System

Abstract

An object of the system according to the embodiment is to fully automate routine work and efficiently handle the routine work.SOLUTION: A system includes an automation unit, an avatar unit, a caption display unit, a translation unit, and a confirmation unit. The automation unit fully automates the routine work. The avatar part corresponds to the work processed by the automatic part by a AI avatar having concrete technical specifications. The caption display part displays the contents spoken by the avatar part in the front as captions. The translation portion automatically translates the content spoken by the avatar portion. The confirmation unit performs identity verification by the avatar unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not fully automate routine tasks, leaving room for improvement in efficiency.

[0005] The system according to the embodiment aims to fully automate routine tasks and respond efficiently. [Means for solving the problem]

[0006] The system according to the embodiment includes an automation unit, an avatar unit, a subtitle display unit, a translation unit, and a verification unit. The automation unit fully automates routine tasks. The avatar unit handles tasks processed by the automation unit using an AI avatar with specific technical specifications. The subtitle display unit displays subtitles in front of the user that represent what is spoken by the avatar unit. The translation unit automatically translates what is spoken by the avatar unit. The verification unit uses the avatar unit to verify the user's identity. [Effects of the Invention]

[0007] The system according to the embodiment can fully automate routine tasks and handle them efficiently. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A counter work efficiency improvement system according to an embodiment of the present invention fully automates routine tasks and uses a friendly AI avatar to handle them. The counter work efficiency improvement system includes an automation unit that fully automates routine tasks, an avatar unit that uses a friendly AI avatar to handle tasks processed by the automation unit, a subtitle display unit that displays subtitles in front of the user what is spoken by the avatar unit, a translation unit that automatically translates what is spoken by the avatar unit, and a verification unit that verifies the user's identity using the avatar unit. For example, the counter work efficiency improvement system fully automates routine tasks at government offices and banks. For example, an AI avatar automatically performs procedures such as issuing a resident registration card and opening an account. Next, the counter work efficiency improvement system uses a friendly AI avatar to handle tasks. The AI ​​avatar has a human-like appearance and voice, giving the user a friendly impression. Furthermore, the counter work efficiency improvement system displays subtitles in front of the user to prevent mishearing. For example, what the AI ​​avatar is saying is displayed as subtitles. The counter work efficiency improvement system also includes an automatic translation function that can handle languages ​​other than Japanese. For example, responses can be provided in the user's native language, such as English or Chinese. Furthermore, the counter work efficiency system operates an AI avatar in the background when atypical content arises. For example, when complex procedures or special handling are required, the AI ​​avatar can be operated to handle the situation. Furthermore, the counter work efficiency system does not have to be located in a government office; it can be located in a corner of a supermarket or convenience store, for example. This allows the counter work efficiency system to streamline human resources, expand reception hours, and liberalize reception locations. This allows the counter work efficiency system to streamline human resources, expand reception hours, and liberalize reception locations. For example, the number of staff working at the counter can be halved. It also allows reception hours to be extended, allowing services to be provided on weekends, holidays, and at night. Furthermore, the liberalization of reception locations eliminates the need for users to go to the government office.

[0029] A counter work efficiency improvement system according to an embodiment includes an automation unit, an avatar unit, a subtitle display unit, a translation unit, and a confirmation unit. The automation unit fully automates routine tasks. For example, the automation unit automatically performs procedures such as issuing resident registration certificates and opening accounts. The automation unit can also automate tasks such as document processing and data entry. The avatar unit responds with a friendly AI avatar. For example, the avatar unit has a human-like appearance and voice, giving the user a friendly impression. The avatar unit can also respond appropriately to user questions. The subtitle display unit displays subtitles in front of the user what is spoken by the avatar unit. For example, the subtitle display unit displays what the AI ​​avatar is saying as subtitles. The subtitle display unit can also adjust the display position, font size, color, and the like. The translation unit automatically translates what is spoken by the avatar unit. For example, the translation unit responds in the user's native language, such as English or Chinese. The translation unit can also adjust the translation algorithm used and the supported languages. The verification unit verifies the identity of the customer using the avatar unit. For example, the verification unit may verify the identity of the customer using a My Number card. The verification unit may also use authentication technologies such as facial recognition technology and fingerprint authentication. As a result, the counter work efficiency improvement system according to the embodiment aims to improve the efficiency of counter work by fully automating routine tasks and using friendly AI avatars.

[0030] The counter work efficiency improvement system is equipped with an operation unit that corresponds to the specific content of non-routine work. The operation unit corresponds to the non-routine content. For example, the operation unit corresponds to cases where complex procedures or special responses are required. The operation unit can also provide customized responses in accordance with user requests. Furthermore, the operation unit can also provide exception handling and customized responses under specific conditions. For example, the operation unit can provide exception handling under specific conditions. The operation unit can also provide customized responses. This makes it possible to handle non-routine content and provide flexible responses.

[0031] The counter work efficiency improvement system includes a location setting unit that freely sets the location of the counter. The location setting unit freely sets the location of the counter. For example, the location setting unit can handle not only a counter at a government office or bank, but also a corner of a supermarket or convenience store. The location setting unit can also set an online virtual counter. Furthermore, the location setting unit can set not only a physical location, but also an online virtual counter. For example, the location setting unit sets an online virtual counter so that the user can complete procedures from home. This allows the location of the counter to be freely set, improving user convenience.

[0032] The counter operation efficiency improvement system includes an identity verification means using facial recognition technology. The identity verification means using facial recognition technology performs identity verification using facial recognition technology. For example, the identity verification means using facial recognition technology photographs the user's face with a camera and performs identity verification using a facial recognition algorithm. The identity verification means using facial recognition technology can also use deep learning technology to improve the accuracy of the facial recognition algorithm. The identity verification means using facial recognition technology can also use pattern recognition technology to improve the accuracy of the facial recognition algorithm. For example, the identity verification means using facial recognition technology improves the accuracy of the facial recognition algorithm using deep learning technology. The identity verification means using facial recognition technology also improves the accuracy of the facial recognition algorithm using pattern recognition technology. As a result, the accuracy of identity verification is improved by using facial recognition technology.

[0033] The automation department can automate procedures such as issuing resident registration certificates and opening accounts. The automation department automates procedures such as issuing resident registration certificates and opening accounts. For example, the automation department automates the procedure for issuing resident registration certificates. The automation department can also automatically collect the documents required for opening an account and automate the procedure. Furthermore, the automation department can automate tasks such as document processing and data entry. For example, the automation department automates document processing. The automation department can also automate data entry. This allows procedures such as issuing resident registration certificates and opening accounts to be performed automatically, thereby improving the efficiency of operations.

[0034] The avatar unit has specific appearance and voice specifications, and can give the user a friendly impression. The avatar unit has a human-like appearance and voice, and can give the user a friendly impression. For example, the avatar unit can adjust the appearance design to give the user a friendly impression. The avatar unit can also adjust the tone and accent of the voice to give the user a friendly impression. Furthermore, the avatar unit can respond appropriately to the user's questions. For example, the avatar unit can respond appropriately to the user's questions. The avatar unit can also provide customized responses according to the user's requests. This allows the user to use the system with peace of mind thanks to the friendly AI avatar.

[0035] The subtitle display unit can display what the AI ​​avatar is saying as subtitles. The subtitle display unit displays what the AI ​​avatar is saying as subtitles. For example, the subtitle display unit displays what the AI ​​avatar is saying as subtitles. The subtitle display unit can also adjust the display position, font size, color, etc. Furthermore, the subtitle display unit can customize the display content. For example, the subtitle display unit can customize the display content and display it in a format that is easy for the user to see. This makes the subtitle display accessible to people with hearing impairments.

[0036] The translation unit can support multiple languages ​​(e.g., English and Chinese). The translation unit can support multiple languages. For example, the translation unit can support the user's native language, such as English or Chinese. The translation unit can also adjust the translation algorithm used and the supported languages. Furthermore, the translation unit can use machine learning technology to improve the accuracy of the translation. For example, the translation unit can improve the accuracy of the translation using machine learning technology. The translation unit can also improve the accuracy of the translation using context analysis technology. This makes it possible to support foreign users using the automatic translation function.

[0037] The verification unit can verify the identity of the user using a My Number card. The verification unit can verify the identity of the user using a My Number card. For example, the verification unit can verify the identity of the user by having the user present the My Number card. The verification unit can also verify the identity of the user by reading information from the My Number card using a card reader. The verification unit can also use authentication technology such as facial recognition technology or fingerprint authentication. For example, the verification unit can verify the identity of the user using facial recognition technology. The verification unit can also verify the identity of the user using fingerprint authentication. As a result, the security of identity verification is ensured by using the My Number card.

[0038] The automation unit can apply different automation algorithms depending on the type of business being processed. The automation unit applies different automation algorithms depending on the type of business being processed. For example, the automation unit applies a quick database search algorithm to issue resident registration certificates. The automation unit can also apply a detailed identity verification algorithm to open accounts. Furthermore, the automation unit can apply an accurate calculation algorithm to pay taxes. This improves processing efficiency by applying the optimal algorithm for each type of business.

[0039] The automation unit can determine the priority of tasks to be processed based on the user's past usage history. The automation unit determines the priority of tasks to be processed based on the user's past usage history. For example, the automation unit prioritizes tasks that the user uses frequently. The automation unit can also prioritize tasks that are highly important based on the user's past usage history. Furthermore, the automation unit can adjust the priority based on the time period in which the user used the task in the past. In this way, by determining the priority based on the user's past usage history, tasks that are important to the user can be prioritized.

[0040] The automation unit can automatically collect the necessary documents and information depending on the content of the business being processed. The automation unit automatically collects the necessary documents and information depending on the content of the business being processed. For example, the automation unit automatically collects the personal information required for issuing a resident registration card. The automation unit can also automatically collect the identification documents required for opening an account. Furthermore, the automation unit can automatically collect the tax payment information required for paying taxes. In this way, the efficiency of business operations is improved by automatically collecting the necessary documents and information.

[0041] The automation unit can customize the content of the business to be processed based on the user's geographic location information. The automation unit customizes the content of the business to be processed based on the user's geographic location information. For example, the automation unit prioritizes specific procedures in the area where the user lives. The automation unit can also provide information on the nearest counter based on the user's current location. Furthermore, the automation unit can automate region-specific procedures based on the user's geographic location information. This makes it possible to accommodate region-specific procedures by customizing the content of the business based on geographic location information.

[0042] The automation unit can optimize the content of the tasks to be processed based on the user's social media activity. The automation unit optimizes the content of the tasks to be processed based on the user's social media activity. For example, the automation unit prioritizes procedures that the user frequently mentions on social media. The automation unit can also predict and suggest necessary procedures from the user's social media activity. Furthermore, the automation unit can optimize the content of tasks based on the user's feedback on social media. In this way, by optimizing the content of tasks based on social media activity, it is possible to predict and provide necessary procedures for the user.

[0043] The automation unit can adjust the content of the work to be processed based on the user's past feedback. The automation unit adjusts the content of the work to be processed based on the user's past feedback. For example, the automation unit improves a procedure that the user was dissatisfied with in the past. The automation unit can also optimize the work content based on the user's past feedback. Furthermore, the automation unit can extract and reflect important areas for improvement from the user's past feedback. In this way, adjusting the work content based on the user's past feedback improves user satisfaction.

[0044] The avatar department can select different avatar appearances and voices depending on the type of business being handled. The avatar department can select different avatar appearances and voices depending on the type of business being handled. For example, the avatar department can select an avatar with an official appearance and voice for issuing a resident registration certificate. The avatar department can also select an avatar with a friendly appearance and voice for opening an account. Furthermore, the avatar department can select an avatar with a trustworthy appearance and voice for paying taxes. This allows for more appropriate handling by selecting an avatar according to the type of business.

[0045] The avatar unit can customize the avatar's actions and gestures according to the content of the corresponding business. The avatar unit customizes the avatar's actions and gestures according to the content of the corresponding business. For example, the avatar unit can set an avatar that performs polite actions and gestures for issuing a resident registration certificate. The avatar unit can also set an avatar that performs friendly actions and gestures for opening an account. Furthermore, the avatar unit can set an avatar that performs reliable actions and gestures for paying taxes. In this way, customizing actions and gestures according to the content of the business enables more natural responses.

[0046] The avatar department can change the avatar's clothing and background depending on the type of work being performed. The avatar department can change the avatar's clothing and background depending on the type of work being performed. For example, the avatar department can set official clothing and background for issuing a resident registration certificate. The avatar department can also set friendly clothing and background for opening an account. Furthermore, the avatar department can also set reliable clothing and background for paying taxes. This allows for more appropriate responses by changing the clothing and background depending on the type of work being performed.

[0047] The avatar unit can customize the content of the corresponding business based on the user's geographic location information. The avatar unit customizes the content of the corresponding business based on the user's geographic location information. For example, the avatar unit prioritizes specific procedures in the area where the user lives. The avatar unit can also provide information on the nearest counter based on the user's current location. Furthermore, the avatar unit can automate region-specific procedures based on the user's geographic location information. This makes it possible to accommodate region-specific procedures by customizing the content of the business based on geographic location information.

[0048] The avatar department can optimize the content of corresponding tasks based on the user's social media activity. The avatar department optimizes the content of corresponding tasks based on the user's social media activity. For example, the avatar department prioritizes procedures that the user frequently mentions on social media. The avatar department can also predict and suggest necessary procedures from the user's social media activity. Furthermore, the avatar department can optimize the content of tasks based on the user's social media feedback. In this way, by optimizing the content of tasks based on social media activity, it is possible to predict and provide necessary procedures for the user.

[0049] The avatar unit can adjust the content of the corresponding work based on the user's past feedback. The avatar unit adjusts the content of the corresponding work based on the user's past feedback. For example, the avatar unit improves a procedure that the user was dissatisfied with in the past. The avatar unit can also optimize the work content based on the user's past feedback. Furthermore, the avatar unit can extract and reflect important points for improvement from the user's past feedback. In this way, adjusting the work content based on the user's past feedback improves user satisfaction.

[0050] The subtitle display unit can apply different fonts and colors depending on the type of content to be displayed. The subtitle display unit can apply different fonts and colors depending on the type of content to be displayed. For example, the subtitle display unit can apply a bold font and red color to important information. The subtitle display unit can also apply a standard font and black color to regular information. Furthermore, the subtitle display unit can apply an italic font and yellow color to information that requires attention. In this way, by applying fonts and colors depending on the type of content, the visibility of the information is improved.

[0051] The subtitle display unit can determine the priority of the content to be displayed based on the user's past usage history. The subtitle display unit determines the priority of the content to be displayed based on the user's past usage history. For example, the subtitle display unit prioritizes displaying information that the user frequently uses. The subtitle display unit can also prioritize information of high importance based on the user's past usage history. Furthermore, the subtitle display unit can adjust the priority based on the time period in which the user used the content in the past. In this way, by determining the priority based on the user's past usage history, it is possible to prioritize displaying information that is important to the user.

[0052] The subtitle display unit can adjust the level of detail of the content to be displayed according to the user's level of expertise. The subtitle display unit adjusts the level of detail of the content to be displayed according to the user's level of expertise. For example, the subtitle display unit can display a concise explanation to a user with little expertise. The subtitle display unit can also display a detailed explanation to a user with a lot of expertise. Furthermore, the subtitle display unit can customize the display content according to the user's level of expertise. In this way, by adjusting the display content according to the user's level of expertise, it is possible to provide information that is easy for the user to understand.

[0053] The subtitle display unit can customize the content to be displayed based on the user's geographical location information. The subtitle display unit customizes the content to be displayed based on the user's geographical location information. For example, the subtitle display unit prioritizes displaying specific information for the area where the user lives. The subtitle display unit can also display information about the nearest service center based on the user's current location. Furthermore, the subtitle display unit can display information specific to the area based on the user's geographical location information. This makes it possible to accommodate information specific to the area by customizing the display content based on the geographical location information.

[0054] The subtitle display unit can optimize the content to be displayed based on the user's social media activity. The subtitle display unit optimizes the content to be displayed based on the user's social media activity. For example, the subtitle display unit prioritizes displaying information that the user frequently mentions on social media. The subtitle display unit can also predict and display necessary information based on the user's social media activity. Furthermore, the subtitle display unit can optimize the display content based on the user's social media feedback. In this way, by optimizing the display content based on the social media activity, necessary information for the user can be predicted and provided.

[0055] The subtitle display unit can adjust the content to be displayed based on the user's past feedback. The subtitle display unit adjusts the content to be displayed based on the user's past feedback. For example, the subtitle display unit improves the display of information that the user was dissatisfied with in the past. The subtitle display unit can also optimize the display content based on the user's past feedback. Furthermore, the subtitle display unit can extract and reflect important improvements from the user's past feedback. In this way, adjusting the display content based on the user's past feedback improves user satisfaction.

[0056] The translation unit can apply different translation algorithms depending on the type of language supported. The translation unit applies different translation algorithms depending on the type of language supported. For example, the translation unit applies a natural language processing algorithm to English. The translation unit can also apply a context analysis algorithm to Chinese. Furthermore, the translation unit can apply a machine learning algorithm to Spanish. This improves translation accuracy by applying the optimal algorithm depending on the type of language.

[0057] The translation unit can determine the priority of corresponding content based on the user's past usage history. The translation unit determines the priority of corresponding content based on the user's past usage history. For example, the translation unit gives priority to translating languages ​​that the user uses frequently. The translation unit can also give priority to content that is highly important based on the user's past usage history. Furthermore, the translation unit can adjust the priority based on the time period in which the user used the service in the past. In this way, by determining the priority based on the past usage history, content that is important to the user can be translated preferentially.

[0058] The translation unit can adjust the level of detail of the corresponding content according to the user's level of expertise. The translation unit adjusts the level of detail of the corresponding content according to the user's level of expertise. For example, the translation unit can provide a concise translation for a user with little expertise. The translation unit can also provide a detailed translation for a user with a lot of expertise. Furthermore, the translation unit can customize the translation content according to the user's level of expertise. In this way, by adjusting the translation content according to the level of expertise, it is possible to provide information that is easy for the user to understand.

[0059] The translation unit can customize the corresponding content based on the user's geographic location information. The translation unit customizes the corresponding content based on the user's geographic location information. For example, the translation unit prioritizes translating specific information for the area where the user lives. The translation unit can also translate information about the nearest service center based on the user's current location. Furthermore, the translation unit can translate region-specific information based on the user's geographic location information. This makes it possible to accommodate region-specific information by customizing the translation content based on the geographic location information.

[0060] The translation unit can optimize the corresponding content based on the user's social media activity. The translation unit optimizes the corresponding content based on the user's social media activity. For example, the translation unit prioritizes translating information that the user frequently mentions on social media. The translation unit can also predict and translate necessary information from the user's social media activity. Furthermore, the translation unit can optimize the translation content based on the user's social media feedback. In this way, by optimizing the translation content based on social media activity, it is possible to predict and provide necessary information to the user.

[0061] The translation unit can adjust the corresponding content based on the user's past feedback. The translation unit adjusts the corresponding content based on the user's past feedback. For example, the translation unit improves a translation that the user was dissatisfied with in the past. The translation unit can also optimize the translation content based on the user's past feedback. Furthermore, the translation unit can extract and reflect important improvements from the user's past feedback. In this way, adjusting the translation content based on the user's past feedback improves user satisfaction.

[0062] The verification unit can apply different verification means depending on the type of identity verification that is supported. The verification unit applies different verification means depending on the type of identity verification that is supported. For example, the verification unit applies facial recognition technology for identity verification using a My Number card. The verification unit can also apply OCR technology for identity verification using a passport. Furthermore, the verification unit can also apply barcode scanning technology for identity verification using a driver's license. This improves the accuracy of verification by applying the most appropriate means depending on the type of identity verification.

[0063] The confirmation unit can determine the priority of corresponding identity verification based on the user's past usage history. The confirmation unit determines the priority of corresponding identity verification based on the user's past usage history. For example, the confirmation unit preferentially applies identity verification methods that the user uses frequently. The confirmation unit can also prioritize identity verification methods that are highly important based on the user's past usage history. Furthermore, the confirmation unit can adjust the priority based on the time period in which the user used the method in the past. In this way, by determining the priority based on the past usage history, it is possible to preferentially apply verification methods that are important to the user.

[0064] The verification unit can adjust the level of detail of the corresponding identity verification depending on the user's level of expertise. The verification unit adjusts the level of detail of the corresponding identity verification depending on the user's level of expertise. For example, the verification unit can provide a simple identity verification method for a user with little expertise. The verification unit can also provide a detailed identity verification method for a user with extensive expertise. Furthermore, the verification unit can customize the identity verification method depending on the user's level of expertise. In this way, by adjusting the verification method depending on the level of expertise, it is possible to provide a verification method that is easy for the user to understand.

[0065] The verification unit can customize the content of the corresponding identity verification based on the user's geographic location information. The verification unit customizes the content of the corresponding identity verification based on the user's geographic location information. For example, the verification unit prioritizes a specific identity verification method for the region where the user lives. The verification unit can also provide information on the nearest counter based on the user's current location. Furthermore, the verification unit can provide a region-specific identity verification method based on the user's geographic location information. This makes it possible to support region-specific verification methods by customizing the verification content based on the geographic location information.

[0066] The verification unit can optimize the content of the corresponding identity verification based on the user's social media activity. The verification unit optimizes the content of the corresponding identity verification based on the user's social media activity. For example, the verification unit prioritizes identity verification methods that the user frequently mentions on social media. The verification unit can also predict and provide a required identity verification method from the user's social media activity. Furthermore, the verification unit can optimize the identity verification method based on the user's social media feedback. In this way, by optimizing the verification content based on the social media activity, it is possible to predict and provide a required verification method for the user.

[0067] The verification unit can adjust the content of the corresponding identity verification based on the user's past feedback. The verification unit adjusts the content of the corresponding identity verification based on the user's past feedback. For example, the verification unit improves an identity verification method that the user was dissatisfied with in the past. The verification unit can also optimize the identity verification method based on the user's past feedback. Furthermore, the verification unit can extract and reflect important improvements from the user's past feedback. In this way, user satisfaction is improved by adjusting the verification content based on the user's past feedback.

[0068] The operation unit can apply different operation means depending on the type of corresponding non-routine content. The operation unit applies different operation means depending on the type of corresponding non-routine content. For example, the operation unit provides detailed guidance for complex procedures. The operation unit can also provide professional support when special handling is required. Furthermore, the operation unit can provide operation means customized according to the user's request. This improves the accuracy of handling by applying the optimal operation means according to the non-routine content.

[0069] The operation unit can determine the priority of the corresponding non-standard content based on the user's past usage history. The operation unit determines the priority of the corresponding non-standard content based on the user's past usage history. For example, the operation unit prioritizes processing of non-standard content that the user frequently uses. The operation unit can also prioritize non-standard content that is highly important based on the user's past usage history. Furthermore, the operation unit can adjust the priority based on the time period in which the user used the content in the past. In this way, by determining the priority based on the past usage history, non-standard content that is important to the user can be processed with priority.

[0070] The operation unit can customize the corresponding non-standard content based on the user's geographic location information. The operation unit customizes the corresponding non-standard content based on the user's geographic location information. For example, the operation unit prioritizes non-standard content specific to the area where the user lives. The operation unit can also provide information on the nearest service center based on the user's current location. Furthermore, the operation unit can provide non-standard content specific to the area based on the user's geographic location information. This makes it possible to accommodate non-standard content specific to the area by customizing the non-standard content based on the geographic location information.

[0071] The operation unit can optimize the corresponding non-standard content based on the user's social media activity. The operation unit optimizes the corresponding non-standard content based on the user's social media activity. For example, the operation unit prioritizes non-standard content that the user frequently mentions on social media. The operation unit can also predict and provide necessary non-standard content from the user's social media activity. Furthermore, the operation unit can optimize the non-standard content based on the user's social media feedback. In this way, by optimizing the non-standard content based on the social media activity, necessary content for the user can be predicted and provided.

[0072] The location setting unit can apply different location setting means depending on the type of location that is corresponding to the location. The location setting unit applies different location setting means depending on the type of location that is corresponding to the location. For example, the location setting unit applies an official location setting means to a counter at a government office. The location setting unit can also apply a simple location setting means to a corner of a supermarket or convenience store. Furthermore, the location setting unit can also apply a highly secure location setting means to a counter at a bank. This improves the accuracy of setting by applying the most appropriate means depending on the type of location.

[0073] The location setting unit can determine the priority of the corresponding locations based on the user's past usage history. The location setting unit determines the priority of the corresponding locations based on the user's past usage history. For example, the location setting unit prioritizes locations that the user frequently uses. The location setting unit can also prioritize locations that are highly important based on the user's past usage history. Furthermore, the location setting unit can adjust the priority based on the time period in which the user used the location in the past. In this way, by determining the priority based on the past usage history, locations that are important to the user can be prioritized.

[0074] The location setting unit can adjust the level of detail of the corresponding location according to the user's level of expertise. The location setting unit adjusts the level of detail of the corresponding location according to the user's level of expertise. For example, the location setting unit provides a simple location setting for a user with little expertise. The location setting unit can also provide a detailed location setting for a user with much expertise. Furthermore, the location setting unit can customize the location setting according to the user's level of expertise. In this way, by adjusting the location setting according to the level of expertise, it is possible to provide a setting that is easy for the user to understand.

[0075] The location setting unit can customize the content of the corresponding location based on the user's geographical location information. The location setting unit customizes the content of the corresponding location based on the user's geographical location information. For example, the location setting unit prioritizes specific locations in the area where the user lives. The location setting unit can also provide information on the nearest service counter based on the user's current location. Furthermore, the location setting unit can also provide area-specific locations based on the user's geographical location information. This makes it possible to accommodate area-specific locations by customizing the location setting based on the geographical location information.

[0076] The location setting unit can optimize the content of the corresponding location based on the user's social media activity. The location setting unit optimizes the content of the corresponding location based on the user's social media activity. For example, the location setting unit prioritizes locations that the user frequently mentions on social media. The location setting unit can also predict and provide a required location from the user's social media activity. Furthermore, the location setting unit can optimize the location setting based on the user's feedback on social media. In this way, by optimizing the location setting based on the social media activity, it is possible to predict and provide a required location for the user.

[0077] Identity verification means using facial recognition technology can apply different facial recognition algorithms depending on the type of identity verification that is being performed. Identity verification means using facial recognition technology can apply different facial recognition algorithms depending on the type of identity verification that is being performed. For example, identity verification means using facial recognition technology can apply facial recognition technology to identity verification using a My Number card. Identity verification means using facial recognition technology can also apply OCR technology to identity verification using a passport. Furthermore, identity verification means using facial recognition technology can also apply barcode scanning technology to identity verification using a driver's license. This improves the accuracy of verification by applying the optimal algorithm depending on the type of identity verification.

[0078] An identity verification means using facial recognition technology can determine the priority of corresponding identity verification methods based on a user's past usage history. An identity verification means using facial recognition technology determines the priority of corresponding identity verification methods based on a user's past usage history. For example, an identity verification means using facial recognition technology preferentially applies an identity verification method that a user uses frequently. An identity verification means using facial recognition technology can also prioritize an identity verification method that is more important based on a user's past usage history. Furthermore, an identity verification means using facial recognition technology can adjust the priority based on the time period in which the user used the method in the past. In this way, by determining the priority based on the past usage history, it is possible to preferentially apply a verification method that is important to the user.

[0079] An identity verification means using facial recognition technology can adjust the level of detail of the corresponding identity verification depending on the user's level of expertise. An identity verification means using facial recognition technology adjusts the level of detail of the corresponding identity verification depending on the user's level of expertise. For example, an identity verification means using facial recognition technology can provide a simple identity verification method for users with little expertise. Alternatively, an identity verification means using facial recognition technology can provide a detailed identity verification method for users with extensive expertise. Furthermore, an identity verification means using facial recognition technology can customize the identity verification method depending on the user's level of expertise. In this way, by adjusting the verification method depending on the level of expertise, a verification method that is easy for users to understand can be provided.

[0080] An identity verification means using facial recognition technology can customize the content of the corresponding identity verification based on the user's geographic location information. An identity verification means using facial recognition technology customizes the content of the corresponding identity verification based on the user's geographic location information. For example, an identity verification means using facial recognition technology prioritizes a specific identity verification method for the area where the user lives. An identity verification means using facial recognition technology can also provide information on the nearest service center based on the user's current location. Furthermore, an identity verification means using facial recognition technology can provide a region-specific identity verification method based on the user's geographic location information. This makes it possible to accommodate region-specific verification methods by customizing the verification content based on the geographic location information.

[0081] An identity verification method using facial recognition technology can optimize the content of corresponding identity verification based on a user's social media activity. An identity verification method using facial recognition technology optimizes the content of corresponding identity verification based on a user's social media activity. For example, an identity verification method using facial recognition technology prioritizes identity verification methods that a user frequently mentions on social media. An identity verification method using facial recognition technology can also predict and provide a required identity verification method based on a user's social media activity. Furthermore, an identity verification method using facial recognition technology can optimize the identity verification method based on the user's social media feedback. In this way, by optimizing the verification content based on social media activity, it is possible to predict and provide a required identity verification method for a user.

[0082] An identity verification means using facial recognition technology can adjust the content of the corresponding identity verification based on the user's past feedback. An identity verification means using facial recognition technology adjusts the content of the corresponding identity verification based on the user's past feedback. For example, an identity verification means using facial recognition technology improves an identity verification method that a user has been dissatisfied with in the past. An identity verification means using facial recognition technology can also optimize the identity verification method based on the user's past feedback. Furthermore, an identity verification means using facial recognition technology can extract and reflect important improvements from the user's past feedback. In this way, user satisfaction is improved by adjusting the verification content based on the user's past feedback.

[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 counter work efficiency system can customize the avatar's response methods based on the user's past usage history. For example, it can provide a quicker response to users who use the service frequently. It can also provide information about a specific procedure preferentially to users who have performed that procedure many times in the past. It can also analyze the user's preferences and tendencies from past usage history and select the most appropriate response method. This allows it to provide the best service for the user.

[0085] The counter work efficiency improvement system can suggest the optimal counter location based on the user's geographical location information. For example, it can guide the user to the counter closest to their current location. It can also provide services specialized for the area where the user lives. It can also suggest the optimal counter location based on the user's travel route. This improves user convenience and enables efficient service provision.

[0086] The counter work efficiency system can optimize the avatar's response methods based on the user's social media activity. For example, it can prioritize providing information on topics that the user frequently mentions on social media. It can also predict and suggest necessary procedures based on the user's social media activity. Furthermore, it can optimize the avatar's response methods based on the user's feedback on social media. This makes it possible to predict and provide the information the user needs.

[0087] The counter work efficiency system can adjust the level of detail of the information provided depending on the user's level of expertise. For example, it can provide a simple explanation to a user with little expertise, and a detailed explanation to a user with a lot of expertise. Furthermore, it can customize the display format of the information depending on the user's level of expertise. This makes it possible to provide information that is easy for the user to understand.

[0088] The counter work efficiency system can adjust the avatar's response methods based on past user feedback. For example, it can improve procedures that users were dissatisfied with in the past. It can also optimize the avatar's response methods based on past feedback. It can also extract and reflect important areas for improvement from past feedback. This increases user satisfaction and allows for the provision of better service.

[0089] The counter work efficiency system can customize the services it provides based on the user's geographic location information. For example, it can prioritize specific procedures in the area where the user lives. It can also provide information on the nearest counter based on the user's current location. It can also automate procedures specific to the area based on the user's geographic location information. This makes it possible to accommodate procedures specific to the area, improving user convenience.

[0090] The processing flow of the first embodiment will be briefly explained below.

[0091] Step 1: The automation department fully automates routine tasks. For example, it automates procedures such as issuing resident registration certificates and opening accounts. It can also automate tasks such as document processing and data entry. Step 2: The avatar section responds with a friendly AI avatar. For example, it has a human-like appearance and voice, giving the user a friendly impression. It can also respond appropriately to the user's questions. Step 3: The subtitle display unit displays the content spoken by the avatar unit as subtitles in front of the user. For example, the content spoken by an AI avatar can be displayed as subtitles, and the display position, font size, color, etc. can be adjusted. Step 4: The translation unit automatically translates what is said by the avatar unit. For example, it can translate into the user's native language, such as English or Chinese, and the translation algorithm and supported languages ​​can be adjusted. Step 5: The verification unit verifies the identity of the user using the avatar unit. For example, the user can be verified using a My Number card, or other authentication technologies such as facial recognition or fingerprint authentication.

[0092] (Example 2) A counter work efficiency improvement system according to an embodiment of the present invention fully automates routine tasks and uses a friendly AI avatar to handle them. The counter work efficiency improvement system includes an automation unit that fully automates routine tasks, an avatar unit that uses a friendly AI avatar to handle tasks processed by the automation unit, a subtitle display unit that displays subtitles in front of the user what is spoken by the avatar unit, a translation unit that automatically translates what is spoken by the avatar unit, and a verification unit that verifies the user's identity using the avatar unit. For example, the counter work efficiency improvement system fully automates routine tasks at government offices and banks. For example, an AI avatar automatically performs procedures such as issuing a resident registration card and opening an account. Next, the counter work efficiency improvement system uses a friendly AI avatar to handle tasks. The AI ​​avatar has a human-like appearance and voice, giving the user a friendly impression. Furthermore, the counter work efficiency improvement system displays subtitles in front of the user to prevent mishearing. For example, what the AI ​​avatar is saying is displayed as subtitles. The counter work efficiency improvement system also includes an automatic translation function that can handle languages ​​other than Japanese. For example, responses can be provided in the user's native language, such as English or Chinese. Furthermore, the counter work efficiency system operates an AI avatar in the background when atypical content arises. For example, when complex procedures or special handling are required, the AI ​​avatar can be operated to handle the situation. Furthermore, the counter work efficiency system does not have to be located in a government office; it can be located in a corner of a supermarket or convenience store, for example. This allows the counter work efficiency system to streamline human resources, expand reception hours, and liberalize reception locations. This allows the counter work efficiency system to streamline human resources, expand reception hours, and liberalize reception locations. For example, the number of staff working at the counter can be halved. It also allows reception hours to be extended, allowing services to be provided on weekends, holidays, and at night. Furthermore, the liberalization of reception locations eliminates the need for users to go to the government office.

[0093] A counter work efficiency improvement system according to an embodiment includes an automation unit, an avatar unit, a subtitle display unit, a translation unit, and a confirmation unit. The automation unit fully automates routine tasks. For example, the automation unit automatically performs procedures such as issuing resident registration certificates and opening accounts. The automation unit can also automate tasks such as document processing and data entry. The avatar unit responds with a friendly AI avatar. For example, the avatar unit has a human-like appearance and voice, giving the user a friendly impression. The avatar unit can also respond appropriately to user questions. The subtitle display unit displays subtitles in front of the user what is spoken by the avatar unit. For example, the subtitle display unit displays what the AI ​​avatar is saying as subtitles. The subtitle display unit can also adjust the display position, font size, color, and the like. The translation unit automatically translates what is spoken by the avatar unit. For example, the translation unit responds in the user's native language, such as English or Chinese. The translation unit can also adjust the translation algorithm used and the supported languages. The verification unit verifies the identity of the customer using the avatar unit. For example, the verification unit may verify the identity of the customer using a My Number card. The verification unit may also use authentication technologies such as facial recognition technology and fingerprint authentication. As a result, the counter work efficiency improvement system according to the embodiment aims to improve the efficiency of counter work by fully automating routine tasks and using friendly AI avatars.

[0094] The counter work efficiency improvement system is equipped with an operation unit that corresponds to the specific content of non-routine work. The operation unit corresponds to the non-routine content. For example, the operation unit corresponds to cases where complex procedures or special responses are required. The operation unit can also provide customized responses in accordance with user requests. Furthermore, the operation unit can also provide exception handling and customized responses under specific conditions. For example, the operation unit can provide exception handling under specific conditions. The operation unit can also provide customized responses. This makes it possible to handle non-routine content and provide flexible responses.

[0095] The counter work efficiency improvement system includes a location setting unit that freely sets the location of the counter. The location setting unit freely sets the location of the counter. For example, the location setting unit can handle not only a counter at a government office or bank, but also a corner of a supermarket or convenience store. The location setting unit can also set an online virtual counter. Furthermore, the location setting unit can set not only a physical location, but also an online virtual counter. For example, the location setting unit sets an online virtual counter so that the user can complete procedures from home. This allows the location of the counter to be freely set, improving user convenience.

[0096] The counter operation efficiency improvement system includes an identity verification means using facial recognition technology. The identity verification means using facial recognition technology performs identity verification using facial recognition technology. For example, the identity verification means using facial recognition technology photographs the user's face with a camera and performs identity verification using a facial recognition algorithm. The identity verification means using facial recognition technology can also use deep learning technology to improve the accuracy of the facial recognition algorithm. The identity verification means using facial recognition technology can also use pattern recognition technology to improve the accuracy of the facial recognition algorithm. For example, the identity verification means using facial recognition technology improves the accuracy of the facial recognition algorithm using deep learning technology. The identity verification means using facial recognition technology also improves the accuracy of the facial recognition algorithm using pattern recognition technology. As a result, the accuracy of identity verification is improved by using facial recognition technology.

[0097] The automation department can automate procedures such as issuing resident registration certificates and opening accounts. The automation department automates procedures such as issuing resident registration certificates and opening accounts. For example, the automation department automates the procedure for issuing resident registration certificates. The automation department can also automatically collect the documents required for opening an account and automate the procedure. Furthermore, the automation department can automate tasks such as document processing and data entry. For example, the automation department automates document processing. The automation department can also automate data entry. This allows procedures such as issuing resident registration certificates and opening accounts to be performed automatically, thereby improving the efficiency of operations.

[0098] The avatar unit has specific appearance and voice specifications, and can give the user a friendly impression. The avatar unit has a human-like appearance and voice, and can give the user a friendly impression. For example, the avatar unit can adjust the appearance design to give the user a friendly impression. The avatar unit can also adjust the tone and accent of the voice to give the user a friendly impression. Furthermore, the avatar unit can respond appropriately to the user's questions. For example, the avatar unit can respond appropriately to the user's questions. The avatar unit can also provide customized responses according to the user's requests. This allows the user to use the system with peace of mind thanks to the friendly AI avatar.

[0099] The subtitle display unit can display what the AI ​​avatar is saying as subtitles. The subtitle display unit displays what the AI ​​avatar is saying as subtitles. For example, the subtitle display unit displays what the AI ​​avatar is saying as subtitles. The subtitle display unit can also adjust the display position, font size, color, etc. Furthermore, the subtitle display unit can customize the display content. For example, the subtitle display unit can customize the display content and display it in a format that is easy for the user to see. This makes the subtitle display accessible to people with hearing impairments.

[0100] The translation unit can support multiple languages ​​(e.g., English and Chinese). The translation unit can support multiple languages. For example, the translation unit can support the user's native language, such as English or Chinese. The translation unit can also adjust the translation algorithm used and the supported languages. Furthermore, the translation unit can use machine learning technology to improve the accuracy of the translation. For example, the translation unit can improve the accuracy of the translation using machine learning technology. The translation unit can also improve the accuracy of the translation using context analysis technology. This makes it possible to support foreign users using the automatic translation function.

[0101] The verification unit can verify the identity of the user using a My Number card. The verification unit can verify the identity of the user using a My Number card. For example, the verification unit can verify the identity of the user by having the user present the My Number card. The verification unit can also verify the identity of the user by reading information from the My Number card using a card reader. The verification unit can also use authentication technology such as facial recognition technology or fingerprint authentication. For example, the verification unit can verify the identity of the user using facial recognition technology. The verification unit can also verify the identity of the user using fingerprint authentication. As a result, the security of identity verification is ensured by using the My Number card.

[0102] The automation unit can estimate the user's emotions and adjust the automation processing speed based on the estimated emotion analysis results. The automation unit can estimate the user's emotions and adjust the automation processing speed based on the estimated emotion analysis results. For example, if the user is feeling stressed, the automation unit can increase the processing speed to respond quickly. Also, if the user is relaxed, the automation unit can maintain the processing speed at a normal level. Furthermore, if the user is in a hurry, the automation unit can maximize the processing speed to respond. This allows for more appropriate responses by adjusting the processing speed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0103] The automation unit can apply different automation algorithms depending on the type of business being processed. The automation unit applies different automation algorithms depending on the type of business being processed. For example, the automation unit applies a quick database search algorithm to issue resident registration certificates. The automation unit can also apply a detailed identity verification algorithm to open accounts. Furthermore, the automation unit can apply an accurate calculation algorithm to pay taxes. This improves processing efficiency by applying the optimal algorithm for each type of business.

[0104] The automation unit can determine the priority of tasks to be processed based on the user's past usage history. The automation unit determines the priority of tasks to be processed based on the user's past usage history. For example, the automation unit prioritizes tasks that the user uses frequently. The automation unit can also prioritize tasks that are highly important based on the user's past usage history. Furthermore, the automation unit can adjust the priority based on the time period in which the user used the task in the past. In this way, by determining the priority based on the user's past usage history, tasks that are important to the user can be prioritized.

[0105] The automation unit can automatically collect the necessary documents and information depending on the content of the business being processed. The automation unit automatically collects the necessary documents and information depending on the content of the business being processed. For example, the automation unit automatically collects the personal information required for issuing a resident registration card. The automation unit can also automatically collect the identification documents required for opening an account. Furthermore, the automation unit can automatically collect the tax payment information required for paying taxes. In this way, the efficiency of business operations is improved by automatically collecting the necessary documents and information.

[0106] The automation unit can estimate the user's emotions and adjust the order of tasks to be automated based on the estimated emotion analysis results. The automation unit can estimate the user's emotions and adjust the order of tasks to be automated based on the estimated emotion analysis results. For example, if the user is feeling stressed, the automation unit processes tasks in order starting with the easiest. Also, if the user is relaxed, the automation unit can process tasks in the normal order. Furthermore, if the user is in a hurry, the automation unit can prioritize important tasks. This allows for more appropriate responses by adjusting the order of tasks according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0107] The automation unit can customize the content of the business to be processed based on the user's geographic location information. The automation unit customizes the content of the business to be processed based on the user's geographic location information. For example, the automation unit prioritizes specific procedures in the area where the user lives. The automation unit can also provide information on the nearest counter based on the user's current location. Furthermore, the automation unit can automate region-specific procedures based on the user's geographic location information. This makes it possible to accommodate region-specific procedures by customizing the content of the business based on geographic location information.

[0108] The automation unit can optimize the content of the tasks to be processed based on the user's social media activity. The automation unit optimizes the content of the tasks to be processed based on the user's social media activity. For example, the automation unit prioritizes procedures that the user frequently mentions on social media. The automation unit can also predict and suggest necessary procedures from the user's social media activity. Furthermore, the automation unit can optimize the content of tasks based on the user's feedback on social media. In this way, by optimizing the content of tasks based on social media activity, it is possible to predict and provide necessary procedures for the user.

[0109] The automation unit can adjust the content of the work to be processed based on the user's past feedback. The automation unit adjusts the content of the work to be processed based on the user's past feedback. For example, the automation unit improves a procedure that the user was dissatisfied with in the past. The automation unit can also optimize the work content based on the user's past feedback. Furthermore, the automation unit can extract and reflect important areas for improvement from the user's past feedback. In this way, adjusting the work content based on the user's past feedback improves user satisfaction.

[0110] The avatar unit can estimate the user's emotions and adjust the avatar's facial expression and tone of voice based on the estimated emotion analysis results. The avatar unit can estimate the user's emotions and adjust the avatar's facial expression and tone of voice based on the estimated emotion analysis results. For example, if the user is nervous, the avatar can respond with a calm facial expression and tone of voice. Alternatively, if the user is relaxed, the avatar can respond with a bright facial expression and tone of voice. Furthermore, if the user is in a hurry, the avatar can respond quickly and concisely. This allows for a more friendly response by adjusting the avatar's facial expression and tone of voice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0111] The avatar department can select different avatar appearances and voices depending on the type of business being handled. The avatar department can select different avatar appearances and voices depending on the type of business being handled. For example, the avatar department can select an avatar with an official appearance and voice for issuing a resident registration certificate. The avatar department can also select an avatar with a friendly appearance and voice for opening an account. Furthermore, the avatar department can select an avatar with a trustworthy appearance and voice for paying taxes. This allows for more appropriate handling by selecting an avatar according to the type of business.

[0112] The avatar unit can customize the avatar's actions and gestures according to the content of the corresponding business. The avatar unit customizes the avatar's actions and gestures according to the content of the corresponding business. For example, the avatar unit can set an avatar that performs polite actions and gestures for issuing a resident registration certificate. The avatar unit can also set an avatar that performs friendly actions and gestures for opening an account. Furthermore, the avatar unit can set an avatar that performs reliable actions and gestures for paying taxes. In this way, customizing actions and gestures according to the content of the business enables more natural responses.

[0113] The avatar department can change the avatar's clothing and background depending on the type of work being performed. The avatar department can change the avatar's clothing and background depending on the type of work being performed. For example, the avatar department can set official clothing and background for issuing a resident registration certificate. The avatar department can also set friendly clothing and background for opening an account. Furthermore, the avatar department can also set reliable clothing and background for paying taxes. This allows for more appropriate responses by changing the clothing and background depending on the type of work being performed.

[0114] The avatar unit can estimate the user's emotions and adjust the speaking speed of the avatar based on the estimated emotion analysis results. The avatar unit can estimate the user's emotions and adjust the speaking speed of the avatar based on the estimated emotion analysis results. For example, if the user is nervous, the avatar unit can make the avatar speak slowly. Also, if the user is relaxed, the avatar unit can make the avatar speak at a normal speed. Furthermore, if the user is in a hurry, the avatar unit can make the avatar speak quickly. This allows for more appropriate responses by adjusting the speaking speed according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0115] The avatar unit can customize the content of the corresponding business based on the user's geographic location information. The avatar unit customizes the content of the corresponding business based on the user's geographic location information. For example, the avatar unit prioritizes specific procedures in the area where the user lives. The avatar unit can also provide information on the nearest counter based on the user's current location. Furthermore, the avatar unit can automate region-specific procedures based on the user's geographic location information. This makes it possible to accommodate region-specific procedures by customizing the content of the business based on geographic location information.

[0116] The avatar department can optimize the content of corresponding tasks based on the user's social media activity. The avatar department optimizes the content of corresponding tasks based on the user's social media activity. For example, the avatar department prioritizes procedures that the user frequently mentions on social media. The avatar department can also predict and suggest necessary procedures from the user's social media activity. Furthermore, the avatar department can optimize the content of tasks based on the user's social media feedback. In this way, by optimizing the content of tasks based on social media activity, it is possible to predict and provide necessary procedures for the user.

[0117] The avatar unit can adjust the content of the corresponding work based on the user's past feedback. The avatar unit adjusts the content of the corresponding work based on the user's past feedback. For example, the avatar unit improves a procedure that the user was dissatisfied with in the past. The avatar unit can also optimize the work content based on the user's past feedback. Furthermore, the avatar unit can extract and reflect important points for improvement from the user's past feedback. In this way, adjusting the work content based on the user's past feedback improves user satisfaction.

[0118] The subtitle display unit can estimate the user's emotions and adjust the subtitle display speed based on the estimated emotion analysis results. The subtitle display unit can estimate the user's emotions and adjust the subtitle display speed based on the estimated emotion analysis results. For example, the subtitle display unit slows down the subtitle display speed when the user is nervous. The subtitle display unit can also normalize the subtitle display speed when the user is relaxed. Furthermore, the subtitle display unit can speed up the subtitle display speed when the user is in a hurry. This allows for more appropriate responses by adjusting the subtitle display speed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0119] The subtitle display unit can apply different fonts and colors depending on the type of content to be displayed. The subtitle display unit can apply different fonts and colors depending on the type of content to be displayed. For example, the subtitle display unit can apply a bold font and red color to important information. The subtitle display unit can also apply a standard font and black color to regular information. Furthermore, the subtitle display unit can apply an italic font and yellow color to information that requires attention. In this way, by applying fonts and colors depending on the type of content, the visibility of the information is improved.

[0120] The subtitle display unit can determine the priority of the content to be displayed based on the user's past usage history. The subtitle display unit determines the priority of the content to be displayed based on the user's past usage history. For example, the subtitle display unit prioritizes displaying information that the user frequently uses. The subtitle display unit can also prioritize information of high importance based on the user's past usage history. Furthermore, the subtitle display unit can adjust the priority based on the time period in which the user used the content in the past. In this way, by determining the priority based on the user's past usage history, it is possible to prioritize displaying information that is important to the user.

[0121] The subtitle display unit can adjust the level of detail of the content to be displayed according to the user's level of expertise. The subtitle display unit adjusts the level of detail of the content to be displayed according to the user's level of expertise. For example, the subtitle display unit can display a concise explanation to a user with little expertise. The subtitle display unit can also display a detailed explanation to a user with a lot of expertise. Furthermore, the subtitle display unit can customize the display content according to the user's level of expertise. In this way, by adjusting the display content according to the user's level of expertise, it is possible to provide information that is easy for the user to understand.

[0122] The subtitle display unit can estimate the user's emotions and adjust the display position of the subtitles based on the estimated emotion analysis results. The subtitle display unit can estimate the user's emotions and adjust the display position of the subtitles based on the estimated emotion analysis results. For example, if the user is nervous, the subtitle display unit can display the subtitles in the center of the screen. If the user is relaxed, the subtitle display unit can also display the subtitles at the bottom of the screen. Furthermore, if the user is in a hurry, the subtitle display unit can also display the subtitles at the top of the screen. This allows for more appropriate responses by adjusting the display position of the subtitles according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0123] The subtitle display unit can customize the content to be displayed based on the user's geographical location information. The subtitle display unit customizes the content to be displayed based on the user's geographical location information. For example, the subtitle display unit prioritizes displaying specific information for the area where the user lives. The subtitle display unit can also display information about the nearest service center based on the user's current location. Furthermore, the subtitle display unit can display information specific to the area based on the user's geographical location information. This makes it possible to accommodate information specific to the area by customizing the display content based on the geographical location information.

[0124] The subtitle display unit can optimize the content to be displayed based on the user's social media activity. The subtitle display unit optimizes the content to be displayed based on the user's social media activity. For example, the subtitle display unit prioritizes displaying information that the user frequently mentions on social media. The subtitle display unit can also predict and display necessary information based on the user's social media activity. Furthermore, the subtitle display unit can optimize the display content based on the user's social media feedback. In this way, by optimizing the display content based on the social media activity, necessary information for the user can be predicted and provided.

[0125] The subtitle display unit can adjust the content to be displayed based on the user's past feedback. The subtitle display unit adjusts the content to be displayed based on the user's past feedback. For example, the subtitle display unit improves the display of information that the user was dissatisfied with in the past. The subtitle display unit can also optimize the display content based on the user's past feedback. Furthermore, the subtitle display unit can extract and reflect important improvements from the user's past feedback. In this way, adjusting the display content based on the user's past feedback improves user satisfaction.

[0126] The translation unit can estimate the user's emotions and adjust the translation expression based on the estimated emotion analysis results. The translation unit can estimate the user's emotions and adjust the translation expression based on the estimated emotion analysis results. For example, if the user is nervous, the translation unit uses concise and easy-to-understand expressions. If the user is relaxed, the translation unit can also use detailed expressions. If the user is in a hurry, the translation unit can also use quick and concise expressions. This allows for more appropriate responses by adjusting the translation expression based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0127] The translation unit can apply different translation algorithms depending on the type of language supported. The translation unit applies different translation algorithms depending on the type of language supported. For example, the translation unit applies a natural language processing algorithm to English. The translation unit can also apply a context analysis algorithm to Chinese. Furthermore, the translation unit can apply a machine learning algorithm to Spanish. This improves translation accuracy by applying the optimal algorithm depending on the type of language.

[0128] The translation unit can determine the priority of corresponding content based on the user's past usage history. The translation unit determines the priority of corresponding content based on the user's past usage history. For example, the translation unit gives priority to translating languages ​​that the user uses frequently. The translation unit can also give priority to content that is highly important based on the user's past usage history. Furthermore, the translation unit can adjust the priority based on the time period in which the user used the service in the past. In this way, by determining the priority based on the past usage history, content that is important to the user can be translated preferentially.

[0129] The translation unit can adjust the level of detail of the corresponding content according to the user's level of expertise. The translation unit adjusts the level of detail of the corresponding content according to the user's level of expertise. For example, the translation unit can provide a concise translation for a user with little expertise. The translation unit can also provide a detailed translation for a user with a lot of expertise. Furthermore, the translation unit can customize the translation content according to the user's level of expertise. In this way, by adjusting the translation content according to the level of expertise, it is possible to provide information that is easy for the user to understand.

[0130] The translation unit can estimate the user's emotions and adjust the length of the translation based on the estimated emotion analysis results. The translation unit can estimate the user's emotions and adjust the length of the translation based on the estimated emotion analysis results. For example, if the user is nervous, the translation unit can provide a short, to-the-point translation. If the user is relaxed, the translation unit can provide a longer translation with detailed explanations. Furthermore, if the user is in a hurry, the translation unit can provide a quick, concise translation. This allows for more appropriate responses by adjusting the length of the translation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0131] The translation unit can customize the corresponding content based on the user's geographic location information. The translation unit customizes the corresponding content based on the user's geographic location information. For example, the translation unit prioritizes translating specific information for the area where the user lives. The translation unit can also translate information about the nearest service center based on the user's current location. Furthermore, the translation unit can translate region-specific information based on the user's geographic location information. This makes it possible to accommodate region-specific information by customizing the translation content based on the geographic location information.

[0132] The translation unit can optimize the corresponding content based on the user's social media activity. The translation unit optimizes the corresponding content based on the user's social media activity. For example, the translation unit prioritizes translating information that the user frequently mentions on social media. The translation unit can also predict and translate necessary information from the user's social media activity. Furthermore, the translation unit can optimize the translation content based on the user's social media feedback. In this way, by optimizing the translation content based on social media activity, it is possible to predict and provide necessary information to the user.

[0133] The translation unit can adjust the corresponding content based on the user's past feedback. The translation unit adjusts the corresponding content based on the user's past feedback. For example, the translation unit improves a translation that the user was dissatisfied with in the past. The translation unit can also optimize the translation content based on the user's past feedback. Furthermore, the translation unit can extract and reflect important improvements from the user's past feedback. In this way, adjusting the translation content based on the user's past feedback improves user satisfaction.

[0134] The verification unit can estimate the user's emotions and adjust the identity verification method based on the estimated emotion analysis results. The verification unit can estimate the user's emotions and adjust the identity verification method based on the estimated emotion analysis results. For example, if the user is nervous, the verification unit can provide a simple and easy-to-understand identity verification method. If the user is relaxed, the verification unit can also provide a detailed identity verification method. Furthermore, if the user is in a hurry, the verification unit can also provide a quick and concise identity verification method. This allows for more appropriate responses by adjusting the identity verification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0135] The verification unit can apply different verification means depending on the type of identity verification that is supported. The verification unit applies different verification means depending on the type of identity verification that is supported. For example, the verification unit applies facial recognition technology for identity verification using a My Number card. The verification unit can also apply OCR technology for identity verification using a passport. Furthermore, the verification unit can also apply barcode scanning technology for identity verification using a driver's license. This improves the accuracy of verification by applying the most appropriate means depending on the type of identity verification.

[0136] The confirmation unit can determine the priority of corresponding identity verification based on the user's past usage history. The confirmation unit determines the priority of corresponding identity verification based on the user's past usage history. For example, the confirmation unit preferentially applies identity verification methods that the user uses frequently. The confirmation unit can also prioritize identity verification methods that are highly important based on the user's past usage history. Furthermore, the confirmation unit can adjust the priority based on the time period in which the user used the method in the past. In this way, by determining the priority based on the past usage history, it is possible to preferentially apply verification methods that are important to the user.

[0137] The verification unit can adjust the level of detail of the corresponding identity verification depending on the user's level of expertise. The verification unit adjusts the level of detail of the corresponding identity verification depending on the user's level of expertise. For example, the verification unit can provide a simple identity verification method for a user with little expertise. The verification unit can also provide a detailed identity verification method for a user with extensive expertise. Furthermore, the verification unit can customize the identity verification method depending on the user's level of expertise. In this way, by adjusting the verification method depending on the level of expertise, it is possible to provide a verification method that is easy for the user to understand.

[0138] The confirmation unit can estimate the user's emotions and adjust the identity verification procedure based on the estimated emotion analysis result. The confirmation unit can estimate the user's emotions and adjust the identity verification procedure based on the estimated emotion analysis result. For example, the confirmation unit can provide simple and easy-to-understand instructions when the user is nervous. The confirmation unit can also provide detailed instructions when the user is relaxed. Furthermore, the confirmation unit can also provide quick and concise instructions when the user is in a hurry. This allows for more appropriate responses by adjusting the verification procedure according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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.

[0139] The verification unit can customize the content of the corresponding identity verification based on the user's geographic location information. The verification unit customizes the content of the corresponding identity verification based on the user's geographic location information. For example, the verification unit prioritizes a specific identity verification method for the region where the user lives. The verification unit can also provide information on the nearest counter based on the user's current location. Furthermore, the verification unit can provide a region-specific identity verification method based on the user's geographic location information. This makes it possible to support region-specific verification methods by customizing the verification content based on the geographic location information.

[0140] The verification unit can optimize the content of the corresponding identity verification based on the user's social media activity. The verification unit optimizes the content of the corresponding identity verification based on the user's social media activity. For example, the verification unit prioritizes identity verification methods that the user frequently mentions on social media. The verification unit can also predict and provide a required identity verification method from the user's social media activity. Furthermore, the verification unit can optimize the identity verification method based on the user's social media feedback. In this way, by optimizing the verification content based on the social media activity, it is possible to predict and provide a required verification method for the user.

[0141] The verification unit can adjust the content of the corresponding identity verification based on the user's past feedback. The verification unit adjusts the content of the corresponding identity verification based on the user's past feedback. For example, the verification unit improves an identity verification method that the user was dissatisfied with in the past. The verification unit can also optimize the identity verification method based on the user's past feedback. Furthermore, the verification unit can extract and reflect important improvements from the user's past feedback. In this way, user satisfaction is improved by adjusting the verification content based on the user's past feedback.

[0142] The operation unit can estimate the user's emotions and adjust the operation method based on the estimated emotion analysis results. The operation unit can estimate the user's emotions and adjust the operation method based on the estimated emotion analysis results. For example, the operation unit provides a simple and easy-to-understand operation method when the user is nervous. The operation unit can also provide a detailed operation method when the user is relaxed. Furthermore, the operation unit can also provide a quick and concise operation method when the user is in a hurry. This allows for more appropriate responses by adjusting the operation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0143] The operation unit can apply different operation means depending on the type of corresponding non-routine content. The operation unit applies different operation means depending on the type of corresponding non-routine content. For example, the operation unit provides detailed guidance for complex procedures. The operation unit can also provide professional support when special handling is required. Furthermore, the operation unit can provide operation means customized according to the user's request. This improves the accuracy of handling by applying the optimal operation means according to the non-routine content.

[0144] The operation unit can determine the priority of the corresponding non-standard content based on the user's past usage history. The operation unit determines the priority of the corresponding non-standard content based on the user's past usage history. For example, the operation unit prioritizes processing of non-standard content that the user frequently uses. The operation unit can also prioritize non-standard content that is highly important based on the user's past usage history. Furthermore, the operation unit can adjust the priority based on the time period in which the user used the content in the past. In this way, by determining the priority based on the past usage history, non-standard content that is important to the user can be processed with priority.

[0145] The operation unit can estimate the user's emotions and adjust the operation procedure based on the estimated emotion analysis results. The operation unit can estimate the user's emotions and adjust the operation procedure based on the estimated emotion analysis results. For example, the operation unit provides simple and easy-to-understand instructions when the user is nervous. The operation unit can also provide detailed instructions when the user is relaxed. Furthermore, the operation unit can also provide quick and concise instructions when the user is in a hurry. This allows for more appropriate responses by adjusting the operation procedure according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0146] The operation unit can customize the corresponding non-standard content based on the user's geographic location information. The operation unit customizes the corresponding non-standard content based on the user's geographic location information. For example, the operation unit prioritizes non-standard content specific to the area where the user lives. The operation unit can also provide information on the nearest service center based on the user's current location. Furthermore, the operation unit can provide non-standard content specific to the area based on the user's geographic location information. This makes it possible to accommodate non-standard content specific to the area by customizing the non-standard content based on the geographic location information.

[0147] The operation unit can optimize the corresponding non-standard content based on the user's social media activity. The operation unit optimizes the corresponding non-standard content based on the user's social media activity. For example, the operation unit prioritizes non-standard content that the user frequently mentions on social media. The operation unit can also predict and provide necessary non-standard content from the user's social media activity. Furthermore, the operation unit can optimize the non-standard content based on the user's social media feedback. In this way, by optimizing the non-standard content based on the social media activity, necessary content for the user can be predicted and provided.

[0148] The location setting unit can estimate the user's emotions and adjust the location of the counter based on the estimated emotion analysis results. The location setting unit can estimate the user's emotions and adjust the location of the counter based on the estimated emotion analysis results. For example, if the user is nervous, the location setting unit can prioritize a quiet location. Also, if the user is relaxed, the location setting unit can provide a normal location. Furthermore, if the user is in a hurry, the location setting unit can prioritize an easily accessible location. This allows for more appropriate response by adjusting the location of the counter according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0149] The location setting unit can apply different location setting means depending on the type of location that is corresponding to the location. The location setting unit applies different location setting means depending on the type of location that is corresponding to the location. For example, the location setting unit applies an official location setting means to a counter at a government office. The location setting unit can also apply a simple location setting means to a corner of a supermarket or convenience store. Furthermore, the location setting unit can also apply a highly secure location setting means to a counter at a bank. This improves the accuracy of setting by applying the most appropriate means depending on the type of location.

[0150] The location setting unit can determine the priority of the corresponding locations based on the user's past usage history. The location setting unit determines the priority of the corresponding locations based on the user's past usage history. For example, the location setting unit prioritizes locations that the user frequently uses. The location setting unit can also prioritize locations that are highly important based on the user's past usage history. Furthermore, the location setting unit can adjust the priority based on the time period in which the user used the location in the past. In this way, by determining the priority based on the past usage history, locations that are important to the user can be prioritized.

[0151] The location setting unit can adjust the level of detail of the corresponding location according to the user's level of expertise. The location setting unit adjusts the level of detail of the corresponding location according to the user's level of expertise. For example, the location setting unit provides a simple location setting for a user with little expertise. The location setting unit can also provide a detailed location setting for a user with much expertise. Furthermore, the location setting unit can customize the location setting according to the user's level of expertise. In this way, by adjusting the location setting according to the level of expertise, it is possible to provide a setting that is easy for the user to understand.

[0152] The location setting unit can estimate the user's emotions and adjust the location setting procedure based on the estimated emotion analysis results. The location setting unit can estimate the user's emotions and adjust the location setting procedure based on the estimated emotion analysis results. For example, if the user is nervous, the location setting unit can provide simple and easy-to-understand procedures. If the user is relaxed, the location setting unit can also provide detailed procedures. Furthermore, if the user is in a hurry, the location setting unit can also provide quick and concise procedures. This allows for more appropriate responses by adjusting the setting procedure according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0153] The location setting unit can customize the content of the corresponding location based on the user's geographical location information. The location setting unit customizes the content of the corresponding location based on the user's geographical location information. For example, the location setting unit prioritizes specific locations in the area where the user lives. The location setting unit can also provide information on the nearest service counter based on the user's current location. Furthermore, the location setting unit can also provide area-specific locations based on the user's geographical location information. This makes it possible to accommodate area-specific locations by customizing the location setting based on the geographical location information.

[0154] The location setting unit can optimize the content of the corresponding location based on the user's social media activity. The location setting unit optimizes the content of the corresponding location based on the user's social media activity. For example, the location setting unit prioritizes locations that the user frequently mentions on social media. The location setting unit can also predict and provide a required location from the user's social media activity. Furthermore, the location setting unit can optimize the location setting based on the user's feedback on social media. In this way, by optimizing the location setting based on the social media activity, it is possible to predict and provide a required location for the user.

[0155] A personal identification method using facial recognition technology can estimate a user's emotions and adjust the facial recognition method based on the estimated emotion analysis results. A personal identification method using facial recognition technology can estimate a user's emotions and adjust the facial recognition method based on the estimated emotion analysis results. For example, a personal identification method using facial recognition technology can provide a simple and easy-to-understand facial recognition method when a user is nervous. A personal identification method using facial recognition technology can also provide a detailed facial recognition method when a user is relaxed. Furthermore, a personal identification method using facial recognition technology can also provide a quick and concise facial recognition method when a user is in a hurry. This allows for more appropriate responses by adjusting the facial recognition method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0156] Identity verification means using facial recognition technology can apply different facial recognition algorithms depending on the type of identity verification that is being performed. Identity verification means using facial recognition technology can apply different facial recognition algorithms depending on the type of identity verification that is being performed. For example, identity verification means using facial recognition technology can apply facial recognition technology to identity verification using a My Number card. Identity verification means using facial recognition technology can also apply OCR technology to identity verification using a passport. Furthermore, identity verification means using facial recognition technology can also apply barcode scanning technology to identity verification using a driver's license. This improves the accuracy of verification by applying the optimal algorithm depending on the type of identity verification.

[0157] An identity verification means using facial recognition technology can determine the priority of corresponding identity verification methods based on a user's past usage history. An identity verification means using facial recognition technology determines the priority of corresponding identity verification methods based on a user's past usage history. For example, an identity verification means using facial recognition technology preferentially applies an identity verification method that a user uses frequently. An identity verification means using facial recognition technology can also prioritize an identity verification method that is more important based on a user's past usage history. Furthermore, an identity verification means using facial recognition technology can adjust the priority based on the time period in which the user used the method in the past. In this way, by determining the priority based on the past usage history, it is possible to preferentially apply a verification method that is important to the user.

[0158] An identity verification means using facial recognition technology can adjust the level of detail of the corresponding identity verification depending on the user's level of expertise. An identity verification means using facial recognition technology adjusts the level of detail of the corresponding identity verification depending on the user's level of expertise. For example, an identity verification means using facial recognition technology can provide a simple identity verification method for users with little expertise. Alternatively, an identity verification means using facial recognition technology can provide a detailed identity verification method for users with extensive expertise. Furthermore, an identity verification means using facial recognition technology can customize the identity verification method depending on the user's level of expertise. In this way, by adjusting the verification method depending on the level of expertise, a verification method that is easy for users to understand can be provided.

[0159] A personal identification verification method using facial recognition technology can estimate a user's emotions and adjust the facial recognition procedure based on the estimated emotion analysis results. A personal identification verification method using facial recognition technology can estimate a user's emotions and adjust the facial recognition procedure based on the estimated emotion analysis results. For example, a personal identification verification method using facial recognition technology can provide simple and easy-to-understand instructions when a user is nervous. A personal identification verification method using facial recognition technology can also provide detailed instructions when a user is relaxed. Furthermore, a personal identification verification method using facial recognition technology can also provide quick and concise instructions when a user is in a hurry. This allows for more appropriate responses by adjusting the verification procedure according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0160] An identity verification means using facial recognition technology can customize the content of the corresponding identity verification based on the user's geographic location information. An identity verification means using facial recognition technology customizes the content of the corresponding identity verification based on the user's geographic location information. For example, an identity verification means using facial recognition technology prioritizes a specific identity verification method for the area where the user lives. An identity verification means using facial recognition technology can also provide information on the nearest service center based on the user's current location. Furthermore, an identity verification means using facial recognition technology can provide a region-specific identity verification method based on the user's geographic location information. This makes it possible to accommodate region-specific verification methods by customizing the verification content based on the geographic location information.

[0161] An identity verification method using facial recognition technology can optimize the content of corresponding identity verification based on a user's social media activity. An identity verification method using facial recognition technology optimizes the content of corresponding identity verification based on a user's social media activity. For example, an identity verification method using facial recognition technology prioritizes identity verification methods that a user frequently mentions on social media. An identity verification method using facial recognition technology can also predict and provide a required identity verification method based on a user's social media activity. Furthermore, an identity verification method using facial recognition technology can optimize the identity verification method based on the user's social media feedback. In this way, by optimizing the verification content based on social media activity, it is possible to predict and provide a required identity verification method for a user.

[0162] An identity verification means using facial recognition technology can adjust the content of the corresponding identity verification based on the user's past feedback. An identity verification means using facial recognition technology adjusts the content of the corresponding identity verification based on the user's past feedback. For example, an identity verification means using facial recognition technology improves an identity verification method that a user has been dissatisfied with in the past. An identity verification means using facial recognition technology can also optimize the identity verification method based on the user's past feedback. Furthermore, an identity verification means using facial recognition technology can extract and reflect important improvements from the user's past feedback. In this way, user satisfaction is improved by adjusting the verification content based on the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned automation unit, avatar unit, subtitle display unit, translation unit, and verification unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the automation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the avatar unit is realized by the control unit 46A of the smart device 14. For example, the subtitle display unit is realized by the display 40A of the smart device 14. For example, the translation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the verification unit is realized by the camera 42 and the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned automation unit, avatar unit, subtitle display unit, translation unit, and verification unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the automation unit is realized by a specific processing unit 290 of the data processing device 12. For example, the avatar unit is realized by a control unit 46A of the smart glasses 214. For example, the subtitle display unit is realized by a display 40A of the smart glasses 214. For example, the translation unit is realized by a specific processing unit 290 of the data processing device 12. For example, the verification unit is realized by a camera 42 and a control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned automation unit, avatar unit, subtitle display unit, translation unit, and verification unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the automation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the avatar unit is realized by the control unit 46A of the headset type terminal 314. For example, the subtitle display unit is realized by the display 343 of the headset type terminal 314. For example, the translation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the verification unit is realized by the camera 42 and the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned automation unit, avatar unit, subtitle display unit, translation unit, and verification unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the automation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the avatar unit is realized by the control unit 46A of the robot 414. For example, the subtitle display unit is realized by the display 40A of the robot 414. For example, the translation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the verification unit is realized by the camera 42 and the control unit 46A of the robot 414.

[0163] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0164] The counter work efficiency system can estimate the user's emotions and adjust the avatar's response method based on the estimated emotions. For example, if the user is nervous, the avatar can respond with a calm voice and facial expression. If the user is relaxed, the avatar can respond with a cheerful voice and facial expression. Furthermore, if the user is in a hurry, the avatar can respond quickly and concisely. This makes it possible to respond according to the user's emotions and provide more friendly service.

[0165] The counter work efficiency system can customize the avatar's response methods based on the user's past usage history. For example, it can provide a quicker response to users who use the service frequently. It can also provide information about a specific procedure preferentially to users who have performed that procedure many times in the past. It can also analyze the user's preferences and tendencies from past usage history and select the most appropriate response method. This allows it to provide the best service for the user.

[0166] The counter work efficiency improvement system can suggest the optimal counter location based on the user's geographical location information. For example, it can guide the user to the counter closest to their current location. It can also provide services specialized for the area where the user lives. It can also suggest the optimal counter location based on the user's travel route. This improves user convenience and enables efficient service provision.

[0167] The counter work efficiency system can optimize the avatar's response methods based on the user's social media activity. For example, it can prioritize providing information on topics that the user frequently mentions on social media. It can also predict and suggest necessary procedures based on the user's social media activity. Furthermore, it can optimize the avatar's response methods based on the user's feedback on social media. This makes it possible to predict and provide the information the user needs.

[0168] The counter work efficiency system can estimate the user's emotions and adjust the subtitle display method based on the estimated emotions. For example, if the user is nervous, the subtitle display speed can be slowed down. Alternatively, if the user is relaxed, the subtitle display speed can be normal. Furthermore, if the user is in a hurry, the subtitle display speed can be increased. This makes it possible to display subtitles according to the user's emotions, allowing for more appropriate responses.

[0169] The counter work efficiency system can adjust the level of detail of the information provided depending on the user's level of expertise. For example, it can provide a simple explanation to a user with little expertise, and a detailed explanation to a user with a lot of expertise. Furthermore, it can customize the display format of the information depending on the user's level of expertise. This makes it possible to provide information that is easy for the user to understand.

[0170] The counter work efficiency system can estimate the user's emotions and adjust the translation expression based on the estimated emotions. For example, if the user is nervous, concise and easy to understand expressions can be used. If the user is relaxed, detailed expressions can be used. Furthermore, if the user is in a hurry, quick and concise expressions can be used. This makes it possible to translate according to the user's emotions and provide more appropriate service.

[0171] The counter work efficiency system can adjust the avatar's response methods based on past user feedback. For example, it can improve procedures that users were dissatisfied with in the past. It can also optimize the avatar's response methods based on past feedback. It can also extract and reflect important areas for improvement from past feedback. This increases user satisfaction and allows for the provision of better service.

[0172] The counter work efficiency improvement system can estimate the user's emotions and adjust the identity verification method based on the estimated emotions. For example, if the user is nervous, a simple and easy-to-understand identity verification method can be provided. If the user is relaxed, a detailed identity verification method can be provided. Furthermore, if the user is in a hurry, a quick and simple identity verification method can be provided. This makes it possible to verify the user's identity according to their emotions, allowing for more appropriate responses.

[0173] The counter work efficiency system can customize the services it provides based on the user's geographic location information. For example, it can prioritize specific procedures in the area where the user lives. It can also provide information on the nearest counter based on the user's current location. It can also automate procedures specific to the area based on the user's geographic location information. This makes it possible to accommodate procedures specific to the area, improving user convenience.

[0174] The processing flow of the second embodiment will be briefly explained below.

[0175] Step 1: The automation department fully automates routine tasks. For example, it automates procedures such as issuing resident registration certificates and opening accounts. It can also automate tasks such as document processing and data entry. Step 2: The avatar section responds with a friendly AI avatar. For example, it has a human-like appearance and voice, giving the user a friendly impression. It can also respond appropriately to the user's questions. Step 3: The subtitle display unit displays the content spoken by the avatar unit as subtitles in front of the user. For example, the content spoken by an AI avatar can be displayed as subtitles, and the display position, font size, color, etc. can be adjusted. Step 4: The translation unit automatically translates what is said by the avatar unit. For example, it can translate into the user's native language, such as English or Chinese, and the translation algorithm and supported languages ​​can be adjusted. Step 5: The verification unit verifies the identity of the user using the avatar unit. For example, the user can be verified using a My Number card, or other authentication technologies such as facial recognition or fingerprint authentication.

[0176] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0177] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0178] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0179] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0180] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0181] 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.

[0182] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0183] 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.

[0184] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0185] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0186] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0187] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0188] 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.

[0189] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0190] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0191] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0192] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0193] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0194] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0195] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0196] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0197] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0198] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0199] 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.

[0200] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0201] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0202] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0203] 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.

[0204] 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.

[0205] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0206] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0207] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0208] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0209] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0210] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0211] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0212] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0213] 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.

[0214] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0215] 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.

[0216] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0217] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0218] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0219] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0220] 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.

[0221] 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.

[0222] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0223] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0224] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0225] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0226] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0227] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0228] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0229] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0230] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0231] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0232] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0233] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0234] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0235] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0236] 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.

[0237] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0238] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0239] 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.

[0240] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0241] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0242] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0243] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0244] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0245] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0246] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0247] [Explanation of symbols]

[0248] 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. The automation department fully automates routine tasks, an avatar unit that handles the tasks processed by the automation unit using an AI avatar with specific technical specifications; a subtitle display unit that displays subtitles of the content spoken by the avatar unit in front of the user; a translation unit that automatically translates the content spoken by the avatar unit; a confirmation unit that performs identity verification using the avatar unit; Equipped with A system characterized by:

2. Equipped with an operation unit that corresponds to the specific content of non-routine tasks The system of claim 1 .

3. Equipped with a location setting unit that allows you to freely set the location of the counter The system of claim 1 .

4. Equipped with identity verification methods using facial recognition technology The system of claim 1 .

5. The automation unit Automate procedures such as issuing resident certificates and opening accounts The system of claim 1 .

6. The avatar section It has specific appearance and voice specifications, giving users a friendly impression. The system of claim 1 .

7. The subtitle display unit Display what an AI avatar says as subtitles The system of claim 1 .

8. The translation unit Support multiple languages ​​(e.g., English, Chinese) The system of claim 1 .

Images