system

Abstract

The system according to this embodiment aims to allow secure withdrawals of money without the need for a cash card or passbook. [Solution] The system according to the embodiment comprises a registration unit, an authentication unit, and a withdrawal unit. The registration unit registers personal information. The authentication unit performs facial recognition and biometric authentication based on the personal information registered by the registration unit. The withdrawal unit withdraws money based on the information authenticated by the authentication unit.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot 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 an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to withdraw money when a cash card or a passbook is lost.

[0005] The system according to the embodiment aims to safely withdraw money even without a cash card or a passbook.

Means for Solving the Problems

[0006] The system according to the embodiment includes a registration unit, an authentication unit, and a withdrawal unit. The registration unit registers personal information. The authentication unit performs face authentication and biometric authentication based on the personal information registered by the registration unit. The withdrawal unit withdraws money based on the information authenticated by the authentication unit.

Effects of the Invention

[0007] The system according to this embodiment allows for secure withdrawal of money without the need for a cash card or passbook. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The cash cardless system according to an embodiment of the present invention is a system that allows users to withdraw money from their own accounts through facial recognition or biometric authentication even when they forget their wallet or cash card when going out. This cash cardless system ensures security through individual biometric characteristics and achieves strong security for identity verification even without a cash card, by having the user register their "personal information (date of birth)," "facial recognition technology," and "biometric authentication technology (fingerprint)" in advance. For example, when a user withdraws money from a terminal such as an ATM, identity verification is performed by performing facial recognition and biometric authentication, even without a cash card. This system is particularly effective when passbooks and cash cards are lost or destroyed during natural disasters or other emergencies. For example, even during a disaster, by utilizing this cash cardless system, it is possible to have money even in uncertain situations and use it for purchasing essential supplies or for transportation costs to evacuate to a nearby area. The user registers their "personal information (date of birth)," "facial recognition technology," and "biometric authentication technology (fingerprint)" in advance. At this time, the user's facial image and fingerprint data are registered in the system. As a result, the user's biometric characteristics are stored in the system. Next, when a user withdraws money from an ATM or other terminal, facial recognition and biometric authentication are performed. Specifically, the user faces the ATM's camera and places their finger on the fingerprint sensor, and the system matches the user's facial image and fingerprint data. This verifies the user's identity. This system utilizes generative AI to learn "facial recognition," "fingerprints," "iris," "personal information (My Number)," and "account information," and achieves "personal authentication" and "security" by utilizing biometric authentication, which is a unique characteristic of the individual. The generative AI learns the user's biometric characteristics and improves the accuracy of identity verification. This mechanism eliminates the need for users to carry cash cards, allowing them to withdraw money with peace of mind even if they forget their wallet or cash card when going out. Furthermore, in the event of natural disasters or other emergencies, even if passbooks or cash cards are lost or destroyed, the funds can be used to purchase essential supplies or to cover transportation costs for evacuation to nearby areas.This system targets financial institutions such as banks, and could potentially be applied to local governments in the future. For example, if a My Number Card is lost or destroyed due to a natural disaster, earthquake, or fire, it can be handled without the My Number Card until a replacement is issued. This means that the cash cardless system will allow users to withdraw money through facial recognition and biometric authentication without having to carry a cash card.

[0029] The cash cardless system according to this embodiment comprises a registration unit, an authentication unit, and a withdrawal unit. The registration unit registers the user's personal information. Personal information includes, but is not limited to, names, addresses, telephone numbers, facial images, and fingerprint data. The registration unit can, for example, take a picture of the user's face with a camera and store it in the system. The registration unit can also acquire the user's fingerprint data using a fingerprint scanner and store it in the system. For example, the registration unit takes a picture of the user's face with a high-resolution camera and extracts feature points using a facial recognition algorithm. Fingerprint data is acquired using a fingerprint scanner and feature points are extracted using a fingerprint authentication algorithm. The authentication unit performs facial recognition and biometric authentication based on the personal information registered by the registration unit. The authentication unit recognizes the user's facial image using, for example, facial recognition technology using deep learning. The authentication unit can also recognize the user's fingerprint using fingerprint authentication technology. For example, the authentication unit extracts feature points from the facial image and compares them with the registered facial image. Fingerprint authentication extracts feature points from the fingerprint and compares them with the registered fingerprint data. Some or all of the above-described processes in the authentication unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the authentication unit can input the user's facial image into the generating AI and have the generating AI perform facial recognition. The withdrawal unit withdraws money based on the information authenticated by the authentication unit. The withdrawal unit eliminates the need for a cash card by performing facial recognition and biometric authentication at a terminal such as an ATM. For example, the withdrawal unit verifies the user's identity by having the user face the ATM camera and place their finger on the fingerprint sensor, and the system matches the user's facial image and fingerprint data. This verifies the user's identity, and allows them to withdraw money. Some or all of the above-described processes in the withdrawal unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the withdrawal unit can input the user's facial image and fingerprint data into the generating AI and have the generating AI perform identity verification. As a result, the cash cardless system according to the embodiment allows the user to withdraw money through facial recognition and biometric authentication without carrying a cash card.

[0030] The registration unit registers the user's personal information. This personal information includes, but is not limited to, name, address, telephone number, facial image, and fingerprint data. For example, the registration unit can take a picture of the user's face with a camera and save it to the system. The registration unit can also acquire the user's fingerprint data using a fingerprint scanner and save it to the system. Specifically, the registration unit takes a picture of the user's face using a high-resolution camera and extracts feature points using a facial recognition algorithm. This facial recognition algorithm analyzes feature points such as the position and shape of the eyes, nose, and mouth, and the contours of the face, and saves them to a database. Fingerprint data is acquired using a fingerprint scanner, and feature points are extracted using a fingerprint recognition algorithm. The fingerprint recognition algorithm analyzes the fine lines and swirling patterns of the fingerprint and saves these feature points to a database. This allows the registration unit to acquire and save the user's facial image and fingerprint data with high accuracy. Furthermore, the registration unit protects the data using encryption technology to securely manage the user's personal information. For example, facial images and fingerprint data are encrypted using strong encryption algorithms such as AES (Advanced Encryption Standard) to protect them from unauthorized access. This ensures that users' personal information is managed securely and improves the reliability of the system.

[0031] The authentication unit performs facial recognition and biometric authentication based on personal information registered by the registration unit. The authentication unit recognizes the user's face image using, for example, deep learning-based facial recognition technology. Specifically, the authentication unit captures the user's face image with a camera and inputs it into a deep learning model. This deep learning model has been pre-trained on a large amount of facial image data and can perform facial recognition with high accuracy. The model extracts feature points from the user's face image and compares them with registered face images. If the comparison result exceeds a certain threshold, it is determined that the user's facial recognition was successful. The authentication unit can also recognize the user's fingerprint using fingerprint authentication technology. For fingerprint authentication, the user's fingerprint is obtained using a fingerprint scanner and input into a fingerprint authentication algorithm. The algorithm extracts feature points from the fingerprint and compares them with registered fingerprint data. If the comparison result exceeds a certain threshold, it is determined that the user's fingerprint authentication was successful. Some or all of the above-described processes in the authentication unit may be performed using, for example, generative AI, or without using generative AI. For example, the authentication unit inputs the user's facial image into a generating AI, which then performs facial recognition. The generating AI analyzes the user's facial image, extracts feature points, and compares them with registered facial images. This enables the authentication unit to achieve highly accurate facial recognition and biometric authentication, ensuring reliable user identity verification.

[0032] The withdrawal unit withdraws money based on information authenticated by the authentication unit. The withdrawal unit eliminates the need for a cash card by performing facial recognition and biometric authentication at a terminal such as an ATM. Specifically, the withdrawal unit works by having the user face the ATM's camera and place their finger on the fingerprint sensor, at which point the system compares the user's facial image and fingerprint data. Facial recognition involves the ATM's camera capturing the user's facial image and comparing it with the facial image registered in the authentication unit. Fingerprint authentication involves the ATM's fingerprint sensor acquiring the user's fingerprint and comparing it with the fingerprint data registered in the authentication unit. This verifies the user's identity, allowing them to withdraw money. Some or all of the above-described processes in the withdrawal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the withdrawal unit can input the user's facial image and fingerprint data into a generative AI and have the generative AI perform identity verification. The generative AI analyzes the user's facial image and fingerprint data and compares it with the registered data. This enables highly accurate identity verification at the withdrawal unit, allowing users to withdraw money without carrying their cash card. Furthermore, the withdrawal unit can provide voice guidance and a touchscreen interface to enhance user convenience. This allows users to operate intuitively and withdraw money smoothly.

[0033] The authentication unit can verify the user's identity using facial recognition technology and biometric authentication technology. For example, the authentication unit can recognize the user's facial image using facial recognition technology that utilizes deep learning. For example, the authentication unit can extract feature points from the facial image and compare them with registered facial images. The authentication unit can also recognize the user's fingerprint using fingerprint authentication technology. For example, the authentication unit can extract feature points from the fingerprint and compare them with registered fingerprint data. This improves the accuracy of user identity verification by using facial recognition technology and biometric authentication technology. Some or all of the above-described processes in the authentication unit may be performed using, for example, a generative AI, or without a generative AI. For example, the authentication unit can input the user's facial image into a generative AI and have the generative AI perform facial recognition.

[0034] The registration unit can register the user's facial image and fingerprint data. For example, the registration unit can capture the user's facial image with a camera and save it to the system. For example, the registration unit can capture the user's facial image with a high-resolution camera and extract feature points using a facial recognition algorithm. The registration unit can also acquire the user's fingerprint data using a fingerprint scanner and save it to the system. For example, the registration unit can acquire fingerprint data using a fingerprint scanner and extract feature points using a fingerprint recognition algorithm. By registering the user's facial image and fingerprint data, facial recognition and biometric authentication become possible. Some or all of the above processing in the registration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the registration unit can input the user's facial image and fingerprint data into a generative AI and have the generative AI perform the data registration.

[0035] The authentication unit can improve the accuracy of identity verification by learning the user's biometric characteristics using generative AI. For example, the authentication unit can improve the accuracy of identity verification by learning the user's facial image and fingerprint data using generative AI. For example, the authentication unit can improve the accuracy of facial recognition by learning the feature points of the user's facial image using generative AI. The authentication unit can also improve the accuracy of fingerprint recognition by learning the feature points of the user's fingerprint data using generative AI. In this way, the accuracy of identity verification is improved by using generative AI. Some or all of the above processing in the authentication unit is performed using generative AI. For example, the authentication unit can input the user's facial image and fingerprint data into the generative AI and have the generative AI perform the improvement of identity verification accuracy.

[0036] The withdrawal unit can perform facial recognition and biometric authentication when a user withdraws money from an ATM or other terminal. For example, when a user faces the ATM's camera and places their finger on the fingerprint sensor, the system matches the user's facial image and fingerprint data. This verifies the user's identity and allows them to withdraw money. The withdrawal unit can, for example, capture a facial image of the user using the ATM's camera and match it using a facial recognition algorithm. The withdrawal unit can also acquire the user's fingerprint data using a fingerprint sensor and match it using a fingerprint recognition algorithm. This eliminates the need for a cash card when the user performs facial recognition and biometric authentication at an ATM or other terminal. Some or all of the above processing in the withdrawal unit is performed using a generative AI. For example, the withdrawal unit can input the user's facial image and fingerprint data into the generative AI and have the generative AI perform identity verification.

[0037] The withdrawal system allows users to withdraw money without a cash card, even during disasters. For example, even during disasters, the withdrawal system can eliminate the need for a cash card by performing facial recognition and biometric authentication at ATMs and other terminals. For example, even during disasters, the withdrawal system can capture the user's facial image using the ATM's camera and verify it using a facial recognition algorithm. Furthermore, even during disasters, the withdrawal system can acquire the user's fingerprint data using a fingerprint sensor and verify it using a fingerprint authentication algorithm. This allows users to withdraw money without a cash card even during disasters, ensuring safe use. Some or all of the above processes in the withdrawal system are performed using a generation AI. For example, even during disasters, the withdrawal system can input the user's facial image and fingerprint data into the generation AI and have the generation AI perform identity verification.

[0038] The registration unit can analyze a user's past registration history and select the optimal registration method during registration. For example, the registration unit can suggest similar procedures based on information the user has previously registered. For instance, the registration unit may prioritize suggesting registration methods the user has used in the past (online, in-person, etc.). The registration unit can also suggest the optimal registration method for a specific time period based on the user's past registration history. In this way, the optimal registration method can be selected by analyzing the user's past registration history. Some or all of the above processing in the registration unit is performed using a generative AI. For example, the registration unit can input the user's past registration history data into the generative AI and have the generative AI select the optimal registration method.

[0039] The registration unit can filter registration information based on the user's current living situation and areas of interest during registration. For example, the registration unit prioritizes registering relevant information based on the user's current occupation and living situation. For example, the registration unit filters and registers relevant information based on the user's areas of interest. The registration unit can also register only the necessary information based on the user's current living situation. This allows only the necessary information to be registered by filtering registration information based on the user's current living situation and areas of interest. Some or all of the above processing in the registration unit is performed using a generation AI. For example, the registration unit can input data on the user's living situation and areas of interest into the generation AI and have the generation AI perform the information filtering.

[0040] The registration unit can prioritize registering highly relevant information by considering the user's geographical location during registration. For example, the registration unit can prioritize registering information about nearby services and facilities based on the user's current location. For example, the registration unit can prioritize registering region-specific information based on the user's geographical location. The registration unit can also prioritize registering highly relevant information by considering the user's travel history. In this way, highly relevant information can be prioritized by considering the user's geographical location. Some or all of the above processing in the registration unit is performed using a generation AI. For example, the registration unit can input the user's geographical location data into the generation AI and have the generation AI prioritize the information.

[0041] The registration unit can analyze a user's social media activity and register relevant information during registration. For example, the registration unit can analyze the content of a user's social media posts and register relevant information. For example, the registration unit can refer to the activities of a user's social media followers and friends and register relevant information. The registration unit can also analyze a user's social media interests and register relevant information. In this way, relevant information can be registered by analyzing the user's social media activity. Some or all of the above processing in the registration unit is performed using a generative AI. For example, the registration unit can input the user's social media activity data into the generative AI and have the generative AI perform the information analysis.

[0042] The authentication unit can optimize the authentication algorithm during authentication by taking into account changes in the user's biometric characteristics. For example, the authentication unit adjusts the authentication algorithm by taking into account changes in the user's face (hairstyle, presence or absence of glasses, etc.). For example, the authentication unit optimizes the authentication algorithm by taking into account changes in the user's fingerprints (scars, dryness, etc.). Furthermore, the authentication unit can learn changes in the user's biometric characteristics in real time and update the authentication algorithm. This improves the accuracy of authentication by taking into account changes in the user's biometric characteristics. Some or all of the above processing in the authentication unit is performed using generative AI. For example, the authentication unit can input the user's biometric characteristic data into the generative AI and have the generative AI perform the optimization of the authentication algorithm.

[0043] The authentication unit can improve authentication accuracy by referring to the user's past authentication history during authentication. For example, the authentication unit can improve authentication accuracy based on the user's past authentication history. For example, the authentication unit can learn specific patterns from the user's past authentication history to improve authentication accuracy. The authentication unit can also analyze the user's past authentication history and optimize the authentication algorithm. This improves authentication accuracy by referring to the user's past authentication history. Some or all of the above processes in the authentication unit are performed using generative AI. For example, the authentication unit can input the user's past authentication history data into the generative AI and have the generative AI perform the authentication accuracy improvement.

[0044] The authentication unit can improve authentication accuracy by considering the user's geographical location information during authentication. For example, the authentication unit can improve authentication accuracy based on the user's current location. For example, the authentication unit can propose a region-specific authentication method based on the user's geographical location information. The authentication unit can also improve authentication accuracy by considering the user's travel history. In this way, authentication accuracy is improved by considering the user's geographical location information. Some or all of the above processing in the authentication unit is performed using a generation AI. For example, the authentication unit can input the user's geographical location information data into the generation AI and have the generation AI perform the authentication accuracy improvement.

[0045] The authentication unit can improve the accuracy of authentication by referring to the user's relevant literature during authentication. For example, the authentication unit can improve the accuracy of authentication by referring to the user's relevant literature. For example, the authentication unit can learn specific patterns from the user's relevant literature to improve the accuracy of authentication. The authentication unit can also analyze the user's relevant literature and optimize the authentication algorithm. As a result, the accuracy of authentication is improved by referring to the user's relevant literature. Some or all of the above processes in the authentication unit are performed using a generative AI. For example, the authentication unit can input the user's relevant literature data into the generative AI and have the generative AI perform the authentication accuracy improvement.

[0046] The withdrawal unit can analyze the user's past withdrawal history to select the optimal withdrawal method at the time of withdrawal. For example, the withdrawal unit can propose the optimal withdrawal method based on the user's past withdrawal history. For example, the withdrawal unit can learn specific patterns from the user's past withdrawal history and propose the optimal withdrawal method. The withdrawal unit can also analyze the user's past withdrawal history and optimize the withdrawal method. In this way, the optimal withdrawal method can be selected by analyzing the user's past withdrawal history. Some or all of the above processing in the withdrawal unit is performed using a generative AI. For example, the withdrawal unit can input the user's past withdrawal history data into the generative AI and have the generative AI perform the selection of the optimal withdrawal method.

[0047] The withdrawal unit can customize the withdrawal method based on the user's current living situation when a withdrawal occurs. For example, the withdrawal unit can suggest the optimal withdrawal method based on the user's current living situation. For example, the withdrawal unit can customize the withdrawal method considering the user's current living situation. The withdrawal unit can also provide the necessary withdrawal method based on the user's current living situation. This improves user convenience by customizing the withdrawal method based on the user's current living situation. Some or all of the above processing in the withdrawal unit is performed using a generation AI. For example, the withdrawal unit can input user living situation data into the generation AI and have the generation AI perform the customization of the withdrawal method.

[0048] The withdrawal unit can select the optimal withdrawal method by considering the user's geographical location information during the withdrawal process. For example, the withdrawal unit can propose the optimal withdrawal method based on the user's current location. For example, the withdrawal unit can propose a region-specific withdrawal method based on the user's geographical location information. The withdrawal unit can also propose the optimal withdrawal method by considering the user's travel history. In this way, the optimal withdrawal method can be selected by considering the user's geographical location information. Some or all of the above processing in the withdrawal unit is performed using a generation AI. For example, the withdrawal unit can input the user's geographical location information data into the generation AI and have the generation AI perform the selection of the optimal withdrawal method.

[0049] The withdrawal unit can analyze the user's social media activity and propose a withdrawal method during the withdrawal process. For example, the withdrawal unit can analyze the content of the user's social media posts and propose the optimal withdrawal method. For example, the withdrawal unit can propose the optimal withdrawal method by referring to the activities of the user's social media followers and friends. The withdrawal unit can also analyze the user's social media interests and propose the optimal withdrawal method. In this way, the optimal withdrawal method can be proposed by analyzing the user's social media activity. Some or all of the above processing in the withdrawal unit is performed using a generative AI. For example, the withdrawal unit can input the user's social media activity data into the generative AI and have the generative AI execute the proposal of a withdrawal method.

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

[0051] A cashless system can analyze a user's past withdrawal history and suggest the optimal withdrawal method. For example, if a user has frequently used a particular ATM in the past, that ATM will be prioritized for suggestion. Furthermore, if a user has made withdrawals during specific time periods in the past, the system can suggest the most suitable withdrawal method for those times. Additionally, if a user has withdrawn a specific amount in the past, the system can prioritize suggesting that amount. In this way, by analyzing a user's past withdrawal history, the system can suggest the optimal withdrawal method. The suggestion of withdrawal methods is performed using generative AI. For example, withdrawal history data can be input into the generative AI, and the AI ​​can then generate suggestions for the optimal withdrawal method.

[0052] A cashless system can customize withdrawal methods based on the user's current living situation. For example, it can suggest the optimal withdrawal method based on the user's current living situation. For example, it can customize the withdrawal method considering the user's current living situation. It can also provide the user with the necessary withdrawal method based on their current living situation. This improves user convenience by customizing withdrawal methods based on the user's current living situation. Customization of withdrawal methods is performed using generative AI. For example, living situation data can be input into the generative AI, and the generative AI can be made to perform the customization of withdrawal methods.

[0053] A cashless system can select the optimal withdrawal method by considering the user's geographical location. For example, it can suggest the optimal withdrawal method based on the user's current location. For example, it can suggest region-specific withdrawal methods based on the user's geographical location. It can also suggest the optimal withdrawal method by considering the user's travel history. In this way, the optimal withdrawal method can be selected by considering the user's geographical location. The selection of the withdrawal method is performed using a generative AI. For example, geographical location data can be input into the generative AI, and the generative AI can be made to select the optimal withdrawal method.

[0054] A cashless system can analyze a user's social media activity and suggest withdrawal methods. For example, it can analyze the content of a user's social media posts and suggest the most suitable withdrawal method. For example, it can refer to the activities of a user's social media followers and friends to suggest the most suitable withdrawal method. It can also analyze a user's social media interests and suggest the most suitable withdrawal method. In this way, by analyzing a user's social media activity, the system can suggest the most suitable withdrawal method. The suggestion of withdrawal methods is performed using generative AI. For example, social media activity data can be input into the generative AI, and the generative AI can then generate suggestions for withdrawal methods.

[0055] A cashless system can analyze a user's past withdrawal history and suggest the optimal withdrawal method. For example, if a user has frequently used a particular ATM in the past, that ATM will be prioritized for suggestion. Furthermore, if a user has made withdrawals during specific time periods in the past, the system can suggest the most suitable withdrawal method for those times. Additionally, if a user has withdrawn a specific amount in the past, the system can prioritize suggesting that amount. In this way, by analyzing a user's past withdrawal history, the system can suggest the optimal withdrawal method. The suggestion of withdrawal methods is performed using generative AI. For example, withdrawal history data can be input into the generative AI, and the AI ​​can then generate suggestions for the optimal withdrawal method.

[0056] A cashless system can select the optimal withdrawal method by considering the user's geographical location. For example, it can suggest the optimal withdrawal method based on the user's current location. For example, it can suggest region-specific withdrawal methods based on the user's geographical location. It can also suggest the optimal withdrawal method by considering the user's travel history. In this way, the optimal withdrawal method can be selected by considering the user's geographical location. The selection of the withdrawal method is performed using a generative AI. For example, geographical location data can be input into the generative AI, and the generative AI can be made to select the optimal withdrawal method.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The registration unit registers the user's personal information. This personal information includes name, address, telephone number, facial image, and fingerprint data. The registration unit takes a picture of the user's face with a camera and saves it to the system. It also acquires the user's fingerprint data using a fingerprint scanner and saves it to the system. Feature points are extracted from the facial image and fingerprint data using facial recognition algorithms and fingerprint recognition algorithms, respectively. Step 2: The authentication unit performs facial recognition and biometric authentication based on the personal information registered by the registration unit. The authentication unit recognizes the user's facial image and fingerprints using deep learning-based facial recognition technology and fingerprint authentication technology. It extracts feature points from the facial image and fingerprints and compares them with the registered data. The processing in the authentication unit can also be performed using generative AI. Step 3: The withdrawal unit withdraws money based on the information authenticated by the authentication unit. The withdrawal unit eliminates the need for a cash card by performing facial recognition and biometric authentication at the ATM or other terminal. The user faces the ATM camera and places their finger on the fingerprint sensor, and the system matches the user's facial image and fingerprint data to verify their identity. The processing in the withdrawal unit can also be performed using generative AI.

[0059] (Example of form 2) The cash cardless system according to an embodiment of the present invention is a system that allows users to withdraw money from their own accounts through facial recognition or biometric authentication even when they forget their wallet or cash card when going out. This cash cardless system ensures security through individual biometric characteristics and achieves strong security for identity verification even without a cash card, by having the user register their "personal information (date of birth)," "facial recognition technology," and "biometric authentication technology (fingerprint)" in advance. For example, when a user withdraws money from a terminal such as an ATM, identity verification is performed by performing facial recognition and biometric authentication, even without a cash card. This system is particularly effective when passbooks and cash cards are lost or destroyed during natural disasters or other emergencies. For example, even during a disaster, by utilizing this cash cardless system, it is possible to have money even in uncertain situations and use it for purchasing essential supplies or for transportation costs to evacuate to a nearby area. The user registers their "personal information (date of birth)," "facial recognition technology," and "biometric authentication technology (fingerprint)" in advance. At this time, the user's facial image and fingerprint data are registered in the system. As a result, the user's biometric characteristics are stored in the system. Next, when a user withdraws money from an ATM or other terminal, facial recognition and biometric authentication are performed. Specifically, the user faces the ATM's camera and places their finger on the fingerprint sensor, and the system matches the user's facial image and fingerprint data. This verifies the user's identity. This system utilizes generative AI to learn "facial recognition," "fingerprints," "iris," "personal information (My Number)," and "account information," and achieves "personal authentication" and "security" by utilizing biometric authentication, which is a unique characteristic of the individual. The generative AI learns the user's biometric characteristics and improves the accuracy of identity verification. This mechanism eliminates the need for users to carry cash cards, allowing them to withdraw money with peace of mind even if they forget their wallet or cash card when going out. Furthermore, in the event of natural disasters or other emergencies, even if passbooks or cash cards are lost or destroyed, the funds can be used to purchase essential supplies or to cover transportation costs for evacuation to nearby areas.This system targets financial institutions such as banks, and could potentially be applied to local governments in the future. For example, if a My Number Card is lost or destroyed due to a natural disaster, earthquake, or fire, it can be handled without the My Number Card until a replacement is issued. This means that the cash cardless system will allow users to withdraw money through facial recognition and biometric authentication without having to carry a cash card.

[0060] The cash cardless system according to this embodiment comprises a registration unit, an authentication unit, and a withdrawal unit. The registration unit registers the user's personal information. Personal information includes, but is not limited to, names, addresses, telephone numbers, facial images, and fingerprint data. The registration unit can, for example, take a picture of the user's face with a camera and store it in the system. The registration unit can also acquire the user's fingerprint data using a fingerprint scanner and store it in the system. For example, the registration unit takes a picture of the user's face with a high-resolution camera and extracts feature points using a facial recognition algorithm. Fingerprint data is acquired using a fingerprint scanner and feature points are extracted using a fingerprint authentication algorithm. The authentication unit performs facial recognition and biometric authentication based on the personal information registered by the registration unit. The authentication unit recognizes the user's facial image using, for example, facial recognition technology using deep learning. The authentication unit can also recognize the user's fingerprint using fingerprint authentication technology. For example, the authentication unit extracts feature points from the facial image and compares them with the registered facial image. Fingerprint authentication extracts feature points from the fingerprint and compares them with the registered fingerprint data. Some or all of the above-described processes in the authentication unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the authentication unit can input the user's facial image into the generating AI and have the generating AI perform facial recognition. The withdrawal unit withdraws money based on the information authenticated by the authentication unit. The withdrawal unit eliminates the need for a cash card by performing facial recognition and biometric authentication at a terminal such as an ATM. For example, the withdrawal unit verifies the user's identity by having the user face the ATM camera and place their finger on the fingerprint sensor, and the system matches the user's facial image and fingerprint data. This verifies the user's identity, and allows them to withdraw money. Some or all of the above-described processes in the withdrawal unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the withdrawal unit can input the user's facial image and fingerprint data into the generating AI and have the generating AI perform identity verification. As a result, the cash cardless system according to the embodiment allows the user to withdraw money through facial recognition and biometric authentication without carrying a cash card.

[0061] The registration unit registers the user's personal information. This personal information includes, but is not limited to, name, address, telephone number, facial image, and fingerprint data. For example, the registration unit can take a picture of the user's face with a camera and save it to the system. The registration unit can also acquire the user's fingerprint data using a fingerprint scanner and save it to the system. Specifically, the registration unit takes a picture of the user's face using a high-resolution camera and extracts feature points using a facial recognition algorithm. This facial recognition algorithm analyzes feature points such as the position and shape of the eyes, nose, and mouth, and the contours of the face, and saves them to a database. Fingerprint data is acquired using a fingerprint scanner, and feature points are extracted using a fingerprint recognition algorithm. The fingerprint recognition algorithm analyzes the fine lines and swirling patterns of the fingerprint and saves these feature points to a database. This allows the registration unit to acquire and save the user's facial image and fingerprint data with high accuracy. Furthermore, the registration unit protects the data using encryption technology to securely manage the user's personal information. For example, facial images and fingerprint data are encrypted using strong encryption algorithms such as AES (Advanced Encryption Standard) to protect them from unauthorized access. This ensures that users' personal information is managed securely and improves the reliability of the system.

[0062] The authentication unit performs facial recognition and biometric authentication based on personal information registered by the registration unit. The authentication unit recognizes the user's face image using, for example, deep learning-based facial recognition technology. Specifically, the authentication unit captures the user's face image with a camera and inputs it into a deep learning model. This deep learning model has been pre-trained on a large amount of facial image data and can perform facial recognition with high accuracy. The model extracts feature points from the user's face image and compares them with registered face images. If the comparison result exceeds a certain threshold, it is determined that the user's facial recognition was successful. The authentication unit can also recognize the user's fingerprint using fingerprint authentication technology. For fingerprint authentication, the user's fingerprint is obtained using a fingerprint scanner and input into a fingerprint authentication algorithm. The algorithm extracts feature points from the fingerprint and compares them with registered fingerprint data. If the comparison result exceeds a certain threshold, it is determined that the user's fingerprint authentication was successful. Some or all of the above-described processes in the authentication unit may be performed using, for example, generative AI, or without using generative AI. For example, the authentication unit inputs the user's facial image into a generating AI, which then performs facial recognition. The generating AI analyzes the user's facial image, extracts feature points, and compares them with registered facial images. This enables the authentication unit to achieve highly accurate facial recognition and biometric authentication, ensuring reliable user identity verification.

[0063] The withdrawal unit withdraws money based on information authenticated by the authentication unit. The withdrawal unit eliminates the need for a cash card by performing facial recognition and biometric authentication at a terminal such as an ATM. Specifically, the withdrawal unit works by having the user face the ATM's camera and place their finger on the fingerprint sensor, at which point the system compares the user's facial image and fingerprint data. Facial recognition involves the ATM's camera capturing the user's facial image and comparing it with the facial image registered in the authentication unit. Fingerprint authentication involves the ATM's fingerprint sensor acquiring the user's fingerprint and comparing it with the fingerprint data registered in the authentication unit. This verifies the user's identity, allowing them to withdraw money. Some or all of the above-described processes in the withdrawal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the withdrawal unit can input the user's facial image and fingerprint data into a generative AI and have the generative AI perform identity verification. The generative AI analyzes the user's facial image and fingerprint data and compares it with the registered data. This enables highly accurate identity verification at the withdrawal unit, allowing users to withdraw money without carrying their cash card. Furthermore, the withdrawal unit can provide voice guidance and a touchscreen interface to enhance user convenience. This allows users to operate intuitively and withdraw money smoothly.

[0064] The authentication unit can verify the user's identity using facial recognition technology and biometric authentication technology. For example, the authentication unit can recognize the user's facial image using facial recognition technology that utilizes deep learning. For example, the authentication unit can extract feature points from the facial image and compare them with registered facial images. The authentication unit can also recognize the user's fingerprint using fingerprint authentication technology. For example, the authentication unit can extract feature points from the fingerprint and compare them with registered fingerprint data. This improves the accuracy of user identity verification by using facial recognition technology and biometric authentication technology. Some or all of the above-described processes in the authentication unit may be performed using, for example, a generative AI, or without a generative AI. For example, the authentication unit can input the user's facial image into a generative AI and have the generative AI perform facial recognition.

[0065] The registration unit can register the user's facial image and fingerprint data. For example, the registration unit can capture the user's facial image with a camera and save it to the system. For example, the registration unit can capture the user's facial image with a high-resolution camera and extract feature points using a facial recognition algorithm. The registration unit can also acquire the user's fingerprint data using a fingerprint scanner and save it to the system. For example, the registration unit can acquire fingerprint data using a fingerprint scanner and extract feature points using a fingerprint recognition algorithm. By registering the user's facial image and fingerprint data, facial recognition and biometric authentication become possible. Some or all of the above processing in the registration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the registration unit can input the user's facial image and fingerprint data into a generative AI and have the generative AI perform the data registration.

[0066] The authentication unit can improve the accuracy of identity verification by learning the user's biometric characteristics using generative AI. For example, the authentication unit can improve the accuracy of identity verification by learning the user's facial image and fingerprint data using generative AI. For example, the authentication unit can improve the accuracy of facial recognition by learning the feature points of the user's facial image using generative AI. The authentication unit can also improve the accuracy of fingerprint recognition by learning the feature points of the user's fingerprint data using generative AI. In this way, the accuracy of identity verification is improved by using generative AI. Some or all of the above processing in the authentication unit is performed using generative AI. For example, the authentication unit can input the user's facial image and fingerprint data into the generative AI and have the generative AI perform the improvement of identity verification accuracy.

[0067] The withdrawal unit can perform facial recognition and biometric authentication when a user withdraws money from an ATM or other terminal. For example, when a user faces the ATM's camera and places their finger on the fingerprint sensor, the system matches the user's facial image and fingerprint data. This verifies the user's identity and allows them to withdraw money. The withdrawal unit can, for example, capture a facial image of the user using the ATM's camera and match it using a facial recognition algorithm. The withdrawal unit can also acquire the user's fingerprint data using a fingerprint sensor and match it using a fingerprint recognition algorithm. This eliminates the need for a cash card when the user performs facial recognition and biometric authentication at an ATM or other terminal. Some or all of the above processing in the withdrawal unit is performed using a generative AI. For example, the withdrawal unit can input the user's facial image and fingerprint data into the generative AI and have the generative AI perform identity verification.

[0068] The withdrawal system allows users to withdraw money without a cash card, even during disasters. For example, even during disasters, the withdrawal system can eliminate the need for a cash card by performing facial recognition and biometric authentication at ATMs and other terminals. For example, even during disasters, the withdrawal system can capture the user's facial image using the ATM's camera and verify it using a facial recognition algorithm. Furthermore, even during disasters, the withdrawal system can acquire the user's fingerprint data using a fingerprint sensor and verify it using a fingerprint authentication algorithm. This allows users to withdraw money without a cash card even during disasters, ensuring safe use. Some or all of the above processes in the withdrawal system are performed using a generation AI. For example, even during disasters, the withdrawal system can input the user's facial image and fingerprint data into the generation AI and have the generation AI perform identity verification.

[0069] The registration unit can estimate the user's emotions and adjust the registration process based on those emotions. For example, if the user is nervous, the registration unit will provide guidance in a calm voice and carefully explain each step of the process. If the user is in a hurry, the registration unit will simplify the steps of the process to allow for quick registration completion. If the user is relaxed, the registration unit will provide guidance with detailed explanations to ensure the user can proceed with the process with confidence. By adjusting the registration process guidance according to the user's emotions, user convenience is improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the registration unit are performed using generative AI. For example, the registration unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0070] The registration unit can analyze a user's past registration history and select the optimal registration method during registration. For example, the registration unit can suggest similar procedures based on information the user has previously registered. For instance, the registration unit may prioritize suggesting registration methods the user has used in the past (online, in-person, etc.). The registration unit can also suggest the optimal registration method for a specific time period based on the user's past registration history. In this way, the optimal registration method can be selected by analyzing the user's past registration history. Some or all of the above processing in the registration unit is performed using a generative AI. For example, the registration unit can input the user's past registration history data into the generative AI and have the generative AI select the optimal registration method.

[0071] The registration unit can filter registration information based on the user's current living situation and areas of interest during registration. For example, the registration unit prioritizes registering relevant information based on the user's current occupation and living situation. For example, the registration unit filters and registers relevant information based on the user's areas of interest. The registration unit can also register only the necessary information based on the user's current living situation. This allows only the necessary information to be registered by filtering registration information based on the user's current living situation and areas of interest. Some or all of the above processing in the registration unit is performed using a generation AI. For example, the registration unit can input data on the user's living situation and areas of interest into the generation AI and have the generation AI perform the information filtering.

[0072] The registration unit can estimate the user's emotions and determine the priority of information to register based on the estimated emotions. For example, if the user is nervous, the registration unit may prioritize registering important information, allowing for the addition of detailed information later. For example, if the user is relaxed, the registration unit may prioritize registering detailed information to ensure a smooth process. Furthermore, if the user is in a hurry, the registration unit may prioritize registering minimal information to complete the process quickly. This ensures a smooth registration process by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the registration unit are performed using generative AI. For example, the registration unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0073] The registration unit can prioritize registering highly relevant information by considering the user's geographical location during registration. For example, the registration unit can prioritize registering information about nearby services and facilities based on the user's current location. For example, the registration unit can prioritize registering region-specific information based on the user's geographical location. The registration unit can also prioritize registering highly relevant information by considering the user's travel history. In this way, highly relevant information can be prioritized by considering the user's geographical location. Some or all of the above processing in the registration unit is performed using a generation AI. For example, the registration unit can input the user's geographical location data into the generation AI and have the generation AI prioritize the information.

[0074] The registration unit can analyze a user's social media activity and register relevant information during registration. For example, the registration unit can analyze the content of a user's social media posts and register relevant information. For example, the registration unit can refer to the activities of a user's social media followers and friends and register relevant information. The registration unit can also analyze a user's social media interests and register relevant information. In this way, relevant information can be registered by analyzing the user's social media activity. Some or all of the above processing in the registration unit is performed using a generative AI. For example, the registration unit can input the user's social media activity data into the generative AI and have the generative AI perform the information analysis.

[0075] The authentication unit can estimate the user's emotions and adjust the authentication process based on those emotions. For example, if the user is nervous, the authentication unit will provide authentication instructions in a calm voice. For example, if the user is relaxed, the authentication unit will provide authentication instructions in a cheerful voice. The authentication unit can also provide quick and concise authentication instructions if the user is in a hurry. By adjusting the authentication process according to the user's emotions, user convenience is improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the authentication unit are performed using generative AI. For example, the authentication unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0076] The authentication unit can optimize the authentication algorithm during authentication by taking into account changes in the user's biometric characteristics. For example, the authentication unit adjusts the authentication algorithm by taking into account changes in the user's face (hairstyle, presence or absence of glasses, etc.). For example, the authentication unit optimizes the authentication algorithm by taking into account changes in the user's fingerprints (scars, dryness, etc.). Furthermore, the authentication unit can learn changes in the user's biometric characteristics in real time and update the authentication algorithm. This improves the accuracy of authentication by taking into account changes in the user's biometric characteristics. Some or all of the above processing in the authentication unit is performed using generative AI. For example, the authentication unit can input the user's biometric characteristic data into the generative AI and have the generative AI perform the optimization of the authentication algorithm.

[0077] The authentication unit can improve authentication accuracy by referring to the user's past authentication history during authentication. For example, the authentication unit can improve authentication accuracy based on the user's past authentication history. For example, the authentication unit can learn specific patterns from the user's past authentication history to improve authentication accuracy. The authentication unit can also analyze the user's past authentication history and optimize the authentication algorithm. This improves authentication accuracy by referring to the user's past authentication history. Some or all of the above processes in the authentication unit are performed using generative AI. For example, the authentication unit can input the user's past authentication history data into the generative AI and have the generative AI perform the authentication accuracy improvement.

[0078] The authentication unit can estimate the user's emotions and determine authentication priorities based on those emotions. For example, if the user is nervous, the authentication unit may prioritize important authentication steps, allowing for the addition of more detailed authentication later. For example, if the user is relaxed, the authentication unit may prioritize detailed authentication to ensure a smooth process. Furthermore, if the user is in a hurry, the authentication unit may prioritize minimal authentication to complete the process quickly. This ensures a smooth authentication process by prioritizing authentication according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the authentication unit are performed using generative AI. For example, the authentication unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0079] The authentication unit can improve authentication accuracy by considering the user's geographical location information during authentication. For example, the authentication unit can improve authentication accuracy based on the user's current location. For example, the authentication unit can propose a region-specific authentication method based on the user's geographical location information. The authentication unit can also improve authentication accuracy by considering the user's travel history. In this way, authentication accuracy is improved by considering the user's geographical location information. Some or all of the above processing in the authentication unit is performed using a generation AI. For example, the authentication unit can input the user's geographical location information data into the generation AI and have the generation AI perform the authentication accuracy improvement.

[0080] The authentication unit can improve the accuracy of authentication by referring to the user's relevant literature during authentication. For example, the authentication unit can improve the accuracy of authentication by referring to the user's relevant literature. For example, the authentication unit can learn specific patterns from the user's relevant literature to improve the accuracy of authentication. The authentication unit can also analyze the user's relevant literature and optimize the authentication algorithm. As a result, the accuracy of authentication is improved by referring to the user's relevant literature. Some or all of the above processes in the authentication unit are performed using a generative AI. For example, the authentication unit can input the user's relevant literature data into the generative AI and have the generative AI perform the authentication accuracy improvement.

[0081] The drawer unit can estimate the user's emotions and adjust the drawer method based on the estimated emotions. For example, if the user is nervous, the drawer unit will guide them through the drawer in a calm voice. For example, if the user is relaxed, the drawer unit will guide them through the drawer in a cheerful voice. The drawer unit can also provide quick and concise guidance if the user is in a hurry. By adjusting the drawer method according to the user's emotions, user convenience is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the drawer unit is performed using generative AI. For example, the drawer unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0082] The withdrawal unit can analyze the user's past withdrawal history to select the optimal withdrawal method at the time of withdrawal. For example, the withdrawal unit can propose the optimal withdrawal method based on the user's past withdrawal history. For example, the withdrawal unit can learn specific patterns from the user's past withdrawal history and propose the optimal withdrawal method. The withdrawal unit can also analyze the user's past withdrawal history and optimize the withdrawal method. In this way, the optimal withdrawal method can be selected by analyzing the user's past withdrawal history. Some or all of the above processing in the withdrawal unit is performed using a generative AI. For example, the withdrawal unit can input the user's past withdrawal history data into the generative AI and have the generative AI perform the selection of the optimal withdrawal method.

[0083] The withdrawal unit can customize the withdrawal method based on the user's current living situation when a withdrawal occurs. For example, the withdrawal unit can suggest the optimal withdrawal method based on the user's current living situation. For example, the withdrawal unit can customize the withdrawal method considering the user's current living situation. The withdrawal unit can also provide the necessary withdrawal method based on the user's current living situation. This improves user convenience by customizing the withdrawal method based on the user's current living situation. Some or all of the above processing in the withdrawal unit is performed using a generation AI. For example, the withdrawal unit can input user living situation data into the generation AI and have the generation AI perform the customization of the withdrawal method.

[0084] The retrieval unit can estimate the user's emotions and determine the priority of retrievals based on the estimated emotions. For example, if the user is nervous, the retrieval unit may prioritize important retrievals, allowing for the addition of more detailed retrievals later. For example, if the user is relaxed, the retrieval unit may prioritize detailed retrievals to ensure a smooth process. Also, if the user is in a hurry, the retrieval unit may prioritize minimal retrievals to complete the process quickly. In this way, the retrieval process proceeds smoothly by determining the priority of retrievals according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the retrieval unit is performed using generative AI. For example, the retrieval unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0085] The withdrawal unit can select the optimal withdrawal method by considering the user's geographical location information during the withdrawal process. For example, the withdrawal unit can propose the optimal withdrawal method based on the user's current location. For example, the withdrawal unit can propose a region-specific withdrawal method based on the user's geographical location information. The withdrawal unit can also propose the optimal withdrawal method by considering the user's travel history. In this way, the optimal withdrawal method can be selected by considering the user's geographical location information. Some or all of the above processing in the withdrawal unit is performed using a generation AI. For example, the withdrawal unit can input the user's geographical location information data into the generation AI and have the generation AI perform the selection of the optimal withdrawal method.

[0086] The withdrawal unit can analyze the user's social media activity and propose a withdrawal method during the withdrawal process. For example, the withdrawal unit can analyze the content of the user's social media posts and propose the optimal withdrawal method. For example, the withdrawal unit can propose the optimal withdrawal method by referring to the activities of the user's social media followers and friends. The withdrawal unit can also analyze the user's social media interests and propose the optimal withdrawal method. In this way, the optimal withdrawal method can be proposed by analyzing the user's social media activity. Some or all of the above processing in the withdrawal unit is performed using a generative AI. For example, the withdrawal unit can input the user's social media activity data into the generative AI and have the generative AI execute the proposal of a withdrawal method.

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

[0088] A cashless system can estimate the user's emotions and customize the ATM's operation screen based on those emotions. For example, if the user is nervous, the screen's color scheme can be changed to calming colors and the operation procedure can be simplified. If the user is relaxed, detailed explanations can be displayed on the screen, and the operation procedure can be guided carefully. Furthermore, if the user is in a hurry, the screen can be simplified to allow them to withdraw money in the shortest possible steps. In this way, customizing the operation screen according to the user's emotions improves user convenience. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Customization of the operation screen is performed using generative AI. For example, the data necessary for customizing the operation screen can be input into the generative AI, and the customization can be executed by the generative AI.

[0089] A cashless system can analyze a user's past withdrawal history and suggest the optimal withdrawal method. For example, if a user has frequently used a particular ATM in the past, that ATM will be prioritized for suggestion. Furthermore, if a user has made withdrawals during specific time periods in the past, the system can suggest the most suitable withdrawal method for those times. Additionally, if a user has withdrawn a specific amount in the past, the system can prioritize suggesting that amount. In this way, by analyzing a user's past withdrawal history, the system can suggest the optimal withdrawal method. The suggestion of withdrawal methods is performed using generative AI. For example, withdrawal history data can be input into the generative AI, and the AI ​​can then generate suggestions for the optimal withdrawal method.

[0090] A cashless system can customize withdrawal methods based on the user's current living situation. For example, it can suggest the optimal withdrawal method based on the user's current living situation. For example, it can customize the withdrawal method considering the user's current living situation. It can also provide the user with the necessary withdrawal method based on their current living situation. This improves user convenience by customizing withdrawal methods based on the user's current living situation. Customization of withdrawal methods is performed using generative AI. For example, living situation data can be input into the generative AI, and the generative AI can be made to perform the customization of withdrawal methods.

[0091] A cashless system can estimate a user's emotions and prioritize withdrawals based on those emotions. For example, if a user is stressed, important withdrawals can be prioritized, allowing for additional detailed withdrawals later. If a user is relaxed, detailed withdrawals can be prioritized to ensure a smooth transaction. If a user is in a hurry, minimal withdrawals can be prioritized to complete the transaction quickly. This ensures a smooth withdrawal process by prioritizing withdrawals according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Determining withdrawal priorities is also performed using generative AI. For example, emotion data can be input into the generative AI, and the generative AI can then perform the priority determination.

[0092] A cashless system can select the optimal withdrawal method by considering the user's geographical location. For example, it can suggest the optimal withdrawal method based on the user's current location. For example, it can suggest region-specific withdrawal methods based on the user's geographical location. It can also suggest the optimal withdrawal method by considering the user's travel history. In this way, the optimal withdrawal method can be selected by considering the user's geographical location. The selection of the withdrawal method is performed using a generative AI. For example, geographical location data can be input into the generative AI, and the generative AI can be made to select the optimal withdrawal method.

[0093] A cashless system can estimate the user's emotions and adjust the withdrawal method based on those emotions. For example, if the user is nervous, a calm voice will guide them through the withdrawal process. If the user is relaxed, a cheerful voice will guide them through the withdrawal process. If the user is in a hurry, quick and concise instructions can be provided. This improves user convenience by adjusting the withdrawal method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Adjustment of the withdrawal method is performed using generative AI. For example, emotion data can be input into the generative AI, and the generative AI can be made to adjust the withdrawal method.

[0094] A cashless system can analyze a user's social media activity and suggest withdrawal methods. For example, it can analyze the content of a user's social media posts and suggest the most suitable withdrawal method. For example, it can refer to the activities of a user's social media followers and friends to suggest the most suitable withdrawal method. It can also analyze a user's social media interests and suggest the most suitable withdrawal method. In this way, by analyzing a user's social media activity, the system can suggest the most suitable withdrawal method. The suggestion of withdrawal methods is performed using generative AI. For example, social media activity data can be input into the generative AI, and the generative AI can then generate suggestions for withdrawal methods.

[0095] A cashless system can estimate the user's emotions and adjust the ATM's operating procedures based on those emotions. For example, if the user is nervous, the operating procedures can be simplified and guides can be displayed to prevent errors. If the user is relaxed, detailed operating procedures can be displayed to improve understanding. If the user is in a hurry, the shortest possible procedure can be displayed to allow for quick withdrawal of money. This improves the convenience of operation by adjusting the operating procedures according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Adjustment of operating procedures is performed using generative AI. For example, emotion data can be input into the generative AI, and the generative AI can be made to adjust the operating procedures.

[0096] A cashless system can analyze a user's past withdrawal history and suggest the optimal withdrawal method. For example, if a user has frequently used a particular ATM in the past, that ATM will be prioritized for suggestion. Furthermore, if a user has made withdrawals during specific time periods in the past, the system can suggest the most suitable withdrawal method for those times. Additionally, if a user has withdrawn a specific amount in the past, the system can prioritize suggesting that amount. In this way, by analyzing a user's past withdrawal history, the system can suggest the optimal withdrawal method. The suggestion of withdrawal methods is performed using generative AI. For example, withdrawal history data can be input into the generative AI, and the AI ​​can then generate suggestions for the optimal withdrawal method.

[0097] A cashless system can select the optimal withdrawal method by considering the user's geographical location. For example, it can suggest the optimal withdrawal method based on the user's current location. For example, it can suggest region-specific withdrawal methods based on the user's geographical location. It can also suggest the optimal withdrawal method by considering the user's travel history. In this way, the optimal withdrawal method can be selected by considering the user's geographical location. The selection of the withdrawal method is performed using a generative AI. For example, geographical location data can be input into the generative AI, and the generative AI can be made to select the optimal withdrawal method.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The registration unit registers the user's personal information. This personal information includes name, address, telephone number, facial image, and fingerprint data. The registration unit takes a picture of the user's face with a camera and saves it to the system. It also acquires the user's fingerprint data using a fingerprint scanner and saves it to the system. Feature points are extracted from the facial image and fingerprint data using facial recognition algorithms and fingerprint recognition algorithms, respectively. Step 2: The authentication unit performs facial recognition and biometric authentication based on the personal information registered by the registration unit. The authentication unit recognizes the user's facial image and fingerprints using deep learning-based facial recognition technology and fingerprint authentication technology. It extracts feature points from the facial image and fingerprints and compares them with the registered data. The processing in the authentication unit can also be performed using generative AI. Step 3: The withdrawal unit withdraws money based on the information authenticated by the authentication unit. The withdrawal unit eliminates the need for a cash card by performing facial recognition and biometric authentication at the ATM or other terminal. The user faces the ATM camera and places their finger on the fingerprint sensor, and the system matches the user's facial image and fingerprint data to verify their identity. The processing in the withdrawal unit can also be performed using generative AI.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0102] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0103] Each of the multiple elements described above, including the registration unit, authentication unit, and withdrawal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the registration unit acquires the user's facial image and fingerprint data using the camera 42 and fingerprint scanner of the smart device 14 and stores them in the system using the control unit 46A. The authentication unit is implemented in the identification processing unit 290 of the data processing unit 12 and performs facial recognition and fingerprint recognition. The withdrawal unit is implemented in the control unit 46A of the smart device 14 and eliminates the need for a cash card by performing facial recognition and biometric authentication at a terminal such as an ATM. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0110] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0111] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0112] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0113] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0114] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0116] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0118] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] Each of the multiple elements described above, including the registration unit, authentication unit, and withdrawal unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the registration unit acquires the user's facial image and fingerprint data using the camera 42 and fingerprint scanner of the smart glasses 214 and stores them in the system using the control unit 46A. The authentication unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and performs facial recognition and fingerprint recognition. The withdrawal unit is implemented, for example, in the control unit 46A of the smart glasses 214 and eliminates the need for a cash card by performing facial recognition and biometric authentication at a terminal such as an ATM. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0123] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0124] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0126] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0127] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0128] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0129] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0130] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0134] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements described above, including the registration unit, authentication unit, and withdrawal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the registration unit acquires the user's facial image and fingerprint data using the camera 42 and fingerprint scanner of the headset terminal 314 and stores them in the system using the control unit 46A. The authentication unit is implemented in the identification processing unit 290 of the data processing unit 12 and performs facial recognition and fingerprint recognition. The withdrawal unit is implemented in the control unit 46A of the headset terminal 314 and eliminates the need for a cash card by performing facial recognition and biometric authentication at a terminal such as an ATM. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0137] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0140] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0144] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the registration unit, authentication unit, and withdrawal unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the registration unit acquires the user's facial image and fingerprint data using the camera 42 and fingerprint scanner of the robot 414 and stores them in the system using the control unit 46A. The authentication unit is implemented in the identification processing unit 290 of the data processing unit 12 and performs facial recognition and fingerprint recognition. The withdrawal unit is implemented in the control unit 46A of the robot 414 and eliminates the need for a cash card by performing facial recognition and biometric authentication at a terminal such as an ATM. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0153] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0155] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0156] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0157] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0161] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0162] 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.

[0163] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0164] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0165] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0166] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0168] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0169] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0170] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0171] (Note 1) The registration section where you register your personal information, An authentication unit that performs facial recognition and biometric authentication based on personal information registered by the registration unit, The system includes a withdrawal unit that withdraws money based on information authenticated by the authentication unit. A system characterized by the following features. (Note 2) The authentication unit, User identity verification is performed using facial recognition and biometric authentication technologies. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned registration unit is Register the user's facial image and fingerprint data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The authentication unit, We use generative AI to learn the user's biometric characteristics and improve the accuracy of identity verification. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned drawer section is When a user withdraws money from an ATM or other terminal, facial recognition and biometric authentication are performed. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned drawer section is You can withdraw money without a cash card even during a disaster. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned registration unit is The system estimates the user's emotions and adjusts the registration process guidance based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned registration unit is During registration, the system analyzes the user's past registration history and selects the most suitable registration method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned registration unit is During registration, registration information is filtered based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned registration unit is The system estimates the user's emotions and prioritizes the information to be registered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned registration unit is During registration, the system prioritizes registering highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned registration unit is During registration, the system analyzes the user's social media activity and registers relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The authentication unit, The system estimates the user's emotions and adjusts the authentication process based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The authentication unit, During authentication, the authentication algorithm is optimized to take into account changes in the user's biometric characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 15) The authentication unit, During authentication, the system improves authentication accuracy by referencing the user's past authentication history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The authentication unit, The system estimates the user's emotions and determines authentication priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The authentication unit, During authentication, the accuracy of authentication is improved by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The authentication unit, During authentication, the system improves authentication accuracy by referencing the user's relevant literature. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned drawer section is It estimates the user's emotions and adjusts the drawer method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned drawer section is When a user attempts to withdraw funds, the system analyzes their past withdrawal history to select the most suitable withdrawal method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned drawer section is When making a withdrawal, the withdrawal method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned drawer section is It estimates the user's emotions and determines the priority of drawers based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned drawer section is When a user attempts to withdraw funds, the system selects the optimal withdrawal method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned drawer section is During the withdrawal process, the system analyzes the user's social media activity and suggests withdrawal methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The registration section where you register your personal information, An authentication unit that performs facial recognition and biometric authentication based on personal information registered by the registration unit, The system includes a withdrawal unit that withdraws money based on information authenticated by the authentication unit. A system characterized by the following features.

2. The authentication unit, User identity verification is performed using facial recognition and biometric authentication technologies. The system according to feature 1.

3. The aforementioned registration unit is Register the user's facial image and fingerprint data. The system according to feature 1.

4. The authentication unit, By using generative AI to learn the user's biometric characteristics, we improve the accuracy of identity verification. The system according to feature 1.

5. The aforementioned drawer section is When a user withdraws money from an ATM or other terminal, facial recognition and biometric authentication are performed. The system according to feature 1.

6. The aforementioned drawer section is You can withdraw money without a cash card even during a disaster. The system according to feature 1.

7. The aforementioned registration unit is The system estimates the user's emotions and adjusts the registration process guidance based on those estimated emotions. The system according to feature 1.

8. The aforementioned registration unit is During registration, the system analyzes the user's past registration history and selects the most suitable registration method. The system according to feature 1.

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