system

Abstract

The system according to the embodiment aims to allow safe withdrawal of cash without a cash card or bankbook. [Solution] A system according to an embodiment includes a face authentication unit, a biometric authentication unit, a transaction terminal, and a withdrawal unit. The face authentication unit performs face authentication. The biometric authentication unit performs biometric authentication based on face data authenticated by the face authentication unit. The transaction terminal performs a transaction based on the biometric data authenticated by the biometric authentication unit. The withdrawal unit provides cash after the transaction is confirmed by the transaction terminal.

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 it is difficult to withdraw cash if a cash card or passbook is lost.

[0005] The system according to the embodiment aims to allow safe withdrawal of cash without a cash card or bankbook. [Means for solving the problem]

[0006] The system according to the embodiment includes a face authentication unit, a biometric authentication unit, a transaction terminal, and a withdrawal unit. The face authentication unit performs face authentication. The biometric authentication unit performs biometric authentication based on face data authenticated by the face authentication unit. The transaction terminal performs a transaction based on the biometric data authenticated by the biometric authentication unit. The withdrawal unit provides cash after the transaction is confirmed by the transaction terminal. [Effects of the Invention]

[0007] The system according to the embodiment allows users to safely withdraw cash without a cash card or bankbook. [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) A cash cardless system according to an embodiment of the present invention utilizes facial recognition and biometric authentication to enable users to withdraw money from their accounts without carrying a cash card or bankbook. This cash cardless system combines facial recognition and biometric authentication technologies to verify identity using personal characteristics and biometric data. For example, a user accesses a transaction terminal and performs facial recognition and biometric authentication. Facial recognition technology captures the user's facial features with a camera and compares them with pre-registered facial data. Meanwhile, biometric authentication technology uses biometric data such as fingerprints and irises to verify identity. This allows users to withdraw money from their accounts without carrying a cash card or bankbook. Next, if authentication is successful, the user enters the amount they wish to withdraw. At this time, the system verifies the user's account information and displays the available amount. Once the user confirms the amount and confirms the withdrawal, cash is provided from the transaction terminal. This system is particularly effective during natural disasters and other emergencies. Even if a bankbook or cash card is lost or missing, the user can still withdraw money by verifying their identity through facial recognition and biometric authentication. This allows users to use the cash card to purchase basic supplies or cover transportation costs for evacuation to nearby areas, even in uncertain times. Furthermore, this system also offers excellent security. Facial and biometric authentication verify personal identity using individual characteristics, reducing the risk of fraud. Furthermore, since there is no need to carry a cash card or bankbook, the risk of loss or theft is also reduced. Thus, a cash cardless system utilizing facial and biometric authentication is a groundbreaking idea that improves user convenience and security. This allows users to withdraw money from their accounts without carrying a cash card or bankbook. Even in the event of a natural disaster or other disaster, if a bankbook or cash card is lost or lost, users can still withdraw money by verifying their identity through facial and biometric authentication. This allows users to use the cash card to purchase basic supplies or cover transportation costs for evacuation to nearby areas, even in uncertain times.Furthermore, the use of facial recognition and biometric authentication reduces the risk of fraudulent use, and since there is no need to carry cash cards or passbooks, the risk of loss or theft is also reduced.

[0029] A cash cardless system according to an embodiment includes a facial authentication unit, a biometric authentication unit, a transaction terminal, and a withdrawal unit. The facial authentication unit captures a user's facial features with a camera and compares them with pre-registered facial data. For example, when a user stands in front of the camera, the camera automatically captures the user's facial features and compares them with pre-registered facial data. The facial authentication unit can also accurately capture facial features under different lighting conditions. For example, the facial authentication unit can capture facial features under both bright and dark lighting conditions, improving authentication accuracy. The facial authentication unit can also capture a user's facial features from multiple angles. For example, the facial authentication unit instructs the user to move their face left and right in front of the camera to capture facial features from multiple angles. The biometric authentication unit uses biometric data such as fingerprints and irises to verify the user's identity. For example, when a user places their finger on a fingerprint sensor, the biometric authentication unit captures fingerprint data and compares it with pre-registered fingerprint data. The biometric authentication unit can also perform iris authentication. For example, when a user brings their eye close to the iris authentication sensor, the biometric authentication unit acquires iris data and compares it with pre-registered iris data. The transaction terminal confirms the user's account information and displays the amount available for withdrawal. For example, when a user inputs the amount they wish to withdraw, the transaction terminal confirms the account information and displays the amount available for withdrawal. The transaction terminal can also present optimal transaction options by referring to the user's past transaction history. For example, the transaction terminal automatically displays transactions that the user has frequently performed in the past as candidates. The withdrawal unit provides cash from the transaction terminal. For example, the withdrawal unit provides cash when the user confirms the withdrawal. The withdrawal unit can also estimate the user's emotions and adjust the timing of the withdrawal based on the estimated user emotions. For example, if the user is nervous, the withdrawal unit delays the timing of the withdrawal and waits for the user to calm down. In this way, the cash cardless system according to the embodiment allows a user to withdraw money from their account without carrying a cash card or passbook.

[0030] The facial authentication unit can capture the user's facial features with a camera and compare them with pre-registered facial data. For example, when a user stands in front of the camera, the camera automatically captures the user's facial features and compares them with pre-registered facial data. The facial authentication unit can also accurately capture facial features under different lighting conditions. For example, the facial authentication unit can capture facial features under both bright and dark lighting conditions, improving authentication accuracy. Furthermore, the facial authentication unit can capture the user's facial features from multiple angles. For example, the facial authentication unit can instruct the user to move their face left and right in front of the camera and capture facial features from multiple angles. This improves the accuracy of facial authentication.

[0031] The biometric authentication unit can verify the identity of a user using biometric data including fingerprints and irises. For example, when a user places a finger on a fingerprint sensor, the biometric authentication unit acquires fingerprint data and compares it with pre-registered fingerprint data. The biometric authentication unit can also perform iris authentication. For example, when a user brings their eye close to the iris authentication sensor, the biometric authentication unit acquires iris data and compares it with pre-registered iris data. Furthermore, the biometric authentication unit can also perform authentication using biometric data other than fingerprints and irises (e.g., vein patterns). For example, when fingerprint authentication is insufficient, the biometric authentication unit performs authentication by supplementally using vein pattern authentication. This improves the accuracy of biometric authentication.

[0032] The transaction terminal can check the user's account information and display the amount available for withdrawal. For example, when the user inputs the amount they wish to withdraw, the transaction terminal checks the account information and displays the amount available for withdrawal. The transaction terminal can also present optimal transaction options by referring to the user's past transaction history. For example, the transaction terminal can automatically display transactions that the user has frequently performed in the past as candidates. Furthermore, the transaction terminal can present optimal transaction options by taking into account the user's current location information. For example, the transaction terminal can suggest the ATM closest to the user's current location. This allows the user to check the amount available for withdrawal.

[0033] The withdrawal unit can provide cash from the transaction terminal. For example, the withdrawal unit provides cash when the user confirms the withdrawal. The withdrawal unit can also estimate the user's emotions and adjust the timing of the withdrawal based on the estimated user's emotions. For example, if the user is nervous, the withdrawal unit can delay the timing of the withdrawal and wait for the user to calm down. Furthermore, the withdrawal unit can present optimal withdrawal options by referring to the user's past withdrawal history. For example, the withdrawal unit can automatically display the amounts that the user has frequently withdrawn in the past as candidates. This allows the user to withdraw cash.

[0034] The cash cardless system further includes a data protection unit. This data protection unit protects data acquired by the facial authentication unit and the biometric authentication unit. For example, the data protection unit encrypts the data acquired by the facial authentication unit and the biometric authentication unit to protect it from unauthorized access. The data protection unit also performs access control to ensure that only authenticated users can access the data. Furthermore, the data protection unit regularly backs up data to prevent data loss. For example, the data protection unit backs up data to cloud storage so that the data can be restored in the event of a disaster. This strengthens the protection of the acquired data.

[0035] The cash cardless system further includes a disaster response unit. This disaster response unit performs identity verification and transactions through facial recognition and biometric authentication even during natural disasters or other disasters. The disaster response unit, for example, includes a special protocol for verifying identity through facial recognition and biometric authentication even during disasters. For example, the disaster response unit performs priority processing for quickly performing facial recognition and biometric authentication even during disasters. The disaster response unit also includes special security measures for strengthening data protection even during disasters. For example, the disaster response unit strengthens data encryption to protect data from unauthorized access even during disasters. Furthermore, the disaster response unit includes special procedures for quickly performing transactions even during disasters. For example, the disaster response unit provides simplified procedures for quickly performing transactions even during disasters. This makes it possible to verify identity and perform transactions even during disasters.

[0036] During facial authentication, the facial authentication unit can capture the user's facial features from multiple angles, improving authentication accuracy. For example, the facial authentication unit instructs the user to move their face left and right in front of the camera, thereby acquiring facial features from multiple angles. The facial authentication unit can also have the camera automatically track the user's face, thereby capturing facial features from different angles. Furthermore, the facial authentication unit can instruct the user to move their face up and down, thereby acquiring facial features from different heights. This improves authentication accuracy by capturing facial features from multiple angles.

[0037] The face authentication unit can perform face authentication by taking into account changes in the user's facial features over time. For example, the face authentication unit performs authentication by comparing past face data with current face data, taking into account that the user's facial features change over time. The face authentication unit can also register multiple face data and perform authentication, taking into account that the user's facial features change depending on the season or physical condition. Furthermore, the face authentication unit can also perform authentication by periodically updating face data, taking into account that the user's facial features change with age. This improves authentication accuracy by taking into account changes in the face over time.

[0038] During face authentication, the face authentication unit can capture the user's facial features under different lighting conditions, thereby improving authentication accuracy. For example, the face authentication unit can capture facial features under both bright and dark lighting conditions, thereby improving authentication accuracy. The face authentication unit can also acquire face data under multiple lighting conditions in order to accurately capture facial features even when the color temperature of the lighting is different. Furthermore, the face authentication unit can also acquire face data under different lighting conditions in order to accurately capture facial features even when the lighting angle is different. This improves authentication accuracy by capturing facial features under different lighting conditions.

[0039] During facial authentication, the facial authentication unit can perform authentication by combining the user's facial features with other biometric data (e.g., voiceprint). For example, the facial authentication unit performs voiceprint authentication simultaneously with facial authentication, and performs authentication when both data match. Furthermore, if facial authentication is insufficient, the facial authentication unit can perform authentication by complementarily using voiceprint authentication. Furthermore, the facial authentication unit can combine both facial authentication and voiceprint authentication to improve authentication accuracy. This improves authentication accuracy by combining with other biometric data.

[0040] The biometric authentication unit can perform authentication using biometric data other than fingerprints and irises during biometric authentication. For example, if fingerprint authentication is insufficient, the biometric authentication unit can perform authentication by supplementally using vein pattern authentication. Also, if iris authentication is insufficient, the biometric authentication unit can perform authentication by supplementally using vein pattern authentication. Furthermore, if both fingerprint authentication and iris authentication are insufficient, the biometric authentication unit can also perform authentication by using vein pattern authentication. This improves authentication accuracy by using biometric data other than fingerprints and irises.

[0041] The biometric authentication unit can improve authentication accuracy by acquiring a user's biometric data multiple times during biometric authentication. For example, the biometric authentication unit can improve authentication accuracy by performing fingerprint authentication multiple times and comparing the multiple pieces of data. The biometric authentication unit can also improve authentication accuracy by performing iris authentication multiple times and comparing the multiple pieces of data. Furthermore, the biometric authentication unit can also improve authentication accuracy by performing vein pattern authentication multiple times and comparing the multiple pieces of data. As a result, authentication accuracy is improved by acquiring biometric data multiple times.

[0042] The biometric authentication unit can improve authentication accuracy by acquiring a user's biometric data under different environmental conditions during biometric authentication. For example, the biometric authentication unit can acquire biometric data under both bright and dark lighting conditions to improve authentication accuracy. The biometric authentication unit can also acquire biometric data under both high and low temperatures to improve authentication accuracy. Furthermore, the biometric authentication unit can also acquire biometric data under both high and low humidity conditions to improve authentication accuracy. As a result, authentication accuracy is improved by acquiring biometric data under different environmental conditions.

[0043] During biometric authentication, the biometric authentication unit can perform authentication by combining the user's biometric data with other authentication means (e.g., password). For example, the biometric authentication unit performs password authentication simultaneously with biometric authentication, and performs authentication if both sets of data match. Furthermore, if biometric authentication is insufficient, the biometric authentication unit can perform authentication by complementarily using password authentication. Furthermore, the biometric authentication unit can combine both biometric authentication and password authentication to improve authentication accuracy. This improves authentication accuracy by combining with other authentication means.

[0044] The trading terminal can present optimal trading options by referring to the user's past trading history. For example, the trading terminal can automatically display transactions that the user has frequently performed in the past as candidates. The trading terminal can also predict and suggest transactions to be performed in a specific time period based on the user's past trading history. Furthermore, the trading terminal can analyze the user's past trading patterns and suggest optimal trading options. In this way, optimal trading options are presented by referring to the user's past trading history.

[0045] The trading terminal can present optimal trading options taking into account the user's current location information. For example, the trading terminal can suggest the ATM closest to the user's current location. The trading terminal can also suggest optimal withdrawal options based on the user's current location. Furthermore, the trading terminal can also suggest optimal trading options taking into account the distance from the user's current location. In this way, optimal trading options are presented taking into account the current location information.

[0046] The trading terminal can present optimal trading options by taking into account device information of the user. For example, if the user is using a smartphone, the trading terminal can provide trading options that fit the screen size. Also, if the user is using a tablet, the trading terminal can provide trading options that are optimized for a large screen. Furthermore, if the user is using a smartwatch, the trading terminal can provide simple, highly visible trading options. In this way, optimal trading options are presented by taking into account device information.

[0047] The trading terminal can analyze the user's social media activity to present relevant trading options. For example, the trading terminal can suggest optimal trading options based on the user's social media mentions. The trading terminal can also suggest trading options related to specific events based on the user's social media activity. Furthermore, the trading terminal can analyze the user's social media activity to suggest the most relevant trading options. As a result, relevant trading options are presented by analyzing the social media activity.

[0048] The withdrawal unit can present optimal withdrawal options by referring to the user's past withdrawal history. For example, the withdrawal unit can automatically display as candidates the amounts that the user has frequently withdrawn in the past. The withdrawal unit can also predict and suggest the amount to be withdrawn in a specific time period based on the user's past withdrawal history. Furthermore, the withdrawal unit can analyze the user's past withdrawal patterns and suggest optimal withdrawal options. In this way, optimal withdrawal options are presented by referring to the user's past withdrawal history.

[0049] The withdrawal unit can present optimal withdrawal options taking into account the user's current location information. For example, the withdrawal unit can suggest the ATM closest to the user's current location. The withdrawal unit can also suggest optimal withdrawal options based on the user's current location. Furthermore, the withdrawal unit can also suggest optimal withdrawal options taking into account the distance from the user's current location. In this way, optimal withdrawal options are presented by taking into account the current location information.

[0050] The drawer unit can present optimal drawer options in consideration of device information of the user. For example, if the user is using a smartphone, the drawer unit can provide drawer options that fit the screen size. Also, if the user is using a tablet, the drawer unit can provide drawer options that are optimized for a large screen. Furthermore, if the user is using a smartwatch, the drawer unit can provide drawer options that are concise and highly visible. In this way, optimal drawer options are presented in consideration of device information.

[0051] The drawer unit can analyze the user's social media activity to present relevant drawer options. For example, the drawer unit can suggest the most suitable drawer option based on the places mentioned by the user on social media. The drawer unit can also suggest drawer options related to a specific event from the user's social media activity. Furthermore, the drawer unit can analyze the user's social media activity to suggest the most relevant drawer option. As a result, relevant drawer options are presented by analyzing the social media activity.

[0052] The data protection unit can select the optimal protection method by referring to the user's past data protection history. For example, the data protection unit automatically displays as candidates the data protection methods that the user has frequently used in the past. The data protection unit can also predict and suggest the protection method to be used during a specific time period based on the user's past data protection history. Furthermore, the data protection unit can analyze the user's past data protection patterns and suggest the optimal protection method. In this way, the optimal protection method is selected by referring to the past data protection history.

[0053] The data protection unit can select an optimal protection method by taking into account device information of the user. For example, if the user is using a smartphone, the data protection unit can provide a protection method optimized for the device. Also, if the user is using a tablet, the data protection unit can provide a protection method optimized for a large screen. Furthermore, if the user is using a smartwatch, the data protection unit can provide a simple and highly visible protection method. In this way, the optimal protection method is selected by taking into account device information.

[0054] The disaster response unit can select the optimal response method by referring to past disaster data. The disaster response unit can, for example, propose the optimal evacuation route based on past disaster data. The disaster response unit can also propose a list of necessary supplies based on past disaster data. Furthermore, the disaster response unit can analyze past disaster data and propose the most effective response method. In this way, the optimal response method is selected by referring to past disaster data.

[0055] The disaster response unit can select the optimal response method by taking into account the user's location information. The disaster response unit can, for example, propose an optimal evacuation route based on the user's current location. The disaster response unit can also propose an optimal evacuation site based on the user's current location. Furthermore, the disaster response unit can also propose the optimal response method by taking into account the distance from the user's current location. In this way, the optimal response method is selected by taking into account the location information.

[0056] During facial authentication, the facial authentication unit can capture the user's facial features from multiple angles, improving authentication accuracy. For example, the facial authentication unit instructs the user to move their face left and right in front of the camera, thereby acquiring facial features from multiple angles. The facial authentication unit can also have the camera automatically track the user's face, thereby capturing facial features from different angles. Furthermore, the facial authentication unit can instruct the user to move their face up and down, thereby acquiring facial features from different heights. This improves authentication accuracy by capturing facial features from multiple angles.

[0057] The face authentication unit can perform face authentication by taking into account changes in the user's facial features over time. For example, the face authentication unit performs authentication by comparing past face data with current face data, taking into account that the user's facial features change over time. The face authentication unit can also register multiple face data and perform authentication, taking into account that the user's facial features change depending on the season or physical condition. Furthermore, the face authentication unit can also perform authentication by periodically updating face data, taking into account that the user's facial features change with age. This improves authentication accuracy by taking into account changes in the face over time.

[0058] During face authentication, the face authentication unit can capture the user's facial features under different lighting conditions, thereby improving authentication accuracy. For example, the face authentication unit can capture facial features under both bright and dark lighting conditions, thereby improving authentication accuracy. The face authentication unit can also acquire face data under multiple lighting conditions in order to accurately capture facial features even when the color temperature of the lighting is different. Furthermore, the face authentication unit can also acquire face data under different lighting conditions in order to accurately capture facial features even when the lighting angle is different. This improves authentication accuracy by capturing facial features under different lighting conditions.

[0059] During facial authentication, the facial authentication unit can perform authentication by combining the user's facial features with other biometric data (e.g., voiceprint). For example, the facial authentication unit performs voiceprint authentication simultaneously with facial authentication, and performs authentication when both data match. Furthermore, if facial authentication is insufficient, the facial authentication unit can perform authentication by complementarily using voiceprint authentication. Furthermore, the facial authentication unit can combine both facial authentication and voiceprint authentication to improve authentication accuracy. This improves authentication accuracy by combining with other biometric data.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The cash cardless system can analyze a user's transaction history and provide optimal transaction options. For example, the transaction terminal can automatically display transactions that the user has frequently performed in the past as candidates. The transaction terminal can also learn the user's transaction patterns and prioritize the user's preferred transaction options. Furthermore, the transaction terminal can predict and suggest transactions to be performed during a specific time period based on the user's transaction history. This allows users to perform transactions quickly and efficiently.

[0062] The biometric authentication unit can take the user's health condition into consideration when acquiring the user's biometric data. For example, the biometric authentication unit can widen the tolerance range for authentication if the user's fingerprint is dry. Also, the biometric authentication unit can adjust the accuracy of authentication if the user's iris is tired. Furthermore, if the user's vein pattern has changed, the unit can also perform authentication using complementary authentication means. This improves the accuracy of biometric authentication according to the user's health condition.

[0063] The trading terminal can provide optimal transaction options taking into account the user's current location information. For example, the trading terminal can suggest the ATM closest to the user's current location. The trading terminal can also suggest optimal withdrawal options based on the user's current location. Furthermore, the trading terminal can also suggest optimal transaction options taking into account the distance from the user's current location. This allows the user to conduct transactions efficiently.

[0064] The cash cardless system can provide optimal trading options by taking into account device information of the user. For example, if the user is using a smartphone, the trading terminal can provide trading options that fit the screen size. Also, if the user is using a tablet, the trading terminal can provide trading options that are optimized for a large screen. Furthermore, if the user is using a smartwatch, the trading terminal can provide simple, highly visible trading options. In this way, optimal trading options are presented by taking into account device information.

[0065] The cash cardless system further includes a disaster response unit. This disaster response unit performs identity verification and transactions through facial recognition and biometric authentication even during natural disasters or other disasters. The disaster response unit, for example, includes a special protocol for verifying identity through facial recognition and biometric authentication even during disasters. For example, the disaster response unit performs priority processing for quickly performing facial recognition and biometric authentication even during disasters. The disaster response unit also includes special security measures for strengthening data protection even during disasters. For example, the disaster response unit strengthens data encryption to protect data from unauthorized access even during disasters. Furthermore, the disaster response unit includes special procedures for quickly performing transactions even during disasters. For example, the disaster response unit provides simplified procedures for quickly performing transactions even during disasters. This makes it possible to verify identity and perform transactions even during disasters.

[0066] The trading terminal can analyze the user's social media activity to present relevant trading options. For example, the trading terminal can suggest optimal trading options based on the locations mentioned by the user on social media. The trading terminal can also suggest trading options related to specific events based on the user's social media activity. Furthermore, the trading terminal can analyze the user's social media activity to suggest the most relevant trading options. As a result, relevant trading options are presented by analyzing the social media activity.

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

[0068] Step 1: The facial recognition unit captures the user's facial features with a camera and compares them with pre-registered facial data. For example, when a user stands in front of the camera, the camera automatically captures the user's facial features and compares them with pre-registered facial data. The facial recognition unit can also accurately capture facial features under different lighting conditions. For example, the facial recognition unit can capture facial features in both bright and dark lighting conditions, improving authentication accuracy. Furthermore, the facial recognition unit can capture the user's facial features from multiple angles. For example, the facial recognition unit can instruct the user to move their face left and right in front of the camera and capture facial features from multiple angles. Step 2: The biometric authentication unit verifies the user's identity using biometric data such as fingerprints and irises. For example, when the user places their finger on the fingerprint sensor, the biometric authentication unit acquires fingerprint data and compares it with pre-registered fingerprint data. The biometric authentication unit can also perform iris authentication. For example, when the user brings their eye close to the iris authentication sensor, the biometric authentication unit acquires iris data and compares it with pre-registered iris data. Step 3: The trading terminal checks the user's account information and displays the amount available for withdrawal. For example, when the user inputs the amount they wish to withdraw, the trading terminal checks the account information and displays the amount available for withdrawal. The trading terminal can also refer to the user's past transaction history to present optimal trading options. For example, the trading terminal automatically displays transactions that the user has frequently performed in the past as candidates. Step 4: The withdrawal unit provides cash from the transaction terminal. For example, the withdrawal unit provides cash when the user confirms the withdrawal. The withdrawal unit can also estimate the user's emotions and adjust the timing of the withdrawal based on the estimated user emotions. For example, if the user is nervous, the withdrawal unit delays the timing of the withdrawal and waits for the user to calm down.

[0069] (Example 2) A cash cardless system according to an embodiment of the present invention utilizes facial recognition and biometric authentication to enable users to withdraw money from their accounts without carrying a cash card or bankbook. This cash cardless system combines facial recognition and biometric authentication technologies to verify identity using personal characteristics and biometric data. For example, a user accesses a transaction terminal and performs facial recognition and biometric authentication. Facial recognition technology captures the user's facial features with a camera and compares them with pre-registered facial data. Meanwhile, biometric authentication technology uses biometric data such as fingerprints and irises to verify identity. This allows users to withdraw money from their accounts without carrying a cash card or bankbook. Next, if authentication is successful, the user enters the amount they wish to withdraw. At this time, the system verifies the user's account information and displays the available amount. Once the user confirms the amount and confirms the withdrawal, cash is provided from the transaction terminal. This system is particularly effective during natural disasters and other emergencies. Even if a bankbook or cash card is lost or missing, the user can still withdraw money by verifying their identity through facial recognition and biometric authentication. This allows users to use the cash card to purchase basic supplies or cover transportation costs for evacuation to nearby areas, even in uncertain times. Furthermore, this system also offers excellent security. Facial and biometric authentication verify personal identity using individual characteristics, reducing the risk of fraud. Furthermore, since there is no need to carry a cash card or bankbook, the risk of loss or theft is also reduced. Thus, a cash cardless system utilizing facial and biometric authentication is a groundbreaking idea that improves user convenience and security. This allows users to withdraw money from their accounts without carrying a cash card or bankbook. Even in the event of a natural disaster or other disaster, if a bankbook or cash card is lost or lost, users can still withdraw money by verifying their identity through facial and biometric authentication. This allows users to use the cash card to purchase basic supplies or cover transportation costs for evacuation to nearby areas, even in uncertain times.Furthermore, the use of facial recognition and biometric authentication reduces the risk of fraudulent use, and since there is no need to carry cash cards or passbooks, the risk of loss or theft is also reduced.

[0070] A cash cardless system according to an embodiment includes a facial authentication unit, a biometric authentication unit, a transaction terminal, and a withdrawal unit. The facial authentication unit captures a user's facial features with a camera and compares them with pre-registered facial data. For example, when a user stands in front of the camera, the camera automatically captures the user's facial features and compares them with pre-registered facial data. The facial authentication unit can also accurately capture facial features under different lighting conditions. For example, the facial authentication unit can capture facial features under both bright and dark lighting conditions, improving authentication accuracy. The facial authentication unit can also capture a user's facial features from multiple angles. For example, the facial authentication unit instructs the user to move their face left and right in front of the camera to capture facial features from multiple angles. The biometric authentication unit uses biometric data such as fingerprints and irises to verify the user's identity. For example, when a user places their finger on a fingerprint sensor, the biometric authentication unit captures fingerprint data and compares it with pre-registered fingerprint data. The biometric authentication unit can also perform iris authentication. For example, when a user brings their eye close to the iris authentication sensor, the biometric authentication unit acquires iris data and compares it with pre-registered iris data. The transaction terminal confirms the user's account information and displays the amount available for withdrawal. For example, when a user inputs the amount they wish to withdraw, the transaction terminal confirms the account information and displays the amount available for withdrawal. The transaction terminal can also present optimal transaction options by referring to the user's past transaction history. For example, the transaction terminal automatically displays transactions that the user has frequently performed in the past as candidates. The withdrawal unit provides cash from the transaction terminal. For example, the withdrawal unit provides cash when the user confirms the withdrawal. The withdrawal unit can also estimate the user's emotions and adjust the timing of the withdrawal based on the estimated user emotions. For example, if the user is nervous, the withdrawal unit delays the timing of the withdrawal and waits for the user to calm down. In this way, the cash cardless system according to the embodiment allows a user to withdraw money from their account without carrying a cash card or passbook.

[0071] The facial authentication unit can capture the user's facial features with a camera and compare them with pre-registered facial data. For example, when a user stands in front of the camera, the camera automatically captures the user's facial features and compares them with pre-registered facial data. The facial authentication unit can also accurately capture facial features under different lighting conditions. For example, the facial authentication unit can capture facial features under both bright and dark lighting conditions, improving authentication accuracy. Furthermore, the facial authentication unit can capture the user's facial features from multiple angles. For example, the facial authentication unit can instruct the user to move their face left and right in front of the camera and capture facial features from multiple angles. This improves the accuracy of facial authentication.

[0072] The biometric authentication unit can verify the identity of a user using biometric data including fingerprints and irises. For example, when a user places a finger on a fingerprint sensor, the biometric authentication unit acquires fingerprint data and compares it with pre-registered fingerprint data. The biometric authentication unit can also perform iris authentication. For example, when a user brings their eye close to the iris authentication sensor, the biometric authentication unit acquires iris data and compares it with pre-registered iris data. Furthermore, the biometric authentication unit can also perform authentication using biometric data other than fingerprints and irises (e.g., vein patterns). For example, when fingerprint authentication is insufficient, the biometric authentication unit performs authentication by supplementally using vein pattern authentication. This improves the accuracy of biometric authentication.

[0073] The transaction terminal can check the user's account information and display the amount available for withdrawal. For example, when the user inputs the amount they wish to withdraw, the transaction terminal checks the account information and displays the amount available for withdrawal. The transaction terminal can also present optimal transaction options by referring to the user's past transaction history. For example, the transaction terminal can automatically display transactions that the user has frequently performed in the past as candidates. Furthermore, the transaction terminal can present optimal transaction options by taking into account the user's current location information. For example, the transaction terminal can suggest the ATM closest to the user's current location. This allows the user to check the amount available for withdrawal.

[0074] The withdrawal unit can provide cash from the transaction terminal. For example, the withdrawal unit provides cash when the user confirms the withdrawal. The withdrawal unit can also estimate the user's emotions and adjust the timing of the withdrawal based on the estimated user's emotions. For example, if the user is nervous, the withdrawal unit can delay the timing of the withdrawal and wait for the user to calm down. Furthermore, the withdrawal unit can present optimal withdrawal options by referring to the user's past withdrawal history. For example, the withdrawal unit can automatically display the amounts that the user has frequently withdrawn in the past as candidates. This allows the user to withdraw cash.

[0075] The cash cardless system further includes a data protection unit. This data protection unit protects data acquired by the facial authentication unit and the biometric authentication unit. For example, the data protection unit encrypts the data acquired by the facial authentication unit and the biometric authentication unit to protect it from unauthorized access. The data protection unit also performs access control to ensure that only authenticated users can access the data. Furthermore, the data protection unit regularly backs up data to prevent data loss. For example, the data protection unit backs up data to cloud storage so that the data can be restored in the event of a disaster. This strengthens the protection of the acquired data.

[0076] The cash cardless system further includes a disaster response unit. This disaster response unit performs identity verification and transactions through facial recognition and biometric authentication even during natural disasters or other disasters. The disaster response unit, for example, includes a special protocol for verifying identity through facial recognition and biometric authentication even during disasters. For example, the disaster response unit performs priority processing for quickly performing facial recognition and biometric authentication even during disasters. The disaster response unit also includes special security measures for strengthening data protection even during disasters. For example, the disaster response unit strengthens data encryption to protect data from unauthorized access even during disasters. Furthermore, the disaster response unit includes special procedures for quickly performing transactions even during disasters. For example, the disaster response unit provides simplified procedures for quickly performing transactions even during disasters. This makes it possible to verify identity and perform transactions even during disasters.

[0077] The face authentication unit can estimate the user's emotions and adjust the accuracy of face authentication based on the estimated user emotions. For example, if the user is nervous, the face authentication unit widens the tolerance range for face authentication and performs authentication. Furthermore, if the user is relaxed, the face authentication unit can perform face authentication with normal accuracy. Furthermore, if the user is impatient, the face authentication unit can prioritize authentication speed and perform authentication with slightly lower accuracy. For example, the face authentication unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Emotion estimation is performed using, for example, facial expression recognition technology. In this way, the accuracy of face authentication is adjusted according to the user's emotion.

[0078] During facial authentication, the facial authentication unit can capture the user's facial features from multiple angles, improving authentication accuracy. For example, the facial authentication unit instructs the user to move their face left and right in front of the camera, thereby acquiring facial features from multiple angles. The facial authentication unit can also have the camera automatically track the user's face, thereby capturing facial features from different angles. Furthermore, the facial authentication unit can instruct the user to move their face up and down, thereby acquiring facial features from different heights. This improves authentication accuracy by capturing facial features from multiple angles.

[0079] The face authentication unit can perform face authentication by taking into account changes in the user's facial features over time. For example, the face authentication unit performs authentication by comparing past face data with current face data, taking into account that the user's facial features change over time. The face authentication unit can also register multiple face data and perform authentication, taking into account that the user's facial features change depending on the season or physical condition. Furthermore, the face authentication unit can also perform authentication by periodically updating face data, taking into account that the user's facial features change with age. This improves authentication accuracy by taking into account changes in the face over time.

[0080] The face authentication unit can estimate the user's emotions and adjust the timing of face authentication based on the estimated user emotions. For example, if the user is relaxed, the face authentication unit performs face authentication at a normal timing. Furthermore, if the user is nervous, the face authentication unit can delay the timing of face authentication to wait for the user to calm down. Furthermore, if the user is in a hurry, the face authentication unit can advance the timing of face authentication to perform authentication quickly. For example, the face authentication unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Emotion estimation is performed using, for example, facial expression recognition technology. As a result, the timing of face authentication is adjusted according to the user's emotions.

[0081] During face authentication, the face authentication unit can capture the user's facial features under different lighting conditions, thereby improving authentication accuracy. For example, the face authentication unit can capture facial features under both bright and dark lighting conditions, thereby improving authentication accuracy. The face authentication unit can also acquire face data under multiple lighting conditions in order to accurately capture facial features even when the color temperature of the lighting is different. Furthermore, the face authentication unit can also acquire face data under different lighting conditions in order to accurately capture facial features even when the lighting angle is different. This improves authentication accuracy by capturing facial features under different lighting conditions.

[0082] During facial authentication, the facial authentication unit can perform authentication by combining the user's facial features with other biometric data (e.g., voiceprint). For example, the facial authentication unit performs voiceprint authentication simultaneously with facial authentication, and performs authentication when both data match. Furthermore, if facial authentication is insufficient, the facial authentication unit can perform authentication by complementarily using voiceprint authentication. Furthermore, the facial authentication unit can combine both facial authentication and voiceprint authentication to improve authentication accuracy. This improves authentication accuracy by combining with other biometric data.

[0083] The biometric authentication unit can estimate the user's emotions and adjust the accuracy of biometric authentication based on the estimated user emotions. For example, if the user is nervous, the biometric authentication unit widens the tolerance range for biometric authentication and performs authentication. Also, if the user is relaxed, the biometric authentication unit can perform biometric authentication with normal accuracy. Furthermore, if the user is impatient, the biometric authentication unit can prioritize authentication speed and perform authentication with slightly lower accuracy. For example, the biometric authentication unit captures the user's fingerprint or iris with a sensor and estimates the emotion using an emotion estimation algorithm. Emotion estimation is performed, for example, based on changes in the fingerprint or iris. In this way, the accuracy of biometric authentication is adjusted according to the user's emotions.

[0084] The biometric authentication unit can perform authentication using biometric data other than fingerprints and irises during biometric authentication. For example, if fingerprint authentication is insufficient, the biometric authentication unit can perform authentication by supplementally using vein pattern authentication. Also, if iris authentication is insufficient, the biometric authentication unit can perform authentication by supplementally using vein pattern authentication. Furthermore, if both fingerprint authentication and iris authentication are insufficient, the biometric authentication unit can also perform authentication by using vein pattern authentication. This improves authentication accuracy by using biometric data other than fingerprints and irises.

[0085] The biometric authentication unit can improve authentication accuracy by acquiring a user's biometric data multiple times during biometric authentication. For example, the biometric authentication unit can improve authentication accuracy by performing fingerprint authentication multiple times and comparing the multiple pieces of data. The biometric authentication unit can also improve authentication accuracy by performing iris authentication multiple times and comparing the multiple pieces of data. Furthermore, the biometric authentication unit can also improve authentication accuracy by performing vein pattern authentication multiple times and comparing the multiple pieces of data. As a result, authentication accuracy is improved by acquiring biometric data multiple times.

[0086] The biometric authentication unit can estimate the user's emotion and adjust the timing of biometric authentication based on the estimated user emotion. For example, if the user is relaxed, the biometric authentication unit performs biometric authentication at a normal timing. Furthermore, if the user is nervous, the biometric authentication unit can delay the timing of biometric authentication and wait for the user to calm down. Furthermore, if the user is in a hurry, the biometric authentication unit can advance the timing of biometric authentication to perform authentication quickly. For example, the biometric authentication unit captures the user's fingerprint or iris with a sensor and estimates the emotion using an emotion estimation algorithm. Emotion estimation is performed, for example, based on changes in the fingerprint or iris. In this way, the timing of biometric authentication is adjusted according to the user's emotion.

[0087] The biometric authentication unit can improve authentication accuracy by acquiring a user's biometric data under different environmental conditions during biometric authentication. For example, the biometric authentication unit can acquire biometric data under both bright and dark lighting conditions to improve authentication accuracy. The biometric authentication unit can also acquire biometric data under both high and low temperatures to improve authentication accuracy. Furthermore, the biometric authentication unit can also acquire biometric data under both high and low humidity conditions to improve authentication accuracy. As a result, authentication accuracy is improved by acquiring biometric data under different environmental conditions.

[0088] During biometric authentication, the biometric authentication unit can perform authentication by combining the user's biometric data with other authentication means (e.g., password). For example, the biometric authentication unit performs password authentication simultaneously with biometric authentication, and performs authentication if both sets of data match. Furthermore, if biometric authentication is insufficient, the biometric authentication unit can perform authentication by complementarily using password authentication. Furthermore, the biometric authentication unit can combine both biometric authentication and password authentication to improve authentication accuracy. This improves authentication accuracy by combining with other authentication means.

[0089] The trading terminal can estimate the user's emotions and adjust the operation method of the trading terminal based on the estimated user emotions. For example, if the user is nervous, the trading terminal can provide a simple operation method and minimize operation steps. Furthermore, if the user is relaxed, the trading terminal can provide detailed operation options and suggest a customizable operation method. Furthermore, if the user is in a hurry, the trading terminal can prioritize voice operation to enable quick operation. For example, the trading terminal captures the user's facial expressions with a camera and estimates the emotion using an emotion estimation algorithm. Emotion estimation is performed, for example, using facial expression recognition technology. As a result, the operation method of the trading terminal is adjusted according to the user's emotions.

[0090] The trading terminal can present optimal trading options by referring to the user's past trading history. For example, the trading terminal can automatically display transactions that the user has frequently performed in the past as candidates. The trading terminal can also predict and suggest transactions to be performed in a specific time period based on the user's past trading history. Furthermore, the trading terminal can analyze the user's past trading patterns and suggest optimal trading options. In this way, optimal trading options are presented by referring to the user's past trading history.

[0091] The trading terminal can present optimal trading options taking into account the user's current location information. For example, the trading terminal can suggest the ATM closest to the user's current location. The trading terminal can also suggest optimal withdrawal options based on the user's current location. Furthermore, the trading terminal can also suggest optimal trading options taking into account the distance from the user's current location. In this way, optimal trading options are presented taking into account the current location information.

[0092] The trading terminal can estimate the user's emotions and adjust the display content of the trading terminal based on the estimated user emotions. For example, if the user is nervous, the trading terminal can provide simple, highly visible display content. Furthermore, if the user is relaxed, the trading terminal can provide display content including detailed information. Furthermore, if the user is in a hurry, the trading terminal can provide display content that focuses on the main points. For example, the trading terminal captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Emotion estimation is performed using, for example, facial expression recognition technology. As a result, the display content of the trading terminal is adjusted according to the user's emotions.

[0093] The trading terminal can present optimal trading options by taking into account device information of the user. For example, if the user is using a smartphone, the trading terminal can provide trading options that fit the screen size. Also, if the user is using a tablet, the trading terminal can provide trading options that are optimized for a large screen. Furthermore, if the user is using a smartwatch, the trading terminal can provide simple, highly visible trading options. In this way, optimal trading options are presented by taking into account device information.

[0094] The trading terminal can analyze the user's social media activity to present relevant trading options. For example, the trading terminal can suggest optimal trading options based on the user's social media mentions. The trading terminal can also suggest trading options related to specific events based on the user's social media activity. Furthermore, the trading terminal can analyze the user's social media activity to suggest the most relevant trading options. As a result, relevant trading options are presented by analyzing the social media activity.

[0095] The drawer unit can estimate the user's emotion and adjust the timing of drawer operation based on the estimated user's emotion. For example, when the user is relaxed, the drawer unit performs drawer operation at a normal timing. Furthermore, when the user is nervous, the drawer unit can delay the timing of drawer operation to wait for the user to calm down. Furthermore, when the user is in a hurry, the drawer unit can advance the timing of drawer operation to perform drawer operation quickly. For example, the drawer unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The emotion estimation is performed using, for example, facial expression recognition technology. As a result, the timing of drawer operation is adjusted according to the user's emotion.

[0096] The withdrawal unit can present optimal withdrawal options by referring to the user's past withdrawal history. For example, the withdrawal unit can automatically display as candidates the amounts that the user has frequently withdrawn in the past. The withdrawal unit can also predict and suggest the amount to be withdrawn in a specific time period based on the user's past withdrawal history. Furthermore, the withdrawal unit can analyze the user's past withdrawal patterns and suggest optimal withdrawal options. In this way, optimal withdrawal options are presented by referring to the user's past withdrawal history.

[0097] The withdrawal unit can present optimal withdrawal options taking into account the user's current location information. For example, the withdrawal unit can suggest the ATM closest to the user's current location. The withdrawal unit can also suggest optimal withdrawal options based on the user's current location. Furthermore, the withdrawal unit can also suggest optimal withdrawal options taking into account the distance from the user's current location. In this way, optimal withdrawal options are presented by taking into account the current location information.

[0098] The withdrawal unit can estimate the user's emotions and adjust the withdrawal amount based on the estimated user emotions. For example, if the user is relaxed, the withdrawal unit can withdraw a normal amount. If the user is nervous, the withdrawal unit can set the withdrawal amount to a small amount and wait for the user to calm down. Furthermore, if the user is in a hurry, the withdrawal unit can quickly set the withdrawal amount and withdraw quickly. For example, the withdrawal unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Emotion estimation is performed using, for example, facial expression recognition technology. As a result, the withdrawal amount is adjusted according to the user's emotions.

[0099] The drawer unit can present optimal drawer options in consideration of device information of the user. For example, if the user is using a smartphone, the drawer unit can provide drawer options that fit the screen size. Also, if the user is using a tablet, the drawer unit can provide drawer options that are optimized for a large screen. Furthermore, if the user is using a smartwatch, the drawer unit can provide drawer options that are concise and highly visible. In this way, optimal drawer options are presented in consideration of device information.

[0100] The drawer unit can analyze the user's social media activity to present relevant drawer options. For example, the drawer unit can suggest the most suitable drawer option based on the places mentioned by the user on social media. The drawer unit can also suggest drawer options related to a specific event from the user's social media activity. Furthermore, the drawer unit can analyze the user's social media activity to suggest the most relevant drawer option. As a result, relevant drawer options are presented by analyzing the social media activity.

[0101] The data protection unit can estimate the user's emotions and adjust the data protection method based on the estimated user emotions. For example, if the user is nervous, the data protection unit can widen the tolerance range of data protection and perform data protection. Also, if the user is relaxed, the data protection unit can perform data protection with normal accuracy. Furthermore, if the user is impatient, the data protection unit can prioritize protection speed and perform data protection with slightly lower accuracy. For example, the data protection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Emotion estimation is performed using, for example, facial expression recognition technology. In this way, the data protection method is adjusted according to the user's emotions.

[0102] The data protection unit can select the optimal protection method by referring to the user's past data protection history. For example, the data protection unit automatically displays as candidates the data protection methods that the user has frequently used in the past. The data protection unit can also predict and suggest the protection method to be used during a specific time period based on the user's past data protection history. Furthermore, the data protection unit can analyze the user's past data protection patterns and suggest the optimal protection method. In this way, the optimal protection method is selected by referring to the past data protection history.

[0103] The data protection unit can estimate the user's emotions and determine the priority of data protection based on the estimated user emotions. For example, if the user is nervous, the data protection unit can prioritize the protection of important data. Also, if the user is relaxed, the data protection unit can protect data with normal priority. Furthermore, if the user is impatient, the data protection unit can prioritize data that needs to be protected quickly. For example, the data protection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Emotion estimation is performed using, for example, facial expression recognition technology. In this way, the priority of data protection is determined according to the user's emotions.

[0104] The data protection unit can select an optimal protection method by taking into account device information of the user. For example, if the user is using a smartphone, the data protection unit can provide a protection method optimized for the device. Also, if the user is using a tablet, the data protection unit can provide a protection method optimized for a large screen. Furthermore, if the user is using a smartwatch, the data protection unit can provide a simple and highly visible protection method. In this way, the optimal protection method is selected by taking into account device information.

[0105] The disaster response unit can estimate the user's emotions and adjust a disaster response method based on the estimated user emotions. For example, if the user is nervous, the disaster response unit can provide a calm response method to reduce the user's anxiety. Furthermore, if the user is relaxed, the disaster response unit can provide a normal response method. Furthermore, if the user is anxious, the disaster response unit can provide a quick response method to ensure the user's safety. For example, the disaster response unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Emotion estimation is performed using, for example, facial expression recognition technology. In this way, a disaster response method can be adjusted according to the user's emotions.

[0106] The disaster response unit can select the optimal response method by referring to past disaster data. The disaster response unit can, for example, propose the optimal evacuation route based on past disaster data. The disaster response unit can also propose a list of necessary supplies based on past disaster data. Furthermore, the disaster response unit can analyze past disaster data and propose the most effective response method. In this way, the optimal response method is selected by referring to past disaster data.

[0107] The disaster response unit can estimate the user's emotions and determine the priority of disaster response based on the estimated user emotions. For example, if the user is nervous, the disaster response unit prioritizes important responses. Furthermore, if the user is relaxed, the disaster response unit can respond with normal priority. Furthermore, if the user is impatient, the disaster response unit can prioritize matters that require a quick response. For example, the disaster response unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Emotion estimation is performed using, for example, facial expression recognition technology. As a result, the priority of disaster response is determined according to the user's emotions.

[0108] The disaster response unit can select the optimal response method by taking into account the user's location information. The disaster response unit can, for example, propose an optimal evacuation route based on the user's current location. The disaster response unit can also propose an optimal evacuation site based on the user's current location. Furthermore, the disaster response unit can also propose the optimal response method by taking into account the distance from the user's current location. In this way, the optimal response method is selected by taking into account the location information.

[0109] The face authentication unit can estimate the user's emotions and adjust the accuracy of face authentication based on the estimated user emotions. For example, if the user is nervous, the face authentication unit widens the tolerance range for face authentication and performs authentication. Furthermore, if the user is relaxed, the face authentication unit can perform face authentication with normal accuracy. Furthermore, if the user is impatient, the face authentication unit can prioritize authentication speed and perform authentication with slightly lower accuracy. For example, the face authentication unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Emotion estimation is performed using, for example, facial expression recognition technology. In this way, the accuracy of face authentication is adjusted according to the user's emotion.

[0110] During facial authentication, the facial authentication unit can capture the user's facial features from multiple angles, improving authentication accuracy. For example, the facial authentication unit instructs the user to move their face left and right in front of the camera, thereby acquiring facial features from multiple angles. The facial authentication unit can also have the camera automatically track the user's face, thereby capturing facial features from different angles. Furthermore, the facial authentication unit can instruct the user to move their face up and down, thereby acquiring facial features from different heights. This improves authentication accuracy by capturing facial features from multiple angles.

[0111] The face authentication unit can perform face authentication by taking into account changes in the user's facial features over time. For example, the face authentication unit performs authentication by comparing past face data with current face data, taking into account that the user's facial features change over time. The face authentication unit can also register multiple face data and perform authentication, taking into account that the user's facial features change depending on the season or physical condition. Furthermore, the face authentication unit can also perform authentication by periodically updating face data, taking into account that the user's facial features change with age. This improves authentication accuracy by taking into account changes in the face over time.

[0112] The face authentication unit can estimate the user's emotions and adjust the timing of face authentication based on the estimated user emotions. For example, if the user is relaxed, the face authentication unit performs face authentication at a normal timing. Furthermore, if the user is nervous, the face authentication unit can delay the timing of face authentication to wait for the user to calm down. Furthermore, if the user is in a hurry, the face authentication unit can advance the timing of face authentication to perform authentication quickly. For example, the face authentication unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Emotion estimation is performed using, for example, facial expression recognition technology. As a result, the timing of face authentication is adjusted according to the user's emotions.

[0113] During face authentication, the face authentication unit can capture the user's facial features under different lighting conditions, thereby improving authentication accuracy. For example, the face authentication unit can capture facial features under both bright and dark lighting conditions, thereby improving authentication accuracy. The face authentication unit can also acquire face data under multiple lighting conditions in order to accurately capture facial features even when the color temperature of the lighting is different. Furthermore, the face authentication unit can also acquire face data under different lighting conditions in order to accurately capture facial features even when the lighting angle is different. This improves authentication accuracy by capturing facial features under different lighting conditions.

[0114] During facial authentication, the facial authentication unit can perform authentication by combining the user's facial features with other biometric data (e.g., voiceprint). For example, the facial authentication unit performs voiceprint authentication simultaneously with facial authentication, and performs authentication when both data match. Furthermore, if facial authentication is insufficient, the facial authentication unit can perform authentication by complementarily using voiceprint authentication. Furthermore, the facial authentication unit can combine both facial authentication and voiceprint authentication to improve authentication accuracy. This improves authentication accuracy by combining with other biometric data. === Hard Collateral 1-1 === Each of the multiple elements, including the face authentication unit, biometric authentication unit, transaction terminal, withdrawal unit, data protection unit, and disaster response unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the face authentication unit captures the user's facial features using the camera 42 of the smart device 14 and compares them with pre-registered face data by the control unit 46A. The biometric authentication unit verifies the user's identity using the fingerprint sensor or iris authentication sensor of the smart device 14. The transaction terminal verifies the user's account information using the display 40A and control unit 46A of the smart device 14 and displays the amount available for withdrawal. The withdrawal unit provides cash via the control unit 46A of the smart device 14. The data protection unit encrypts data acquired by the face authentication unit and biometric authentication unit using the specific processing unit 290 of the data processing device 12 to protect it from unauthorized access. The disaster response unit performs priority processing using the specific processing unit 290 of the data processing device 12 to quickly perform face authentication and biometric authentication even during a disaster. === Hard Collateral 1-2 === Each of the multiple elements, including the face authentication unit, biometric authentication unit, transaction terminal, withdrawal unit, data protection unit, and disaster response unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the face authentication unit captures the user's facial features using the camera 42 of the smart glasses 214 and compares them with pre-registered face data by the control unit 46A. The biometric authentication unit verifies the user's identity using the fingerprint sensor or iris authentication sensor of the smart glasses 214. The transaction terminal verifies the user's account information using the display and control unit 46A of the smart glasses 214 and displays the amount available for withdrawal. The withdrawal unit provides cash via the control unit 46A of the smart glasses 214. The data protection unit encrypts data acquired by the face authentication unit and biometric authentication unit using the specific processing unit 290 of the data processing device 12 to protect it from unauthorized access. The disaster response unit performs priority processing using the specific processing unit 290 of the data processing device 12 to quickly perform face authentication and biometric authentication even during a disaster. === Hard Collateral 1-3 === Each of the multiple elements, including the face authentication unit, biometric authentication unit, transaction terminal, withdrawal unit, data protection unit, and disaster response unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the face authentication unit captures the user's facial features using the camera 42 of the headset terminal 314 and compares them with pre-registered face data by the control unit 46A. The biometric authentication unit verifies the user's identity using the fingerprint sensor or iris authentication sensor of the headset terminal 314. The transaction terminal verifies the user's account information using the display 343 and control unit 46A of the headset terminal 314 and displays the amount available for withdrawal. The withdrawal unit provides cash via the control unit 46A of the headset terminal 314. The data protection unit encrypts data acquired by the face authentication unit and biometric authentication unit using the specific processing unit 290 of the data processing device 12 to protect it from unauthorized access. The disaster response unit performs priority processing using the specific processing unit 290 of the data processing device 12 to quickly perform face authentication and biometric authentication even during a disaster. === Hard Collateral 1-4 === Each of the multiple elements, including the face authentication unit, biometric authentication unit, transaction terminal, withdrawal unit, data protection unit, and disaster response unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the face authentication unit captures the user's facial features using the camera 42 of the robot 414 and compares them with pre-registered face data by the control unit 46A. The biometric authentication unit verifies the user's identity using the fingerprint sensor or iris authentication sensor of the robot 414. The transaction terminal verifies the user's account information using the display and control unit 46A of the robot 414 and displays the amount available for withdrawal. The withdrawal unit provides cash via the control unit 46A of the robot 414. The data protection unit encrypts data acquired by the face authentication unit and biometric authentication unit using the specific processing unit 290 of the data processing device 12 to protect it from unauthorized access. The disaster response unit performs priority processing using the specific processing unit 290 of the data processing device 12 to quickly perform face authentication and biometric authentication even during a disaster.

[0115] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0116] The cash cardless system can analyze a user's transaction history and provide optimal transaction options. For example, the transaction terminal can automatically display transactions that the user has frequently performed in the past as candidates. The transaction terminal can also learn the user's transaction patterns and prioritize the user's preferred transaction options. Furthermore, the transaction terminal can predict and suggest transactions to be performed during a specific time period based on the user's transaction history. This allows users to perform transactions quickly and efficiently.

[0117] When capturing the user's facial features, the facial recognition unit can analyze the user's facial expression and estimate their emotions. For example, if the user is smiling, the facial recognition unit can widen the tolerance range for recognition. Also, if the user is nervous, the facial recognition unit can increase the accuracy of recognition. Furthermore, if the user is impatient, the facial recognition unit can prioritize authentication speed and perform authentication quickly. In this way, the accuracy of facial recognition is adjusted according to the user's emotions.

[0118] The biometric authentication unit can take the user's health condition into consideration when acquiring the user's biometric data. For example, the biometric authentication unit can widen the tolerance range for authentication if the user's fingerprint is dry. Also, the biometric authentication unit can adjust the accuracy of authentication if the user's iris is tired. Furthermore, if the user's vein pattern has changed, the unit can also perform authentication using complementary authentication means. This improves the accuracy of biometric authentication according to the user's health condition.

[0119] The trading terminal can provide optimal transaction options taking into account the user's current location information. For example, the trading terminal can suggest the ATM closest to the user's current location. The trading terminal can also suggest optimal withdrawal options based on the user's current location. Furthermore, the trading terminal can also suggest optimal transaction options taking into account the distance from the user's current location. This allows the user to conduct transactions efficiently.

[0120] The withdrawal unit can estimate the user's emotions and adjust the withdrawal amount based on the estimated user's emotions. For example, if the user is relaxed, the withdrawal unit can withdraw a normal amount. If the user is nervous, the withdrawal unit can set the withdrawal amount to a small amount and wait for the user to calm down. Furthermore, if the user is in a hurry, the withdrawal unit can quickly set the withdrawal amount and quickly withdraw. In this way, the withdrawal amount is adjusted according to the user's emotions.

[0121] The cash cardless system can provide optimal trading options by taking into account device information of the user. For example, if the user is using a smartphone, the trading terminal can provide trading options that fit the screen size. Also, if the user is using a tablet, the trading terminal can provide trading options that are optimized for a large screen. Furthermore, if the user is using a smartwatch, the trading terminal can provide simple, highly visible trading options. In this way, optimal trading options are presented by taking into account device information.

[0122] The cash cardless system further includes a disaster response unit. This disaster response unit performs identity verification and transactions through facial recognition and biometric authentication even during natural disasters or other disasters. The disaster response unit, for example, includes a special protocol for verifying identity through facial recognition and biometric authentication even during disasters. For example, the disaster response unit performs priority processing for quickly performing facial recognition and biometric authentication even during disasters. The disaster response unit also includes special security measures for strengthening data protection even during disasters. For example, the disaster response unit strengthens data encryption to protect data from unauthorized access even during disasters. Furthermore, the disaster response unit includes special procedures for quickly performing transactions even during disasters. For example, the disaster response unit provides simplified procedures for quickly performing transactions even during disasters. This makes it possible to verify identity and perform transactions even during disasters.

[0123] The face authentication unit can estimate the user's emotions and adjust the timing of face authentication based on the estimated user's emotions. For example, if the user is relaxed, the face authentication unit can perform face authentication at a normal timing. If the user is nervous, the face authentication unit can delay the timing of face authentication to wait for the user to calm down. Furthermore, if the user is in a hurry, the face authentication unit can also advance the timing of face authentication to perform authentication quickly. In this way, the timing of face authentication is adjusted according to the user's emotions.

[0124] The biometric authentication unit can estimate the user's emotions and adjust the accuracy of biometric authentication based on the estimated user's emotions. For example, if the user is nervous, the biometric authentication unit can widen the tolerance range for biometric authentication and perform authentication. If the user is relaxed, the biometric authentication unit can perform biometric authentication with normal accuracy. Furthermore, if the user is impatient, the biometric authentication unit can prioritize authentication speed and perform authentication with slightly lower accuracy. In this way, the accuracy of biometric authentication is adjusted according to the user's emotions.

[0125] The trading terminal can analyze the user's social media activity to present relevant trading options. For example, the trading terminal can suggest optimal trading options based on the locations mentioned by the user on social media. The trading terminal can also suggest trading options related to specific events based on the user's social media activity. Furthermore, the trading terminal can analyze the user's social media activity to suggest the most relevant trading options. As a result, relevant trading options are presented by analyzing the social media activity.

[0126] The processing flow of the second embodiment will be briefly explained below.

[0127] Step 1: The facial recognition unit captures the user's facial features with a camera and compares them with pre-registered facial data. For example, when a user stands in front of the camera, the camera automatically captures the user's facial features and compares them with pre-registered facial data. The facial recognition unit can also accurately capture facial features under different lighting conditions. For example, the facial recognition unit can capture facial features in both bright and dark lighting conditions, improving authentication accuracy. Furthermore, the facial recognition unit can capture the user's facial features from multiple angles. For example, the facial recognition unit can instruct the user to move their face left and right in front of the camera and capture facial features from multiple angles. Step 2: The biometric authentication unit verifies the user's identity using biometric data such as fingerprints and irises. For example, when the user places their finger on the fingerprint sensor, the biometric authentication unit acquires fingerprint data and compares it with pre-registered fingerprint data. The biometric authentication unit can also perform iris authentication. For example, when the user brings their eye close to the iris authentication sensor, the biometric authentication unit acquires iris data and compares it with pre-registered iris data. Step 3: The trading terminal checks the user's account information and displays the amount available for withdrawal. For example, when the user inputs the amount they wish to withdraw, the trading terminal checks the account information and displays the amount available for withdrawal. The trading terminal can also refer to the user's past transaction history to present optimal trading options. For example, the trading terminal automatically displays transactions that the user has frequently performed in the past as candidates. Step 4: The withdrawal unit provides cash from the transaction terminal. For example, the withdrawal unit provides cash when the user confirms the withdrawal. The withdrawal unit can also estimate the user's emotions and adjust the timing of the withdrawal based on the estimated user emotions. For example, if the user is nervous, the withdrawal unit delays the timing of the withdrawal and waits for the user to calm down.

[0128] 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.

[0129] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0130] 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.

[0131] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0132] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0146] 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.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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 identification processing unit 290 using these models.

[0159] 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.

[0160] 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.

[0161] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0162] 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.

[0163] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0164] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0165] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification 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 the same process as the identification processing unit 290 using these models.

[0176] 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.

[0177] 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.

[0178] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0179] 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.

[0180] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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).

[0185] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0186] 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."

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] [Explanation of symbols]

[0200] 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 performs face authentication; a biometric authentication unit that performs biometric authentication based on the face data authenticated by the face authentication unit; a transaction terminal that performs a transaction based on biometric data authenticated by the biometric authentication unit; a cash drawer that provides cash after a transaction is confirmed by the transaction terminal. A system characterized by:

2. The face authentication unit The camera captures the user's facial features and compares them with pre-registered facial data. The system of claim 1 .

3. The biometric authentication unit Verify identity using biometric data, including fingerprints and irises The system of claim 1 .

4. The trading terminal Verify the user's account information and display the amount available for withdrawal The system of claim 1 .

5. The drawer portion is providing cash from said transaction terminal The system of claim 1 .

6. The device further includes a data protection unit that protects data acquired by the face authentication unit and the biometric authentication unit. The system of claim 1 .

7. It will also have a disaster response department that will verify identity through facial recognition and biometric authentication and carry out transactions even in the event of a natural disaster or other emergency. The system of claim 1 .

8. The face authentication unit Estimate the user's emotions and adjust the accuracy of facial recognition based on the estimated user emotions. The system of claim 1 .

9. The face authentication unit During facial recognition, the user's facial features are captured from multiple angles, improving authentication accuracy. The system of claim 1 .

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