system

Abstract

An object of a system according to an embodiment is to enable a user to complete a procedure online.SOLUTION: A system according to an embodiment includes a procedure reception unit, an identity verification unit, and a procedure execution unit. The procedure reception unit receives a procedure request from a user. The identity verification unit performs identity verification using eKYC on the basis of the procedure request accepted by the procedure acceptance unit. The procedure execution unit executes the procedure for which the identity verification is completed by the identity verification unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology requires users to visit a store to complete certain procedures, which can be time-consuming and cumbersome.

[0005] The system according to the embodiment aims to enable users to complete procedures online. [Means for solving the problem]

[0006] The system according to the embodiment includes a procedure reception unit, an identity verification unit, and a procedure execution unit. The procedure reception unit receives a procedure request from a user. The identity verification unit performs identity verification using eKYC based on the procedure request received by the procedure reception unit. The procedure execution unit executes a procedure for which identity verification has been completed by the identity verification unit. [Effects of the Invention]

[0007] The system according to the embodiment may allow users to complete transactions online. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The online procedure system according to an embodiment of the present invention allows users to complete procedures online. This system uses generation AI to automatically complete all procedures that previously required going to a store. As a result, the online procedure system eliminates the need for users to go to a store and allows users to complete procedures at their preferred time. For example, various procedures, such as applying for family discounts, electricity set discounts, and gas set discounts, can be easily completed online. Furthermore, by using eKYC, identity verification can also be completed online, reducing the burden on users. Furthermore, by conducting preliminary interviews and providing instructions on what to bring, the workload for both users and stores can be minimized.

[0029] The online procedure system according to the embodiment includes a procedure reception unit, an identity verification unit, and a procedure execution unit. The procedure reception unit receives procedure requests from users. For example, the procedure reception unit receives procedure requests via an online form. The procedure reception unit can also receive procedure requests via telephone or email. The identity verification unit performs identity verification using eKYC based on the procedure request received by the procedure reception unit. For example, the user uploads an identification document, and a generation AI analyzes the contents of the document to verify the user's identity. The identity verification unit can also verify the user's identity by comparing the document with a database. The procedure execution unit executes procedures for which identity verification has been completed by the identity verification unit. For example, the procedure execution unit executes application procedures for a family discount. The procedure execution unit can also execute application procedures for an electricity set discount or a gas set discount. This allows the online procedure system to allow users to complete procedures online. For example, users can complete procedures 24 hours a day, even outside of store business hours.

[0030] The procedure reception unit can analyze the user's past procedure history, predict the next procedure that will be required, and automatically suggest it. In the procedure reception unit, for example, the generation AI analyzes the user's past procedure history and predicts the next procedure that will be required. For example, for a user who has previously applied for a family discount, the generation AI automatically suggests the next renewal procedure. In addition, in the procedure reception unit, the generation AI reminds the user of the next procedure that is required based on the user's procedure history. For example, when the time to renew the electricity set discount approaches, the generation AI automatically sends a notification. In addition, in the procedure reception unit, the generation AI learns the user's procedure patterns and predicts and suggests the next procedure that will be required. For example, it automatically makes a suggestion when it is time to apply for a gas set discount. This allows the user to predict and suggest the next procedure that they will need.

[0031] The procedure reception unit can analyze the user's voice input and automate the procedure using voice recognition technology. For example, when a user gives voice instructions for a procedure, the generation AI uses voice recognition technology to analyze the content and automatically carry out the procedure. For example, a user may give a voice instruction such as, "I would like to apply for a family discount." The procedure reception unit also analyzes the voice input and builds a system in which the generation AI automates the procedure. For example, if a user says, "I would like to update my electricity set discount," the generation AI automatically proceeds with the procedure. The procedure reception unit also uses voice recognition technology to analyze the user's voice instructions and automate the procedure. For example, the procedure can be completed simply by giving the voice instruction, "I would like to apply for a gas set discount." This allows the user to automate procedures with voice input.

[0032] The procedure reception unit uses the user's location information to provide the congestion status of the nearest shop in real time, allowing the user to determine the need for a visit. In the procedure reception unit, for example, the generation AI uses the user's location information to provide the congestion status of the nearest shop in real time. For example, it displays the congestion status of a shop the user is considering visiting. In addition, the procedure reception unit uses the user's location information to analyze the congestion status of the nearest shop and determine the need for a visit. For example, if it is crowded, it suggests online procedures. In addition, the procedure reception unit uses the user's location information to provide the congestion status of the nearest shop in real time, allowing the user to determine the need for a visit. For example, it suggests a time of day when it is less crowded. This allows the user to check the congestion status of the nearest shop in real time.

[0033] The procedure reception unit works in conjunction with other services and can automate multiple procedures at once. For example, the generation AI in the procedure reception unit works in conjunction with other services such as banking and insurance to automate multiple procedures at once. For example, applying for a family discount and simultaneously carrying out bank account change procedures. The procedure reception unit also works in conjunction with other services to build a system in which the generation AI automates multiple procedures at once. For example, applying for an electricity set discount and simultaneously carrying out insurance renewal procedures. The procedure reception unit also works in conjunction with other services to automate multiple procedures at once. For example, applying for a gas set discount and simultaneously carrying out credit card renewal procedures. This allows multiple procedures to be automated at once.

[0034] The identity verification unit can analyze multiple identity verification documents simultaneously, enabling more accurate identity verification. For example, the generation AI can analyze multiple identity verification documents simultaneously, enabling more accurate identity verification. For example, it can analyze a driver's license and a passport at the same time. The identity verification unit can also build a system in which the generation AI can analyze multiple identity verification documents simultaneously, enabling more accurate identity verification. For example, it can analyze a health insurance card and a resident registration card at the same time. The identity verification unit can also build a system in which the generation AI can analyze multiple identity verification documents simultaneously, enabling more accurate identity verification. For example, it can analyze a My Number card and bank account information at the same time. This enables more accurate identity verification.

[0035] The identity verification unit can perform double identity verification by combining a user's facial recognition and voice authentication. For example, the generation AI performs double identity verification by combining a user's facial recognition and voice authentication. For example, identity verification is performed using facial recognition, and additional verification is performed using voice authentication. The identity verification unit also combines facial recognition and voice authentication to build a system in which the generation AI performs highly accurate identity verification. For example, identity verification is performed using facial recognition, and a verification code is read out using voice authentication. The identity verification unit also combines a user's facial recognition and voice authentication to perform double identity verification. For example, identity verification is performed using facial recognition, and a secret question is answered using voice authentication. This double identity verification improves security.

[0036] The identity verification unit can link with other authentication systems and perform identity verification by combining multiple authentication methods. For example, the generation AI links with other authentication systems, such as fingerprint authentication and iris authentication, to perform identity verification by combining multiple authentication methods. For example, facial authentication and fingerprint authentication are performed simultaneously. The identity verification unit can also link with other authentication systems to build a system in which the generation AI combines multiple authentication methods to perform identity verification. For example, voice authentication and iris authentication are performed simultaneously. The identity verification unit can also link with other authentication systems to build a system in which the generation AI combines multiple authentication methods to perform identity verification. For example, facial authentication and fingerprint authentication are performed simultaneously. This improves the accuracy of identity verification by combining multiple authentication methods.

[0037] The identity verification unit can analyze the user's past identity verification history and automate preparations for the next identity verification to be performed quickly. For example, the identity verification unit uses a generation AI to analyze the user's past identity verification history and automate preparations for the next identity verification to be performed quickly. For example, past verification documents are automatically retrieved. The identity verification unit also builds a system in which the generation AI automates preparations for the next identity verification to be performed quickly based on the user's identity verification history. For example, past verification information is automatically input. The identity verification unit also builds a system in which the generation AI analyzes the user's past identity verification history and automates preparations for the next identity verification to be performed quickly. For example, past verification documents are automatically retrieved. This automates preparations for the next identity verification to be performed quickly.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The procedure reception unit analyzes the user's past procedure history and can predict and automatically suggest the next procedure that will be required. For example, for a user who has previously applied for a family discount, it will automatically suggest the next renewal procedure. The procedure reception unit also uses the generation AI to remind the user of the next procedure that is required based on the user's procedure history. For example, when the time to renew an electricity set discount approaches, the generation AI will automatically send a notification. The procedure reception unit also uses the generation AI to learn the user's procedure patterns and predict and suggest the next procedure that will be required. For example, it will automatically make a suggestion when it is time to apply for a gas set discount. This allows the user to predict and suggest the next procedure that they will need to perform.

[0040] The procedure reception unit can analyze the user's voice input and automate the procedure using voice recognition technology. For example, when a user gives voice instructions for a procedure, the generation AI uses voice recognition technology to analyze the content and automatically carry out the procedure. For example, a user might say, "I would like to apply for a family discount." The procedure reception unit also analyzes the voice input and builds a system in which the generation AI automates the procedure. For example, if a user says, "I would like to update my electricity set discount," the generation AI automatically proceeds with the procedure. The procedure reception unit also uses voice recognition technology to analyze the user's voice instructions and automate the procedure. For example, the procedure can be completed simply by saying, "I would like to apply for a gas set discount." This allows users to automate procedures with voice input.

[0041] The procedure reception unit uses the user's location information to provide the congestion status of the nearest shop in real time, allowing the user to determine whether a visit is necessary. For example, the generation AI uses the user's location information to provide the congestion status of the nearest shop in real time. For example, it displays the congestion status of a shop the user is considering visiting. The procedure reception unit also uses the user's location information to analyze the congestion status of the nearest shop and determine whether a visit is necessary. For example, if it is crowded, it suggests online procedures. The procedure reception unit also uses the user's location information to provide the congestion status of the nearest shop in real time, allowing the user to determine whether a visit is necessary. For example, it suggests a time of day when it is less crowded. This allows the user to check the congestion status of the nearest shop in real time.

[0042] The procedure reception unit works in conjunction with other services and can automate multiple procedures at once. For example, the generation AI works in conjunction with other services such as banking and insurance to automate multiple procedures at once. For example, applying for a family discount and simultaneously carrying out bank account change procedures. The procedure reception unit also works in conjunction with other services to build a system in which the generation AI automates multiple procedures at once. For example, applying for an electricity set discount and simultaneously carrying out insurance renewal procedures. The procedure reception unit also works in conjunction with other services to automate multiple procedures at once. For example, applying for a gas set discount and simultaneously carrying out credit card renewal procedures. This allows multiple procedures to be automated at once.

[0043] The identity verification unit can analyze multiple identity verification documents simultaneously, enabling more accurate identity verification. For example, the generation AI can analyze multiple identity verification documents simultaneously, enabling more accurate identity verification. For example, it can analyze a driver's license and a passport at the same time. The identity verification unit can also build a system in which the generation AI can perform highly accurate identity verification by analyzing multiple identity verification documents simultaneously. For example, it can analyze a health insurance card and a resident registration card at the same time. The identity verification unit can also build a system in which the generation AI can analyze multiple identity verification documents simultaneously, enabling more accurate identity verification. For example, it can analyze a My Number card and bank account information at the same time. This enables more accurate identity verification.

[0044] The identity verification unit can perform double identity verification by combining a user's facial recognition and voice authentication. For example, the generation AI can perform double identity verification by combining a user's facial recognition and voice authentication. For example, identity verification is performed using facial recognition, and additional verification is performed using voice authentication. The identity verification unit can also combine facial recognition and voice authentication to build a system in which the generation AI performs highly accurate identity verification. For example, identity verification is performed using facial recognition, and a verification code is read out using voice authentication. The identity verification unit can also combine a user's facial recognition and voice authentication to perform double identity verification. For example, identity verification is performed using facial recognition, and a secret question is answered using voice authentication. This double identity verification improves security.

[0045] The identity verification unit can link with other authentication systems and perform identity verification by combining multiple authentication methods. For example, the generation AI can link with other authentication systems such as fingerprint authentication and iris authentication to perform identity verification by combining multiple authentication methods. For example, facial authentication and fingerprint authentication can be performed simultaneously. The identity verification unit can also link with other authentication systems to build a system in which the generation AI can perform identity verification by combining multiple authentication methods. For example, voice authentication and iris authentication can be performed simultaneously. The identity verification unit can also link with other authentication systems to build a system in which the generation AI can perform identity verification by combining multiple authentication methods. For example, facial authentication and fingerprint authentication can be performed simultaneously. This improves the accuracy of identity verification by combining multiple authentication methods.

[0046] The identity verification unit can analyze the user's past identity verification history and automate preparations for the next identity verification to be performed quickly. For example, the generation AI analyzes the user's past identity verification history and automates preparations for the next identity verification to be performed quickly. For example, past verification documents may be automatically retrieved. The identity verification unit also builds a system in which the generation AI automates preparations for the next identity verification to be performed quickly, based on the user's identity verification history. For example, past verification information may be automatically input. The identity verification unit also builds a system in which the generation AI analyzes the user's past identity verification history and automates preparations for the next identity verification to be performed quickly. For example, past verification documents may be automatically retrieved. This automates preparations for the next identity verification to be performed quickly.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The procedure reception unit receives a procedure request from a user. For example, the procedure reception unit receives a procedure request via an online form. The procedure reception unit can also receive a procedure request via telephone or email. Step 2: The identity verification unit uses eKYC to verify the identity of the user based on the procedure request accepted by the procedure acceptance unit. For example, the user uploads an identification document, and the generation AI analyzes the contents to verify the user's identity. The identity verification unit can also verify the user's identity by checking against a database. Step 3: The procedure execution unit executes the procedure for which the identity verification unit has completed identity verification. For example, the procedure execution unit executes the application procedure for a family discount. The procedure execution unit can also execute the application procedure for an electricity set discount or a gas set discount.

[0049] (Example 2) The online procedure system according to an embodiment of the present invention allows users to complete procedures online. This system uses generation AI to automatically complete all procedures that previously required going to a store. As a result, the online procedure system eliminates the need for users to go to a store and allows users to complete procedures at their preferred time. For example, various procedures, such as applying for family discounts, electricity set discounts, and gas set discounts, can be easily completed online. Furthermore, by using eKYC, identity verification can also be completed online, reducing the burden on users. Furthermore, by conducting preliminary interviews and providing instructions on what to bring, the workload for both users and stores can be minimized.

[0050] The online procedure system according to the embodiment includes a procedure reception unit, an identity verification unit, and a procedure execution unit. The procedure reception unit receives procedure requests from users. For example, the procedure reception unit receives procedure requests via an online form. The procedure reception unit can also receive procedure requests via telephone or email. The identity verification unit performs identity verification using eKYC based on the procedure request received by the procedure reception unit. For example, the user uploads an identification document, and a generation AI analyzes the contents of the document to verify the user's identity. The identity verification unit can also verify the user's identity by comparing the document with a database. The procedure execution unit executes procedures for which identity verification has been completed by the identity verification unit. For example, the procedure execution unit executes application procedures for a family discount. The procedure execution unit can also execute application procedures for an electricity set discount or a gas set discount. This allows the online procedure system to allow users to complete procedures online. For example, users can complete procedures 24 hours a day, even outside of store business hours.

[0051] The procedure reception unit can analyze the user's past procedure history, predict the next procedure that will be required, and automatically suggest it. In the procedure reception unit, for example, the generation AI analyzes the user's past procedure history and predicts the next procedure that will be required. For example, for a user who has previously applied for a family discount, the generation AI automatically suggests the next renewal procedure. In addition, in the procedure reception unit, the generation AI reminds the user of the next procedure that is required based on the user's procedure history. For example, when the time to renew the electricity set discount approaches, the generation AI automatically sends a notification. In addition, in the procedure reception unit, the generation AI learns the user's procedure patterns and predicts and suggests the next procedure that will be required. For example, it automatically makes a suggestion when it is time to apply for a gas set discount. This allows the user to predict and suggest the next procedure that they will need.

[0052] The procedure reception unit can analyze the user's voice input and automate the procedure using voice recognition technology. For example, when a user gives voice instructions for a procedure, the generation AI uses voice recognition technology to analyze the content and automatically carry out the procedure. For example, a user may give a voice instruction such as, "I would like to apply for a family discount." The procedure reception unit also analyzes the voice input and builds a system in which the generation AI automates the procedure. For example, if a user says, "I would like to update my electricity set discount," the generation AI automatically proceeds with the procedure. The procedure reception unit also uses voice recognition technology to analyze the user's voice instructions and automate the procedure. For example, the procedure can be completed simply by giving the voice instruction, "I would like to apply for a gas set discount." This allows the user to automate procedures with voice input.

[0053] The procedure reception unit can use the emotion estimation function to analyze the user's emotional state and make suggestions to simplify the procedure if the user is feeling stressed. For example, the procedure reception unit uses the generation AI to analyze the user's emotional state and make suggestions to simplify the procedure if the user is feeling stressed. For example, if the user is nervous, the procedure reception unit provides step-by-step guidance through the procedure. The procedure reception unit can also use the emotion estimation function to analyze the user's emotional state in real time and simplify the procedure if the user is feeling stressed. For example, the procedure reception unit can break down a complex procedure into simple steps and provide guidance. The procedure reception unit can also use the generation AI to analyze the user's emotional state and make suggestions to simplify the procedure if the user is feeling stressed. For example, if the user is feeling anxious, the procedure reception unit can display a message to support the user through the procedure. This allows the procedure to be simplified according to the user's emotional state.

[0054] The procedure reception unit uses the user's location information to provide the congestion status of the nearest shop in real time, allowing the user to determine the need for a visit. In the procedure reception unit, for example, the generation AI uses the user's location information to provide the congestion status of the nearest shop in real time. For example, it displays the congestion status of a shop the user is considering visiting. In addition, the procedure reception unit uses the user's location information to analyze the congestion status of the nearest shop and determine the need for a visit. For example, if it is crowded, it suggests online procedures. In addition, the procedure reception unit uses the user's location information to provide the congestion status of the nearest shop in real time, allowing the user to determine the need for a visit. For example, it suggests a time of day when it is less crowded. This allows the user to check the congestion status of the nearest shop in real time.

[0055] The procedure reception unit works in conjunction with other services and can automate multiple procedures at once. For example, the generation AI in the procedure reception unit works in conjunction with other services such as banking and insurance to automate multiple procedures at once. For example, applying for a family discount and simultaneously carrying out bank account change procedures. The procedure reception unit also works in conjunction with other services to build a system in which the generation AI automates multiple procedures at once. For example, applying for an electricity set discount and simultaneously carrying out insurance renewal procedures. The procedure reception unit also works in conjunction with other services to automate multiple procedures at once. For example, applying for a gas set discount and simultaneously carrying out credit card renewal procedures. This allows multiple procedures to be automated at once.

[0056] The procedure reception unit can use the emotion estimation function to monitor the user's emotions in real time when performing a procedure and provide an interface for eliciting positive emotions. The procedure reception unit, for example, uses the emotion estimation function to monitor the user's emotions in real time when performing a procedure and provide an interface for eliciting positive emotions. For example, it displays an encouraging message. The procedure reception unit also provides an interface for the generation AI to monitor the user's emotions in real time and elicit positive emotions. For example, it visually displays the progress of the procedure. The procedure reception unit also uses the emotion estimation function to monitor the user's emotions in real time and provide an interface for eliciting positive emotions. For example, it presents success stories. This allows the user to perform the procedure while maintaining positive emotions.

[0057] The identity verification unit can analyze multiple identity verification documents simultaneously, enabling more accurate identity verification. For example, the generation AI can analyze multiple identity verification documents simultaneously, enabling more accurate identity verification. For example, it can analyze a driver's license and a passport at the same time. The identity verification unit can also build a system in which the generation AI can analyze multiple identity verification documents simultaneously, enabling more accurate identity verification. For example, it can analyze a health insurance card and a resident registration card at the same time. The identity verification unit can also build a system in which the generation AI can analyze multiple identity verification documents simultaneously, enabling more accurate identity verification. For example, it can analyze a My Number card and bank account information at the same time. This enables more accurate identity verification.

[0058] The identity verification unit can perform double identity verification by combining a user's facial recognition and voice authentication. For example, the generation AI performs double identity verification by combining a user's facial recognition and voice authentication. For example, identity verification is performed using facial recognition, and additional verification is performed using voice authentication. The identity verification unit also combines facial recognition and voice authentication to build a system in which the generation AI performs highly accurate identity verification. For example, identity verification is performed using facial recognition, and a verification code is read out using voice authentication. The identity verification unit also combines a user's facial recognition and voice authentication to perform double identity verification. For example, identity verification is performed using facial recognition, and a secret question is answered using voice authentication. This double identity verification improves security.

[0059] The identity verification unit can use the emotion estimation function to analyze the user's emotion during identity verification and provide guidance to help them relax if they appear nervous. For example, the identity verification unit can use the emotion estimation function to analyze the user's emotion during identity verification and provide guidance to help them relax if they appear nervous. For example, it can provide guidance on breathing techniques to help them relax. The identity verification unit also uses the generation AI to analyze the user's emotion in real time and provide guidance to help them relax if they appear nervous. For example, it can play music to help them relax. The identity verification unit also uses the emotion estimation function to analyze the user's emotion during identity verification and provide guidance to help them relax if they appear nervous. For example, it can display a video to help them relax. This allows the user to relax while identity verification is performed.

[0060] The identity verification unit can link with other authentication systems and perform identity verification by combining multiple authentication methods. For example, the generation AI links with other authentication systems, such as fingerprint authentication and iris authentication, to perform identity verification by combining multiple authentication methods. For example, facial authentication and fingerprint authentication are performed simultaneously. The identity verification unit can also link with other authentication systems to build a system in which the generation AI combines multiple authentication methods to perform identity verification. For example, voice authentication and iris authentication are performed simultaneously. The identity verification unit can also link with other authentication systems to build a system in which the generation AI combines multiple authentication methods to perform identity verification. For example, facial authentication and fingerprint authentication are performed simultaneously. This improves the accuracy of identity verification by combining multiple authentication methods.

[0061] The identity verification unit can analyze the user's past identity verification history and automate preparations for the next identity verification to be performed quickly. For example, the identity verification unit uses a generation AI to analyze the user's past identity verification history and automate preparations for the next identity verification to be performed quickly. For example, past verification documents are automatically retrieved. The identity verification unit also builds a system in which the generation AI automates preparations for the next identity verification to be performed quickly based on the user's identity verification history. For example, past verification information is automatically input. The identity verification unit also builds a system in which the generation AI analyzes the user's past identity verification history and automates preparations for the next identity verification to be performed quickly. For example, past verification documents are automatically retrieved. This automates preparations for the next identity verification to be performed quickly.

[0062] The identity verification unit can use the emotion estimation function to monitor the user's emotions in real time during identity verification and provide an interface for eliciting positive emotions. For example, the identity verification unit can use the emotion estimation function to monitor the user's emotions in real time during identity verification and provide an interface for eliciting positive emotions. For example, it can display an encouraging message. The identity verification unit also provides an interface for the generation AI to monitor the user's emotions in real time and elicit positive emotions. For example, it can visually display the progress of the procedure. The identity verification unit also uses the emotion estimation function to monitor the user's emotions in real time and provide an interface for eliciting positive emotions. For example, it can present success stories. This allows identity verification to be performed while keeping the user's emotions positive.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The procedure reception unit analyzes the user's past procedure history and can predict and automatically suggest the next procedure that will be required. For example, for a user who has previously applied for a family discount, it will automatically suggest the next renewal procedure. The procedure reception unit also uses the generation AI to remind the user of the next procedure that is required based on the user's procedure history. For example, when the time to renew an electricity set discount approaches, the generation AI will automatically send a notification. The procedure reception unit also uses the generation AI to learn the user's procedure patterns and predict and suggest the next procedure that will be required. For example, it will automatically make a suggestion when it is time to apply for a gas set discount. This allows the user to predict and suggest the next procedure that they will need to perform.

[0065] The procedure reception unit can analyze the user's voice input and automate the procedure using voice recognition technology. For example, when a user gives voice instructions for a procedure, the generation AI uses voice recognition technology to analyze the content and automatically carry out the procedure. For example, a user might say, "I would like to apply for a family discount." The procedure reception unit also analyzes the voice input and builds a system in which the generation AI automates the procedure. For example, if a user says, "I would like to update my electricity set discount," the generation AI automatically proceeds with the procedure. The procedure reception unit also uses voice recognition technology to analyze the user's voice instructions and automate the procedure. For example, the procedure can be completed simply by saying, "I would like to apply for a gas set discount." This allows users to automate procedures with voice input.

[0066] The procedure reception unit can use the emotion estimation function to analyze the user's emotional state and make suggestions to simplify the procedure if the user is feeling stressed. For example, the generation AI can analyze the user's emotional state and make suggestions to simplify the procedure if the user is feeling stressed. For example, if the user is nervous, the procedure reception unit can guide the user through the procedure step by step. The procedure reception unit can also use the emotion estimation function to analyze the user's emotional state in real time and simplify the procedure if the user is feeling stressed. For example, the procedure reception unit can guide the user through a complicated procedure by dividing it into simple steps. The procedure reception unit can also use the generation AI to analyze the user's emotional state and make suggestions to simplify the procedure if the user is feeling stressed. For example, if the user is feeling anxious, the procedure reception unit can display a message to support the user through the procedure. This makes it possible to simplify the procedure according to the user's emotional state.

[0067] The procedure reception unit uses the user's location information to provide the congestion status of the nearest shop in real time, allowing the user to determine whether a visit is necessary. For example, the generation AI uses the user's location information to provide the congestion status of the nearest shop in real time. For example, it displays the congestion status of a shop the user is considering visiting. The procedure reception unit also uses the user's location information to analyze the congestion status of the nearest shop and determine whether a visit is necessary. For example, if it is crowded, it suggests online procedures. The procedure reception unit also uses the user's location information to provide the congestion status of the nearest shop in real time, allowing the user to determine whether a visit is necessary. For example, it suggests a time of day when it is less crowded. This allows the user to check the congestion status of the nearest shop in real time.

[0068] The procedure reception unit works in conjunction with other services and can automate multiple procedures at once. For example, the generation AI works in conjunction with other services such as banking and insurance to automate multiple procedures at once. For example, applying for a family discount and simultaneously carrying out bank account change procedures. The procedure reception unit also works in conjunction with other services to build a system in which the generation AI automates multiple procedures at once. For example, applying for an electricity set discount and simultaneously carrying out insurance renewal procedures. The procedure reception unit also works in conjunction with other services to automate multiple procedures at once. For example, applying for a gas set discount and simultaneously carrying out credit card renewal procedures. This allows multiple procedures to be automated at once.

[0069] The procedure reception unit can use the emotion estimation function to monitor the user's emotions in real time as they perform the procedure and provide an interface for eliciting positive emotions. For example, the emotion estimation function can be used to monitor the user's emotions in real time as they perform the procedure and provide an interface for eliciting positive emotions. For example, an encouraging message can be displayed. The procedure reception unit also uses the generation AI to monitor the user's emotions in real time and provide an interface for eliciting positive emotions. For example, the procedure progress can be visually displayed. The procedure reception unit also uses the emotion estimation function to monitor the user's emotions in real time and provide an interface for eliciting positive emotions. For example, success stories can be presented. This allows the user to perform the procedure while maintaining positive emotions.

[0070] The identity verification unit can analyze multiple identity verification documents simultaneously, enabling more accurate identity verification. For example, the generation AI can analyze multiple identity verification documents simultaneously, enabling more accurate identity verification. For example, it can analyze a driver's license and a passport at the same time. The identity verification unit can also build a system in which the generation AI can perform highly accurate identity verification by analyzing multiple identity verification documents simultaneously. For example, it can analyze a health insurance card and a resident registration card at the same time. The identity verification unit can also build a system in which the generation AI can analyze multiple identity verification documents simultaneously, enabling more accurate identity verification. For example, it can analyze a My Number card and bank account information at the same time. This enables more accurate identity verification.

[0071] The identity verification unit can perform double identity verification by combining a user's facial recognition and voice authentication. For example, the generation AI can perform double identity verification by combining a user's facial recognition and voice authentication. For example, identity verification is performed using facial recognition, and additional verification is performed using voice authentication. The identity verification unit can also combine facial recognition and voice authentication to build a system in which the generation AI performs highly accurate identity verification. For example, identity verification is performed using facial recognition, and a verification code is read out using voice authentication. The identity verification unit can also combine a user's facial recognition and voice authentication to perform double identity verification. For example, identity verification is performed using facial recognition, and a secret question is answered using voice authentication. This double identity verification improves security.

[0072] The identity verification unit can use the emotion estimation function to analyze the user's emotion during identity verification and provide guidance to help them relax if they appear nervous. For example, the emotion estimation function can be used to analyze the user's emotion during identity verification and provide guidance to help them relax if they appear nervous. For example, the function can provide guidance on breathing techniques to help them relax. The identity verification unit also uses the generation AI to analyze the user's emotion in real time and provide guidance to help them relax if they appear nervous. For example, the function can play music to help them relax. The identity verification unit also uses the emotion estimation function to analyze the user's emotion during identity verification and provide guidance to help them relax if they appear nervous. For example, the function can display a video to help them relax. This allows the user to relax while identity verification is performed.

[0073] The identity verification unit can link with other authentication systems and perform identity verification by combining multiple authentication methods. For example, the generation AI can link with other authentication systems such as fingerprint authentication and iris authentication to perform identity verification by combining multiple authentication methods. For example, facial authentication and fingerprint authentication can be performed simultaneously. The identity verification unit can also link with other authentication systems to build a system in which the generation AI can perform identity verification by combining multiple authentication methods. For example, voice authentication and iris authentication can be performed simultaneously. The identity verification unit can also link with other authentication systems to build a system in which the generation AI can perform identity verification by combining multiple authentication methods. For example, facial authentication and fingerprint authentication can be performed simultaneously. This improves the accuracy of identity verification by combining multiple authentication methods.

[0074] The identity verification unit can analyze the user's past identity verification history and automate preparations for the next identity verification to be performed quickly. For example, the generation AI analyzes the user's past identity verification history and automates preparations for the next identity verification to be performed quickly. For example, past verification documents may be automatically retrieved. The identity verification unit also builds a system in which the generation AI automates preparations for the next identity verification to be performed quickly, based on the user's identity verification history. For example, past verification information may be automatically input. The identity verification unit also builds a system in which the generation AI analyzes the user's past identity verification history and automates preparations for the next identity verification to be performed quickly. For example, past verification documents may be automatically retrieved. This automates preparations for the next identity verification to be performed quickly.

[0075] The processing flow of the second embodiment will be briefly explained below.

[0076] Step 1: The procedure reception unit receives a procedure request from a user. For example, the procedure reception unit receives a procedure request via an online form. The procedure reception unit can also receive a procedure request via telephone or email. Step 2: The identity verification unit uses eKYC to verify the identity of the user based on the procedure request accepted by the procedure acceptance unit. For example, the user uploads an identification document, and the generation AI analyzes the contents to verify the user's identity. The identity verification unit can also verify the user's identity by checking against a database. Step 3: The procedure execution unit executes the procedure for which the identity verification unit has completed identity verification. For example, the procedure execution unit executes the application procedure for a family discount. The procedure execution unit can also execute the application procedure for an electricity set discount or a gas set discount.

[0077] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0078] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0079] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0080] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0081] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0082] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0083] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0084] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0085] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0086] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0087] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0088] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0089] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0090] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0091] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0092] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0093] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0094] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0095] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0096] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0097] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0098] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0099] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0100] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0101] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0102] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0103] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0104] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0105] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0106] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0107] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0110] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0111] 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.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0118] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0126] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0127] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0128] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0129] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0130] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0131] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0132] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0133] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0134] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0135] 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.

[0136] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0137] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0138] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0139] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0140] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0141] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0142] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0143] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0144] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a procedure reception unit that receives a procedure request from a user; an identity verification unit that performs identity verification using eKYC based on the procedure request accepted by the procedure acceptance unit; a procedure execution unit that executes a procedure for which identity verification has been completed by the identity verification unit. A system characterized by:

2. The procedure reception unit Analyzes the user's past procedure history, predicts the next procedure required, and automatically suggests it.

2. The system of claim 1.

3. The procedure reception unit Analyzes user voice input and automates procedures using voice recognition technology 2. The system of claim 1.

4. The procedure reception unit Analyzes the user's emotional state and suggests ways to simplify procedures if the user is feeling stressed.

2. The system of claim 1.

5. The procedure reception unit Uses user location information to provide real-time information on the congestion status of nearby shops, helping users decide whether or not to visit.

2. The system of claim 1.

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