System

Abstract

An object of a system according to an embodiment is to safely execute a transaction without using a cash card.SOLUTION: A system according to an embodiment includes a face authentication unit, a biometric authentication unit, and a transaction execution unit. The face authentication unit recognizes a face of a user. The biometric authentication unit recognizes biometric information of a user. The transaction execution unit executes a transaction based on the information recognized by the face authentication unit and the biometric authentication 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 that transactions using cash cards involve the risk of the card being lost or stolen.

[0005] The system according to the embodiment aims to execute transactions safely without using a cash card. [Means for solving the problem]

[0006] The system according to the embodiment includes a face authentication unit, a biometric authentication unit, and a transaction execution unit. The face authentication unit recognizes the face of a user. The biometric authentication unit recognizes biometric information of the user. The transaction execution unit executes a transaction based on the information recognized by the face authentication unit and the biometric authentication unit. [Effects of the Invention]

[0007] The system according to the embodiment can safely execute transactions without using a cash card. [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 cash cardless system according to an embodiment of the present invention is a system that utilizes facial recognition and biometric authentication technology to perform banking transactions without using a cash card. As a result, the cash cardless system allows banking transactions to be performed without using a cash card.

[0029] The cash cardless system according to the embodiment includes a facial authentication unit, a biometric authentication unit, and a transaction execution unit. The facial authentication unit recognizes a user's face. For example, the facial authentication unit uses a camera to capture a user's face, and AI analyzes the image to extract facial feature points. The facial authentication unit can also use a facial image matching algorithm to compare the image with pre-registered facial data. The facial authentication unit can also perform facial authentication based on facial feature points. For example, the facial authentication unit performs facial authentication based on the facial contours and the positions of the eyes, nose, and mouth. The biometric authentication unit recognizes a user's biometric information. For example, the biometric authentication unit uses a fingerprint scanner to perform fingerprint authentication. The biometric authentication unit can also use an iris scanner to perform iris authentication. The biometric authentication unit can also use a vein scanner to perform vein authentication. For example, the biometric authentication unit uses a fingerprint scanner to acquire fingerprint data, and AI analyzes the data to extract fingerprint feature points. The iris scanner captures a user's iris, and AI analyzes the image to extract iris feature points. The vein scanner captures the vein patterns of the user's palm or fingers, and AI analyzes the image to extract vein features. The transaction execution unit executes transactions based on the information recognized by the facial authentication unit and biometric authentication unit. For example, the transaction execution unit combines the authentication results of the facial authentication unit and the biometric authentication unit, and AI analyzes the data to verify the identity of the user. The transaction execution unit can also approve transactions based on the authentication results. The transaction execution unit can also record transaction details and manage transaction history. For example, the transaction execution unit saves transaction details in a database and manages transaction history. This allows the cash cardless system to conduct banking transactions without using a cash card.

[0030] The facial authentication unit can analyze a user's walking pattern in addition to facial authentication data to perform composite biometric authentication. For example, the facial authentication unit analyzes a user's walking pattern in addition to facial authentication data to perform composite biometric authentication. For example, when a user stands in front of an ATM, a camera automatically captures a picture of the user's face, and AI analyzes the image to perform facial authentication and simultaneously analyzes the walking pattern. The facial authentication unit also analyzes a user's walking pattern in addition to facial authentication data to perform composite biometric authentication. For example, when a user conducts a transaction at a bank teller, a camera captures the user's face, AI analyzes the image to perform facial authentication, and simultaneously analyzes the walking pattern. The facial authentication unit also analyzes a user's walking pattern in addition to facial authentication data to perform composite biometric authentication. For example, when a user uses online banking, a camera captures the user's face, AI analyzes the image to perform facial authentication, and simultaneously analyzes the walking pattern. This composite biometric authentication improves security.

[0031] The facial authentication unit can analyze subtle facial movements of a user to perform additional authentication to prevent counterfeiting. For example, the facial authentication unit analyzes subtle facial movements of a user to perform additional authentication to prevent counterfeiting. For example, when a user stands in front of an ATM, a camera automatically photographs the user's face, and AI analyzes the image to detect subtle movements and perform additional authentication to prevent counterfeiting. The facial authentication unit also analyzes subtle facial movements of the user to perform additional authentication to prevent counterfeiting. For example, when a user conducts a transaction at a bank teller, a camera photographs the user's face, AI analyzes the image to detect subtle movements and perform additional authentication to prevent counterfeiting. The facial authentication unit also analyzes subtle facial movements of the user to perform additional authentication to prevent counterfeiting. For example, when a user uses online banking, a camera photographs the user's face, AI analyzes the image to detect subtle movements and perform additional authentication to prevent counterfeiting. This additional authentication to prevent counterfeiting improves security.

[0032] The facial recognition unit can also be applied to access control for public transportation or offices other than banking transactions. For example, when a user passes through a station ticket gate, a camera automatically captures a picture of the user's face, and AI analyzes the image to verify the user's identity. The facial recognition unit can also be applied to access control for public transportation or offices other than banking transactions. For example, when a user stands at the entrance to an office, a camera automatically captures a picture of the user's face, and AI analyzes the image to verify the user's identity. The facial recognition unit can also be applied to access control for public transportation or offices other than banking transactions. For example, when a user enters a public facility, a camera automatically captures a picture of the user's face, and AI analyzes the image to verify the user's identity. This expands the range of applications of facial recognition technology.

[0033] The facial recognition unit can use facial recognition data to monitor a user's health condition and issue a warning if an abnormality is detected. The facial recognition unit can, for example, use facial recognition data to monitor a user's health condition and issue a warning if an abnormality is detected. For example, when a user stands in front of an ATM, a camera automatically captures a picture of the user's face, and AI analyzes the image to monitor the health condition. The facial recognition unit can also use facial recognition data to monitor a user's health condition and issue a warning if an abnormality is detected. For example, when a user conducts a transaction at a bank teller, a camera captures the user's face, and AI analyzes the image to monitor the health condition. The facial recognition unit can also use facial recognition data to monitor a user's health condition and issue a warning if an abnormality is detected. For example, when a user uses online banking, a camera captures the user's face, and AI analyzes the image to monitor the health condition. This allows the user's health condition to be monitored and a warning to be issued if an abnormality is detected.

[0034] The biometric authentication unit can simultaneously measure the temperature or humidity of the finger during fingerprint authentication to perform additional authentication to prevent counterfeiting. For example, when a user stands in front of an ATM, the temperature and humidity are measured as the user places their finger on the fingerprint scanner, and AI analyzes the data to perform additional authentication to prevent counterfeiting. The biometric authentication unit can also simultaneously measure the temperature and humidity of the finger during fingerprint authentication to perform additional authentication to prevent counterfeiting. For example, when a user conducts a transaction at a bank teller, the temperature and humidity are measured as the user places their finger on the fingerprint scanner, and AI analyzes the data to perform additional authentication to prevent counterfeiting. The biometric authentication unit can also simultaneously measure the temperature and humidity of the finger during fingerprint authentication to perform additional authentication to prevent counterfeiting. For example, when a user uses online banking, the temperature and humidity are measured as the user places their finger on the fingerprint scanner, and AI analyzes the data to perform additional authentication to prevent counterfeiting. This additional authentication to prevent counterfeiting of fingerprint authentication improves security.

[0035] The biometric authentication unit analyzes the pupil's reaction speed during iris authentication to improve the accuracy of identity verification. The biometric authentication unit analyzes the pupil's reaction speed during iris authentication to improve the accuracy of identity verification. For example, when a user stands in front of an ATM, an iris scanner measures the pupil's reaction speed, and AI analyzes the data to verify the user's identity. The biometric authentication unit also analyzes the pupil's reaction speed during iris authentication to improve the accuracy of identity verification. For example, when a user conducts a transaction at a bank teller, an iris scanner measures the pupil's reaction speed, and AI analyzes the data to verify the user's identity. The biometric authentication unit also analyzes the pupil's reaction speed during iris authentication to improve the accuracy of identity verification. For example, when a user uses online banking, an iris scanner measures the pupil's reaction speed, and AI analyzes the data to verify the user's identity. This improves the accuracy of iris authentication.

[0036] The biometric authentication unit can also apply biometric authentication technology to patient authentication at medical institutions and when receiving medication. For example, a patient performs fingerprint authentication at the hospital reception desk, and AI analyzes the data to verify their identity. The biometric authentication unit can also apply biometric authentication technology to patient authentication at medical institutions and when receiving medication. For example, a patient performs iris authentication when receiving medication at a pharmacy, and AI analyzes the data to verify their identity. The biometric authentication unit can also apply biometric authentication technology to patient authentication at medical institutions and when receiving medication. For example, a patient performs vein authentication when entering a hospital examination room, and AI analyzes the data to verify their identity. This expands the range of applications of biometric authentication technology.

[0037] The biometric authentication unit can use the biometric authentication data to monitor the user's fitness state and use it for health management. The biometric authentication unit, for example, uses the biometric authentication data to monitor the user's fitness state and use it for health management. For example, a user performs fingerprint authentication at a fitness gym, and AI analyzes the data to monitor the fitness state. The biometric authentication unit also uses the biometric authentication data to monitor the user's fitness state and use it for health management. For example, a user measures their heart rate with a smartwatch, and AI analyzes the data to monitor the fitness state. The biometric authentication unit also uses the biometric authentication data to monitor the user's fitness state and use it for health management. For example, a user performs vein authentication while running, and AI analyzes the data to monitor the fitness state. This allows the user's fitness state to be monitored and use it for health management.

[0038] The transaction execution unit can enhance security by adding voiceprint authentication to the combination of facial recognition and biometric authentication, thereby providing triple authentication. For example, the transaction execution unit can enhance security by adding voiceprint authentication to the combination of facial recognition and biometric authentication, thereby providing triple authentication. For example, when a user stands in front of an ATM, facial recognition, fingerprint authentication, and voiceprint authentication are performed, and AI analyzes the data to verify the user's identity. The transaction execution unit can also enhance security by adding voiceprint authentication to the combination of facial recognition and biometric authentication, thereby providing triple authentication. For example, when a user conducts a transaction at a bank teller, facial recognition, iris authentication, and voiceprint authentication are performed, and AI analyzes the data to verify the user's identity. The transaction execution unit can also enhance security by adding voiceprint authentication to the combination of facial recognition and biometric authentication, thereby providing triple authentication. For example, when a user uses online banking, facial recognition, vein authentication, and voiceprint authentication are performed, and AI analyzes the data to verify the user's identity. This triple authentication enhances security.

[0039] The transaction execution unit integrates facial recognition and biometric authentication data, and AI performs risk assessment in real time, thereby ensuring the security of transactions. For example, the transaction execution unit integrates facial recognition and biometric authentication data, and AI performs risk assessment in real time, thereby ensuring the security of transactions. For example, when a user stands in front of an ATM, facial recognition and fingerprint authentication data are integrated, and AI analyzes the data to perform risk assessment. The transaction execution unit also integrates facial recognition and biometric authentication data, and AI performs risk assessment in real time, thereby ensuring the security of transactions. For example, when a user conducts a transaction at a bank teller, facial recognition and iris authentication data are integrated, and AI analyzes the data to perform risk assessment. The transaction execution unit also integrates facial recognition and biometric authentication data, and AI performs risk assessment in real time, thereby ensuring the security of transactions. For example, when a user uses online banking, facial recognition and vein authentication data are integrated, and AI analyzes the data to perform risk assessment. This ensures the security of transactions by performing risk assessment in real time.

[0040] The transaction execution unit can also apply the system that combines facial recognition and biometric authentication to online shopping and purchasing digital content. The transaction execution unit, for example, applies the system that combines facial recognition and biometric authentication to online shopping and purchasing digital content. For example, a user performs facial recognition and fingerprint authentication on an online shopping site, and AI analyzes the data to verify the user's identity. The transaction execution unit also applies the system that combines facial recognition and biometric authentication to online shopping and purchasing digital content. For example, a user performs facial recognition and iris authentication on a digital content purchasing site, and AI analyzes the data to verify the user's identity. The transaction execution unit also applies the system that combines facial recognition and biometric authentication to online shopping and purchasing digital content. For example, a user performs facial recognition and vein authentication on a digital content purchasing site, and AI analyzes the data to verify the user's identity. This expands the range of applications of the system that combines facial recognition and biometric authentication.

[0041] The transaction execution unit can also apply a system that combines facial recognition and biometric authentication to hotel check-ins and rental car pickups. For example, the transaction execution unit applies a system that combines facial recognition and biometric authentication to hotel check-ins and rental car pickups. For example, a user performs facial recognition and fingerprint authentication at the hotel check-in counter, and AI analyzes the data to verify their identity. The transaction execution unit can also apply a system that combines facial recognition and biometric authentication to hotel check-ins and rental car pickups. For example, a user performs facial recognition and iris authentication at the rental car pickup counter, and AI analyzes the data to verify their identity. The transaction execution unit can also apply a system that combines facial recognition and biometric authentication to hotel check-ins and rental car pickups. For example, a user performs facial recognition and vein authentication when reserving a rental car online, and AI analyzes the data to verify their identity. This expands the range of applications of a system that combines facial recognition and biometric authentication.

[0042] The transaction execution unit can detect abnormal patterns when the AI ​​analyzes facial data and biometric data and issue a warning in real time. For example, when the AI ​​analyzes facial data and biometric data, the transaction execution unit detects abnormal patterns and issues a warning in real time. For example, when a user stands in front of an ATM, the transaction execution unit analyzes facial and fingerprint authentication data and issues a warning if an abnormal pattern is detected. The transaction execution unit can also detect abnormal patterns when the AI ​​analyzes facial data and biometric data and issue a warning in real time. For example, when a user conducts a transaction at a bank teller, the transaction execution unit analyzes facial and iris authentication data and issues a warning if an abnormal pattern is detected. The transaction execution unit can also detect abnormal patterns when the AI ​​analyzes facial data and biometric data and issue a warning in real time. For example, when a user uses online banking, the transaction execution unit analyzes facial and vein authentication data and issues a warning if an abnormal pattern is detected. This makes it possible to detect abnormal patterns and issue a warning in real time.

[0043] The transaction execution unit allows AI to analyze transaction data and compare it with past transaction history to detect abnormal transactions. For example, the transaction execution unit allows AI to analyze transaction data and compare it with past transaction history to detect abnormal transactions. For example, when a user conducts a transaction at an ATM, the AI ​​compares it with past transaction history to detect abnormal transactions and issue a warning. The transaction execution unit also allows AI to analyze transaction data and compare it with past transaction history to detect abnormal transactions. For example, when a user conducts a transaction at a bank teller, the AI ​​compares it with past transaction history to detect abnormal transactions and issue a warning. The transaction execution unit also allows AI to analyze transaction data and compare it with past transaction history to detect abnormal transactions. For example, when a user uses online banking, the AI ​​compares it with past transaction history to detect abnormal transactions and issue a warning. This makes it possible to detect abnormal transactions.

[0044] The transaction execution unit can use AI to integrate authentication data across different devices and perform comprehensive risk assessments to enhance security. For example, the transaction execution unit uses AI to integrate authentication data across different devices and perform comprehensive risk assessments to enhance security. For example, when a user conducts a transaction using an ATM, smartphone, or PC, the authentication data from each device is integrated, and the AI ​​performs a comprehensive risk assessment. The transaction execution unit also uses AI to integrate authentication data across different devices and perform comprehensive risk assessments to enhance security. For example, when a user conducts a transaction at a bank teller, using a smartwatch, or using a tablet, the authentication data from each device is integrated, and the AI ​​performs a comprehensive risk assessment. The transaction execution unit also uses AI to integrate authentication data across different devices and perform comprehensive risk assessments to enhance security. For example, when a user conducts a transaction using online banking, a smart speaker, or a desktop, the authentication data from each device is integrated, and the AI ​​performs a comprehensive risk assessment. This enhances security by integrating authentication data across different devices and performing comprehensive risk assessments.

[0045] The transaction execution unit can use AI to analyze user behavior patterns and detect abnormal behavior in order to enhance security. For example, when a user conducts a transaction at an ATM, the AI ​​compares the user's behavior with past behavior patterns to detect abnormal behavior and issue a warning. The transaction execution unit also uses AI to analyze user behavior patterns and detect abnormal behavior in order to enhance security. For example, when a user conducts a transaction at a bank teller, the AI ​​compares the user's behavior with past behavior patterns to detect abnormal behavior and issue a warning. The transaction execution unit also uses AI to analyze user behavior patterns and detect abnormal behavior in order to enhance security. For example, when a user uses online banking, the AI ​​compares the user's behavior with past behavior patterns to detect abnormal behavior and issue a warning. In this way, security is enhanced by analyzing user behavior patterns and detecting abnormal behavior.

[0046] The transaction execution unit uses AI to analyze a user's transaction history and propose the optimal transaction method. For example, when a user conducts a transaction at an ATM, AI analyzes the past transaction history and proposes the optimal transaction method. In addition, the transaction execution unit uses AI to analyze a user's transaction history and propose the optimal transaction method. For example, when a user conducts a transaction at a bank teller, AI analyzes the past transaction history and proposes the optimal transaction method. In addition, the transaction execution unit uses AI to analyze a user's transaction history and propose the optimal transaction method. For example, when a user uses online banking, AI analyzes the past transaction history and proposes the optimal transaction method. In this way, the user's transaction history can be analyzed and the optimal transaction method can be proposed.

[0047] The transaction execution unit uses AI to analyze the user's location information and provide guidance to the nearest ATM or bank teller. For example, the transaction execution unit uses AI to analyze the user's location information and provide guidance to the nearest ATM or bank teller. For example, when the user is using a smartphone, the AI ​​analyzes the location information and provides guidance to the nearest ATM. The transaction execution unit also uses AI to analyze the user's location information and provide guidance to the nearest ATM or bank teller. For example, when the user is driving a car, the AI ​​analyzes the location information and provides guidance to the nearest bank teller. The transaction execution unit also uses AI to analyze the user's location information and provide guidance to the nearest ATM or bank teller. For example, when the user is using public transportation, the AI ​​analyzes the location information and provides guidance to the nearest ATM. In this way, the user's location information can be analyzed and guidance to the nearest ATM or bank teller can be provided.

[0048] To improve convenience, the trading execution unit can use AI to analyze the user's schedule and suggest the optimal trading time. For example, when a user is using a calendar app on their smartphone, the AI ​​analyzes the schedule and suggests the optimal trading time. Also, to improve convenience, the trading execution unit can use AI to analyze the user's schedule and suggest the optimal trading time. For example, when a user is using a schedule management software on their computer, the AI ​​analyzes the schedule and suggests the optimal trading time. Also, to improve convenience, the trading execution unit can use AI to analyze the user's schedule and suggest the optimal trading time. For example, when a user is using a schedule app on their tablet, the AI ​​analyzes the schedule and suggests the optimal trading time. In this way, the trading execution unit can analyze the user's schedule and suggest the optimal trading time.

[0049] The trading execution unit can use AI to analyze user preferences and provide customized trading options to improve convenience. For example, the trading execution unit can use AI to analyze user preferences and provide customized trading options to improve convenience. For example, the AI ​​can suggest optimal trading options based on trading options previously selected by the user. Furthermore, the trading execution unit can use AI to analyze user preferences and provide customized trading options to improve convenience. For example, if a user often trades during a specific time period, the AI ​​can suggest the optimal trading option for that time period. Furthermore, the trading execution unit can use AI to analyze user preferences and provide customized trading options to improve convenience. For example, if a user prefers a specific trading method, the AI ​​can suggest the optimal trading option for that method. In this way, the user's preferences can be analyzed and customized trading options can be provided.

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

[0051] The cash cardless system can further include a location information acquisition unit. The location information acquisition unit acquires the user's current location and guides the user to the nearest ATM or bank counter. For example, when a user is using a smartphone, the location information acquisition unit acquires the user's current location and guides the user to the nearest ATM. The location information acquisition unit can also evaluate the security of a transaction based on the user's location information. For example, if a user conducts a transaction in an unusual location, the location information acquisition unit transmits that information to the transaction execution unit and evaluates the security of the transaction. This improves convenience and security by utilizing location information.

[0052] The cash cardless system can further include a health monitoring unit. The health monitoring unit monitors the user's health condition in real time and issues a warning if an abnormality is detected. For example, when a user stands in front of an ATM, the health monitoring unit measures the user's heart rate and blood pressure and issues a warning if an abnormality is detected. The health monitoring unit can also analyze the user's health data and provide health management advice. For example, when a user conducts a transaction at a bank teller, the health monitoring unit analyzes the health data and provides health management advice. This allows the user's health condition to be monitored and used to help with health management.

[0053] The cash cardless system can further include a behavior analysis unit. The behavior analysis unit analyzes the user's behavior patterns and detects abnormal behavior. For example, when a user conducts a transaction at an ATM, the behavior analysis unit compares the user's behavior with past behavior patterns to detect abnormal behavior and issue a warning. The behavior analysis unit can also evaluate the safety of the transaction based on the user's behavior patterns. For example, when a user conducts a transaction at a bank teller, the behavior analysis unit analyzes the user's behavior pattern and evaluates the safety of the transaction. In this way, security is strengthened by analyzing the user's behavior patterns and detecting abnormal behavior.

[0054] The cash cardless system can further include a schedule management unit. The schedule management unit analyzes the user's schedule and suggests the optimal transaction time. For example, when the user is using a calendar app on their smartphone, the schedule management unit analyzes the schedule and suggests the optimal transaction time. The schedule management unit can also send transaction reminders based on the user's schedule. For example, if the user has a transaction scheduled at a bank teller, the schedule management unit sends a reminder to ensure the user does not forget to make the transaction. This makes it possible to analyze the user's schedule and suggest the optimal transaction time.

[0055] The cash cardless system can also monitor the user's health condition and issue a warning if an abnormality is detected. For example, when a user stands in front of an ATM, the facial recognition unit uses facial recognition data to monitor the user's health condition and issues a warning if an abnormality is detected. When a user conducts a transaction at a bank teller, the facial recognition unit uses facial recognition data to monitor the user's health condition and issues a warning if an abnormality is detected. When a user uses online banking, the facial recognition unit uses facial recognition data to monitor the user's health condition and issues a warning if an abnormality is detected. This makes it possible to monitor the user's health condition and issue a warning if an abnormality is detected.

[0056] The cash cardless system can also analyze the user's walking pattern to perform composite biometric authentication. For example, when a user stands in front of an ATM, the facial recognition unit analyzes their walking pattern in addition to the facial recognition data to perform composite biometric authentication. When the user conducts a transaction at a bank teller, the facial recognition unit analyzes their walking pattern in addition to the facial recognition data to perform composite biometric authentication. When the user uses online banking, the facial recognition unit analyzes their walking pattern in addition to the facial recognition data to perform composite biometric authentication. This composite biometric authentication improves security.

[0057] The cash cardless system can also analyze the subtle movements of the user's face to perform additional authentication to prevent counterfeiting. For example, when a user stands in front of an ATM, the facial recognition unit analyzes the subtle movements of the user's face to perform additional authentication to prevent counterfeiting. When the user conducts a transaction at a bank teller, the facial recognition unit analyzes the subtle movements of the user's face to perform additional authentication to prevent counterfeiting. Furthermore, when the user uses online banking, the facial recognition unit analyzes the subtle movements of the user's face to perform additional authentication to prevent counterfeiting. This additional authentication to prevent counterfeiting improves security.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The facial recognition unit recognizes the user's face. For example, the facial recognition unit uses a camera to take a picture of the user's face, and AI analyzes the image to extract facial features. The facial recognition unit can also use a facial image matching algorithm to compare the image with pre-registered facial data. Furthermore, the facial recognition unit performs facial recognition based on the contours of the face and the positions of the eyes, nose, and mouth. Step 2: The biometric authentication unit recognizes the user's biometric information. For example, the biometric authentication unit uses a fingerprint scanner to perform fingerprint authentication. The biometric authentication unit can also use an iris scanner to perform iris authentication. The biometric authentication unit can also use a vein scanner to perform vein authentication. Specifically, the fingerprint scanner acquires fingerprint data, and AI analyzes the data to extract fingerprint feature points. The iris scanner photographs the user's iris, and AI analyzes the image to extract iris feature points. The vein scanner photographs the vein patterns of the user's palm or fingers, and AI analyzes the image to extract vein feature points. Step 3: The transaction execution unit executes the transaction based on the information recognized by the facial authentication unit and biometric authentication unit. For example, the transaction execution unit integrates the authentication results of the facial authentication unit and the biometric authentication unit, and AI analyzes the data to verify the identity of the person. The transaction execution unit can also approve the transaction based on the authentication results. Furthermore, the transaction execution unit can record the transaction details and manage the transaction history. Specifically, it saves the transaction details in a database and manages the transaction history.

[0060] (Example 2) The cash cardless system according to an embodiment of the present invention is a system that utilizes facial recognition and biometric authentication technology to perform banking transactions without using a cash card. As a result, the cash cardless system allows banking transactions to be performed without using a cash card.

[0061] The cash cardless system according to the embodiment includes a facial authentication unit, a biometric authentication unit, and a transaction execution unit. The facial authentication unit recognizes a user's face. For example, the facial authentication unit uses a camera to capture a user's face, and AI analyzes the image to extract facial feature points. The facial authentication unit can also use a facial image matching algorithm to compare the image with pre-registered facial data. The facial authentication unit can also perform facial authentication based on facial feature points. For example, the facial authentication unit performs facial authentication based on the facial contours and the positions of the eyes, nose, and mouth. The biometric authentication unit recognizes a user's biometric information. For example, the biometric authentication unit uses a fingerprint scanner to perform fingerprint authentication. The biometric authentication unit can also use an iris scanner to perform iris authentication. The biometric authentication unit can also use a vein scanner to perform vein authentication. For example, the biometric authentication unit uses a fingerprint scanner to acquire fingerprint data, and AI analyzes the data to extract fingerprint feature points. The iris scanner captures a user's iris, and AI analyzes the image to extract iris feature points. The vein scanner captures the vein patterns of the user's palm or fingers, and AI analyzes the image to extract vein features. The transaction execution unit executes transactions based on the information recognized by the facial authentication unit and biometric authentication unit. For example, the transaction execution unit combines the authentication results of the facial authentication unit and the biometric authentication unit, and AI analyzes the data to verify the identity of the user. The transaction execution unit can also approve transactions based on the authentication results. The transaction execution unit can also record transaction details and manage transaction history. For example, the transaction execution unit saves transaction details in a database and manages transaction history. This allows the cash cardless system to conduct banking transactions without using a cash card.

[0062] The facial authentication unit can analyze changes in a user's facial expression in real time and improve the accuracy of identity verification using an emotion estimation function. The facial authentication unit, for example, analyzes changes in a user's facial expression in real time and improves the accuracy of identity verification using an emotion estimation function. For example, when a user stands in front of an ATM, a camera automatically captures a picture of the user's face, and AI analyzes the image to detect changes in facial expression and estimate emotions. The facial authentication unit also analyzes changes in a user's facial expression in real time and improves the accuracy of identity verification using an emotion estimation function. For example, when a user conducts a transaction at a bank teller, a camera captures the user's face, and AI analyzes the image to detect changes in facial expression and estimate emotions. The facial authentication unit also analyzes changes in a user's facial expression in real time and improves the accuracy of identity verification using an emotion estimation function. For example, when a user uses online banking, a camera captures the user's face, and AI analyzes the image to detect changes in facial expression and estimate emotions. This improves the accuracy of identity verification.

[0063] The facial authentication unit can analyze a user's walking pattern in addition to facial authentication data to perform composite biometric authentication. For example, the facial authentication unit analyzes a user's walking pattern in addition to facial authentication data to perform composite biometric authentication. For example, when a user stands in front of an ATM, a camera automatically captures a picture of the user's face, and AI analyzes the image to perform facial authentication and simultaneously analyzes the walking pattern. The facial authentication unit also analyzes a user's walking pattern in addition to facial authentication data to perform composite biometric authentication. For example, when a user conducts a transaction at a bank teller, a camera captures the user's face, AI analyzes the image to perform facial authentication, and simultaneously analyzes the walking pattern. The facial authentication unit also analyzes a user's walking pattern in addition to facial authentication data to perform composite biometric authentication. For example, when a user uses online banking, a camera captures the user's face, AI analyzes the image to perform facial authentication, and simultaneously analyzes the walking pattern. This composite biometric authentication improves security.

[0064] The facial authentication unit can analyze subtle facial movements of a user to perform additional authentication to prevent counterfeiting. For example, the facial authentication unit analyzes subtle facial movements of a user to perform additional authentication to prevent counterfeiting. For example, when a user stands in front of an ATM, a camera automatically photographs the user's face, and AI analyzes the image to detect subtle movements and perform additional authentication to prevent counterfeiting. The facial authentication unit also analyzes subtle facial movements of the user to perform additional authentication to prevent counterfeiting. For example, when a user conducts a transaction at a bank teller, a camera photographs the user's face, AI analyzes the image to detect subtle movements and perform additional authentication to prevent counterfeiting. The facial authentication unit also analyzes subtle facial movements of the user to perform additional authentication to prevent counterfeiting. For example, when a user uses online banking, a camera photographs the user's face, AI analyzes the image to detect subtle movements and perform additional authentication to prevent counterfeiting. This additional authentication to prevent counterfeiting improves security.

[0065] The facial recognition unit can also be applied to access control for public transportation or offices other than banking transactions. For example, when a user passes through a station ticket gate, a camera automatically captures a picture of the user's face, and AI analyzes the image to verify the user's identity. The facial recognition unit can also be applied to access control for public transportation or offices other than banking transactions. For example, when a user stands at the entrance to an office, a camera automatically captures a picture of the user's face, and AI analyzes the image to verify the user's identity. The facial recognition unit can also be applied to access control for public transportation or offices other than banking transactions. For example, when a user enters a public facility, a camera automatically captures a picture of the user's face, and AI analyzes the image to verify the user's identity. This expands the range of applications of facial recognition technology.

[0066] The facial recognition unit can use facial recognition data to monitor a user's health condition and issue a warning if an abnormality is detected. The facial recognition unit can, for example, use facial recognition data to monitor a user's health condition and issue a warning if an abnormality is detected. For example, when a user stands in front of an ATM, a camera automatically captures a picture of the user's face, and AI analyzes the image to monitor the health condition. The facial recognition unit can also use facial recognition data to monitor a user's health condition and issue a warning if an abnormality is detected. For example, when a user conducts a transaction at a bank teller, a camera captures the user's face, and AI analyzes the image to monitor the health condition. The facial recognition unit can also use facial recognition data to monitor a user's health condition and issue a warning if an abnormality is detected. For example, when a user uses online banking, a camera captures the user's face, and AI analyzes the image to monitor the health condition. This allows the user's health condition to be monitored and a warning to be issued if an abnormality is detected.

[0067] The facial recognition unit can measure a user's stress level using an emotion estimation function and make suggestions for relaxation. The facial recognition unit, for example, uses the emotion estimation function to measure a user's stress level and make suggestions for relaxation. For example, when a user stands in front of an ATM, a camera automatically photographs the user's face, and an AI analyzes the image to measure the user's stress level and make suggestions for relaxation. The facial recognition unit also uses the emotion estimation function to measure a user's stress level and make suggestions for relaxation. For example, when a user conducts a transaction at a bank teller, a camera photographs the user's face, an AI analyzes the image to measure the user's stress level, and makes suggestions for relaxation. The facial recognition unit also uses the emotion estimation function to measure a user's stress level and make suggestions for relaxation. For example, when a user uses online banking, a camera photographs the user's face, an AI analyzes the image to measure the user's stress level, and makes suggestions for relaxation. In this way, the user's stress level can be measured and suggestions for relaxation can be made.

[0068] The biometric authentication unit can simultaneously measure the temperature or humidity of the finger during fingerprint authentication to perform additional authentication to prevent counterfeiting. For example, when a user stands in front of an ATM, the temperature and humidity are measured as the user places their finger on the fingerprint scanner, and AI analyzes the data to perform additional authentication to prevent counterfeiting. The biometric authentication unit can also simultaneously measure the temperature and humidity of the finger during fingerprint authentication to perform additional authentication to prevent counterfeiting. For example, when a user conducts a transaction at a bank teller, the temperature and humidity are measured as the user places their finger on the fingerprint scanner, and AI analyzes the data to perform additional authentication to prevent counterfeiting. The biometric authentication unit can also simultaneously measure the temperature and humidity of the finger during fingerprint authentication to perform additional authentication to prevent counterfeiting. For example, when a user uses online banking, the temperature and humidity are measured as the user places their finger on the fingerprint scanner, and AI analyzes the data to perform additional authentication to prevent counterfeiting. This additional authentication to prevent counterfeiting of fingerprint authentication improves security.

[0069] The biometric authentication unit analyzes the pupil's reaction speed during iris authentication to improve the accuracy of identity verification. The biometric authentication unit analyzes the pupil's reaction speed during iris authentication to improve the accuracy of identity verification. For example, when a user stands in front of an ATM, an iris scanner measures the pupil's reaction speed, and AI analyzes the data to verify the user's identity. The biometric authentication unit also analyzes the pupil's reaction speed during iris authentication to improve the accuracy of identity verification. For example, when a user conducts a transaction at a bank teller, an iris scanner measures the pupil's reaction speed, and AI analyzes the data to verify the user's identity. The biometric authentication unit also analyzes the pupil's reaction speed during iris authentication to improve the accuracy of identity verification. For example, when a user uses online banking, an iris scanner measures the pupil's reaction speed, and AI analyzes the data to verify the user's identity. This improves the accuracy of iris authentication.

[0070] The biometric authentication unit can analyze blood flow patterns during vein authentication and use an emotion estimation function to improve the accuracy of identity verification. For example, when a user stands in front of an ATM, a vein scanner measures the blood flow pattern, and an AI analyzes the data to estimate the emotion and verify the user's identity. The biometric authentication unit also analyzes blood flow patterns during vein authentication and uses the emotion estimation function to improve the accuracy of identity verification. For example, when a user stands in front of an ATM, a vein scanner measures the blood flow pattern, and an AI analyzes the data to estimate the emotion and verify the user's identity. The biometric authentication unit also analyzes blood flow patterns during vein authentication and uses the emotion estimation function to improve the accuracy of identity verification. For example, when a user conducts a transaction at a bank teller, a vein scanner measures the blood flow pattern, and an AI analyzes the data to estimate the emotion and verify the user's identity. The biometric authentication unit also analyzes blood flow patterns during vein authentication and uses the emotion estimation function to improve the accuracy of identity verification. For example, when a user uses online banking, a vein scanner measures the blood flow pattern, and an AI analyzes the data to estimate the emotion and verify the user's identity. This improves the accuracy of vein authentication.

[0071] The biometric authentication unit can also apply biometric authentication technology to patient authentication at medical institutions and when receiving medication. For example, a patient performs fingerprint authentication at the hospital reception desk, and AI analyzes the data to verify their identity. The biometric authentication unit can also apply biometric authentication technology to patient authentication at medical institutions and when receiving medication. For example, a patient performs iris authentication when receiving medication at a pharmacy, and AI analyzes the data to verify their identity. The biometric authentication unit can also apply biometric authentication technology to patient authentication at medical institutions and when receiving medication. For example, a patient performs vein authentication when entering a hospital examination room, and AI analyzes the data to verify their identity. This expands the range of applications of biometric authentication technology.

[0072] The biometric authentication unit can use the biometric authentication data to monitor the user's fitness state and use it for health management. The biometric authentication unit, for example, uses the biometric authentication data to monitor the user's fitness state and use it for health management. For example, a user performs fingerprint authentication at a fitness gym, and AI analyzes the data to monitor the fitness state. The biometric authentication unit also uses the biometric authentication data to monitor the user's fitness state and use it for health management. For example, a user measures their heart rate with a smartwatch, and AI analyzes the data to monitor the fitness state. The biometric authentication unit also uses the biometric authentication data to monitor the user's fitness state and use it for health management. For example, a user performs vein authentication while running, and AI analyzes the data to monitor the fitness state. This allows the user's fitness state to be monitored and use it for health management.

[0073] The biometric authentication unit can use an emotion estimation function to analyze the user's emotional state during biometric authentication and provide customized services. For example, the biometric authentication unit can use the emotion estimation function to analyze the user's emotional state during biometric authentication and provide customized services. For example, when a user stands in front of an ATM, emotion estimation is performed simultaneously with fingerprint authentication to provide services according to the user's emotional state. The biometric authentication unit can also use the emotion estimation function to analyze the user's emotional state during biometric authentication and provide customized services. For example, when a user conducts a transaction at a bank teller, emotion estimation is performed simultaneously with iris authentication to provide services according to the user's emotional state. The biometric authentication unit can also use the emotion estimation function to analyze the user's emotional state during biometric authentication and provide customized services. For example, when a user uses online banking, emotion estimation is performed simultaneously with vein authentication to provide services according to the user's emotional state. This makes it possible to provide customized services according to the user's emotional state.

[0074] The transaction execution unit can enhance security by adding voiceprint authentication to the combination of facial recognition and biometric authentication, thereby providing triple authentication. For example, the transaction execution unit can enhance security by adding voiceprint authentication to the combination of facial recognition and biometric authentication, thereby providing triple authentication. For example, when a user stands in front of an ATM, facial recognition, fingerprint authentication, and voiceprint authentication are performed, and AI analyzes the data to verify the user's identity. The transaction execution unit can also enhance security by adding voiceprint authentication to the combination of facial recognition and biometric authentication, thereby providing triple authentication. For example, when a user conducts a transaction at a bank teller, facial recognition, iris authentication, and voiceprint authentication are performed, and AI analyzes the data to verify the user's identity. The transaction execution unit can also enhance security by adding voiceprint authentication to the combination of facial recognition and biometric authentication, thereby providing triple authentication. For example, when a user uses online banking, facial recognition, vein authentication, and voiceprint authentication are performed, and AI analyzes the data to verify the user's identity. This triple authentication enhances security.

[0075] The transaction execution unit integrates facial recognition and biometric authentication data, and AI performs risk assessment in real time, thereby ensuring the security of transactions. For example, the transaction execution unit integrates facial recognition and biometric authentication data, and AI performs risk assessment in real time, thereby ensuring the security of transactions. For example, when a user stands in front of an ATM, facial recognition and fingerprint authentication data are integrated, and AI analyzes the data to perform risk assessment. The transaction execution unit also integrates facial recognition and biometric authentication data, and AI performs risk assessment in real time, thereby ensuring the security of transactions. For example, when a user conducts a transaction at a bank teller, facial recognition and iris authentication data are integrated, and AI analyzes the data to perform risk assessment. The transaction execution unit also integrates facial recognition and biometric authentication data, and AI performs risk assessment in real time, thereby ensuring the security of transactions. For example, when a user uses online banking, facial recognition and vein authentication data are integrated, and AI analyzes the data to perform risk assessment. This ensures the security of transactions by performing risk assessment in real time.

[0076] The transaction execution unit can improve the security of transactions by using an emotion estimation function to analyze the emotional state of a user during facial authentication and biometric authentication. The transaction execution unit, for example, uses the emotion estimation function to analyze the emotional state of a user during facial authentication and biometric authentication to improve the security of transactions. For example, when a user stands in front of an ATM, the transaction execution unit analyzes facial authentication and fingerprint authentication data to estimate emotions, thereby improving the security of transactions. The transaction execution unit also uses the emotion estimation function to analyze the emotional state of a user during facial authentication and biometric authentication to improve the security of transactions. For example, when a user performs a transaction at a bank teller, the transaction execution unit analyzes facial authentication and iris authentication data to estimate emotions, thereby improving the security of transactions. The transaction execution unit also uses the emotion estimation function to analyze the emotional state of a user during facial authentication and biometric authentication to improve the security of transactions. For example, when a user uses online banking, the transaction execution unit analyzes facial authentication and vein authentication data to estimate emotions, thereby improving the security of transactions. In this way, the use of the emotion estimation function improves the security of transactions.

[0077] The transaction execution unit can also apply the system that combines facial recognition and biometric authentication to online shopping and purchasing digital content. The transaction execution unit, for example, applies the system that combines facial recognition and biometric authentication to online shopping and purchasing digital content. For example, a user performs facial recognition and fingerprint authentication on an online shopping site, and AI analyzes the data to verify the user's identity. The transaction execution unit also applies the system that combines facial recognition and biometric authentication to online shopping and purchasing digital content. For example, a user performs facial recognition and iris authentication on a digital content purchasing site, and AI analyzes the data to verify the user's identity. The transaction execution unit also applies the system that combines facial recognition and biometric authentication to online shopping and purchasing digital content. For example, a user performs facial recognition and vein authentication on a digital content purchasing site, and AI analyzes the data to verify the user's identity. This expands the range of applications of the system that combines facial recognition and biometric authentication.

[0078] The transaction execution unit can also apply a system that combines facial recognition and biometric authentication to hotel check-ins and rental car pickups. For example, the transaction execution unit applies a system that combines facial recognition and biometric authentication to hotel check-ins and rental car pickups. For example, a user performs facial recognition and fingerprint authentication at the hotel check-in counter, and AI analyzes the data to verify their identity. The transaction execution unit can also apply a system that combines facial recognition and biometric authentication to hotel check-ins and rental car pickups. For example, a user performs facial recognition and iris authentication at the rental car pickup counter, and AI analyzes the data to verify their identity. The transaction execution unit can also apply a system that combines facial recognition and biometric authentication to hotel check-ins and rental car pickups. For example, a user performs facial recognition and vein authentication when reserving a rental car online, and AI analyzes the data to verify their identity. This expands the range of applications of a system that combines facial recognition and biometric authentication.

[0079] The transaction execution unit adds an emotion estimation function to a system that combines facial recognition and biometric authentication, and can provide customized services according to the emotional state of a user. The transaction execution unit, for example, adds an emotion estimation function to a system that combines facial recognition and biometric authentication, and provides customized services according to the emotional state of a user. For example, when a user stands in front of an ATM, the transaction execution unit analyzes facial recognition and fingerprint authentication data, performs emotion estimation, and provides services according to the user's emotional state. The transaction execution unit also adds an emotion estimation function to a system that combines facial recognition and biometric authentication, and provides customized services according to the user's emotional state. For example, when a user performs a transaction at a bank teller, the transaction execution unit analyzes facial recognition and iris authentication data, performs emotion estimation, and provides services according to the user's emotional state. The transaction execution unit also adds an emotion estimation function to a system that combines facial recognition and biometric authentication, and provides customized services according to the user's emotional state. For example, when a user uses online banking, the transaction execution unit analyzes facial recognition and vein authentication data, performs emotion estimation, and provides services according to the user's emotional state. This makes it possible to provide customized services according to the user's emotional state.

[0080] The transaction execution unit can detect abnormal patterns when the AI ​​analyzes facial data and biometric data and issue a warning in real time. For example, when the AI ​​analyzes facial data and biometric data, the transaction execution unit detects abnormal patterns and issues a warning in real time. For example, when a user stands in front of an ATM, the transaction execution unit analyzes facial and fingerprint authentication data and issues a warning if an abnormal pattern is detected. The transaction execution unit can also detect abnormal patterns when the AI ​​analyzes facial data and biometric data and issue a warning in real time. For example, when a user conducts a transaction at a bank teller, the transaction execution unit analyzes facial and iris authentication data and issues a warning if an abnormal pattern is detected. The transaction execution unit can also detect abnormal patterns when the AI ​​analyzes facial data and biometric data and issue a warning in real time. For example, when a user uses online banking, the transaction execution unit analyzes facial and vein authentication data and issues a warning if an abnormal pattern is detected. This makes it possible to detect abnormal patterns and issue a warning in real time.

[0081] The transaction execution unit allows AI to analyze transaction data and compare it with past transaction history to detect abnormal transactions. For example, the transaction execution unit allows AI to analyze transaction data and compare it with past transaction history to detect abnormal transactions. For example, when a user conducts a transaction at an ATM, the AI ​​compares it with past transaction history to detect abnormal transactions and issue a warning. The transaction execution unit also allows AI to analyze transaction data and compare it with past transaction history to detect abnormal transactions. For example, when a user conducts a transaction at a bank teller, the AI ​​compares it with past transaction history to detect abnormal transactions and issue a warning. The transaction execution unit also allows AI to analyze transaction data and compare it with past transaction history to detect abnormal transactions. For example, when a user uses online banking, the AI ​​compares it with past transaction history to detect abnormal transactions and issue a warning. This makes it possible to detect abnormal transactions.

[0082] The transaction execution unit uses the AI's emotion estimation function to analyze the user's emotional state and detect signs of fraudulent transactions. For example, the transaction execution unit uses the AI's emotion estimation function to analyze the user's emotional state and detect signs of fraudulent transactions. For example, when a user stands in front of an ATM, the AI ​​analyzes facial recognition and fingerprint authentication data to estimate emotions and detect signs of fraudulent transactions. The transaction execution unit also uses the AI's emotion estimation function to analyze the user's emotional state and detect signs of fraudulent transactions. For example, when a user conducts a transaction at a bank teller, the AI ​​analyzes facial recognition and iris authentication data to estimate emotions and detect signs of fraudulent transactions. The transaction execution unit also uses the AI's emotion estimation function to analyze the user's emotional state and detect signs of fraudulent transactions. For example, when a user uses online banking, the AI ​​analyzes facial recognition and vein authentication data to estimate emotions and detect signs of fraudulent transactions. In this way, the emotion estimation function can be used to detect signs of fraudulent transactions.

[0083] The transaction execution unit can use AI to integrate authentication data across different devices and perform comprehensive risk assessments to enhance security. For example, the transaction execution unit uses AI to integrate authentication data across different devices and perform comprehensive risk assessments to enhance security. For example, when a user conducts a transaction using an ATM, smartphone, or PC, the authentication data from each device is integrated, and the AI ​​performs a comprehensive risk assessment. The transaction execution unit also uses AI to integrate authentication data across different devices and perform comprehensive risk assessments to enhance security. For example, when a user conducts a transaction at a bank teller, using a smartwatch, or using a tablet, the authentication data from each device is integrated, and the AI ​​performs a comprehensive risk assessment. The transaction execution unit also uses AI to integrate authentication data across different devices and perform comprehensive risk assessments to enhance security. For example, when a user conducts a transaction using online banking, a smart speaker, or a desktop, the authentication data from each device is integrated, and the AI ​​performs a comprehensive risk assessment. This enhances security by integrating authentication data across different devices and performing comprehensive risk assessments.

[0084] The transaction execution unit can use AI to analyze user behavior patterns and detect abnormal behavior in order to enhance security. For example, when a user conducts a transaction at an ATM, the AI ​​compares the user's behavior with past behavior patterns to detect abnormal behavior and issue a warning. The transaction execution unit also uses AI to analyze user behavior patterns and detect abnormal behavior in order to enhance security. For example, when a user conducts a transaction at a bank teller, the AI ​​compares the user's behavior with past behavior patterns to detect abnormal behavior and issue a warning. The transaction execution unit also uses AI to analyze user behavior patterns and detect abnormal behavior in order to enhance security. For example, when a user uses online banking, the AI ​​compares the user's behavior with past behavior patterns to detect abnormal behavior and issue a warning. In this way, security is enhanced by analyzing user behavior patterns and detecting abnormal behavior.

[0085] The transaction execution unit can use the emotion estimation function to perform risk assessment based on the user's emotional state in order to enhance security. The transaction execution unit, for example, uses the emotion estimation function to perform risk assessment based on the user's emotional state in order to enhance security. For example, when a user stands in front of an ATM, the transaction execution unit analyzes facial authentication and fingerprint authentication data to perform emotion estimation and risk assessment. The transaction execution unit also uses the emotion estimation function to perform risk assessment based on the user's emotional state in order to enhance security. For example, when a user performs a transaction at a bank teller, the transaction execution unit analyzes facial authentication and iris authentication data to perform emotion estimation and risk assessment. The transaction execution unit also uses the emotion estimation function to perform risk assessment based on the user's emotional state in order to enhance security. For example, when a user uses online banking, the transaction execution unit analyzes facial authentication and vein authentication data to perform emotion estimation and risk assessment. In this way, the emotion estimation function can be used to perform risk assessment based on the user's emotional state.

[0086] The transaction execution unit uses AI to analyze a user's transaction history and propose the optimal transaction method. For example, when a user conducts a transaction at an ATM, AI analyzes the past transaction history and proposes the optimal transaction method. In addition, the transaction execution unit uses AI to analyze a user's transaction history and propose the optimal transaction method. For example, when a user conducts a transaction at a bank teller, AI analyzes the past transaction history and proposes the optimal transaction method. In addition, the transaction execution unit uses AI to analyze a user's transaction history and propose the optimal transaction method. For example, when a user uses online banking, AI analyzes the past transaction history and proposes the optimal transaction method. In this way, the user's transaction history can be analyzed and the optimal transaction method can be proposed.

[0087] The transaction execution unit uses AI to analyze the user's location information and provide guidance to the nearest ATM or bank teller. For example, the transaction execution unit uses AI to analyze the user's location information and provide guidance to the nearest ATM or bank teller. For example, when the user is using a smartphone, the AI ​​analyzes the location information and provides guidance to the nearest ATM. The transaction execution unit also uses AI to analyze the user's location information and provide guidance to the nearest ATM or bank teller. For example, when the user is driving a car, the AI ​​analyzes the location information and provides guidance to the nearest bank teller. The transaction execution unit also uses AI to analyze the user's location information and provide guidance to the nearest ATM or bank teller. For example, when the user is using public transportation, the AI ​​analyzes the location information and provides guidance to the nearest ATM. In this way, the user's location information can be analyzed and guidance to the nearest ATM or bank teller can be provided.

[0088] The transaction execution unit can use the AI's emotion estimation function to suggest a transaction method that matches the user's emotional state. For example, the transaction execution unit uses the AI's emotion estimation function to suggest a transaction method that matches the user's emotional state. For example, when a user stands in front of an ATM, the AI ​​analyzes facial recognition and fingerprint authentication data, estimates emotions, and suggests a transaction method that matches the user's emotional state. The transaction execution unit also uses the AI's emotion estimation function to suggest a transaction method that matches the user's emotional state. For example, when a user conducts a transaction at a bank teller, the AI ​​analyzes facial recognition and iris authentication data, estimates emotions, and suggests a transaction method that matches the user's emotional state. The transaction execution unit also uses the AI's emotion estimation function to suggest a transaction method that matches the user's emotional state. For example, when a user uses online banking, the AI ​​analyzes facial recognition and vein authentication data, estimates emotions, and suggests a transaction method that matches the user's emotional state. In this way, the emotion estimation function can be used to suggest a transaction method that matches the user's emotional state.

[0089] To improve convenience, the trading execution unit can use AI to analyze the user's schedule and suggest the optimal trading time. For example, when a user is using a calendar app on their smartphone, the AI ​​analyzes the schedule and suggests the optimal trading time. Also, to improve convenience, the trading execution unit can use AI to analyze the user's schedule and suggest the optimal trading time. For example, when a user is using a schedule management software on their computer, the AI ​​analyzes the schedule and suggests the optimal trading time. Also, to improve convenience, the trading execution unit can use AI to analyze the user's schedule and suggest the optimal trading time. For example, when a user is using a schedule app on their tablet, the AI ​​analyzes the schedule and suggests the optimal trading time. In this way, the trading execution unit can analyze the user's schedule and suggest the optimal trading time.

[0090] The trading execution unit can use AI to analyze user preferences and provide customized trading options to improve convenience. For example, the trading execution unit can use AI to analyze user preferences and provide customized trading options to improve convenience. For example, the AI ​​can suggest optimal trading options based on trading options previously selected by the user. Furthermore, the trading execution unit can use AI to analyze user preferences and provide customized trading options to improve convenience. For example, if a user often trades during a specific time period, the AI ​​can suggest the optimal trading option for that time period. Furthermore, the trading execution unit can use AI to analyze user preferences and provide customized trading options to improve convenience. For example, if a user prefers a specific trading method, the AI ​​can suggest the optimal trading option for that method. In this way, the user's preferences can be analyzed and customized trading options can be provided.

[0091] The transaction execution unit can use the emotion estimation function to provide customized services according to the user's emotional state in order to improve convenience. The transaction execution unit, for example, uses the emotion estimation function to provide customized services according to the user's emotional state in order to improve convenience. For example, when a user stands in front of an ATM, the transaction execution unit analyzes facial authentication and fingerprint authentication data, estimates emotions, and provides services according to the user's emotional state. The transaction execution unit also uses the emotion estimation function to provide customized services according to the user's emotional state in order to improve convenience. For example, when a user performs a transaction at a bank teller, the transaction execution unit analyzes facial authentication and iris authentication data, estimates emotions, and provides services according to the user's emotional state. The transaction execution unit also uses the emotion estimation function to provide customized services according to the user's emotional state in order to improve convenience. For example, when a user uses online banking, the transaction execution unit analyzes facial authentication and vein authentication data, estimates emotions, and provides services according to the user's emotional state. In this way, by using the emotion estimation function, customized services according to the user's emotional state can be provided.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The cash cardless system can further include a voice recognition unit. The voice recognition unit analyzes the user's voice and recognizes voice commands. For example, when a user stands in front of an ATM and verbally instructs the details of a transaction, the voice recognition unit analyzes the instruction and transmits it to the transaction execution unit. The voice recognition unit can also analyze the user's voiceprint to verify the user's identity. For example, when a user performs a transaction at a bank teller, the voice recognition unit analyzes the voiceprint to verify the user's identity. Furthermore, the voice recognition unit can analyze the tone and speed of the user's voice and estimate the user's emotional state using an emotion estimation function. This provides a variety of transaction methods using voice recognition.

[0094] The cash cardless system can further include a location information acquisition unit. The location information acquisition unit acquires the user's current location and guides the user to the nearest ATM or bank counter. For example, when a user is using a smartphone, the location information acquisition unit acquires the user's current location and guides the user to the nearest ATM. The location information acquisition unit can also evaluate the security of a transaction based on the user's location information. For example, if a user conducts a transaction in an unusual location, the location information acquisition unit transmits that information to the transaction execution unit and evaluates the security of the transaction. This improves convenience and security by utilizing location information.

[0095] The cash cardless system can further include a health monitoring unit. The health monitoring unit monitors the user's health condition in real time and issues a warning if an abnormality is detected. For example, when a user stands in front of an ATM, the health monitoring unit measures the user's heart rate and blood pressure and issues a warning if an abnormality is detected. The health monitoring unit can also analyze the user's health data and provide health management advice. For example, when a user conducts a transaction at a bank teller, the health monitoring unit analyzes the health data and provides health management advice. This allows the user's health condition to be monitored and used to help with health management.

[0096] The cash cardless system can further include a behavior analysis unit. The behavior analysis unit analyzes the user's behavior patterns and detects abnormal behavior. For example, when a user conducts a transaction at an ATM, the behavior analysis unit compares the user's behavior with past behavior patterns to detect abnormal behavior and issue a warning. The behavior analysis unit can also evaluate the safety of the transaction based on the user's behavior patterns. For example, when a user conducts a transaction at a bank teller, the behavior analysis unit analyzes the user's behavior pattern and evaluates the safety of the transaction. In this way, security is strengthened by analyzing the user's behavior patterns and detecting abnormal behavior.

[0097] The cash cardless system can further include a schedule management unit. The schedule management unit analyzes the user's schedule and suggests the optimal transaction time. For example, when the user is using a calendar app on their smartphone, the schedule management unit analyzes the schedule and suggests the optimal transaction time. The schedule management unit can also send transaction reminders based on the user's schedule. For example, if the user has a transaction scheduled at a bank teller, the schedule management unit sends a reminder to ensure the user does not forget to make the transaction. This makes it possible to analyze the user's schedule and suggest the optimal transaction time.

[0098] The cash cardless system can also provide customized services based on the user's emotional state. For example, when a user stands in front of an ATM, the system analyzes facial and fingerprint authentication data, performs emotion estimation, and provides services according to the user's emotional state. When a user conducts a transaction at a bank teller, the system analyzes facial and iris authentication data, performs emotion estimation, and provides services according to the user's emotional state. When a user uses online banking, the system analyzes facial and vein authentication data, performs emotion estimation, and provides services according to the user's emotional state. In this way, the emotion estimation function can be used to provide customized services according to the user's emotional state.

[0099] The cash cardless system can also measure the user's stress level and make suggestions for relaxation. For example, when a user stands in front of an ATM, the facial recognition unit uses the emotion estimation function to measure the user's stress level and make suggestions for relaxation. When the user conducts a transaction at a bank teller, the facial recognition unit uses the emotion estimation function to measure the user's stress level and make suggestions for relaxation. When the user uses online banking, the facial recognition unit uses the emotion estimation function to measure the user's stress level and make suggestions for relaxation. In this way, the user's stress level can be measured and suggestions for relaxation can be made.

[0100] The cash cardless system can also monitor the user's health condition and issue a warning if an abnormality is detected. For example, when a user stands in front of an ATM, the facial recognition unit uses facial recognition data to monitor the user's health condition and issues a warning if an abnormality is detected. When a user conducts a transaction at a bank teller, the facial recognition unit uses facial recognition data to monitor the user's health condition and issues a warning if an abnormality is detected. When a user uses online banking, the facial recognition unit uses facial recognition data to monitor the user's health condition and issues a warning if an abnormality is detected. This makes it possible to monitor the user's health condition and issue a warning if an abnormality is detected.

[0101] The cash cardless system can also analyze the user's walking pattern to perform composite biometric authentication. For example, when a user stands in front of an ATM, the facial recognition unit analyzes their walking pattern in addition to the facial recognition data to perform composite biometric authentication. When the user conducts a transaction at a bank teller, the facial recognition unit analyzes their walking pattern in addition to the facial recognition data to perform composite biometric authentication. When the user uses online banking, the facial recognition unit analyzes their walking pattern in addition to the facial recognition data to perform composite biometric authentication. This composite biometric authentication improves security.

[0102] The cash cardless system can also analyze the subtle movements of the user's face to perform additional authentication to prevent counterfeiting. For example, when a user stands in front of an ATM, the facial recognition unit analyzes the subtle movements of the user's face to perform additional authentication to prevent counterfeiting. When the user conducts a transaction at a bank teller, the facial recognition unit analyzes the subtle movements of the user's face to perform additional authentication to prevent counterfeiting. Furthermore, when the user uses online banking, the facial recognition unit analyzes the subtle movements of the user's face to perform additional authentication to prevent counterfeiting. This additional authentication to prevent counterfeiting improves security.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The facial recognition unit recognizes the user's face. For example, the facial recognition unit uses a camera to take a picture of the user's face, and AI analyzes the image to extract facial features. The facial recognition unit can also use a facial image matching algorithm to compare the image with pre-registered facial data. Furthermore, the facial recognition unit performs facial recognition based on the contours of the face and the positions of the eyes, nose, and mouth. Step 2: The biometric authentication unit recognizes the user's biometric information. For example, the biometric authentication unit uses a fingerprint scanner to perform fingerprint authentication. The biometric authentication unit can also use an iris scanner to perform iris authentication. The biometric authentication unit can also use a vein scanner to perform vein authentication. Specifically, the fingerprint scanner acquires fingerprint data, and AI analyzes the data to extract fingerprint feature points. The iris scanner photographs the user's iris, and AI analyzes the image to extract iris feature points. The vein scanner photographs the vein patterns of the user's palm or fingers, and AI analyzes the image to extract vein feature points. Step 3: The transaction execution unit executes the transaction based on the information recognized by the facial authentication unit and biometric authentication unit. For example, the transaction execution unit integrates the authentication results of the facial authentication unit and the biometric authentication unit, and AI analyzes the data to verify the identity of the person. The transaction execution unit can also approve the transaction based on the authentication results. Furthermore, the transaction execution unit can record the transaction details and manage the transaction history. Specifically, it saves the transaction details in a database and manages the transaction history.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0107] 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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 AI 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.

[0122] 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.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 AI 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.

[0137] 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.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 7, a 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] 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 AI 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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]

[0172] 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 face authentication unit that recognizes the face of a user; a biometric authentication unit that recognizes biometric information of a user; a transaction execution unit that executes a transaction based on information recognized by the face authentication unit and the biometric authentication unit. A system characterized by:

2. The face authentication unit Analyzing the user's facial expression changes in real time to improve the accuracy of identity verification 2. The system of claim 1.

3. The face authentication unit In addition to facial recognition data, the user's walking patterns are analyzed to perform combined biometric authentication.

2. The system of claim 1.

4. The face authentication unit Analyzes the subtle movements of the user's face and performs additional authentication to prevent counterfeiting 2. The system of claim 1.

5. The face authentication unit It can also be applied to public transportation and office access control, in addition to banking transactions.

2. The system of claim 1.

Images