues a warning when stress or anxiety is high. The detection unit also uses the emotion estimation function to analyze the user's emotional state in real time and issue a warning when an abnormal transaction is performed. For example, the detection unit issues a warning when the emotion score is high. In this way, by using the emotion estimation function to analyze the user's emotional state and issue a warning when an abnormal transaction is performed, cash card fraud can be prevented.

[0068] The detection unit can detect anomalies by analyzing the time or location of a transaction. In the detection unit, for example, the generation AI analyzes the time of a transaction and detects abnormal transactions. For example, it detects transactions outside of normal trading hours. In the detection unit, the generation AI also analyzes the location of a transaction and detects abnormal transactions. For example, it detects transactions in areas that are not normally used. In the detection unit, the generation AI also detects abnormal transactions based on the time and location of a transaction. For example, it detects transactions outside of normal trading hours or in areas that are not normally used. In this way, by analyzing the time and location of a transaction and detecting anomalies, it is possible to prevent cash card fraud.

[0069] The detection unit can use the emotion estimation function to analyze the user's emotional response when an abnormal transaction is performed and respond immediately. The detection unit, for example, uses the emotion estimation function to analyze the user's emotional response when an abnormal transaction is performed and respond immediately. For example, it issues a warning if the emotion score is high. The detection unit also analyzes the user's emotional response when the generation AI performs an abnormal transaction and responds immediately. For example, it issues a warning if stress or anxiety increases. The detection unit also uses the emotion estimation function to analyze the user's emotional response in real time when an abnormal transaction is performed and respond immediately. For example, it issues a warning if the emotion score is high. In this way, by using the emotion estimation function to analyze the user's emotional response when an abnormal transaction is performed and responding immediately, it is possible to prevent cash card fraud.

[0070] The detection unit can detect abnormalities by analyzing the IP address and device information of the access source. For example, the generation AI in the detection unit analyzes the IP address of the access source and detects abnormal access. For example, it detects access from an IP address that is not normally used. The detection unit also analyzes the device information of the access source and detects abnormal access. For example, it detects access from an IP address that is not normally used. The detection unit also detects abnormal access by the generation AI based on the IP address and device information of the access source. For example, it detects access from an IP address or device that is not normally used. In this way, by analyzing the IP address and device information of the access source and detecting abnormalities, it is possible to prevent cash card fraud.

[0071] The detection unit can detect anomalies by analyzing access frequency and patterns. In the detection unit, for example, the generation AI analyzes access frequency and detects abnormal access. For example, it detects continuous access in a short period of time. In the detection unit, the generation AI analyzes access patterns and detects abnormal access. For example, it detects access that does not match normal access patterns. In the detection unit, the generation AI detects abnormal access based on access frequency and patterns. For example, it detects continuous access in a short period of time or access that does not match normal access patterns. In this way, by analyzing access frequency and patterns and detecting anomalies, it is possible to prevent cash card fraud.

[0072] The detection unit can analyze the user's emotional state using the emotion estimation function and issue a warning when unauthorized access is attempted. The detection unit, for example, uses the emotion estimation function to analyze the user's emotional state and issue a warning when unauthorized access is attempted. For example, the detection unit issues a warning when the emotion score is high. The detection unit also uses the generation AI to analyze the user's emotional state and issue a warning when unauthorized access is attempted. For example, the detection unit issues a warning when stress or anxiety increases. The detection unit also uses the emotion estimation function to analyze the user's emotional state in real time and issue a warning when unauthorized access is attempted. For example, the detection unit issues a warning when the emotion score is high. In this way, by using the emotion estimation function to analyze the user's emotional state and issue a warning when unauthorized access is attempted, cash card fraud can be prevented.

[0073] The detection unit can detect anomalies by analyzing the geographical information of the access source. In the detection unit, for example, the generation AI analyzes the geographical information of the access source and detects anomalous access. For example, it detects access from areas that are not normally used. The detection unit also detects anomalous access based on the geographical information of the access source by the generation AI. For example, it detects access from outside the normal trading area. The detection unit also detects anomalous access by analyzing the geographical information of the access source by the generation AI. For example, it detects access from countries or areas that are not normally used. In this way, by analyzing the geographical information of the access source and detecting anomalies, it is possible to prevent cash card fraud.

[0074] The detection unit can detect abnormalities by analyzing the operation history of the accessing device. For example, the generation AI in the detection unit analyzes the operation history of the accessing device and detects abnormal access. For example, it detects behavior that does not match normal operation patterns. The detection unit also detects abnormal access based on the operation history of the accessing device by the generation AI. For example, it detects behavior outside of normal usage hours. The detection unit also detects abnormal access by analyzing the operation history of the accessing device by the generation AI. For example, it detects behavior that does not match normal operation patterns or abnormal behavior. In this way, by analyzing the operation history of the accessing device and detecting abnormalities, it is possible to prevent cash card fraud.

[0075] The detection unit uses the emotion estimation function to analyze the user's emotional response when unauthorized access is attempted and can respond immediately. The detection unit, for example, uses the emotion estimation function to analyze the user's emotional response when unauthorized access is attempted and responds immediately. For example, it issues a warning if the emotion score is high. The detection unit also analyzes the user's emotional response when unauthorized access is attempted using the generation AI and responds immediately. For example, it issues a warning if stress or anxiety increases. The detection unit also uses the emotion estimation function to analyze the user's emotional response in real time when unauthorized access is attempted and responds immediately. For example, it issues a warning if the emotion score is high. In this way, by using the emotion estimation function to analyze the user's emotional response when unauthorized access is attempted and responding immediately, it is possible to prevent cash card fraud.

[0076] The notification unit can immediately suspend the use of the cash card when fraudulent activity is detected and notify the user. For example, when the generation AI detects fraudulent activity, the notification unit immediately suspends the use of the cash card and notifies the user. For example, the notification unit suspends the use of the cash card when an abnormal transaction is detected. Furthermore, when the generation AI detects fraudulent activity, the notification unit temporarily suspends the use of the cash card and notifies the user. For example, the notification unit suspends the use of the cash card when unauthorized access is detected. Furthermore, when the generation AI detects fraudulent activity, the notification unit immediately suspends the use of the cash card and notifies the user. For example, the notification unit suspends the use of the cash card when an abnormal transaction or unauthorized access is detected. In this way, by immediately suspending the use of the cash card and notifying the user when fraudulent activity is detected, cash card fraud can be prevented.

[0077] The notification unit can temporarily freeze the user's bank account when fraudulent activity is detected, thereby minimizing damage. For example, when the generation AI detects fraudulent activity, the notification unit temporarily freezes the user's bank account, thereby minimizing damage. For example, it freezes the bank account when an abnormal transaction is detected. The notification unit can also temporarily freeze the user's bank account when the generation AI detects fraudulent activity, thereby preventing damage. For example, it freezes the bank account when unauthorized access is detected. The notification unit can also temporarily freeze the user's bank account when the generation AI detects fraudulent activity, thereby minimizing damage. For example, it freezes the bank account when an abnormal transaction or unauthorized access is detected. This makes it possible to temporarily freeze the user's bank account when fraudulent activity is detected, thereby minimizing damage and preventing cash card fraud.

[0078] The notification unit uses the emotion estimation function to analyze the user's emotional state and take appropriate action when fraudulent activity is detected. The notification unit, for example, uses the emotion estimation function to analyze the user's emotional state and take appropriate action when fraudulent activity is detected. For example, it issues a warning if the emotion score is high. The notification unit also uses the generation AI to analyze the user's emotional state and take appropriate action when fraudulent activity is detected. For example, it issues a warning if stress or anxiety increases. The notification unit also uses the emotion estimation function to analyze the user's emotional state in real time and take appropriate action when fraudulent activity is detected. For example, it issues a warning if the emotion score is high. In this way, by using the emotion estimation function to analyze the user's emotional state and taking appropriate action when fraudulent activity is detected, it is possible to prevent cash card fraud.

[0079] The notification unit can send a notification to the user's family or trusted contacts when fraudulent activity is detected, and request their cooperation. For example, when the generation AI detects fraudulent activity, the notification unit sends a notification to the user's family or trusted contacts and requests their cooperation. For example, a notification is sent when an abnormal transaction is detected. In addition, when the generation AI detects fraudulent activity, the notification unit sends a notification to the user's family or trusted contacts and requests their cooperation. For example, a notification is sent when unauthorized access is detected. In addition, when the generation AI detects fraudulent activity, the notification unit sends a notification to the user's family or trusted contacts and requests their cooperation. For example, a notification is sent when an abnormal transaction or unauthorized access is detected. In this way, by sending a notification to the user's family or trusted contacts and requesting their cooperation when fraudulent activity is detected, cash card fraud can be prevented.

[0080] The notification unit can analyze the user's emotional response when fraudulent activity is detected using the emotion estimation function and take the optimal response. For example, the notification unit uses the emotion estimation function to analyze the user's emotional response when fraudulent activity is detected and take the optimal response. For example, it issues a warning if the emotion score is high. The notification unit also analyzes the user's emotional response when the generation AI detects fraudulent activity and take the optimal response. For example, it issues a warning if stress or anxiety increases. The notification unit also uses the emotion estimation function to analyze the user's emotional response in real time when fraudulent activity is detected and take the optimal response. For example, it issues a warning if the emotion score is high. In this way, by using the emotion estimation function to analyze the user's emotional response when fraudulent activity is detected and take the optimal response, it is possible to prevent cash card fraud.

[0081] The notification unit can immediately send a notification to the user's smartphone when it detects an abnormal transaction or unauthorized access. For example, the notification unit immediately sends a notification to the user's smartphone when the generation AI detects an abnormal transaction. For example, it sends a notification when an abnormal transaction is detected. Furthermore, the notification unit immediately sends a notification to the user's smartphone when the generation AI detects unauthorized access. For example, it sends a notification when unauthorized access is detected. Furthermore, the notification unit immediately sends a notification to the user's smartphone when the generation AI detects an abnormal transaction or unauthorized access. For example, it sends a notification when an abnormal transaction or unauthorized access is detected. In this way, by immediately sending a notification to the user's smartphone when an abnormal transaction or unauthorized access is detected, cash card fraud can be prevented.

[0082] The notification unit can send a detailed notification to the user's email address when an abnormal transaction or unauthorized access is detected. For example, the notification unit sends a detailed notification to the user's email address when the generation AI detects an abnormal transaction. For example, a detailed notification is sent when an abnormal transaction is detected. Furthermore, the notification unit sends a detailed notification to the user's email address when the generation AI detects unauthorized access. For example, a detailed notification is sent when unauthorized access is detected. Furthermore, the notification unit sends a detailed notification to the user's email address when the generation AI detects an abnormal transaction or unauthorized access. For example, a detailed notification is sent when an abnormal transaction or unauthorized access is detected. In this way, by sending a detailed notification to the user's email address when an abnormal transaction or unauthorized access is detected, cash card fraud can be prevented.

[0083] The notification unit can analyze the user's emotional state using the emotion estimation function and send a notification at an appropriate time. The notification unit, for example, uses the emotion estimation function to analyze the user's emotional state and send a notification at an appropriate time. For example, a notification is sent when the emotion score is high. The notification unit also uses the generation AI to analyze the user's emotional state and send a notification at an appropriate time. For example, a notification is sent when stress or anxiety increases. The notification unit also uses the emotion estimation function to analyze the user's emotional state in real time and send a notification at an appropriate time. For example, a notification is sent when the emotion score is high. In this way, by using the emotion estimation function to analyze the user's emotional state and sending a notification at an appropriate time, cash card fraud can be prevented.

[0084] The notification unit can send a notification to the user's SNS account when it detects an abnormal transaction or unauthorized access. The notification unit, for example, sends a notification to the user's SNS account when the generation AI detects an abnormal transaction. For example, it sends a notification to the SNS account when an abnormal transaction is detected. Furthermore, the notification unit sends a notification to the user's SNS account when the generation AI detects unauthorized access. For example, it sends a notification to the SNS account when unauthorized access is detected. Furthermore, the notification unit sends a notification to the user's SNS account when the generation AI detects an abnormal transaction or unauthorized access. For example, it sends a notification to the SNS account when an abnormal transaction or unauthorized access is detected. In this way, by sending a notification to the user's SNS account when an abnormal transaction or unauthorized access is detected, it is possible to prevent cash card fraud.

[0085] The notification unit can send a notification to the user's family or trusted contacts when an abnormal transaction or unauthorized access is detected. For example, the notification unit sends a notification to the user's family or trusted contacts when the generation AI detects an abnormal transaction. For example, it sends a notification to family members when an abnormal transaction is detected. Furthermore, the notification unit sends a notification to the user's family or trusted contacts when the generation AI detects unauthorized access. For example, it sends a notification to family members when unauthorized access is detected. Furthermore, the notification unit sends a notification to the user's family or trusted contacts when the generation AI detects an abnormal transaction or unauthorized access. For example, it sends a notification to family members when an abnormal transaction or unauthorized access is detected. In this way, by sending a notification to the user's family or trusted contacts when an abnormal transaction or unauthorized access is detected, cash card fraud can be prevented.

[0086] The notification unit can use the emotion estimation function to analyze the user's emotional response and send the notification in the optimal manner. The notification unit, for example, uses the emotion estimation function to analyze the user's emotional response and send the notification in the optimal manner. For example, an appropriate notification method is selected when the emotion score is high. The notification unit also uses the generation AI to analyze the user's emotional response and send the notification in the optimal manner. For example, an appropriate notification method is selected when stress or anxiety is high. The notification unit also uses the emotion estimation function to analyze the user's emotional response in real time and send the notification in the optimal manner. For example, an appropriate notification method is selected when the emotion score is high. In this way, by using the emotion estimation function to analyze the user's emotional response and sending the notification in the optimal manner, it is possible to prevent cash card fraud.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The cash card fraud detection tool can further include a purchase history analysis unit that analyzes the user's purchase history. The purchase history analysis unit analyzes the user's past purchase history and learns normal purchasing patterns. For example, it can learn the types of products and services the user usually purchases, their purchase frequency, and purchase amounts, and based on this, detect abnormal purchasing behavior. For example, it can detect transactions at high-priced items that the user does not normally purchase or at suspicious stores. The purchase history analysis unit also detects abnormal purchasing behavior based on the user's purchase history. For example, it can detect transactions at stores that the user does not normally use or at abnormally high prices. In this way, cash card fraud can be prevented by analyzing the user's purchase history and detecting abnormal purchasing behavior.

[0089] The cash card fraud detection tool may further include a health data analysis unit that analyzes the user's health data. The health data analysis unit analyzes the user's health data to detect abnormal health conditions. For example, it can analyze data such as the user's heart rate, blood pressure, and body temperature to detect abnormal fluctuations. For example, it detects abnormalities when the heart rate rises suddenly or when the blood pressure is abnormally high. The health data analysis unit also detects abnormal health conditions based on the user's health data. For example, it detects data that does not match the user's normal health condition. In this way, cash card fraud can be prevented by analyzing the user's health data and detecting abnormal health conditions.

[0090] The cash card fraud detection tool can further include a voice analysis unit that analyzes the user's voice data. The voice analysis unit analyzes the user's voice data and detects abnormal voice patterns. For example, it can analyze the tone, rhythm, volume, etc. of the user's voice to detect abnormal fluctuations. For example, it detects an abnormality when the voice tone changes suddenly or when the volume is abnormally high. The voice analysis unit also detects abnormal voice patterns based on the user's voice data. For example, it detects data that does not match normal voice patterns. In this way, by analyzing the user's voice data and detecting abnormal voice patterns, cash card fraud can be prevented.

[0091] The cash card fraud detection tool can further include an emotion analysis unit that analyzes the user's emotional state. The emotion analysis unit analyzes the user's emotional state and detects abnormal emotional reactions. For example, it can analyze the user's facial expressions, voice, behavioral patterns, etc. to detect abnormal emotional reactions. For example, it can detect abnormalities when stress or anxiety increases. The emotion analysis unit also detects abnormal emotional reactions based on the user's emotional state. For example, it can detect data that does not match a normal emotional state. In this way, cash card fraud can be prevented by analyzing the user's emotional state and detecting abnormal emotional reactions.

[0092] The cash card fraud detection tool can further include a behavior history analysis unit that analyzes the user's behavior history. The behavior history analysis unit analyzes the user's behavior history and detects abnormal behavior patterns. For example, it can analyze the user's movement history and the history of services used to detect abnormal behavior patterns. For example, it can detect behavior in areas that the user does not normally use or abnormally frequent use of services. The behavior history analysis unit also detects abnormal behavior patterns based on the user's behavior history. For example, it can detect data that does not match normal behavior patterns. In this way, cash card fraud can be prevented by analyzing the user's behavior history and detecting abnormal behavior patterns.

[0093] The cash card fraud detection tool can further include an emotion analysis unit that analyzes the user's emotional state. The emotion analysis unit analyzes the user's emotional state and detects abnormal emotional reactions. For example, it can analyze the user's facial expressions, voice, behavioral patterns, etc. to detect abnormal emotional reactions. For example, it can detect abnormalities when stress or anxiety increases. The emotion analysis unit also detects abnormal emotional reactions based on the user's emotional state. For example, it can detect data that does not match a normal emotional state. In this way, cash card fraud can be prevented by analyzing the user's emotional state and detecting abnormal emotional reactions.

[0094] The cash card fraud detection tool may further include a biometric information analysis unit that analyzes the user's biometric information. The biometric information analysis unit analyzes the user's biometric information and detects abnormal biometric reactions. For example, it can analyze the user's fingerprint or facial authentication data to detect abnormal biometric reactions. For example, an abnormality is detected when the fingerprint authentication does not match or when the facial authentication does not match. The biometric information analysis unit also detects abnormal biometric reactions based on the user's biometric information. For example, it detects data that does not match normal biometric information. In this way, cash card fraud can be prevented by analyzing the user's biometric information and detecting abnormal biometric reactions.

[0095] The cash card fraud detection tool can further include an emotion analysis unit that analyzes the user's emotional state. The emotion analysis unit analyzes the user's emotional state and detects abnormal emotional reactions. For example, it can analyze the user's facial expressions, voice, behavioral patterns, etc. to detect abnormal emotional reactions. For example, it can detect abnormalities when stress or anxiety increases. The emotion analysis unit also detects abnormal emotional reactions based on the user's emotional state. For example, it can detect data that does not match a normal emotional state. In this way, cash card fraud can be prevented by analyzing the user's emotional state and detecting abnormal emotional reactions.

[0096] The cash card fraud detection tool can further include a social media analysis unit that analyzes the user's social media activity. The social media analysis unit analyzes the user's social media activity and detects abnormal activity. For example, it can detect abnormal activity by analyzing the content of the user's posts, location information, activity times, etc. For example, it can detect posts that do not match normal activity patterns or posts that are posted abnormally frequently. The social media analysis unit also detects abnormal activity based on the user's social media activity. For example, it can detect data that does not match normal activity patterns. In this way, cash card fraud can be prevented by analyzing the user's social media activity and detecting abnormal activity.

[0097] The cash card fraud detection tool can further include an emotion analysis unit that analyzes the user's emotional state. The emotion analysis unit analyzes the user's emotional state and detects abnormal emotional reactions. For example, it can analyze the user's facial expressions, voice, behavioral patterns, etc. to detect abnormal emotional reactions. For example, it can detect abnormalities when stress or anxiety increases. The emotion analysis unit also detects abnormal emotional reactions based on the user's emotional state. For example, it can detect data that does not match a normal emotional state. In this way, cash card fraud can be prevented by analyzing the user's emotional state and detecting abnormal emotional reactions.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The monitoring unit uses the generation AI to monitor the usage of the cash card. For example, the monitoring unit monitors the cash card's transaction data and usage history in real time. The monitoring unit can also learn patterns such as the area, time of day, and type of transaction in which the cash card is usually used, and use this information to detect abnormal transactions. Step 2: The detection unit analyzes the transaction data and usage history monitored by the monitoring unit to detect abnormal transactions. For example, the detection unit detects large withdrawals in areas not normally used, or successive transactions in a short period of time. The detection unit can also detect unauthorized access to cash cards. For example, it can detect when the cash card PIN code is entered incorrectly multiple times in a short period of time, or when access is made from a device not normally used. Step 3: The notification unit notifies the user of any abnormal transactions or unauthorized access detected by the detection unit. For example, if the notification unit detects an abnormal transaction, it sends a notification to the user's smartphone, requesting confirmation of the transaction. Also, if unauthorized access is detected, it issues a warning to the user, urging them to take necessary measures.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A monitoring unit that uses a generating AI to monitor the usage of cash cards; a detection unit that analyzes the transaction data and usage history monitored by the monitoring unit and detects abnormal transactions; a notification unit that notifies a user of the abnormal transaction and unauthorized access detected by the detection unit. A system characterized by:

2. The monitoring unit The abnormality is detected by using the biological information of the user in combination.

2. The system of claim 1.

3. The monitoring unit Analyzing the user's social media activity to detect the abnormal transactions.

2. The system of claim 1.

4. The detection unit Compare with past transaction history to detect the anomaly 2. The system of claim 1.

5. The notification unit When fraudulent activity is detected, the use of the cash card is immediately suspended and the user is notified.

2. The system of claim 1.

6. The monitoring unit Monitoring the user's emotional state and detecting the anomalous transactions when stress or anxiety increases.

2. The system of claim 1.

Images