System

Abstract

To provide a system according to an embodiment which enables the elderly to perform online application.SOLUTION: A system according to an embodiment includes: a voice reception part; an authentication part; an input part; and a processing part. The voice reception part receives applications by voice. The authentication part authenticates a user by using his / her my-number card when start of the application is received by the voice reception part. The input part has application information inputted by an interactive voice input to an applicant authenticated by the authentication part. The processing part processes the application information inputted by the input part.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, the procedures for elderly people to apply online were complicated, making it difficult for them to complete the application easily.

[0005] The system according to the embodiment aims to enable even elderly people to easily apply online. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice reception unit, an authentication unit, an input unit, and a processing unit. The voice reception unit receives the start of an application by voice. The authentication unit performs authentication using a My Number card when the start of the application is received by the voice reception unit. The input unit allows an applicant authenticated by the authentication unit to input application information by interactive voice input. The processing unit processes the application information input by the input unit. Effect of the Invention

[0007] The system according to the embodiment allows even elderly people to easily apply online. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The online application system according to the embodiment of the present invention is a system that allows even elderly people to easily apply online to local governments. This online application system uses a smartphone as an entry point, accepts voice messages, uses a My Number card for authentication, and completes the online application through interactive voice input. For example, when an elderly person starts an application using a smartphone, the online application system recognizes the voice and starts the application process by simply issuing a simple command such as "I will start the application." For example, if the elderly person says, "I would like to apply for a copy of my resident's certificate," the system will sequentially ask for the information necessary for the application. Next, the online application system uses the smartphone's camera to read the My Number card to verify the identity of the applicant. This ensures that the applicant is the applicant. For example, when applying for a copy of a resident's certificate, authentication is completed by simply holding the My Number card over the smartphone's camera. Finally, the online application system asks the applicant for the necessary information through voice through interactive voice input, and the applicant responds to the request through voice. For example, when the system asks, "Please tell me your address," the applicant responds, "1-1-1 Nishi-Shinjuku, Shinjuku-ku, Tokyo." In this way, all the information necessary for the application is input by voice, and the online application is completed. This will allow even elderly people to easily apply online, accelerating the digital transformation (DX) of local governments.

[0029] The online application system according to the embodiment includes a voice reception unit, an authentication unit, an input unit, and a processing unit. The voice reception unit starts the application process when the applicant issues a voice instruction to start the application. For example, when the applicant says "I will start the application," the voice reception unit recognizes the voice and starts the application process. In addition, when the applicant says "I would like to apply for a copy of my resident's certificate," the voice reception unit can sequentially ask for information necessary for the application. The authentication unit reads the My Number card using a smartphone camera and performs identity verification. For example, when the applicant holds the My Number card up to the smartphone camera, the authentication unit reads the card information and performs identity verification. The input unit asks the applicant for necessary information by interactive voice input, and the applicant responds by voice. For example, when the input unit asks "Please tell me your address," the applicant can respond "1-1-1 Nishi-Shinjuku, Shinjuku-ku, Tokyo." The processing unit processes the application information input by the input unit and completes the online application. For example, the processing unit registers the input information in a database and completes the application procedure. As a result, the online application system according to the embodiment allows even elderly people to easily apply online, accelerating digital transformation in local governments.

[0030] The voice reception unit starts the application process when the applicant issues a voice command to start the application. For example, when the applicant says, "I'm going to start the application," the system recognizes the voice and starts the application process. The voice recognition technology incorporates advanced algorithms using natural language processing (NLP) and can accurately understand the applicant's speech. The voice reception unit analyzes the applicant's voice in real time and generates an appropriate response. For example, when the applicant says, "I would like to apply for a copy of my resident's certificate," the voice reception unit understands the intention and generates a prompt to ask the next required information. The voice reception unit converts the applicant's speech into text data, facilitating processing within the system. In addition, the voice reception unit also has filtering technology to remove background noise from the applicant's speech and obtain clear voice data. This allows the voice reception unit to accurately recognize the voice and smoothly progress the application process no matter what environment the applicant is in. In addition, the voice reception unit is capable of supporting multiple languages ​​and can also handle applications in foreign languages. This allows the voice reception unit to easily start an application by voice, greatly improving the convenience of online applications.

[0031] The authentication unit uses the smartphone camera to read the My Number card and verify the identity of the applicant. For example, when the applicant holds the My Number card up to the smartphone camera, the authentication unit reads the information on the card and verifies the applicant's identity. The authentication unit uses optical character recognition (OCR) technology to accurately read the information written on the My Number card. OCR technology analyzes high-resolution images and converts letters and numbers into digital data. In addition, the authentication unit also has NFC (near-field communication) technology to read information from the IC chip of the card. This allows the authentication unit to obtain not only the information written on the surface of the card, but also detailed information in the IC chip. The authentication unit compares the obtained information with the database in the system to verify the identity. For example, the authentication unit compares information such as the applicant's name, address, and date of birth with the database to see if they match. The authentication unit also uses facial recognition technology to compare the applicant's face with the photo on the My Number card, further improving the accuracy of identity verification. This allows the authentication unit to perform identity verification with high accuracy and prevent fraudulent applications. The authentication department also provides real-time feedback to the applicant on the progress of the authentication process to support a smooth authentication procedure, allowing the applicant to apply online with peace of mind and improving the reliability of the entire system.

[0032] The input unit asks the applicant for necessary information through interactive voice input, and the applicant responds by voice. For example, when the input unit asks, "Please tell me your address," the applicant can respond, "1-1-1 Nishi-Shinjuku, Shinjuku-ku, Tokyo." The input unit converts the applicant's response into text data using voice recognition technology to facilitate processing within the system. The input unit is equipped with natural language processing (NLP) technology to analyze the content of the applicant's response and accurately extract the necessary information. For example, if the applicant responds, "My address is 1-1-1 Nishi-Shinjuku, Shinjuku-ku, Tokyo," the input unit recognizes the keyword "address" and extracts the subsequent text as address information. If the applicant's response is incomplete or unclear, the input unit can ask additional questions to complete the necessary information. For example, if the applicant only responds, "Shinjuku-ku," the input unit will ask again, "Please tell me your specific address." The input unit saves the applicant's voice data so that it can be checked and reprocessed later. The input unit is also multilingual and can handle applications in foreign languages. In this way, the input unit can enable the applicant to easily provide the necessary information by voice, and can greatly improve the convenience of online application. Furthermore, the input unit can also analyze the applicant's voice data and provide feedback to identify improvements in the application process. In this way, the input unit can improve the user experience of the entire system.

[0033] The processing unit processes the application information input by the input unit and completes the online application. For example, the processing unit registers the input information in a database and completes the application procedure. The processing unit checks the consistency of the application information and checks whether there is any missing or incorrect information. For example, it checks whether the address information is entered correctly and whether all necessary documents are available. The processing unit transmits the application information to the relevant local government or institution and automatically proceeds with the necessary procedures. For example, it transmits the application information for a copy of the resident's certificate to the city hall system and starts the issuance procedure. The processing unit monitors the progress of the application procedure in real time and provides feedback to the applicant. For example, it notifies the applicant when the application is accepted or when additional documents are required. After the application procedure is completed, the processing unit sends a confirmation message to the applicant so that the application contents can be confirmed or corrected. In this way, the processing unit completes the application procedure quickly and accurately and allows the applicant to apply online with peace of mind. In addition, the processing unit is equipped with encryption technology to ensure the security of the application information and protects the applicant's personal information. In this way, the processing unit can improve the reliability and safety of online applications. Additionally, the processor may analyze the application data and provide feedback to identify improvements to the application process, thereby enabling the processor to improve the overall system efficiency and user experience.

[0034] The online application system is equipped with an application content confirmation unit that checks the information entered by the applicant and prompts them to make corrections if necessary. The application content confirmation unit checks the information entered by the applicant and prompts them to make corrections if necessary. For example, if there is an error in the information entered, the application content confirmation unit points out the error and prompts them to make corrections. In addition, if the information entered is incomplete, the application content confirmation unit can point out the incomplete part and request additional information. This makes it possible to check and correct the application content, improving the accuracy of the application.

[0035] The online application system includes an application result notification unit that notifies the applicant that the application has been completed. The application result notification unit notifies the applicant that the application has been completed. For example, the application result notification unit sends a notification to the applicant by email or SMS when the application has been completed. The application result notification unit can also allow the applicant to select the notification method they prefer (e.g., email, SMS, app notification, etc.). This allows the application result to be notified promptly.

[0036] The voice reception unit can select an appropriate reception method by referring to the applicant's past application history. For example, the voice reception unit preferentially presents application contents that the applicant has frequently made in the past. The voice reception unit can also preferentially recognize voice commands that the applicant has used in the past. Furthermore, the voice reception unit can automatically select an optimal voice reception flow from the applicant's past application history. This provides an optimal reception method based on the past application history.

[0037] The voice reception unit can improve the accuracy of voice recognition by taking into account the applicant's current situation and environmental sounds. For example, when the applicant is in a noisy environment, the voice reception unit can improve the accuracy of voice recognition using noise canceling technology. Also, when the applicant is in a quiet environment, the voice reception unit can use a normal voice recognition mode. Furthermore, the voice reception unit can analyze the environmental sounds around the applicant in real time and automatically apply the optimal voice recognition settings. This improves the accuracy of voice recognition and makes the application process proceed smoothly.

[0038] The authentication unit can select an appropriate authentication method by referring to the applicant's past authentication history. For example, the authentication unit preferentially suggests authentication methods that the applicant has used in the past. The authentication unit can also automatically select the optimal authentication method from the applicant's past authentication history. Furthermore, the authentication unit can also preferentially provide authentication methods that the applicant has frequently used in the past. In this way, the optimal authentication method based on the past authentication history is provided.

[0039] The authentication unit can improve the accuracy of authentication by taking into account the applicant's current situation and the ambient light. For example, if the applicant is in a dark environment, the authentication unit can increase the sensitivity of the camera to improve the accuracy of authentication. Also, if the applicant is in a bright environment, the authentication unit can perform authentication with normal camera settings. Furthermore, the authentication unit can analyze the ambient light around the applicant in real time and automatically apply optimal camera settings. This improves the accuracy of authentication and makes the application process proceed smoothly.

[0040] The input unit can select an appropriate input method by referring to the applicant's past input history. For example, the input unit preferentially presents information that the applicant has frequently input in the past. The input unit can also preferentially recognize voice commands that the applicant has used in the past. Furthermore, the input unit can automatically select an optimal interactive voice input flow from the applicant's past input history. This provides an optimal input method based on the past input history.

[0041] The input unit may improve the accuracy of the voice recognition by taking into account the applicant's current situation and environmental sounds. For example, the input unit may improve the accuracy of the voice recognition by using noise canceling technology when the applicant is in a noisy environment. Also, the input unit may use a normal voice recognition mode when the applicant is in a quiet environment. Furthermore, the input unit may analyze the environmental sounds around the applicant in real time and automatically apply the optimal voice recognition settings. This improves the accuracy of the voice recognition and makes the application process proceed smoothly.

[0042] The processing unit can select an appropriate processing method by referring to past processing history. For example, when a similar application has been made in the past, the processing unit selects the optimal processing method based on the processing history. Also, the processing unit can automatically select the most efficient processing method from the past processing history. Furthermore, the processing unit can analyze the past processing history and propose an optimal processing flow. In this way, an optimal processing method based on the past processing history is provided.

[0043] The processing unit can improve the accuracy of the processing based on the applicant's current situation and environment. For example, the processing unit can provide a quick processing method when the applicant is in a hurry. Also, the processing unit can provide a normal processing method when the applicant is relaxed. Furthermore, the processing unit can analyze the applicant's current situation in real time and automatically apply the optimal processing method. This improves the accuracy of the processing and makes the application process proceed smoothly.

[0044] The application content confirmation unit can select an appropriate confirmation method by referring to past confirmation history. For example, if a similar application has been made in the past, the application content confirmation unit selects the optimal confirmation method based on the confirmation history. Furthermore, the application content confirmation unit can automatically select the most efficient confirmation method from the past confirmation history. Furthermore, the application content confirmation unit can analyze the past confirmation history and propose an optimal confirmation flow. This provides the optimal confirmation method based on the past confirmation history.

[0045] The application result notification unit can select an appropriate notification method by referring to past notification history. For example, if a similar application has been made in the past, the application result notification unit selects the optimal notification method based on the notification history. The application result notification unit can also automatically select the most efficient notification method from the past notification history. Furthermore, the application result notification unit can also analyze the past notification history and propose the optimal notification flow. This provides the optimal notification method based on the past notification history.

[0046] The application result notification unit can prioritize notifications that are highly relevant based on the applicant's geographic location information. For example, if the applicant is in a specific area, the application result notification unit prioritizes notification of application results related to that area. In addition, the application result notification unit can prioritize notification of application results from the nearest municipality based on the applicant's location information. Furthermore, the application result notification unit can analyze the applicant's geographic location information in real time and suggest the optimal notification method. This provides the optimal notification based on the applicant's geographic location information.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The online application system can adjust not only the tone and speed of the voice, but also the language used when inputting the applicant's voice. For example, if the applicant is elderly, more polite and easy-to-understand language can be used. If the applicant is younger, casual language can be used. Furthermore, if the applicant is a foreigner, multiple languages ​​such as simple Japanese and English can be supported. This provides optimal voice input according to the applicant's attributes.

[0049] The online application system's application content confirmation section can translate the contents entered by the applicant in real time and confirm in multiple languages. For example, the contents entered by the applicant in Japanese can be translated into English and confirmed in English. Also, the contents entered by the applicant in Chinese can be translated into Japanese and confirmed in Japanese. Furthermore, the application content confirmation section can automatically select the optimal translation method depending on the language selected by the applicant. This makes it possible to confirm application contents in multiple languages.

[0050] The online application system allows the application result notification unit to prioritize notifications that are highly relevant based on the applicant's geographic location information. For example, if the applicant is in a specific area, the application results related to that area are prioritized. The application result notification unit can also prioritize notification of application results from the nearest municipality based on the applicant's location information. Furthermore, the application result notification unit can analyze the applicant's geographic location information in real time and suggest the optimal notification method. This provides the optimal notification based on the applicant's geographic location information.

[0051] The voice reception unit can select an appropriate reception method by referring to the applicant's past application history. For example, it can preferentially present application details that the applicant has frequently made in the past. The voice reception unit can also preferentially recognize voice commands that the applicant has used in the past. Furthermore, the voice reception unit can automatically select the optimal voice reception flow from the applicant's past application history. This provides the optimal reception method based on the applicant's past application history.

[0052] The voice reception unit can improve the accuracy of voice recognition by taking into account the applicant's current situation and environmental sounds. For example, if the applicant is in a noisy environment, noise canceling technology can be used to improve the accuracy of voice recognition. Also, the voice reception unit can use a normal voice recognition mode if the applicant is in a quiet environment. Furthermore, the voice reception unit can also analyze the environmental sounds around the applicant in real time and automatically apply the optimal voice recognition settings. This improves the accuracy of voice recognition and makes the application process proceed smoothly.

[0053] The authentication unit can select an appropriate authentication method by referring to the applicant's past authentication history. For example, the authentication unit preferentially suggests authentication methods that the applicant has used in the past. The authentication unit can also automatically select the optimal authentication method from the applicant's past authentication history. Furthermore, the authentication unit can also preferentially provide authentication methods that the applicant has frequently used in the past. In this way, the optimal authentication method based on the past authentication history is provided.

[0054] The authentication unit can improve the accuracy of authentication by taking into account the applicant's current situation and the ambient light. For example, if the applicant is in a dark environment, the authentication unit can increase the sensitivity of the camera to improve the accuracy of authentication. Also, if the applicant is in a bright environment, the authentication unit can perform authentication with normal camera settings. Furthermore, the authentication unit can analyze the ambient light around the applicant in real time and automatically apply the optimal camera settings. This improves the accuracy of authentication and makes the application process proceed smoothly.

[0055] The process flow of the first embodiment will be briefly described below.

[0056] Step 1: The voice reception unit starts the application process when the applicant issues a voice command to start the application. For example, if the applicant says, "I'm going to start the application," the system recognizes the voice and starts the application process. Also, if the applicant says, "I'd like to apply for a copy of my resident certificate," the system can ask for the information required for the application in sequence. Step 2: The authentication unit uses the smartphone camera to read the My Number card and verify the identity of the applicant. For example, when the applicant holds the My Number card up to the smartphone camera, the information on the card is read and the identity is verified. Step 3: The input unit asks the applicant for necessary information through interactive voice input, and the applicant responds by voice. For example, when asked "Please tell me your address," the applicant can respond "1-1-1 Nishi-Shinjuku, Shinjuku-ku, Tokyo." Step 4: The processing unit processes the application information input by the input unit and completes the online application, for example, by registering the input information in a database and completing the application procedure.

[0057] (Example 2) The online application system according to the embodiment of the present invention is a system that allows even elderly people to easily apply online to local governments. This online application system uses a smartphone as an entry point, accepts voice messages, uses a My Number card for authentication, and completes the online application through interactive voice input. For example, when an elderly person starts an application using a smartphone, the online application system recognizes the voice and starts the application process by simply issuing a simple command such as "I will start the application." For example, if the elderly person says, "I would like to apply for a copy of my resident's certificate," the system will sequentially ask for the information necessary for the application. Next, the online application system uses the smartphone's camera to read the My Number card to verify the identity of the applicant. This ensures that the applicant is the applicant. For example, when applying for a copy of a resident's certificate, authentication is completed by simply holding the My Number card over the smartphone's camera. Finally, the online application system asks the applicant for the necessary information through voice through interactive voice input, and the applicant responds to the request through voice. For example, when the system asks, "Please tell me your address," the applicant responds, "1-1-1 Nishi-Shinjuku, Shinjuku-ku, Tokyo." In this way, all the information necessary for the application is input by voice, and the online application is completed. This will allow even elderly people to easily apply online, accelerating the digital transformation (DX) of local governments.

[0058] The online application system according to the embodiment includes a voice reception unit, an authentication unit, an input unit, and a processing unit. The voice reception unit starts the application process when the applicant issues a voice instruction to start the application. For example, when the applicant says "I will start the application," the voice reception unit recognizes the voice and starts the application process. In addition, when the applicant says "I would like to apply for a copy of my resident's certificate," the voice reception unit can sequentially ask for information necessary for the application. The authentication unit reads the My Number card using a smartphone camera and performs identity verification. For example, when the applicant holds the My Number card up to the smartphone camera, the authentication unit reads the card information and performs identity verification. The input unit asks the applicant for necessary information by interactive voice input, and the applicant responds by voice. For example, when the input unit asks "Please tell me your address," the applicant can respond "1-1-1 Nishi-Shinjuku, Shinjuku-ku, Tokyo." The processing unit processes the application information input by the input unit and completes the online application. For example, the processing unit registers the input information in a database and completes the application procedure. As a result, the online application system according to the embodiment allows even elderly people to easily apply online, accelerating digital transformation in local governments.

[0059] The voice reception unit starts the application process when the applicant issues a voice command to start the application. For example, when the applicant says, "I'm going to start the application," the system recognizes the voice and starts the application process. The voice recognition technology incorporates advanced algorithms using natural language processing (NLP) and can accurately understand the applicant's speech. The voice reception unit analyzes the applicant's voice in real time and generates an appropriate response. For example, when the applicant says, "I would like to apply for a copy of my resident's certificate," the voice reception unit understands the intention and generates a prompt to ask the next required information. The voice reception unit converts the applicant's speech into text data, facilitating processing within the system. In addition, the voice reception unit also has filtering technology to remove background noise from the applicant's speech and obtain clear voice data. This allows the voice reception unit to accurately recognize the voice and smoothly progress the application process no matter what environment the applicant is in. In addition, the voice reception unit is capable of supporting multiple languages ​​and can also handle applications in foreign languages. This allows the voice reception unit to easily start an application by voice, greatly improving the convenience of online applications.

[0060] The authentication unit uses the smartphone camera to read the My Number card and verify the identity of the applicant. For example, when the applicant holds the My Number card up to the smartphone camera, the authentication unit reads the information on the card and verifies the applicant's identity. The authentication unit uses optical character recognition (OCR) technology to accurately read the information written on the My Number card. OCR technology analyzes high-resolution images and converts letters and numbers into digital data. In addition, the authentication unit also has NFC (near-field communication) technology to read information from the IC chip of the card. This allows the authentication unit to obtain not only the information written on the surface of the card, but also detailed information in the IC chip. The authentication unit compares the obtained information with the database in the system to verify the identity. For example, the authentication unit compares information such as the applicant's name, address, and date of birth with the database to see if they match. The authentication unit also uses facial recognition technology to compare the applicant's face with the photo on the My Number card, further improving the accuracy of identity verification. This allows the authentication unit to perform identity verification with high accuracy and prevent fraudulent applications. The authentication department also provides real-time feedback to the applicant on the progress of the authentication process to support a smooth authentication procedure, allowing the applicant to apply online with peace of mind and improving the reliability of the entire system.

[0061] The input unit asks the applicant for necessary information through interactive voice input, and the applicant responds by voice. For example, when the input unit asks, "Please tell me your address," the applicant can respond, "1-1-1 Nishi-Shinjuku, Shinjuku-ku, Tokyo." The input unit converts the applicant's response into text data using voice recognition technology to facilitate processing within the system. The input unit is equipped with natural language processing (NLP) technology to analyze the content of the applicant's response and accurately extract the necessary information. For example, if the applicant responds, "My address is 1-1-1 Nishi-Shinjuku, Shinjuku-ku, Tokyo," the input unit recognizes the keyword "address" and extracts the subsequent text as address information. If the applicant's response is incomplete or unclear, the input unit can ask additional questions to complete the necessary information. For example, if the applicant only responds, "Shinjuku-ku," the input unit will ask again, "Please tell me your specific address." The input unit saves the applicant's voice data so that it can be checked and reprocessed later. The input unit is also multilingual and can handle applications in foreign languages. In this way, the input unit can enable the applicant to easily provide the necessary information by voice, and can greatly improve the convenience of online application. Furthermore, the input unit can also analyze the applicant's voice data and provide feedback to identify improvements in the application process. In this way, the input unit can improve the user experience of the entire system.

[0062] The processing unit processes the application information input by the input unit and completes the online application. For example, the processing unit registers the input information in a database and completes the application procedure. The processing unit checks the consistency of the application information and checks whether there is any missing or incorrect information. For example, it checks whether the address information is entered correctly and whether all necessary documents are available. The processing unit transmits the application information to the relevant local government or institution and automatically proceeds with the necessary procedures. For example, it transmits the application information for a copy of the resident's certificate to the city hall system and starts the issuance procedure. The processing unit monitors the progress of the application procedure in real time and provides feedback to the applicant. For example, it notifies the applicant when the application is accepted or when additional documents are required. After the application procedure is completed, the processing unit sends a confirmation message to the applicant so that the application contents can be confirmed or corrected. In this way, the processing unit completes the application procedure quickly and accurately and allows the applicant to apply online with peace of mind. In addition, the processing unit is equipped with encryption technology to ensure the security of the application information and protects the applicant's personal information. In this way, the processing unit can improve the reliability and safety of online applications. Additionally, the processor may analyze the application data and provide feedback to identify improvements to the application process, thereby enabling the processor to improve the overall system efficiency and user experience.

[0063] The online application system is equipped with an application content confirmation unit that checks the information entered by the applicant and prompts them to make corrections if necessary. The application content confirmation unit checks the information entered by the applicant and prompts them to make corrections if necessary. For example, if there is an error in the information entered, the application content confirmation unit points out the error and prompts them to make corrections. In addition, if the information entered is incomplete, the application content confirmation unit can point out the incomplete part and request additional information. This makes it possible to check and correct the application content, improving the accuracy of the application.

[0064] The online application system includes an application result notification unit that notifies the applicant that the application has been completed. The application result notification unit notifies the applicant that the application has been completed. For example, the application result notification unit sends a notification to the applicant by email or SMS when the application has been completed. The application result notification unit can also allow the applicant to select the notification method they prefer (e.g., email, SMS, app notification, etc.). This allows the application result to be notified promptly.

[0065] The voice reception unit can estimate the emotion of the applicant and adjust the tone and speed of the voice reception based on the estimated emotion of the applicant. For example, when the applicant is nervous, the voice reception unit performs voice reception at a slow speed with a calm tone. Also, when the applicant is relaxed, the voice reception unit can perform voice reception at a normal speed with a bright tone. Furthermore, when the applicant is in a hurry, the voice reception unit can also perform voice reception at a fast speed with a quick tone. This enables voice reception according to the emotion of the applicant. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0066] The voice reception unit can select an appropriate reception method by referring to the applicant's past application history. For example, the voice reception unit preferentially presents application contents that the applicant has frequently made in the past. The voice reception unit can also preferentially recognize voice commands that the applicant has used in the past. Furthermore, the voice reception unit can automatically select an optimal voice reception flow from the applicant's past application history. This provides an optimal reception method based on the past application history.

[0067] The voice reception unit can improve the accuracy of voice recognition by taking into account the applicant's current situation and environmental sounds. For example, when the applicant is in a noisy environment, the voice reception unit can improve the accuracy of voice recognition using noise canceling technology. Also, when the applicant is in a quiet environment, the voice reception unit can use a normal voice recognition mode. Furthermore, the voice reception unit can analyze the environmental sounds around the applicant in real time and automatically apply the optimal voice recognition settings. This improves the accuracy of voice recognition and makes the application process proceed smoothly.

[0068] The authentication unit can estimate the emotion of the applicant and adjust the difficulty of the authentication process based on the estimated emotion of the applicant. For example, the authentication unit can provide an easy authentication process when the applicant is nervous. The authentication unit can also provide a normal authentication process when the applicant is relaxed. Furthermore, the authentication unit can also provide a quick authentication process when the applicant is in a hurry. This provides an authentication process according to the emotion of the applicant. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0069] The authentication unit can select an appropriate authentication method by referring to the applicant's past authentication history. For example, the authentication unit preferentially suggests authentication methods that the applicant has used in the past. The authentication unit can also automatically select the optimal authentication method from the applicant's past authentication history. Furthermore, the authentication unit can also preferentially provide authentication methods that the applicant has frequently used in the past. In this way, the optimal authentication method based on the past authentication history is provided.

[0070] The authentication unit can improve the accuracy of authentication by taking into account the applicant's current situation and the ambient light. For example, if the applicant is in a dark environment, the authentication unit can increase the sensitivity of the camera to improve the accuracy of authentication. Also, if the applicant is in a bright environment, the authentication unit can perform authentication with normal camera settings. Furthermore, the authentication unit can analyze the ambient light around the applicant in real time and automatically apply optimal camera settings. This improves the accuracy of authentication and makes the application process proceed smoothly.

[0071] The input unit can estimate the emotion of the applicant and adjust the tone and speed of the interactive voice input based on the estimated emotion of the applicant. For example, when the applicant is nervous, the input unit performs the interactive voice input at a slow speed with a calm tone. Also, when the applicant is relaxed, the input unit can perform the interactive voice input at a normal speed with a bright tone. Furthermore, when the applicant is in a hurry, the input unit can also perform the interactive voice input at a fast speed with a quick tone. In this way, an interactive voice input according to the emotion of the applicant is provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0072] The input unit can select an appropriate input method by referring to the applicant's past input history. For example, the input unit preferentially presents information that the applicant has frequently input in the past. The input unit can also preferentially recognize voice commands that the applicant has used in the past. Furthermore, the input unit can automatically select an optimal interactive voice input flow from the applicant's past input history. This provides an optimal input method based on the past input history.

[0073] The input unit may improve the accuracy of the voice recognition by taking into account the applicant's current situation and environmental sounds. For example, the input unit may improve the accuracy of the voice recognition by using noise canceling technology when the applicant is in a noisy environment. Also, the input unit may use a normal voice recognition mode when the applicant is in a quiet environment. Furthermore, the input unit may analyze the environmental sounds around the applicant in real time and automatically apply the optimal voice recognition settings. This improves the accuracy of the voice recognition and makes the application process proceed smoothly.

[0074] The processing unit can estimate the emotion of the applicant and adjust the processing method of the application information based on the estimated emotion of the applicant. For example, the processing unit can provide a simple processing method when the applicant is nervous. Also, the processing unit can provide a normal processing method when the applicant is relaxed. Furthermore, the processing unit can also provide a quick processing method when the applicant is in a hurry. In this way, a processing method according to the emotion of the applicant is provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] The processing unit can select an appropriate processing method by referring to past processing history. For example, when a similar application has been made in the past, the processing unit selects the optimal processing method based on the processing history. Also, the processing unit can automatically select the most efficient processing method from the past processing history. Furthermore, the processing unit can analyze the past processing history and propose an optimal processing flow. In this way, an optimal processing method based on the past processing history is provided.

[0076] The processing unit can improve the accuracy of the processing based on the applicant's current situation and environment. For example, the processing unit can provide a quick processing method when the applicant is in a hurry. Also, the processing unit can provide a normal processing method when the applicant is relaxed. Furthermore, the processing unit can analyze the applicant's current situation in real time and automatically apply the optimal processing method. This improves the accuracy of the processing and makes the application process proceed smoothly.

[0077] The application content confirmation unit can estimate the emotion of the applicant and adjust the confirmation method of the application content based on the estimated emotion of the applicant. For example, the application content confirmation unit can provide a simple confirmation method when the applicant is nervous. Also, the application content confirmation unit can provide a normal confirmation method when the applicant is relaxed. Furthermore, the application content confirmation unit can also provide a quick confirmation method when the applicant is in a hurry. This provides a confirmation method according to the emotion of the applicant. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0078] The application content confirmation unit can select an appropriate confirmation method by referring to past confirmation history. For example, if a similar application has been made in the past, the application content confirmation unit selects the optimal confirmation method based on the confirmation history. Furthermore, the application content confirmation unit can automatically select the most efficient confirmation method from the past confirmation history. Furthermore, the application content confirmation unit can analyze the past confirmation history and propose an optimal confirmation flow. This provides the optimal confirmation method based on the past confirmation history.

[0079] The application result notification unit can estimate the emotion of the applicant and adjust the notification method of the application result based on the estimated emotion of the applicant. For example, if the applicant is nervous, the application result notification unit can notify the application result in a calm tone at a slow speed. Also, if the applicant is relaxed, the application result notification unit can notify the application result in a bright tone at a normal speed. Furthermore, if the applicant is in a hurry, the application result notification unit can also notify the application result in a quick tone at a fast speed. This provides a notification method according to the emotion of the applicant. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0080] The application result notification unit can select an appropriate notification method by referring to past notification history. For example, if a similar application has been made in the past, the application result notification unit selects the optimal notification method based on the notification history. The application result notification unit can also automatically select the most efficient notification method from the past notification history. Furthermore, the application result notification unit can also analyze the past notification history and propose the optimal notification flow. This provides the optimal notification method based on the past notification history.

[0081] The application result notification unit can estimate the emotion of the applicant and determine the priority of the notification of the application result based on the estimated emotion of the applicant. For example, when the applicant is nervous, the application result notification unit can notify the application result with priority and respond quickly. Also, when the applicant is relaxed, the application result notification unit can notify the application result with normal priority. Furthermore, when the applicant is in a hurry, the application result notification unit can also notify the application result with top priority and quickly proceed with the application process. Thereby, notification is performed with priority according to the emotion of the applicant. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0082] The application result notification unit can prioritize notifications that are highly relevant based on the applicant's geographic location information. For example, if the applicant is in a specific area, the application result notification unit prioritizes notification of application results related to that area. In addition, the application result notification unit can prioritize notification of application results from the nearest municipality based on the applicant's location information. Furthermore, the application result notification unit can analyze the applicant's geographic location information in real time and suggest the optimal notification method. This provides the optimal notification based on the applicant's geographic location information.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The online application system can adjust not only the tone and speed of the voice, but also the language used when inputting the applicant's voice. For example, if the applicant is elderly, more polite and easy-to-understand language can be used. If the applicant is younger, casual language can be used. Furthermore, if the applicant is a foreigner, multiple languages ​​such as simple Japanese and English can be supported. This provides optimal voice input according to the applicant's attributes.

[0085] The online application system's application content confirmation section can translate the contents entered by the applicant in real time and confirm in multiple languages. For example, the contents entered by the applicant in Japanese can be translated into English and confirmed in English. Also, the contents entered by the applicant in Chinese can be translated into Japanese and confirmed in Japanese. Furthermore, the application content confirmation section can automatically select the optimal translation method depending on the language selected by the applicant. This makes it possible to confirm application contents in multiple languages.

[0086] The online application system allows the application result notification unit to prioritize notifications that are highly relevant based on the applicant's geographic location information. For example, if the applicant is in a specific area, the application results related to that area are prioritized. The application result notification unit can also prioritize notification of application results from the nearest municipality based on the applicant's location information. Furthermore, the application result notification unit can analyze the applicant's geographic location information in real time and suggest the optimal notification method. This provides the optimal notification based on the applicant's geographic location information.

[0087] The voice reception unit can estimate the emotion of the applicant and adjust the tone and speed of the voice reception based on the estimated emotion of the applicant. For example, if the applicant is nervous, the voice reception unit can perform voice reception at a slow speed with a calm tone. Also, if the applicant is relaxed, the voice reception unit can perform voice reception at a normal speed with a bright tone. Furthermore, if the applicant is in a hurry, the voice reception unit can also perform voice reception at a fast speed with a quick tone. This enables voice reception according to the emotion of the applicant. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0088] The voice reception unit can select an appropriate reception method by referring to the applicant's past application history. For example, it can preferentially present application details that the applicant has frequently made in the past. The voice reception unit can also preferentially recognize voice commands that the applicant has used in the past. Furthermore, the voice reception unit can automatically select the optimal voice reception flow from the applicant's past application history. This provides the optimal reception method based on the applicant's past application history.

[0089] The voice reception unit can improve the accuracy of voice recognition by taking into account the applicant's current situation and environmental sounds. For example, if the applicant is in a noisy environment, noise canceling technology can be used to improve the accuracy of voice recognition. Also, the voice reception unit can use a normal voice recognition mode if the applicant is in a quiet environment. Furthermore, the voice reception unit can also analyze the environmental sounds around the applicant in real time and automatically apply the optimal voice recognition settings. This improves the accuracy of voice recognition and makes the application process proceed smoothly.

[0090] The authentication unit can estimate the emotion of the applicant and adjust the difficulty of the authentication process based on the estimated emotion of the applicant. For example, if the applicant is nervous, an easy authentication process is provided. Also, the authentication unit can provide a normal authentication process if the applicant is relaxed. Furthermore, the authentication unit can also provide a quick authentication process if the applicant is in a hurry. This provides an authentication process according to the emotion of the applicant. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0091] The authentication unit can select an appropriate authentication method by referring to the applicant's past authentication history. For example, the authentication unit preferentially suggests authentication methods that the applicant has used in the past. The authentication unit can also automatically select the optimal authentication method from the applicant's past authentication history. Furthermore, the authentication unit can also preferentially provide authentication methods that the applicant has frequently used in the past. In this way, the optimal authentication method based on the past authentication history is provided.

[0092] The authentication unit can improve the accuracy of authentication by taking into account the applicant's current situation and the ambient light. For example, if the applicant is in a dark environment, the authentication unit can increase the sensitivity of the camera to improve the accuracy of authentication. Also, if the applicant is in a bright environment, the authentication unit can perform authentication with normal camera settings. Furthermore, the authentication unit can analyze the ambient light around the applicant in real time and automatically apply the optimal camera settings. This improves the accuracy of authentication and makes the application process proceed smoothly.

[0093] The input unit can estimate the emotion of the applicant and adjust the tone and speed of the interactive voice input based on the estimated emotion of the applicant. For example, if the applicant is nervous, the input unit can perform interactive voice input at a slow speed with a calm tone. Also, if the applicant is relaxed, the input unit can perform interactive voice input at a normal speed with a bright tone. Furthermore, if the applicant is in a hurry, the input unit can also perform interactive voice input at a fast speed with a quick tone. This provides interactive voice input according to the emotion of the applicant. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0094] The process flow of the second embodiment will be briefly described below.

[0095] Step 1: The voice reception unit starts the application process when the applicant issues a voice command to start the application. For example, if the applicant says, "I'm going to start the application," the system recognizes the voice and starts the application process. Also, if the applicant says, "I'd like to apply for a copy of my resident certificate," the system can ask for the information required for the application in sequence. Step 2: The authentication unit uses the smartphone camera to read the My Number card and verify the identity of the applicant. For example, when the applicant holds the My Number card up to the smartphone camera, the information on the card is read and the identity is verified. Step 3: The input unit asks the applicant for necessary information through interactive voice input, and the applicant responds by voice. For example, when asked "Please tell me your address," the applicant can respond "1-1-1 Nishi-Shinjuku, Shinjuku-ku, Tokyo." Step 4: The processing unit processes the application information input by the input unit and completes the online application, for example, by registering the input information in a database and completing the application procedure.

[0096] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0097] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0098] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0099] Each of the multiple elements including the voice reception unit, authentication unit, input unit, processing unit, application content confirmation unit, and application result notification unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the voice reception unit is realized by the microphone 38B and the control unit 46A of the smart device 14, and recognizes the voice of the applicant to start the application process. The authentication unit reads the My Number card using the camera 42 of the smart device 14 to perform identity verification. The input unit is realized by the control unit 46A of the smart device 14, and asks the applicant for necessary information by interactive voice input. The processing unit is realized by the specific processing unit 290 of the data processing device 12, and processes the input application information. The application content confirmation unit is realized by the specific processing unit 290 of the data processing device 12, and confirms the input information and prompts the applicant to make corrections as necessary. The application result notification unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and notifies the applicant that the application has been completed. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 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.

[0102] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0103] 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.

[0104] 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 the voice according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0106] 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 performed in a secure state.

[0107] Fig. 4 shows an example of 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.

[0108] 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.

[0109] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0110] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0111] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0112] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0113] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0114] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0115] Each of the multiple elements including the voice reception unit, authentication unit, input unit, processing unit, application content confirmation unit, and application result notification unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the voice reception unit is realized by the microphone 238 and the control unit 46A of the smart glasses 214, and recognizes the voice of the applicant to start the application process. The authentication unit reads the My Number card using the camera 42 of the smart glasses 214 to perform identity verification. The input unit is realized by the control unit 46A of the smart glasses 214, and asks the applicant for necessary information by interactive voice input. The processing unit is realized by the specific processing unit 290 of the data processing device 12, and processes the input application information. The application content confirmation unit is realized by the specific processing unit 290 of the data processing device 12, and confirms the input information and prompts correction as necessary. The application result notification unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and notifies the applicant that the application has been completed. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 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.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0119] 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.

[0120] 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 the voice according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0122] 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 performed in a secure state.

[0123] 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.

[0124] 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.

[0125] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0126] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0127] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0128] 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 voice indicating a user input for 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.

[0129] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0130] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements including the voice reception unit, authentication unit, input unit, processing unit, application content confirmation unit, and application result notification unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the voice reception unit is realized by the microphone 238 and the control unit 46A of the headset type terminal 314, and recognizes the voice of the applicant to start the application process. The authentication unit reads the My Number card using the camera 42 of the headset type terminal 314 to perform identity verification. The input unit is realized by the control unit 46A of the headset type terminal 314, and asks the applicant for necessary information by interactive voice input. The processing unit is realized by the specific processing unit 290 of the data processing device 12, and processes the input application information. The application content confirmation unit is realized by the specific processing unit 290 of the data processing device 12, and confirms the input information and prompts the applicant to make corrections as necessary. The application result notification unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12, and notifies the applicant that the application has been completed. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0135] The 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.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0139] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0140] 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.

[0141] 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.

[0142] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0143] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0144] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0145] 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 a voice indicating a user input for 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.

[0146] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0147] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0148] Each of the multiple elements including the voice reception unit, authentication unit, input unit, processing unit, application content confirmation unit, and application result notification unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the voice reception unit is realized by the microphone 238 and the control unit 46A of the robot 414, and recognizes the voice of the applicant to start the application process. The authentication unit reads the My Number card using the camera 42 of the robot 414 to perform identity verification. The input unit is realized by the control unit 46A of the robot 414, and asks the applicant for necessary information by interactive voice input. The processing unit is realized by the specific processing unit 290 of the data processing device 12, and processes the input application information. The application content confirmation unit is realized by the specific processing unit 290 of the data processing device 12, and confirms the input information and prompts the applicant to make corrections as necessary. The application result notification unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and notifies the applicant that the application has been completed. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0149] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0150] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0151] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0152] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0153] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0154] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0155] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0156] 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.

[0157] 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 Universal Serial Bus (USB) 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.

[0158] In addition, 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 upon request from the data processing device 12.

[0159] 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.

[0160] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0161] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0162] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0163] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0164] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0165] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0166] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0167] (Appendix 1) a voice reception unit that receives an application by voice; an authentication unit that performs authentication using a My Number card when the start of the application is accepted by the voice acceptance unit; an input unit that allows an applicant authenticated by the authentication unit to input application information by interactive voice input; a processing unit that processes the application information input by the input unit. A system characterized by: (Appendix 2) Equipping the application confirmation section with which the information entered by the applicant can be checked and corrections can be made if necessary 2. The system of claim 1, (Appendix 3) Equipped with an application result notification section that notifies the applicant that the application has been completed 2. The system of claim 1, (Appendix 4) The voice receiving unit is Infer the applicant's emotions and adjust the tone and speed of voice messages based on the estimated emotions of the applicant 2. The system of claim 1, (Appendix 5) The voice receiving unit is Select the appropriate reception method by referring to the applicant's past application history 2. The system of claim 1, (Appendix 6) The voice receiving unit is Improve speech recognition accuracy based on the applicant's current situation and environmental sounds 2. The system of claim 1, (Appendix 7) The authentication unit Estimating the applicant's emotions and adjusting the difficulty of the authentication process based on the estimated emotions of the applicant 2. The system of claim 1, (Appendix 8) The authentication unit Select the appropriate authentication method by referring to the applicant's past authentication history 2. The system of claim 1, (Appendix 9) The authentication unit Improve authentication accuracy based on the applicant's current situation and ambient light 2. The system of claim 1, (Appendix 10) The input unit is Estimating the emotion of the applicant and adjusting the tone and rate of the interactive voice input based on the estimated emotion of the applicant 2. The system of claim 1, (Appendix 11) The input unit is Select the appropriate input method by referring to the applicant's past input history 2. The system of claim 1, (Appendix 12) The input unit is Improve speech recognition accuracy by taking into account the applicant's current situation and environmental sounds 2. The system of claim 1, (Appendix 13) The processing unit includes: Inferring the applicant's sentiment and adjusting how the application information is processed based on the inferred sentiment 2. The system of claim 1, (Appendix 14) The processing unit includes: Refer to past processing history to select the appropriate processing method 2. The system of claim 1, (Appendix 15) The processing unit includes: Improve the accuracy of processing based on the applicant's current situation and circumstances 2. The system of claim 1, (Appendix 16) The application content confirmation unit Infer the applicant's emotions and adjust the review method for the application based on the estimated emotions of the applicant 3. The system of claim 2, (Appendix 17) The application content confirmation unit Refer to past verification history to select the appropriate verification method 3. The system of claim 2, (Appendix 18) The application result notification unit Infer the applicant's emotions and adjust the notification method for the application result based on the estimated emotions of the applicant. 4. The system of claim 3. (Appendix 19) The application result notification unit Refer to past notification history to select the appropriate notification method 4. The system of claim 3. (Appendix 20) The application result notification unit Estimate the applicant's feelings and determine the priority of notification of the application results based on the estimated applicant's feelings 4. The system of claim 3. (Appendix 21) The application result notification unit Prioritize relevant notifications based on your geographic location 4. The system of claim 3. [Explanation of symbols]

[0168] 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 voice reception unit that receives an application by voice; an authentication unit that performs authentication using a My Number card when the start of the application is accepted by the voice acceptance unit; an input unit that allows an applicant authenticated by the authentication unit to input application information by interactive voice input; a processing unit that processes the application information input by the input unit. A system characterized by:

2. Equipping the application confirmation section with which the information entered by the applicant can be checked and corrections can be made if necessary 2. The system of claim 1 .

3. Equipped with an application result notification section that notifies the applicant that the application has been completed 2. The system of claim 1 .

4. The voice receiving unit is Infer the applicant's emotions and adjust the tone and speed of voice messages based on the estimated emotions of the applicant 2. The system of claim 1 .

5. The voice receiving unit is Select the appropriate reception method by referring to the applicant's past application history 2. The system of claim 1 .

6. The voice receiving unit is Improve speech recognition accuracy based on the applicant's current situation and environmental sounds 2. The system of claim 1 .

7. The authentication unit Estimating the applicant's emotions and adjusting the difficulty of the authentication process based on the estimated emotions of the applicant 2. The system of claim 1 .

8. The authentication unit Select the appropriate authentication method by referring to the applicant's past authentication history 2. The system of claim 1 .

9. The authentication unit Improve authentication accuracy based on the applicant's current situation and ambient lighting 2. The system of claim 1 .

10. The input unit includes: Estimating the emotion of the applicant and adjusting the tone and rate of the interactive voice input based on the estimated emotion of the applicant 2. The system of claim 1 .

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