system

Abstract

An object of a system according to an embodiment is to prevent damage from a special fraud in a transfer using an ATM.SOLUTION: A system includes an ATM, a determination unit, and a confirmation unit. The ATM is loaded with the generated AI. The determination unit checks the purpose and history of the transfer from the user, and determines whether or not the transfer is likely to be a special fraud based on the information obtained by the generation AI. The confirmation unit performs confirmation by an attendant when the determination unit determines that there is a possibility of a special fraud.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 prevent special frauds when transferring funds using ATMs.

[0005] The system according to the embodiment aims to prevent damage caused by special fraud when transferring money using an ATM. [Means for solving the problem]

[0006] The system according to the embodiment includes an ATM, a determination unit, and a confirmation unit. The ATM is equipped with a generation AI. The determination unit checks the purpose and circumstances of the transfer from the user, and determines whether the transfer is likely to be a special fraud based on the information obtained by the generation AI. If the determination unit determines that there is a possibility of special fraud, the confirmation unit has an attendant confirm the transfer. [Effects of the Invention]

[0007] The system according to the embodiment can prevent damage caused by special fraud when transferring money using an ATM. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The special fraud prevention system according to the embodiment of the present invention uses an ATM equipped with a generation AI to check the purpose and circumstances of a user's transfer, determine the possibility of special fraud, and have an attendant check the situation if necessary. This allows the special fraud prevention system to prevent damage caused by special fraud before it occurs.

[0029] A special fraud prevention system according to an embodiment includes an ATM equipped with a generation AI, a determination unit, and a confirmation unit. The generation AI confirms the purpose and circumstances of a transfer from a user. For example, when a user attempts to transfer money at an ATM, the generation AI asks questions such as, "What is the purpose of the transfer?" and "How is this transfer being made?" The generation AI analyzes the purpose and circumstances of the transfer based on the user's answers. The determination unit determines whether the transfer is likely to be a special fraud based on information obtained by the generation AI. For example, the determination unit determines, based on information analyzed by the generation AI, that "this transfer may be fraudulent." If the determination unit determines that there is a possibility of special fraud, the confirmation unit has an attendant verify the transfer. For example, the confirmation unit has an attendant ask the user questions such as, "Is this transfer really necessary?" and "Is the recipient trustworthy?" The special fraud prevention system according to an embodiment thus prevents damage from special fraud.

[0030] The generation AI can refer to a user's past transfer history and detect abnormal patterns. For example, the generation AI analyzes a user's past transfer history and detects abnormal patterns. For example, it issues a warning if the transfer amount or frequency differs from normal. The generation AI also refers to the past transfer history when a user makes a transfer at an ATM and detects abnormal patterns. For example, it displays a warning if the transfer destination suddenly changes. The generation AI also builds a system that analyzes a user's past transfer history and detects abnormal patterns. For example, it issues a warning if the transfer amount suddenly increases. This allows the system to detect abnormal patterns based on past transfer history and increase the possibility of fraud.

[0031] The generation AI can check the reliability of the transfer destination in real time from an online database based on the user's input. For example, the generation AI checks the reliability of the transfer destination in real time from an online database based on the user's input. For example, it checks the reputation and past transaction history of the transfer destination. In addition, when a user makes a transfer at an ATM, the generation AI analyzes the input and checks the reliability of the transfer destination in real time. For example, it checks the company information and reviews of the transfer destination. In addition, the generation AI builds a system that checks the reliability of the transfer destination in real time from an online database based on the user's input. For example, it checks the transfer destination's past transaction history. This checks the reliability of the transfer destination in real time and increases the possibility of fraud.

[0032] The generation AI can analyze the content of a user's answers using natural language processing technology and compare it with typical fraud patterns. For example, the generation AI analyzes the content of a user's answers using natural language processing technology and compares it with typical fraud patterns. For example, it detects specific keywords and phrases. The generation AI also analyzes the content of the answers when a user makes a transfer at an ATM and compares it with typical fraud patterns. For example, it detects specific contexts related to fraud. The generation AI also builds a system that analyzes the content of a user's answers using natural language processing technology and compares it with typical fraud patterns. For example, it detects specific phrases related to fraud. This allows it to compare it with typical fraud patterns and increase the likelihood of fraud.

[0033] The generation AI can generate follow-up questions in response to the user's answers to collect more detailed information. For example, the generation AI generates follow-up questions in response to the user's answers to collect more detailed information. For example, it asks questions such as, "What is the name of the transfer recipient?" or "What is the reason for the transfer?" The generation AI also analyzes the answers given by the user when making a transfer at an ATM and generates follow-up questions to collect more detailed information. For example, it asks questions such as, "Who ordered this transfer?" The generation AI also builds a system that generates follow-up questions in response to the user's answers to collect more detailed information. For example, it asks questions such as, "What is the transfer recipient's address?" This collects more detailed information and increases the possibility of fraud.

[0034] The generation AI can compare the user's answers with the data of other ATM users and detect abnormal patterns. For example, the generation AI compares the user's answers with the data of other ATM users to detect abnormal patterns. For example, it issues a warning if a specific transfer destination is common to multiple users. The generation AI also compares the answers with the data of other users when a user makes a transfer at an ATM to detect abnormal patterns. For example, it issues a warning if the same transfer reason is common to multiple users. The generation AI also builds a system that compares the user's answers with the data of other ATM users to detect abnormal patterns. For example, it issues a warning if a specific transfer amount is common to multiple users. By comparing with other users' data, abnormal patterns can be detected, increasing the possibility of fraud.

[0035] Based on the user's answers, the generation AI can check the transfer recipient's past transaction history and determine the possibility of fraud. For example, the generation AI can check the transfer recipient's past transaction history based on the user's answers and determine the possibility of fraud. For example, it can issue a warning if the transfer recipient has been involved in fraud in the past. Furthermore, when a user makes a transfer at an ATM, the generation AI analyzes the answers and checks the transfer recipient's past transaction history to determine the possibility of fraud. For example, it can display a warning if the transfer recipient has engaged in fraudulent transactions in the past. Furthermore, the generation AI can build a system that checks the transfer recipient's past transaction history based on the user's answers and determines the possibility of fraud. For example, it can issue a warning if the transfer recipient has engaged in transactions that are suspected of being fraudulent in the past. In this way, by checking the transfer recipient's past transaction history and determining the possibility of fraud, fraud victims can be prevented from occurring.

[0036] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0037] The special fraud prevention system can also be equipped with a behavior analysis unit that analyzes user behavior patterns. For example, it can analyze the time and frequency at which a user accesses an ATM, as well as the way the ATM is operated, to detect abnormal behavior patterns. For example, it can issue a warning if the user accesses the ATM at a time other than normal operating hours, or if the operation method suddenly changes. The behavior analysis unit can also detect abnormal behavior when a user transfers money at an ATM by comparing it with past behavior patterns. For example, it can display a warning if the user performs an operation that differs from the normal operating procedure. This allows the user's behavior patterns to be analyzed, thereby increasing the possibility of fraud.

[0038] The special fraud prevention system can further include a biometric authentication unit that acquires the user's biometric authentication information. For example, the user's identity is verified using fingerprint or facial authentication. For example, identity verification can be strengthened by requiring fingerprint authentication when making a transfer. The biometric authentication unit also uses facial authentication to verify the user's identity when making a transfer at an ATM. For example, facial authentication is used to confirm that the user is the registered user. The biometric authentication unit also detects abnormal access based on the user's biometric authentication information. For example, it issues a warning if someone other than the registered user accesses the system. This strengthens identity verification using biometric authentication, reducing the possibility of fraud.

[0039] The special fraud prevention system can further include a location information acquisition unit that acquires user location information. For example, the location information is acquired when a user accesses an ATM, and access from an abnormal location is detected. For example, a warning is issued if access is made from a location different from the user's usual location. The location information acquisition unit also detects abnormal access based on the location information when a user makes a transfer at an ATM. For example, a warning is displayed if access is made from a location other than the user's home or workplace. The location information acquisition unit also detects abnormal behavioral patterns based on the user's location information. For example, a warning is issued if access is made from a location different from the user's usual range of movement. This makes it possible to detect abnormal access using location information and increase the possibility of fraud.

[0040] The special fraud prevention system can further include a transaction history analysis unit that analyzes a user's past transaction history. For example, the system analyzes the user's past transaction history to detect abnormal transaction patterns. For example, it issues a warning if the transaction amount or frequency differs from normal. The transaction history analysis unit also detects abnormal transactions based on the past transaction history when a user transfers money at an ATM. For example, it displays a warning if the transfer destination suddenly changes. The transaction history analysis unit also builds a system that detects abnormal transaction patterns based on the user's past transaction history. For example, it issues a warning if the transaction amount suddenly increases. This makes it possible to detect abnormal transactions based on past transaction history and increase the possibility of fraud.

[0041] The special fraud prevention system can further include a reliability evaluation unit that evaluates the reliability of the user's transfer destination. For example, the reliability is evaluated based on the reputation and past transaction history of the transfer destination. For example, a warning is issued if the transfer destination has been involved in fraud in the past. The reliability evaluation unit also evaluates the reliability of the transfer destination when the user transfers money at an ATM. For example, it checks the company information and reputation of the transfer destination. The reliability evaluation unit also builds a system that evaluates the reliability of the user's transfer destination. For example, it checks the transfer destination's past transaction history. This makes it possible to evaluate the reliability of the transfer destination and reduce the possibility of fraud.

[0042] The processing flow of the first embodiment will be briefly explained below.

[0043] Step 1: The generation AI confirms the purpose and circumstances of the transfer from the user. For example, when a user attempts to transfer money at an ATM, the generation AI asks questions such as "What is the purpose of the transfer?" and "What is the process for this transfer?" and analyzes the purpose and circumstances of the transfer based on the user's answers. Step 2: The judgment unit determines whether the transfer is likely to be a special fraud based on the information obtained by the generation AI. For example, the judgment unit may determine, based on the information analyzed by the generation AI, that "this transfer is likely to be fraud." Step 3: If the judgment unit determines that there is a possibility of special fraud, the confirmation unit will have an attendant check the case. For example, the confirmation unit will have an attendant ask the user questions such as, "Is this transfer really necessary?" or "Is the recipient a trustworthy party?"

[0044] (Example 2) The special fraud prevention system according to the embodiment of the present invention uses an ATM equipped with a generation AI to check the purpose and circumstances of a user's transfer, determine the possibility of special fraud, and have an attendant check the situation if necessary. This allows the special fraud prevention system to prevent damage caused by special fraud before it occurs.

[0045] A special fraud prevention system according to an embodiment includes an ATM equipped with a generation AI, a determination unit, and a confirmation unit. The generation AI confirms the purpose and circumstances of a transfer from a user. For example, when a user attempts to transfer money at an ATM, the generation AI asks questions such as, "What is the purpose of the transfer?" and "How is this transfer being made?" The generation AI analyzes the purpose and circumstances of the transfer based on the user's answers. The determination unit determines whether the transfer is likely to be a special fraud based on information obtained by the generation AI. For example, the determination unit determines, based on information analyzed by the generation AI, that "this transfer may be fraudulent." If the determination unit determines that there is a possibility of special fraud, the confirmation unit has an attendant verify the transfer. For example, the confirmation unit has an attendant ask the user questions such as, "Is this transfer really necessary?" and "Is the recipient trustworthy?" The special fraud prevention system according to an embodiment thus prevents damage from special fraud.

[0046] The generation AI can analyze a user's voice tone and speaking patterns to determine whether they are feeling nervous or anxious. For example, the generation AI can analyze a user's voice tone and speaking patterns in real time to determine whether they are feeling nervous or anxious. For example, it can detect voice tremors and changes in speaking speed. The generation AI can also analyze the user's voice tone and speaking patterns when making a transfer at an ATM to detect abnormal emotional fluctuations. For example, it can detect abnormally high or low tones compared to normal conversation. The generation AI can also analyze the user's voice tone and speaking patterns to issue a warning if they are feeling nervous or anxious. For example, it can display a warning if there is a sudden change in voice tone. This allows the generation AI to analyze the user's emotional state and increase the possibility of fraud.

[0047] The generation AI can refer to a user's past transfer history and detect abnormal patterns. For example, the generation AI analyzes a user's past transfer history and detects abnormal patterns. For example, it issues a warning if the transfer amount or frequency differs from normal. The generation AI also refers to the past transfer history when a user makes a transfer at an ATM and detects abnormal patterns. For example, it displays a warning if the transfer destination suddenly changes. The generation AI also builds a system that analyzes a user's past transfer history and detects abnormal patterns. For example, it issues a warning if the transfer amount suddenly increases. This allows the system to detect abnormal patterns based on past transfer history and increase the possibility of fraud.

[0048] The generation AI can use the emotion estimation function to analyze a user's emotions in real time when making a transfer and issue a warning if there are abnormal emotional fluctuations. For example, the generation AI can use the emotion estimation function to analyze a user's emotions in real time when making a transfer and issue a warning if there are abnormal emotional fluctuations. For example, it can analyze facial expressions and voice tone. The generation AI can also use the emotion estimation function to analyze a user's emotions when making a transfer at an ATM and issue a warning if there are abnormal emotional fluctuations. For example, it can issue a warning if there is a sudden change in facial expression. The generation AI can also use the emotion estimation function to build a system that analyzes a user's emotions in real time when making a transfer and issue a warning if there are abnormal emotional fluctuations. For example, it can issue a warning if there is a sudden change in voice tone. This allows the system to analyze a user's emotional fluctuations in real time and increase the likelihood of fraud.

[0049] The generation AI can check the reliability of the transfer destination in real time from an online database based on the user's input. For example, the generation AI checks the reliability of the transfer destination in real time from an online database based on the user's input. For example, it checks the reputation and past transaction history of the transfer destination. In addition, when a user makes a transfer at an ATM, the generation AI analyzes the input and checks the reliability of the transfer destination in real time. For example, it checks the company information and reviews of the transfer destination. In addition, the generation AI builds a system that checks the reliability of the transfer destination in real time from an online database based on the user's input. For example, it checks the transfer destination's past transaction history. This checks the reliability of the transfer destination in real time and increases the possibility of fraud.

[0050] The generation AI can analyze the content of a user's answers using natural language processing technology and compare it with typical fraud patterns. For example, the generation AI analyzes the content of a user's answers using natural language processing technology and compares it with typical fraud patterns. For example, it detects specific keywords and phrases. The generation AI also analyzes the content of the answers when a user makes a transfer at an ATM and compares it with typical fraud patterns. For example, it detects specific contexts related to fraud. The generation AI also builds a system that analyzes the content of a user's answers using natural language processing technology and compares it with typical fraud patterns. For example, it detects specific phrases related to fraud. This allows it to compare it with typical fraud patterns and increase the likelihood of fraud.

[0051] The generation AI can generate follow-up questions in response to the user's answers to collect more detailed information. For example, the generation AI generates follow-up questions in response to the user's answers to collect more detailed information. For example, it asks questions such as, "What is the name of the transfer recipient?" or "What is the reason for the transfer?" The generation AI also analyzes the answers given by the user when making a transfer at an ATM and generates follow-up questions to collect more detailed information. For example, it asks questions such as, "Who ordered this transfer?" The generation AI also builds a system that generates follow-up questions in response to the user's answers to collect more detailed information. For example, it asks questions such as, "What is the transfer recipient's address?" This collects more detailed information and increases the possibility of fraud.

[0052] The generation AI can use the emotion estimation function to analyze the emotional response to the user's answers and issue a warning if there is a high possibility of fraud. For example, the generation AI uses the emotion estimation function to analyze the emotional response to the user's answers and issue a warning if there is a high possibility of fraud. For example, it detects tension or anxiety while answering. Furthermore, when a user makes a transfer at an ATM, the generation AI uses the emotion estimation function to analyze the emotional response to the answer and displays a warning if there is a high possibility of fraud. For example, it detects changes in the tone of voice while answering. Furthermore, the generation AI uses the emotion estimation function to analyze the emotional response to the user's answers and builds a system that issues a warning if there is a high possibility of fraud. For example, it detects changes in facial expression while answering. In this way, by analyzing emotional responses and issuing a warning if there is a high possibility of fraud, fraud can be prevented before it occurs.

[0053] The generation AI can compare the user's answers with the data of other ATM users and detect abnormal patterns. For example, the generation AI compares the user's answers with the data of other ATM users to detect abnormal patterns. For example, it issues a warning if a specific transfer destination is common to multiple users. The generation AI also compares the answers with the data of other users when a user makes a transfer at an ATM to detect abnormal patterns. For example, it issues a warning if the same transfer reason is common to multiple users. The generation AI also builds a system that compares the user's answers with the data of other ATM users to detect abnormal patterns. For example, it issues a warning if a specific transfer amount is common to multiple users. By comparing with other users' data, abnormal patterns can be detected, increasing the possibility of fraud.

[0054] Based on the user's answers, the generation AI can check the transfer recipient's past transaction history and determine the possibility of fraud. For example, the generation AI can check the transfer recipient's past transaction history based on the user's answers and determine the possibility of fraud. For example, it can issue a warning if the transfer recipient has been involved in fraud in the past. Furthermore, when a user makes a transfer at an ATM, the generation AI analyzes the answers and checks the transfer recipient's past transaction history to determine the possibility of fraud. For example, it can display a warning if the transfer recipient has engaged in fraudulent transactions in the past. Furthermore, the generation AI can build a system that checks the transfer recipient's past transaction history based on the user's answers and determines the possibility of fraud. For example, it can issue a warning if the transfer recipient has engaged in transactions that are suspected of being fraudulent in the past. In this way, by checking the transfer recipient's past transaction history and determining the possibility of fraud, fraud victims can be prevented from occurring.

[0055] The generation AI can use the emotion estimation function to analyze the emotional response to the user's answers and generate follow-up questions to elicit positive emotions. For example, the generation AI uses the emotion estimation function to analyze the emotional response to the user's answers and generate follow-up questions to elicit positive emotions. For example, it asks questions such as, "What good results do you expect from this transfer?" In addition, when a user makes a transfer at an ATM, the generation AI uses the emotion estimation function to analyze the emotional response to the answer and generates follow-up questions to elicit positive emotions. For example, it asks questions such as, "How will you feel if this transfer is successful?" In addition, the generation AI uses the emotion estimation function to build a system that analyzes the emotional response to the user's answers and generates follow-up questions to elicit positive emotions. For example, it asks questions such as, "What good things will happen if this transfer is successful?" In this way, follow-up questions to elicit positive emotions are generated, increasing the user's sense of security.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The special fraud prevention system can also be equipped with a behavior analysis unit that analyzes user behavior patterns. For example, it can analyze the time and frequency at which a user accesses an ATM, as well as the way the ATM is operated, to detect abnormal behavior patterns. For example, it can issue a warning if the user accesses the ATM at a time other than normal operating hours, or if the operation method suddenly changes. The behavior analysis unit can also detect abnormal behavior when a user transfers money at an ATM by comparing it with past behavior patterns. For example, it can display a warning if the user performs an operation that differs from the normal operating procedure. This allows the user's behavior patterns to be analyzed, thereby increasing the possibility of fraud.

[0058] The special fraud prevention system can further include a biometric authentication unit that acquires the user's biometric authentication information. For example, the user's identity is verified using fingerprint or facial authentication. For example, identity verification can be strengthened by requiring fingerprint authentication when making a transfer. The biometric authentication unit also uses facial authentication to verify the user's identity when making a transfer at an ATM. For example, facial authentication is used to confirm that the user is the registered user. The biometric authentication unit also detects abnormal access based on the user's biometric authentication information. For example, it issues a warning if someone other than the registered user accesses the system. This strengthens identity verification using biometric authentication, reducing the possibility of fraud.

[0059] The special fraud prevention system can further include a location information acquisition unit that acquires user location information. For example, the location information is acquired when a user accesses an ATM, and access from an abnormal location is detected. For example, a warning is issued if access is made from a location different from the user's usual location. The location information acquisition unit also detects abnormal access based on the location information when a user makes a transfer at an ATM. For example, a warning is displayed if access is made from a location other than the user's home or workplace. The location information acquisition unit also detects abnormal behavioral patterns based on the user's location information. For example, a warning is issued if access is made from a location different from the user's usual range of movement. This makes it possible to detect abnormal access using location information and increase the possibility of fraud.

[0060] The special fraud prevention system can further include a transaction history analysis unit that analyzes a user's past transaction history. For example, the system analyzes the user's past transaction history to detect abnormal transaction patterns. For example, it issues a warning if the transaction amount or frequency differs from normal. The transaction history analysis unit also detects abnormal transactions based on the past transaction history when a user transfers money at an ATM. For example, it displays a warning if the transfer destination suddenly changes. The transaction history analysis unit also builds a system that detects abnormal transaction patterns based on the user's past transaction history. For example, it issues a warning if the transaction amount suddenly increases. This makes it possible to detect abnormal transactions based on past transaction history and increase the possibility of fraud.

[0061] The special fraud prevention system can further include a reliability evaluation unit that evaluates the reliability of the user's transfer destination. For example, the reliability is evaluated based on the reputation and past transaction history of the transfer destination. For example, a warning is issued if the transfer destination has been involved in fraud in the past. The reliability evaluation unit also evaluates the reliability of the transfer destination when the user transfers money at an ATM. For example, it checks the company information and reputation of the transfer destination. The reliability evaluation unit also builds a system that evaluates the reliability of the user's transfer destination. For example, it checks the transfer destination's past transaction history. This makes it possible to evaluate the reliability of the transfer destination and reduce the possibility of fraud.

[0062] The special fraud prevention system can further include an emotion determination unit that estimates a user's emotions and determines the need for a transfer based on the estimated emotions. For example, when a user makes a transfer, the emotion determination function is used to determine whether the user is feeling nervous or anxious. For example, the tone of voice and speaking patterns are analyzed. The emotion determination unit also uses the emotion determination function to analyze the user's emotions when making a transfer at an ATM and determines the need for the transfer. For example, a warning is displayed if the user is feeling nervous or anxious. The emotion determination unit also constructs a system that estimates a user's emotions and determines the need for a transfer based on the estimated emotions. For example, a warning is issued if the tone of voice suddenly changes. In this way, the possibility of fraud can be increased by analyzing the user's emotions and determining the need for a transfer.

[0063] The special fraud prevention system can further include a risk assessment unit that estimates a user's emotions and assesses the risk of a transfer based on the estimated emotions. For example, when a user makes a transfer, the emotion estimation function is used to determine whether the user is feeling nervous or anxious. For example, facial expressions and tone of voice are analyzed. The risk assessment unit also uses the emotion estimation function to analyze the user's emotions when the user makes a transfer at an ATM and assesses the risk of the transfer. For example, a warning is displayed if the user is feeling nervous or anxious. The risk assessment unit also constructs a system that estimates the user's emotions and assesses the risk of the transfer based on the estimated emotions. For example, a warning is issued if there is a sudden change in facial expression. In this way, by analyzing the user's emotions and assessing the risk of the transfer, the possibility of fraud can be increased.

[0064] The special fraud prevention system can further include an appropriateness determination unit that estimates a user's emotions and determines the appropriateness of a transfer based on the estimated emotions. For example, when a user makes a transfer, the emotion estimation function is used to determine whether the user is feeling nervous or anxious. For example, the tone of voice and speaking patterns are analyzed. The appropriateness determination unit also uses the emotion estimation function to analyze the user's emotions when the user makes a transfer at an ATM and determines the appropriateness of the transfer. For example, a warning is displayed if the user is feeling nervous or anxious. The appropriateness determination unit also constructs a system that estimates a user's emotions and determines the appropriateness of a transfer based on the estimated emotions. For example, a warning is issued if the tone of voice suddenly changes. In this way, by analyzing the user's emotions and determining the appropriateness of a transfer, the possibility of fraud can be reduced.

[0065] The special fraud prevention system can further include a reliability evaluation unit that estimates a user's emotions and evaluates the reliability of the transfer based on the estimated emotions. For example, when a user makes a transfer, the emotion estimation function is used to determine whether the user is feeling nervous or anxious. For example, facial expressions and tone of voice are analyzed. The reliability evaluation unit also uses the emotion estimation function to analyze the user's emotions when the user makes a transfer at an ATM and evaluates the reliability of the transfer. For example, a warning is displayed if the user is feeling nervous or anxious. The reliability evaluation unit also constructs a system that estimates the user's emotions and evaluates the reliability of the transfer based on the estimated emotions. For example, a warning is issued if there is a sudden change in facial expression. In this way, by analyzing the user's emotions and evaluating the reliability of the transfer, the possibility of fraud can be reduced.

[0066] The special fraud prevention system can further include a safety evaluation unit that estimates a user's emotions and evaluates the safety of a transfer based on the estimated emotions. For example, when a user makes a transfer, the emotion estimation function is used to determine whether the user is feeling nervous or anxious. For example, the tone of voice and speaking patterns are analyzed. The safety evaluation unit also uses the emotion estimation function to analyze the user's emotions when the user makes a transfer at an ATM and evaluates the safety of the transfer. For example, a warning is displayed if the user is feeling nervous or anxious. The safety evaluation unit also constructs a system that estimates the user's emotions and evaluates the safety of the transfer based on the estimated emotions. For example, a warning is issued if the tone of voice suddenly changes. In this way, by analyzing the user's emotions and evaluating the safety of the transfer, the possibility of fraud can be reduced.

[0067] The processing flow of the second embodiment will be briefly explained below.

[0068] Step 1: The generation AI confirms the purpose and circumstances of the transfer from the user. For example, when a user attempts to transfer money at an ATM, the generation AI asks questions such as "What is the purpose of the transfer?" and "What is the process for this transfer?" and analyzes the purpose and circumstances of the transfer based on the user's answers. Step 2: The judgment unit determines whether the transfer is likely to be a special fraud based on the information obtained by the generation AI. For example, the judgment unit may determine, based on the information analyzed by the generation AI, that "this transfer is likely to be fraud." Step 3: If the judgment unit determines that there is a possibility of special fraud, the confirmation unit will have an attendant check the case. For example, the confirmation unit will have an attendant ask the user questions such as, "Is this transfer really necessary?" or "Is the recipient a trustworthy party?"

[0069] 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.

[0070] 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.

[0071] 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.

[0072] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0073] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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).

[0078] 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.

[0079] 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.

[0080] 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.

[0081] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0082] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0088] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0097] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 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.

[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 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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."

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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]

[0136] 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. ATMs equipped with generative AI, The generated AI is Check the purpose and circumstances of the transfer from the user, A determination unit that determines whether or not a transfer is likely to be a special fraud based on the information obtained by the generation AI; and a confirmation unit that has an attendant confirm the case when the determination unit determines that there is a possibility of special fraud. A system characterized by:

2. The generated AI is Analyzing the user's tone of voice and speaking patterns to determine whether they are nervous or anxious 2. The system of claim 1.

3. The generated AI is Check the user's past transfer history and detect any abnormal patterns 2. The system of claim 1.

4. The generated AI is The emotions of the user when making a transfer are analyzed in real time, and a warning is issued if there are abnormal emotional fluctuations.

2. The system of claim 1.

5. The generated AI is Based on the user's input, the reliability of the transfer destination is checked in real time from an internet database.

2. The system of claim 1.

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