System

Abstract

To provide a system that promotes the digitalization of public institutions through procedures in a metaverse space.SOLUTION: A system according to the embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives procedures in a metaverse space. The generation unit analyzes the information received by the reception unit, and generates necessary documents and information. The provision unit provides the documents and information generated by the generation unit to public institutions.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, there were issues with the complicated procedures at public institutions and the lack of digitalization, making it difficult to process efficiently.

[0005] The system according to the embodiment aims to promote the digitalization of public institutions through procedures in the metaverse space. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives a procedure in the metaverse space. The generation unit analyzes the information received by the reception unit and generates necessary documents and information. The provision unit provides the documents and information generated by the generation unit to a public institution. Effect of the Invention

[0007] The system according to the embodiment can promote the digitalization of public institutions through procedures in the metaverse space. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The digitalization promotion system according to the embodiment of the present invention is a system that promotes the digitalization of public institutions. This digitalization promotion system utilizes the metaverse space to allow applications and other procedures that previously had to be made at government offices on weekdays to be made online. Furthermore, by utilizing generation AI, it is possible to not only simplify transactions and improve the work efficiency of institutions, but also reduce the hassle and work of citizens. It also promotes economic revitalization in Japan through improving the digital literacy of citizens, obtaining My Number, and obtaining passports. For example, a user accesses the metaverse space and performs the necessary application procedures. For example, procedures that previously required a visit to a government office, such as the issuance of a resident's card or the submission of a change of address notification, can be performed within the metaverse space. At this time, the generation AI analyzes the contents entered by the user and automatically generates the necessary documents and information. This allows the user to easily complete the procedures. Next, the generation AI simplifies the interaction between the user and the public institution. For example, if there is a defect in the document submitted by the user, the generation AI automatically presents a correction proposal and urges the user to resubmit. It also automates the work performed by employees of public institutions, improving work efficiency. This allows the work of public institutions to be performed quickly and efficiently. Furthermore, the use of the metaverse space will improve the digital literacy of citizens. Users can become familiar with digital technology through procedures within the metaverse space. In addition, important procedures such as obtaining a My Number and a passport can be performed online, improving convenience for citizens. Finally, this system will promote the revitalization of Japan's economy. The digitalization of public institutions will improve operational efficiency and reduce costs. In addition, by reducing the effort and work of citizens, they will be able to use their time more effectively. This will invigorate economic activity and is expected to lead to economic growth throughout Japan. As a result, the digitalization promotion system will be able to promote the digitalization of public institutions and improve convenience for citizens.

[0029] The digitalization promotion system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit accepts procedures in the metaverse space. For example, a user can access the metaverse space and perform procedures such as issuing a resident's card and submitting a change of address notification. The reception unit uses a generation AI to analyze the user's input contents and transmits necessary documents and information to the generation unit. The generation unit uses a generation AI to analyze the information accepted from the reception unit and generate necessary documents and information. For example, the generation unit automatically generates documents such as application forms and certificates based on the user's input contents. The generation unit can also use a generation AI to utilize natural language processing technology to analyze the user's input contents and extract necessary information. For example, the generation unit analyzes the text entered by the user, extracts necessary information, and generates documents. The provision unit provides the documents and information generated by the generation unit to a public institution. For example, the provision unit transmits the generated documents to the public institution's system and completes the procedures. The provision unit can also use a generation AI to automatically present correction suggestions if there are any defects in the documents submitted by the user, and encourage the user to resubmit. For example, if there is an omission or error in the document submitted by the user, the generation AI automatically generates a correction proposal and prompts the user to resubmit the document. The provision unit can also automate tasks performed by employees of public institutions to improve work efficiency. For example, the provision unit uses the generation AI to automate tasks such as data entry and document preparation performed by employees of public institutions to improve work efficiency. As a result, the digitalization promotion system according to the embodiment can efficiently carry out procedures in the metaverse space.

[0030] The reception unit accepts procedures in the metaverse space. For example, a user can access the metaverse space and perform procedures such as issuing a resident's certificate or submitting a change of address notification. The reception unit uses a generation AI to analyze the user's input and send the necessary documents and information to the generation unit. Specifically, the user accesses a virtual window in the metaverse space and starts the procedure by voice or text. The generation AI analyzes the user's speech and input text using natural language processing technology and extracts the information necessary for the procedure. For example, if a user inputs "I want to issue a resident's certificate," the AI ​​understands the intention and confirms the necessary information (name, address, date of birth, etc.) with the user. In addition, the reception unit can refer to the user's past procedure history and personal information to speed up the procedure. For example, for users who have performed a similar procedure in the past, the previous information can be automatically reflected in the input form, reducing the effort of inputting information. In addition, the reception unit can provide support in real time if the user has questions or problems during the procedure. For example, a chatbot function can be used to immediately answer the user's questions and smoothly proceed with the procedure. As a result, the reception unit supports users to smoothly complete procedures in the metaverse space, improving the efficiency and convenience of procedures.

[0031] The generation unit uses the generation AI to analyze the information received from the reception unit and generate the necessary documents and information. For example, the generation unit automatically generates documents such as application forms and certificates based on the user's input. The generation unit can also use the generation AI to utilize natural language processing technology to analyze the user's input and extract the necessary information. Specifically, it analyzes the text entered by the user, extracts the necessary information, and generates documents. For example, if the user enters "I would like to submit a transfer notification," the generation AI understands the intention, automatically extracts the information necessary for the transfer notification (new address, date of transfer, old address, etc.), and generates documents in an appropriate format. In addition, the generation unit can refer to multiple data sources and check the consistency of the information to ensure the accuracy of the generated documents. For example, it collates the address information entered by the user with a geographic information system (GIS) to confirm the accurate address notation. In addition, the generation unit can automatically list the necessary attached documents and additional information based on the user's input and notify the user. This allows the user to grasp all the information necessary for the procedure at once, preventing omissions and deficiencies in the procedure. The generation unit can also authenticate the generated document with an electronic signature or timestamp to give it legal validity. This allows the generation unit to quickly and accurately support the procedures that users perform in the metaverse space, and streamline the document generation process.

[0032] The provision unit provides the documents and information generated by the generation unit to the public institution. For example, the provision unit sends the generated documents to the public institution's system and completes the procedure. The provision unit can also use the generation AI to automatically suggest corrections and encourage the user to resubmit documents if there are any defects in the documents submitted by the user, and use the generation AI to automatically send corrections to the user. Specifically, the provision unit sends the generated documents directly to the public institution's electronic application system and tracks the progress of the procedure in real time. For example, in the case of a resident registration certificate issuance procedure, the provision unit sends the generated resident registration certificate to the city hall system and automatically proceeds with the issuance procedure. In addition, if there are any omissions or errors in the documents submitted by the user, the generation AI automatically generates corrections and encourages the user to resubmit the documents. For example, if the user makes an error in the address field of the transfer notification, the AI ​​proposes the correct address and sends a notification encouraging the user to make the correction. In addition, the provision unit can automate tasks performed by employees of public institutions to improve work efficiency. For example, the provision unit uses the generation AI to automate tasks such as data entry and document preparation performed by employees of public institutions to improve work efficiency. This allows public institution employees to focus on more important tasks, improving overall work efficiency. The provision unit can notify the user of the progress of the procedure in real time and confirm the completion of the procedure. For example, when the procedure is completed, a notification is sent to the user, and the result of the procedure can be confirmed. This allows the provision unit to smoothly proceed with the procedure performed by the user in the metaverse space, improving the efficiency and convenience of the procedure.

[0033] The generation unit can analyze the user's input contents using the generation AI and generate necessary documents and information. The generation unit, for example, uses the generation AI to analyze the user's input contents. For example, the generation unit analyzes the text entered by the user, extracts the necessary information, and generates a document. The generation unit can also use the generation AI to utilize natural language processing technology to analyze the user's input contents. For example, the generation unit analyzes the text entered by the user, extracts the necessary information, and generates a document. The generation unit can analyze the user's input contents using the generation AI and automatically generate the necessary documents and information. For example, the generation unit analyzes the text entered by the user, extracts the necessary information, and generates a document. As a result, the generation unit can analyze the user's input contents and automatically generate the necessary documents and information.

[0034] The generation unit can analyze the user's input using natural language processing with the generation AI and extract the necessary information. The generation unit, for example, uses the generation AI to utilize natural language processing technology to analyze the user's input content. For example, the generation unit analyzes the text entered by the user, extracts the necessary information, and generates a document. The generation unit can also use the generation AI to utilize natural language processing technology to analyze the user's input content. For example, the generation unit analyzes the text entered by the user, extracts the necessary information, and generates a document. The generation unit can use the generation AI to utilize natural language processing technology to analyze the user's input content and extract the necessary information. For example, the generation unit analyzes the text entered by the user, extracts the necessary information, and generates a document. This allows the generation unit to more accurately analyze the user's input by using natural language processing.

[0035] The provision unit can automatically present a correction proposal using the generation AI when there is a defect in the document submitted by the user, and encourage the user to resubmit. The provision unit, for example, uses the generation AI to automatically present a correction proposal when there is a defect in the document submitted by the user. For example, if the document submitted by the user has an omission or a typing error, the generation AI automatically generates a correction proposal and encourages the user to resubmit. The provision unit can also automatically present a correction proposal when there is a defect in the document submitted by the user, using the generation AI. For example, if the document submitted by the user has an omission or a typing error, the generation AI automatically generates a correction proposal and encourages the user to resubmit. The provision unit can automatically present a correction proposal when there is a defect in the document submitted by the user, using the generation AI, and encourages the user to resubmit. For example, if the document submitted by the user has an omission or a typing error, the generation AI automatically generates a correction proposal and encourages the user to resubmit. As a result, the provision unit can automatically correct the deficiencies in the document and encourage resubmission, thereby smoothly proceeding with the procedure.

[0036] The provision unit can automate the work performed by employees of a public institution using the generation AI, thereby improving work efficiency. The provision unit, for example, uses the generation AI to automate the work performed by employees of a public institution. For example, the provision unit uses the generation AI to automate work such as data entry and document preparation performed by employees of a public institution, thereby improving work efficiency. The provision unit can also automate the work performed by employees of a public institution using the generation AI. For example, the provision unit uses the generation AI to automate work such as data entry and document preparation performed by employees of a public institution, thereby improving work efficiency. The provision unit can automate the work performed by employees of a public institution using the generation AI, thereby improving work efficiency. For example, the provision unit uses the generation AI to automate work such as data entry and document preparation performed by employees of a public institution, thereby improving work efficiency. In this way, the provision unit can improve the work efficiency of the public institution.

[0037] The digitalization promotion system includes a support unit that provides tutorials and guidelines in the metaverse space and supports the improvement of digital literacy of citizens. The support unit provides, for example, tutorials and guidelines in the metaverse space. For example, the support unit provides a tutorial that explains the operation method and the procedure flow when a user performs a procedure in the metaverse space. The support unit supports the improvement of digital literacy of citizens by providing tutorials and guidelines in the metaverse space. For example, the support unit provides a tutorial that explains the operation method and the procedure flow when a user performs a procedure in the metaverse space. The support unit supports the improvement of digital literacy of citizens by providing tutorials and guidelines in the metaverse space. For example, the support unit provides a tutorial that explains the operation method and the procedure flow when a user performs a procedure in the metaverse space. This allows the support unit to support the improvement of digital literacy of citizens.

[0038] The digitalization promotion system includes a reduction unit that reduces costs by improving business efficiency. The reduction unit, for example, reduces costs by improving business efficiency. For example, the reduction unit uses a generation AI to optimize the business processes of a public institution and reduce costs. The reduction unit can reduce the operating costs of the public institution by reducing costs by improving business efficiency. For example, the reduction unit uses a generation AI to optimize the business processes of a public institution and reduce costs. The reduction unit can reduce the operating costs of the public institution by reducing costs by improving business efficiency. In this way, the reduction unit can achieve cost reduction.

[0039] The digitalization promotion system includes an utilization unit that makes effective use of time by reducing the effort and work of citizens. The utilization unit, for example, makes effective use of time by reducing the effort and work of citizens. For example, the utilization unit uses a generation AI to simplify user procedures and reduce effort and work. By making effective use of time by reducing the effort and work of citizens, the utilization unit can make effective use of users' time. For example, the utilization unit uses a generation AI to simplify user procedures and reduce effort and work. By making effective use of time by reducing the effort and work of citizens, the utilization unit can make effective use of users' time. In this way, the utilization unit can realize effective use of time.

[0040] The reception unit can analyze the user's past application history and propose the optimal procedure method. The reception unit can analyze the user's past application history, for example, using a generation AI. For example, the reception unit can analyze the content of applications made by the user in the past and propose the optimal procedure method. The reception unit can efficiently perform the user's procedures by analyzing the user's past application history and proposing the optimal procedure method. For example, the reception unit automatically displays procedures that the user frequently performed in the past as candidates. In addition, the reception unit preferentially proposes input methods (voice, text, etc.) that the user used in the past. Furthermore, the reception unit predicts and proposes procedures to be performed in a specific time period from the user's past application history. This allows the reception unit to propose the optimal procedure method based on the past application history.

[0041] The reception unit can filter based on the user's current living situation and areas of interest at the time of the procedure. The reception unit, for example, uses a generation AI to analyze the user's current living situation and areas of interest. For example, the reception unit analyzes information such as the user's income, family structure, and place of residence to filter procedures. The reception unit can suggest optimal procedures to the user by filtering based on the user's current living situation and areas of interest at the time of the procedure. For example, if the user moves to a new residence, the reception unit preferentially suggests a move-in notification and issuance of a resident's certificate. In addition, if the user is raising a child, child-rearing-related procedures are preferentially displayed. In addition, if the user is elderly, nursing care and pension-related procedures are preferentially suggested. This allows the reception unit to suggest procedures according to the user's living situation and areas of interest.

[0042] The reception unit can preferentially accept highly relevant applications in consideration of the geographical location information of the user at the time of reception. The reception unit, for example, uses a generation AI to analyze the geographical location information of the user. For example, the reception unit analyzes the GPS data and address information of the user and preferentially accepts highly relevant applications. The reception unit can suggest the optimal procedure for the user by preferentially accepting highly relevant applications in consideration of the geographical location information of the user at the time of reception. For example, if the user lives in a specific area, procedures related to that area are preferentially suggested. In addition, if the user is traveling, procedures required at the travel destination are preferentially displayed. In addition, if the user plans to move, procedures related to the new address are preferentially suggested. This allows the reception unit to preferentially accept highly relevant applications based on the geographical location information.

[0043] The reception unit can analyze the user's social media activity at the time of reception and receive the related application. The reception unit analyzes the user's social media activity, for example, using a generation AI. For example, the reception unit analyzes the user's post content, number of followers, engagement rate, etc., and receives the related application. The reception unit can propose the optimal procedure to the user by analyzing the user's social media activity and receiving the related application at the time of reception. For example, if the user indicates that he or she will participate in a specific event on social media, a procedure related to the event is proposed. In addition, if the user indicates a specific field of interest on social media, procedures related to that field are preferentially displayed. Furthermore, if the user indicates an interest in a specific region on social media, a procedure related to that region is proposed. This allows the reception unit to accept related applications based on social media activity.

[0044] The generation unit can adjust the level of detail of the document to be generated based on the importance of the application content at the time of generation. The generation unit, for example, uses a generation AI to evaluate the importance of the application content. For example, the generation unit evaluates the legal requirements, urgency, and scope of impact of the application content, and adjusts the level of detail of the document to be generated. The generation unit can provide the user with an optimal document by adjusting the level of detail of the document to be generated based on the importance of the application content at the time of generation. For example, in the case of important application content, a document including detailed information is generated. In addition, in the case of general application content, a document including standard information is generated. Furthermore, in the case of simple application content, a document including concise information is generated. This allows the generation unit to generate a document with a level of detail according to the importance of the application content.

[0045] The generation unit can apply different generation algorithms depending on the category of the application at the time of generation. The generation unit, for example, uses a generation AI to analyze the category of the application. For example, the generation unit analyzes categories such as issuance of a resident's certificate, submission of a transfer notification, and acquisition of a passport, and applies a generation algorithm suitable for each. The generation unit can provide the user with an optimal document by applying different generation algorithms depending on the category of the application at the time of generation. For example, in the case of issuance of a resident's certificate, a generation algorithm dedicated to resident's certificates is applied. In addition, in the case of submission of a transfer notification, a generation algorithm dedicated to transfer notifications is applied. Furthermore, in the case of acquisition of a passport, a generation algorithm dedicated to passports is applied. This allows the generation unit to apply a generation algorithm according to the category of the application.

[0046] The generation unit can determine the priority of documents to be generated based on the submission time of the application at the time of generation. The generation unit, for example, uses a generation AI to evaluate the submission time of the application. For example, the generation unit evaluates the submission deadline and urgency of the application and determines the priority of documents to be generated. The generation unit can provide optimal documents to a user by determining the priority of documents to be generated based on the submission time of the application at the time of generation. For example, in the case of an urgent application, documents are generated with the highest priority. In addition, in the case of a normal application, documents are generated with standard priority. Furthermore, in the case of a long-term application, documents are generated at a later date. This allows the generation unit to determine the priority of documents based on the submission time.

[0047] The generation unit can adjust the order of documents to be generated based on the relevance of the application at the time of generation. The generation unit, for example, uses a generation AI to evaluate the relevance of the application. For example, the generation unit evaluates the similarity of the application contents and related laws and regulations, and adjusts the order of documents to be generated. The generation unit can provide optimal documents to the user by adjusting the order of documents to be generated based on the relevance of the application at the time of generation. For example, important and relevant application contents are written in the document with priority. In addition, application contents with general relevance are written in the document in a standard order. Furthermore, application contents with low relevance are written in the document at a later date. This allows the generation unit to adjust the order of documents based on relevance.

[0048] The provision unit can select the optimal provision method by referring to the user's past application history at the time of provision. The provision unit, for example, uses a generation AI to analyze the user's past application history. For example, the provision unit analyzes the contents of applications made by the user in the past and selects the optimal provision method. The provision unit can provide the optimal document to the user by selecting the optimal provision method by referring to the user's past application history at the time of provision. For example, the optimal provision method is selected based on procedures that the user frequently performed in the past. In addition, a provision method that avoids congestion is selected from the user's past application history. Furthermore, the user's past application history is analyzed and the most efficient provision method is selected. This allows the provision unit to select the optimal provision method based on the past application history.

[0049] The providing unit can customize the means of providing documents based on the user's current living situation at the time of providing. The providing unit, for example, uses a generation AI to analyze the user's current living situation. For example, the providing unit analyzes information such as the user's income, family structure, and place of residence, and customizes the means of providing documents. The providing unit can provide optimal documents to the user by customizing the means of providing documents based on the user's current living situation at the time of providing. For example, if the user moves to a new residence, a moving-in notification and issuance of a resident's card are provided preferentially. In addition, if the user is raising a child, childcare-related procedures are provided preferentially. In addition, if the user is elderly, nursing care and pension-related procedures are provided preferentially. This allows the providing unit to provide documents by a means according to the user's living situation.

[0050] The providing unit can select the optimal providing method in consideration of the geographical location information of the user at the time of providing. The providing unit, for example, uses a generation AI to analyze the geographical location information of the user. For example, the providing unit analyzes the GPS data and address information of the user and selects the optimal providing method. The providing unit can provide the optimal document to the user by selecting the optimal providing method in consideration of the geographical location information of the user at the time of providing. For example, if the user lives in a specific area, documents related to that area are provided preferentially. In addition, if the user is traveling, documents necessary at the travel destination are provided preferentially. Furthermore, if the user plans to move, documents related to the new address are provided preferentially. This allows the providing unit to select the optimal providing method based on the geographical location information.

[0051] The providing unit can propose a means of providing documents by analyzing the social media activity of the user at the time of providing. The providing unit, for example, uses a generation AI to analyze the social media activity of the user. For example, the providing unit analyzes the user's posting content, number of followers, engagement rate, etc., and proposes the optimal means of providing. The providing unit can provide optimal documents to the user by proposing a means of providing documents by analyzing the user's social media activity at the time of providing. For example, if the user indicates that he or she will participate in a specific event on social media, documents related to the event are provided. In addition, if the user indicates a specific field of interest on social media, documents related to that field are preferentially provided. Furthermore, if the user indicates an interest in a specific region on social media, documents related to that region are provided. This allows the providing unit to provide documents by the optimal means based on social media activity.

[0052] The support unit can provide the optimal support method by referring to the user's past digital literacy history when providing support. The support unit, for example, uses a generation AI to analyze the user's past digital literacy history. For example, the support unit analyzes the user's past digital literacy operation history and provides the optimal support method. The support unit can provide the optimal support to the user by referring to the user's past digital literacy history when providing support. For example, if the user has performed an operation with high digital literacy in the past, detailed support is provided. Also, if the user has performed an operation with low digital literacy in the past, basic support is provided. Furthermore, the user's past digital literacy history is analyzed and the optimal support method is proposed. This allows the support unit to provide the optimal support method based on the past digital literacy history.

[0053] The support unit can provide the optimal support method by considering the device information of the user when providing support. The support unit, for example, uses a generation AI to analyze the device information of the user. For example, the support unit analyzes information such as the type of device, OS, and browser of the user, and provides the optimal support method. The support unit can provide the optimal support to the user by providing the optimal support method by considering the device information of the user when providing support. For example, when the user is using a smartphone, a support method that matches the screen size is provided. Also, when the user is using a tablet, a support method optimized for a large screen is provided. Furthermore, when the user is using a personal computer, a detailed support method is provided. This allows the support unit to provide the optimal support method based on the device information.

[0054] The reduction department can select the optimal reduction method by referring to past operational efficiency data when making reductions. The reduction department, for example, uses a generation AI to analyze past operational efficiency data. For example, the reduction department analyzes data such as past work hours, resource usage, and quality of deliverables to select the optimal reduction method. The reduction department can reduce the operating costs of the public institution by selecting the optimal reduction method by referring to past operational efficiency data when making reductions. For example, the most effective cost reduction method is selected based on past operational efficiency data. In addition, a reduction method for eliminating waste is selected from the past operational efficiency data. Furthermore, the past operational efficiency data is analyzed to propose the optimal reduction method. This allows the reduction department to select the optimal reduction method based on past operational efficiency data.

[0055] The reduction department can select the optimal reduction method by taking into consideration the geographical distribution of work when reducing operations. The reduction department, for example, uses a generation AI to analyze the geographical distribution of work. For example, the reduction department analyzes the amount of work and resource allocation for each region and selects the optimal reduction method. The reduction department can reduce the operating costs of the public institution by selecting the optimal reduction method by taking into consideration the geographical distribution of work when reducing operations. For example, the optimal reduction method is selected by taking into consideration the efficiency of work in a specific region. In addition, geographically distributed operations are integrated and an efficient reduction method is proposed. Furthermore, the geographical distribution is analyzed and the optimal reduction method is selected. This allows the reduction department to select the optimal reduction method based on the geographical distribution.

[0056] The utilization unit can provide the optimal utilization method by referring to the user's past time management history at the time of utilization. The utilization unit, for example, uses a generation AI to analyze the user's past time management history. For example, the utilization unit analyzes the user's past time management operation history and provides the optimal utilization method. The utilization unit can provide the user with an optimal effective time utilization method by referring to the user's past time management history at the time of utilization. For example, the utilization unit proposes an optimal utilization method based on the user's history of effective time management in the past. In addition, the utilization unit proposes a utilization method for eliminating waste from the user's past time management history. Furthermore, the utilization unit analyzes the user's past time management history and provides the optimal utilization method. As a result, the utilization unit can provide the optimal utilization method based on the past time management history.

[0057] The utilization unit can provide the optimal utilization method by considering the user's device information at the time of utilization. The utilization unit, for example, uses a generation AI to analyze the user's device information. For example, the utilization unit analyzes information such as the user's device type, OS, and browser, and provides the optimal utilization method. The utilization unit can provide the user with an optimal time utilization method by providing the optimal utilization method by considering the user's device information at the time of utilization. For example, if the user is using a smartphone, a utilization method that matches the screen size is provided. Also, if the user is using a tablet, a utilization method optimized for a large screen is provided. Furthermore, if the user is using a personal computer, a detailed utilization method is provided. This allows the utilization unit to provide the optimal utilization method based on the device information.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The reception unit can analyze the user's past application history and propose the optimal procedure method. For example, the reception unit analyzes the content of applications made by the user in the past and proposes the optimal procedure method. The reception unit can efficiently perform procedures for the user by analyzing the user's past application history and proposing the optimal procedure method. For example, the reception unit automatically displays procedures that the user has frequently performed in the past as candidates. In addition, the reception unit preferentially proposes input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit predicts and proposes procedures to be performed in a specific time period based on the user's past application history. This allows the reception unit to propose the optimal procedure method based on the past application history.

[0060] The generation unit can adjust the level of detail of the document to be generated based on the importance of the application content at the time of generation. For example, the generation unit uses a generation AI to evaluate the legal requirements, urgency, and scope of impact of the application content, and adjusts the level of detail of the document to be generated. The generation unit can provide the user with an optimal document by adjusting the level of detail of the document to be generated based on the importance of the application content at the time of generation. For example, in the case of important application content, a document including detailed information is generated. In addition, in the case of general application content, a document including standard information is generated. Furthermore, in the case of simple application content, a document including concise information is generated. This allows the generation unit to generate a document with a level of detail according to the importance of the application content.

[0061] The provision unit can select the optimal provision method by referring to the user's past application history at the time of provision. For example, the provision unit uses a generation AI to analyze the user's past application history. For example, the provision unit analyzes the contents of applications made by the user in the past and selects the optimal provision method. The provision unit can provide the optimal document to the user by selecting the optimal provision method by referring to the user's past application history at the time of provision. For example, the optimal provision method is selected based on procedures that the user frequently performed in the past. In addition, a provision method that avoids congestion is selected from the user's past application history. Furthermore, the user's past application history is analyzed and the most efficient provision method is selected. This allows the provision unit to select the optimal provision method based on the past application history.

[0062] The support unit can provide the optimal support method by considering the user's device information when providing support. For example, the support unit uses a generation AI to analyze the user's device information. For example, the support unit analyzes information such as the user's device type, OS, and browser, and provides the optimal support method. The support unit can provide the optimal support to the user by providing the optimal support method by considering the user's device information when providing support. For example, if the user is using a smartphone, a support method that matches the screen size is provided. Also, if the user is using a tablet, a support method optimized for a large screen is provided. Furthermore, if the user is using a personal computer, a detailed support method is provided. This allows the support unit to provide the optimal support method based on the device information.

[0063] The reduction department can select the optimal reduction method by referring to past operational efficiency data when making reductions. For example, the reduction department uses a generation AI to analyze past operational efficiency data. For example, the reduction department analyzes data such as past work hours, resource usage, and quality of deliverables to select the optimal reduction method. The reduction department can reduce the operating costs of the public institution by selecting the optimal reduction method by referring to past operational efficiency data when making reductions. For example, the most effective cost reduction method is selected based on past operational efficiency data. In addition, a reduction method for eliminating waste is selected from the past operational efficiency data. Furthermore, the past operational efficiency data is analyzed to propose the optimal reduction method. This allows the reduction department to select the optimal reduction method based on past operational efficiency data.

[0064] The process flow of the first embodiment will be briefly described below.

[0065] Step 1: The reception unit accepts procedures in the metaverse space. For example, a user can access the metaverse space and carry out procedures such as issuing a resident's card or submitting a change of address notification. The reception unit uses the generation AI to analyze the user's input and transmits the necessary documents and information to the generation unit. Step 2: The generation unit uses generation AI to analyze the information received from the reception unit and generate the necessary documents and information. For example, the generation unit automatically generates documents such as application forms and certificates based on the user's input. The generation unit can also use generation AI to analyze the user's input using natural language processing technology and extract the necessary information. Step 3: The provision unit provides the documents and information generated by the generation unit to the public institution. For example, the provision unit sends the generated documents to the public institution's system and completes the procedure. Using the generation AI, the provision unit can automatically suggest corrections and encourage the user to resubmit documents if there are any defects in the documents submitted by the user. The provision unit also automates tasks performed by public institution employees, improving work efficiency.

[0066] (Example 2) The digitalization promotion system according to the embodiment of the present invention is a system that promotes the digitalization of public institutions. This digitalization promotion system utilizes the metaverse space to allow applications and other procedures that previously had to be made at government offices on weekdays to be made online. Furthermore, by utilizing generation AI, it is possible to not only simplify transactions and improve the work efficiency of institutions, but also reduce the hassle and work of citizens. It also promotes economic revitalization in Japan through improving the digital literacy of citizens, obtaining My Number, and obtaining passports. For example, a user accesses the metaverse space and performs the necessary application procedures. For example, procedures that previously required a visit to a government office, such as the issuance of a resident's card or the submission of a change of address notification, can be performed within the metaverse space. At this time, the generation AI analyzes the contents entered by the user and automatically generates the necessary documents and information. This allows the user to easily complete the procedures. Next, the generation AI simplifies the interaction between the user and the public institution. For example, if there is a defect in the document submitted by the user, the generation AI automatically presents a correction proposal and urges the user to resubmit. It also automates the work performed by employees of public institutions, improving work efficiency. This allows the work of public institutions to be performed quickly and efficiently. Furthermore, the use of the metaverse space will improve the digital literacy of citizens. Users can become familiar with digital technology through procedures within the metaverse space. In addition, important procedures such as obtaining a My Number and a passport can be performed online, improving convenience for citizens. Finally, this system will promote the revitalization of Japan's economy. The digitalization of public institutions will improve operational efficiency and reduce costs. In addition, by reducing the effort and work of citizens, they will be able to use their time more effectively. This will invigorate economic activity and is expected to lead to economic growth throughout Japan. As a result, the digitalization promotion system will be able to promote the digitalization of public institutions and improve convenience for citizens.

[0067] The digitalization promotion system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit accepts procedures in the metaverse space. For example, a user can access the metaverse space and perform procedures such as issuing a resident's card and submitting a change of address notification. The reception unit uses a generation AI to analyze the user's input contents and transmits necessary documents and information to the generation unit. The generation unit uses a generation AI to analyze the information accepted from the reception unit and generate necessary documents and information. For example, the generation unit automatically generates documents such as application forms and certificates based on the user's input contents. The generation unit can also use a generation AI to utilize natural language processing technology to analyze the user's input contents and extract necessary information. For example, the generation unit analyzes the text entered by the user, extracts necessary information, and generates documents. The provision unit provides the documents and information generated by the generation unit to a public institution. For example, the provision unit transmits the generated documents to the public institution's system and completes the procedures. The provision unit can also use a generation AI to automatically present correction suggestions if there are any defects in the documents submitted by the user, and encourage the user to resubmit. For example, if there is an omission or error in the document submitted by the user, the generation AI automatically generates a correction proposal and prompts the user to resubmit the document. The provision unit can also automate tasks performed by employees of public institutions to improve work efficiency. For example, the provision unit uses the generation AI to automate tasks such as data entry and document preparation performed by employees of public institutions to improve work efficiency. As a result, the digitalization promotion system according to the embodiment can efficiently carry out procedures in the metaverse space.

[0068] The reception unit accepts procedures in the metaverse space. For example, a user can access the metaverse space and perform procedures such as issuing a resident's certificate or submitting a change of address notification. The reception unit uses a generation AI to analyze the user's input and send the necessary documents and information to the generation unit. Specifically, the user accesses a virtual counter in the metaverse space and starts the procedure by voice or text. The generation AI analyzes the user's speech and input text using natural language processing technology and extracts the information necessary for the procedure. For example, if a user inputs "I want to issue a resident's certificate," the AI ​​understands the intention and confirms the necessary information (name, address, date of birth, etc.) with the user. In addition, the reception unit can refer to the user's past procedure history and personal information to speed up the procedure. For example, for users who have performed a similar procedure in the past, the previous information can be automatically reflected in the input form, reducing the effort of inputting information. In addition, the reception unit can provide support in real time if the user has questions or problems during the procedure. For example, a chatbot function can be used to immediately answer the user's questions and smoothly proceed with the procedure. As a result, the reception unit supports users to smoothly complete procedures in the metaverse space, improving the efficiency and convenience of procedures.

[0069] The generation unit uses the generation AI to analyze the information received from the reception unit and generate the necessary documents and information. For example, the generation unit automatically generates documents such as application forms and certificates based on the user's input. The generation unit can also use the generation AI to utilize natural language processing technology to analyze the user's input and extract the necessary information. Specifically, it analyzes the text entered by the user, extracts the necessary information, and generates documents. For example, if the user enters "I would like to submit a transfer notification," the generation AI understands the intention, automatically extracts the information necessary for the transfer notification (new address, date of transfer, old address, etc.), and generates documents in an appropriate format. In addition, the generation unit can refer to multiple data sources and check the consistency of the information to ensure the accuracy of the generated documents. For example, it collates the address information entered by the user with a geographic information system (GIS) to confirm the accurate address notation. In addition, the generation unit can automatically list the necessary attached documents and additional information based on the user's input and notify the user. This allows the user to grasp all the information necessary for the procedure at once, preventing omissions and deficiencies in the procedure. The generation unit can also authenticate the generated document with an electronic signature or timestamp to give it legal validity. This allows the generation unit to quickly and accurately support the procedures that users perform in the metaverse space, and streamline the document generation process.

[0070] The provision unit provides the documents and information generated by the generation unit to the public institution. For example, the provision unit sends the generated documents to the public institution's system and completes the procedure. The provision unit can also use the generation AI to automatically suggest corrections and encourage the user to resubmit documents if there are any defects in the documents submitted by the user, and use the generation AI to automatically send corrections to the user. Specifically, the provision unit sends the generated documents directly to the public institution's electronic application system and tracks the progress of the procedure in real time. For example, in the case of a resident registration certificate issuance procedure, the provision unit sends the generated resident registration certificate to the city hall system and automatically proceeds with the issuance procedure. In addition, if there are any omissions or errors in the documents submitted by the user, the generation AI automatically generates corrections and encourages the user to resubmit the documents. For example, if the user makes an error in the address field of the transfer notification, the AI ​​proposes the correct address and sends a notification encouraging the user to make the correction. In addition, the provision unit can automate tasks performed by employees of public institutions to improve work efficiency. For example, the provision unit uses the generation AI to automate tasks such as data entry and document preparation performed by employees of public institutions to improve work efficiency. This allows public institution employees to focus on more important tasks, improving overall work efficiency. The provision unit can notify the user of the progress of the procedure in real time and confirm the completion of the procedure. For example, when the procedure is completed, a notification is sent to the user, and the result of the procedure can be confirmed. This allows the provision unit to smoothly proceed with the procedure performed by the user in the metaverse space, improving the efficiency and convenience of the procedure.

[0071] The generation unit can analyze the user's input contents using the generation AI and generate necessary documents and information. The generation unit, for example, uses the generation AI to analyze the user's input contents. For example, the generation unit analyzes the text entered by the user, extracts the necessary information, and generates a document. The generation unit can also use the generation AI to utilize natural language processing technology to analyze the user's input contents. For example, the generation unit analyzes the text entered by the user, extracts the necessary information, and generates a document. The generation unit can analyze the user's input contents using the generation AI and automatically generate the necessary documents and information. For example, the generation unit analyzes the text entered by the user, extracts the necessary information, and generates a document. As a result, the generation unit can analyze the user's input contents and automatically generate the necessary documents and information.

[0072] The generation unit can analyze the user's input using natural language processing with the generation AI and extract the necessary information. The generation unit, for example, uses the generation AI to utilize natural language processing technology to analyze the user's input content. For example, the generation unit analyzes the text entered by the user, extracts the necessary information, and generates a document. The generation unit can also use the generation AI to utilize natural language processing technology to analyze the user's input content. For example, the generation unit analyzes the text entered by the user, extracts the necessary information, and generates a document. The generation unit can use the generation AI to utilize natural language processing technology to analyze the user's input content and extract the necessary information. For example, the generation unit analyzes the text entered by the user, extracts the necessary information, and generates a document. This allows the generation unit to more accurately analyze the user's input by using natural language processing.

[0073] The provision unit can automatically present a correction proposal using the generation AI when there is a defect in the document submitted by the user, and encourage the user to resubmit. The provision unit, for example, uses the generation AI to automatically present a correction proposal when there is a defect in the document submitted by the user. For example, if the document submitted by the user has an omission or a typing error, the generation AI automatically generates a correction proposal and encourages the user to resubmit. The provision unit can also automatically present a correction proposal when there is a defect in the document submitted by the user, using the generation AI. For example, if the document submitted by the user has an omission or a typing error, the generation AI automatically generates a correction proposal and encourages the user to resubmit. The provision unit can automatically present a correction proposal when there is a defect in the document submitted by the user, using the generation AI, and encourages the user to resubmit. For example, if the document submitted by the user has an omission or a typing error, the generation AI automatically generates a correction proposal and encourages the user to resubmit. As a result, the provision unit can automatically correct the deficiencies in the document and encourage resubmission, thereby smoothly proceeding with the procedure.

[0074] The provision unit can automate the work performed by employees of a public institution using the generation AI, thereby improving work efficiency. The provision unit, for example, uses the generation AI to automate the work performed by employees of a public institution. For example, the provision unit uses the generation AI to automate work such as data entry and document preparation performed by employees of a public institution, thereby improving work efficiency. The provision unit can also automate the work performed by employees of a public institution using the generation AI. For example, the provision unit uses the generation AI to automate work such as data entry and document preparation performed by employees of a public institution, thereby improving work efficiency. The provision unit can automate the work performed by employees of a public institution using the generation AI, thereby improving work efficiency. For example, the provision unit uses the generation AI to automate work such as data entry and document preparation performed by employees of a public institution, thereby improving work efficiency. In this way, the provision unit can improve the work efficiency of the public institution.

[0075] The digitalization promotion system includes a support unit that provides tutorials and guidelines in the metaverse space and supports the improvement of digital literacy of citizens. The support unit provides, for example, tutorials and guidelines in the metaverse space. For example, the support unit provides a tutorial that explains the operation method and the procedure flow when a user performs a procedure in the metaverse space. The support unit supports the improvement of digital literacy of citizens by providing tutorials and guidelines in the metaverse space. For example, the support unit provides a tutorial that explains the operation method and the procedure flow when a user performs a procedure in the metaverse space. The support unit supports the improvement of digital literacy of citizens by providing tutorials and guidelines in the metaverse space. For example, the support unit provides a tutorial that explains the operation method and the procedure flow when a user performs a procedure in the metaverse space. This allows the support unit to support the improvement of digital literacy of citizens.

[0076] The digitalization promotion system includes a reduction unit that reduces costs by improving business efficiency. The reduction unit, for example, reduces costs by improving business efficiency. For example, the reduction unit uses a generation AI to optimize the business processes of a public institution and reduce costs. The reduction unit can reduce the operating costs of the public institution by reducing costs by improving business efficiency. For example, the reduction unit uses a generation AI to optimize the business processes of a public institution and reduce costs. The reduction unit can reduce the operating costs of the public institution by reducing costs by improving business efficiency. In this way, the reduction unit can achieve cost reduction.

[0077] The digitalization promotion system includes an utilization unit that makes effective use of time by reducing the effort and work of citizens. The utilization unit, for example, makes effective use of time by reducing the effort and work of citizens. For example, the utilization unit uses a generation AI to simplify user procedures and reduce effort and work. By making effective use of time by reducing the effort and work of citizens, the utilization unit can make effective use of users' time. For example, the utilization unit uses a generation AI to simplify user procedures and reduce effort and work. By making effective use of time by reducing the effort and work of citizens, the utilization unit can make effective use of users' time. In this way, the utilization unit can realize effective use of time.

[0078] The reception unit can estimate the user's emotion and customize the interface of the procedure in the metaverse space based on the estimated user's emotion. The reception unit estimates the user's emotion, for example, using a generation AI. For example, the reception unit analyzes the user's facial expression and voice to estimate the emotion. The reception unit customizes the interface of the procedure in the metaverse space based on the estimated user's emotion. For example, when the user is stressed, a simple and intuitive interface is provided to minimize the procedure steps. Also, when the user is relaxed, detailed options are provided and a customizable procedure method is suggested. Furthermore, when the user is in a hurry, voice input is prioritized so that the procedure can be completed quickly. This allows the reception unit to provide an interface according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0079] The reception unit can analyze the user's past application history and propose the optimal procedure method. The reception unit can analyze the user's past application history, for example, using a generation AI. For example, the reception unit can analyze the content of applications made by the user in the past and propose the optimal procedure method. The reception unit can efficiently perform the user's procedures by analyzing the user's past application history and proposing the optimal procedure method. For example, the reception unit automatically displays procedures that the user frequently performed in the past as candidates. In addition, the reception unit preferentially proposes input methods (voice, text, etc.) that the user used in the past. Furthermore, the reception unit predicts and proposes procedures to be performed in a specific time period from the user's past application history. This allows the reception unit to propose the optimal procedure method based on the past application history.

[0080] The reception unit can filter based on the user's current living situation and areas of interest at the time of the procedure. The reception unit, for example, uses a generation AI to analyze the user's current living situation and areas of interest. For example, the reception unit analyzes information such as the user's income, family structure, and place of residence to filter procedures. The reception unit can suggest optimal procedures to the user by filtering based on the user's current living situation and areas of interest at the time of the procedure. For example, if the user moves to a new residence, the reception unit preferentially suggests a move-in notification and issuance of a resident's certificate. In addition, if the user is raising a child, child-rearing-related procedures are preferentially displayed. In addition, if the user is elderly, nursing care and pension-related procedures are preferentially suggested. This allows the reception unit to suggest procedures according to the user's living situation and areas of interest.

[0081] The reception unit can estimate the user's emotion and determine the reception priority based on the estimated user's emotion. The reception unit estimates the user's emotion using, for example, a generation AI. For example, the reception unit analyzes the user's facial expression and voice to estimate the emotion. The reception unit determines the reception priority based on the estimated user's emotion. For example, when the user is nervous, important procedures are processed with priority. Also, when the user is relaxed, normal procedures are processed sequentially. Furthermore, when the user is in a hurry, procedures that require quick processing are processed with priority. This allows the reception unit to perform reception with priority according to the user's emotion. The emotion estimation is realized using, for example, an emotion engine or a generation AI, using an emotion estimation function. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0082] The reception unit can preferentially accept highly relevant applications in consideration of the geographical location information of the user at the time of reception. The reception unit, for example, uses a generation AI to analyze the geographical location information of the user. For example, the reception unit analyzes the GPS data and address information of the user and preferentially accepts highly relevant applications. The reception unit can suggest the optimal procedure for the user by preferentially accepting highly relevant applications in consideration of the geographical location information of the user at the time of reception. For example, if the user lives in a specific area, procedures related to that area are preferentially suggested. In addition, if the user is traveling, procedures required at the travel destination are preferentially displayed. In addition, if the user plans to move, procedures related to the new address are preferentially suggested. This allows the reception unit to preferentially accept highly relevant applications based on the geographical location information.

[0083] The reception unit can analyze the user's social media activity at the time of reception and receive the related application. The reception unit analyzes the user's social media activity, for example, using a generation AI. For example, the reception unit analyzes the user's post content, number of followers, engagement rate, etc., and receives the related application. The reception unit can propose the optimal procedure to the user by analyzing the user's social media activity and receiving the related application at the time of reception. For example, if the user indicates that he or she will participate in a specific event on social media, a procedure related to the event is proposed. In addition, if the user indicates a specific field of interest on social media, procedures related to that field are preferentially displayed. Furthermore, if the user indicates an interest in a specific region on social media, a procedure related to that region is proposed. This allows the reception unit to accept related applications based on social media activity.

[0084] The generation unit can estimate the user's emotion and adjust the expression method of the document to be generated based on the estimated user's emotion. The generation unit estimates the user's emotion, for example, using a generation AI. For example, the generation unit analyzes the user's facial expression and voice to estimate the emotion. The generation unit adjusts the expression method of the document to be generated based on the estimated user's emotion. For example, when the user is relaxed, the generation unit generates a document using polite and detailed expressions. When the user is in a hurry, the generation unit generates a document using concise expressions that focus on the main points. Furthermore, when the user is stressed, the generation unit generates a document using simple and intuitive expressions. This allows the generation unit to generate a document using an expression method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0085] The generation unit can adjust the level of detail of the document to be generated based on the importance of the application content at the time of generation. The generation unit, for example, uses a generation AI to evaluate the importance of the application content. For example, the generation unit evaluates the legal requirements, urgency, and scope of impact of the application content, and adjusts the level of detail of the document to be generated. The generation unit can provide the user with an optimal document by adjusting the level of detail of the document to be generated based on the importance of the application content at the time of generation. For example, in the case of important application content, a document including detailed information is generated. In addition, in the case of general application content, a document including standard information is generated. Furthermore, in the case of simple application content, a document including concise information is generated. This allows the generation unit to generate a document with a level of detail according to the importance of the application content.

[0086] The generation unit can apply different generation algorithms depending on the category of the application at the time of generation. The generation unit, for example, uses a generation AI to analyze the category of the application. For example, the generation unit analyzes categories such as issuance of a resident's certificate, submission of a transfer notification, and acquisition of a passport, and applies a generation algorithm suitable for each. The generation unit can provide the user with an optimal document by applying different generation algorithms depending on the category of the application at the time of generation. For example, in the case of issuance of a resident's certificate, a generation algorithm dedicated to resident's certificates is applied. In addition, in the case of submission of a transfer notification, a generation algorithm dedicated to transfer notifications is applied. Furthermore, in the case of acquisition of a passport, a generation algorithm dedicated to passports is applied. This allows the generation unit to apply a generation algorithm according to the category of the application.

[0087] The generation unit can estimate the user's emotion and adjust the length of the document to be generated based on the estimated user's emotion. The generation unit estimates the user's emotion, for example, using a generation AI. For example, the generation unit analyzes the user's facial expression and voice to estimate the emotion. The generation unit adjusts the length of the document to be generated based on the estimated user's emotion. For example, when the user is in a hurry, a short document that is to the point is generated. When the user is relaxed, a longer document that includes detailed explanations is generated. Furthermore, when the user is stressed, a simple and intuitive document is generated. This allows the generation unit to generate a document with a length according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0088] The generation unit can determine the priority of documents to be generated based on the submission time of the application at the time of generation. The generation unit, for example, uses a generation AI to evaluate the submission time of the application. For example, the generation unit evaluates the submission deadline and urgency of the application and determines the priority of documents to be generated. The generation unit can provide optimal documents to a user by determining the priority of documents to be generated based on the submission time of the application at the time of generation. For example, in the case of an urgent application, documents are generated with the highest priority. In addition, in the case of a normal application, documents are generated with standard priority. Furthermore, in the case of a long-term application, documents are generated at a later date. This allows the generation unit to determine the priority of documents based on the submission time.

[0089] The generation unit can adjust the order of documents to be generated based on the relevance of the application at the time of generation. The generation unit, for example, uses a generation AI to evaluate the relevance of the application. For example, the generation unit evaluates the similarity of the application contents and related laws and regulations, and adjusts the order of documents to be generated. The generation unit can provide optimal documents to the user by adjusting the order of documents to be generated based on the relevance of the application at the time of generation. For example, important and relevant application contents are written in the document with priority. In addition, application contents with general relevance are written in the document in a standard order. Furthermore, application contents with low relevance are written in the document at a later date. This allows the generation unit to adjust the order of documents based on relevance.

[0090] The providing unit can estimate the user's emotion and adjust the display method of the document to be provided based on the estimated user's emotion. The providing unit estimates the user's emotion, for example, using a generation AI. For example, the providing unit analyzes the user's facial expression and voice to estimate the emotion. The providing unit adjusts the display method of the document to be provided based on the estimated user's emotion. For example, when the user is nervous, a display method with simple and high visibility is provided. Also, when the user is relaxed, a display method including detailed information is provided. Furthermore, when the user is in a hurry, a display method with the main points is provided. This allows the providing unit to provide the document in a display method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0091] The provision unit can select the optimal provision method by referring to the user's past application history at the time of provision. The provision unit, for example, uses a generation AI to analyze the user's past application history. For example, the provision unit analyzes the contents of applications made by the user in the past and selects the optimal provision method. The provision unit can provide the optimal document to the user by selecting the optimal provision method by referring to the user's past application history at the time of provision. For example, the optimal provision method is selected based on procedures that the user frequently performed in the past. In addition, a provision method that avoids congestion is selected from the user's past application history. Furthermore, the user's past application history is analyzed and the most efficient provision method is selected. This allows the provision unit to select the optimal provision method based on the past application history.

[0092] The providing unit can customize the means of providing documents based on the user's current living situation at the time of providing. The providing unit, for example, uses a generation AI to analyze the user's current living situation. For example, the providing unit analyzes information such as the user's income, family structure, and place of residence, and customizes the means of providing documents. The providing unit can provide optimal documents to the user by customizing the means of providing documents based on the user's current living situation at the time of providing. For example, if the user moves to a new residence, a moving-in notification and issuance of a resident's card are provided preferentially. In addition, if the user is raising a child, childcare-related procedures are provided preferentially. In addition, if the user is elderly, nursing care and pension-related procedures are provided preferentially. This allows the providing unit to provide documents by a means according to the user's living situation.

[0093] The providing unit can estimate the user's emotion and determine the priority order of documents to be provided based on the estimated user's emotion. The providing unit estimates the user's emotion, for example, using a generation AI. For example, the providing unit analyzes the user's facial expression and voice to estimate the emotion. The providing unit determines the priority order of documents to be provided based on the estimated user's emotion. For example, when the user is nervous, important documents are provided preferentially. Also, when the user is relaxed, normal documents are provided sequentially. Furthermore, when the user is in a hurry, documents that need to be provided quickly are provided preferentially. This allows the providing unit to provide documents in a priority order according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0094] The providing unit can select the optimal providing method in consideration of the geographical location information of the user at the time of providing. The providing unit, for example, uses a generation AI to analyze the geographical location information of the user. For example, the providing unit analyzes the GPS data and address information of the user and selects the optimal providing method. The providing unit can provide the optimal document to the user by selecting the optimal providing method in consideration of the geographical location information of the user at the time of providing. For example, if the user lives in a specific area, documents related to that area are provided preferentially. In addition, if the user is traveling, documents necessary at the travel destination are provided preferentially. Furthermore, if the user plans to move, documents related to the new address are provided preferentially. This allows the providing unit to select the optimal providing method based on the geographical location information.

[0095] The providing unit can propose a means of providing documents by analyzing the social media activity of the user at the time of providing. The providing unit, for example, uses a generation AI to analyze the social media activity of the user. For example, the providing unit analyzes the user's posting content, number of followers, engagement rate, etc., and proposes the optimal means of providing. The providing unit can provide optimal documents to the user by proposing a means of providing documents by analyzing the user's social media activity at the time of providing. For example, if the user indicates that he or she will participate in a specific event on social media, documents related to the event are provided. In addition, if the user indicates a specific field of interest on social media, documents related to that field are preferentially provided. Furthermore, if the user indicates an interest in a specific region on social media, documents related to that region are provided. This allows the providing unit to provide documents by the optimal means based on social media activity.

[0096] The support unit can estimate the user's emotion and adjust the content of the tutorial based on the estimated user's emotion. The support unit estimates the user's emotion, for example, using a generation AI. For example, the support unit analyzes the user's facial expression and voice to estimate the emotion. The support unit adjusts the content of the tutorial based on the estimated user's emotion. For example, if the user is nervous, the support unit provides a tutorial in a calm voice to give a sense of security. Also, if the user is relaxed, the support unit provides a tutorial including detailed explanations to deepen understanding. Furthermore, if the user is in a hurry, the support unit provides a concise tutorial that covers the main points to quickly proceed with the procedure. This allows the support unit to provide a tutorial according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0097] The support unit can provide the optimal support method by referring to the user's past digital literacy history when providing support. The support unit, for example, uses a generation AI to analyze the user's past digital literacy history. For example, the support unit analyzes the user's past digital literacy operation history and provides the optimal support method. The support unit can provide the optimal support to the user by referring to the user's past digital literacy history when providing support. For example, if the user has performed an operation with high digital literacy in the past, detailed support is provided. Also, if the user has performed an operation with low digital literacy in the past, basic support is provided. Furthermore, the user's past digital literacy history is analyzed and the optimal support method is proposed. This allows the support unit to provide the optimal support method based on the past digital literacy history.

[0098] The support unit can estimate the user's emotion and determine the priority of the support based on the estimated user's emotion. The support unit estimates the user's emotion, for example, using a generation AI. For example, the support unit analyzes the user's facial expression and voice to estimate the emotion. The support unit determines the priority of the support based on the estimated user's emotion. For example, when the user is nervous, important support is provided preferentially. Also, when the user is relaxed, normal support is provided sequentially. Furthermore, when the user is in a hurry, items requiring quick support are provided preferentially. This allows the support unit to provide support in a priority order according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0099] The support unit can provide the optimal support method by considering the device information of the user when providing support. The support unit, for example, uses a generation AI to analyze the device information of the user. For example, the support unit analyzes information such as the type of device, OS, and browser of the user, and provides the optimal support method. The support unit can provide the optimal support to the user by providing the optimal support method by considering the device information of the user when providing support. For example, when the user is using a smartphone, a support method that matches the screen size is provided. Also, when the user is using a tablet, a support method optimized for a large screen is provided. Furthermore, when the user is using a personal computer, a detailed support method is provided. This allows the support unit to provide the optimal support method based on the device information.

[0100] The reduction unit can estimate the user's emotion and adjust the cost reduction method based on the estimated user's emotion. The reduction unit estimates the user's emotion, for example, using a generation AI. For example, the reduction unit analyzes the user's facial expression and voice to estimate the emotion. The reduction unit adjusts the cost reduction method based on the estimated user's emotion. For example, when the user is stressed, the reduction unit proposes a simple and intuitive cost reduction method. Also, when the user is relaxed, the reduction unit proposes a detailed cost reduction method. Furthermore, when the user is in a hurry, the reduction unit proposes a cost reduction method that can be quickly executed. In this way, the reduction unit can provide a cost reduction method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0101] The reduction department can select the optimal reduction method by referring to past operational efficiency data when making reductions. The reduction department, for example, uses a generation AI to analyze past operational efficiency data. For example, the reduction department analyzes data such as past work hours, resource usage, and quality of deliverables to select the optimal reduction method. The reduction department can reduce the operating costs of the public institution by selecting the optimal reduction method by referring to past operational efficiency data when making reductions. For example, the most effective cost reduction method is selected based on past operational efficiency data. In addition, a reduction method for eliminating waste is selected from the past operational efficiency data. Furthermore, the past operational efficiency data is analyzed to propose the optimal reduction method. This allows the reduction department to select the optimal reduction method based on past operational efficiency data.

[0102] The reduction unit can estimate the user's emotion and determine the priority of reduction based on the estimated user's emotion. The reduction unit estimates the user's emotion using, for example, a generation AI. For example, the reduction unit analyzes the user's facial expression and voice to estimate the emotion. The reduction unit determines the priority of reduction based on the estimated user's emotion. For example, when the user is nervous, important reduction items are executed with priority. Also, when the user is relaxed, normal reduction items are executed sequentially. Furthermore, when the user is in a hurry, reduction items that need to be executed quickly are executed with priority. Thereby, the reduction unit can perform reduction with priority according to the user's emotion. The estimation of emotion is realized using, for example, an emotion estimation function using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0103] The reduction department can select the optimal reduction method by taking into consideration the geographical distribution of work when reducing operations. The reduction department, for example, uses a generation AI to analyze the geographical distribution of work. For example, the reduction department analyzes the amount of work and resource allocation for each region and selects the optimal reduction method. The reduction department can reduce the operating costs of the public institution by selecting the optimal reduction method by taking into consideration the geographical distribution of work when reducing operations. For example, the optimal reduction method is selected by taking into consideration the efficiency of work in a specific region. In addition, geographically distributed operations are integrated and an efficient reduction method is proposed. Furthermore, the geographical distribution is analyzed and the optimal reduction method is selected. This allows the reduction department to select the optimal reduction method based on the geographical distribution.

[0104] The utilization unit can estimate the user's emotion and adjust the time utilization method based on the estimated user's emotion. The utilization unit estimates the user's emotion, for example, using a generation AI. For example, the utilization unit analyzes the user's facial expression and voice to estimate the emotion. The utilization unit adjusts the time utilization method based on the estimated user's emotion. For example, when the user is stressed, a simple and intuitive time utilization method is proposed. Also, when the user is relaxed, a detailed time utilization method is proposed. Furthermore, when the user is in a hurry, a time utilization method that can be quickly implemented is proposed. In this way, the utilization unit can provide a time utilization method that corresponds to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0105] The utilization unit can provide the optimal utilization method by referring to the user's past time management history at the time of utilization. The utilization unit, for example, uses a generation AI to analyze the user's past time management history. For example, the utilization unit analyzes the user's past time management operation history and provides the optimal utilization method. The utilization unit can provide the user with an optimal effective time utilization method by referring to the user's past time management history at the time of utilization. For example, the utilization unit proposes an optimal utilization method based on the user's history of effective time management in the past. In addition, the utilization unit proposes a utilization method for eliminating waste from the user's past time management history. Furthermore, the utilization unit analyzes the user's past time management history and provides the optimal utilization method. As a result, the utilization unit can provide the optimal utilization method based on the past time management history.

[0106] The utilization unit can estimate the user's emotion and determine the priority order of effective time utilization based on the estimated user's emotion. The utilization unit estimates the user's emotion, for example, using a generation AI. For example, the utilization unit analyzes the user's facial expression and voice to estimate the emotion. The utilization unit determines the priority order of effective time utilization based on the estimated user's emotion. For example, when the user is nervous, important items for effective time utilization are executed with priority. Also, when the user is relaxed, normal items for effective time utilization are executed in sequence. Furthermore, when the user is in a hurry, items for effective time utilization that need to be executed quickly are executed with priority. In this way, the utilization unit can effectively utilize time with priority according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0107] The utilization unit can provide the optimal utilization method by considering the user's device information at the time of utilization. The utilization unit, for example, uses a generation AI to analyze the user's device information. For example, the utilization unit analyzes information such as the user's device type, OS, and browser, and provides the optimal utilization method. The utilization unit can provide the user with an optimal time utilization method by providing the optimal utilization method by considering the user's device information at the time of utilization. For example, if the user is using a smartphone, a utilization method that matches the screen size is provided. Also, if the user is using a tablet, a utilization method optimized for a large screen is provided. Furthermore, if the user is using a personal computer, a detailed utilization method is provided. This allows the utilization unit to provide the optimal utilization method based on the device information.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] The reception unit can analyze the user's past application history and propose the optimal procedure method. For example, the reception unit analyzes the content of applications made by the user in the past and proposes the optimal procedure method. The reception unit can efficiently perform procedures for the user by analyzing the user's past application history and proposing the optimal procedure method. For example, the reception unit automatically displays procedures that the user has frequently performed in the past as candidates. In addition, the reception unit preferentially proposes input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit predicts and proposes procedures to be performed in a specific time period based on the user's past application history. This allows the reception unit to propose the optimal procedure method based on the past application history.

[0110] The generation unit can adjust the level of detail of the document to be generated based on the importance of the application content at the time of generation. For example, the generation unit uses a generation AI to evaluate the legal requirements, urgency, and scope of impact of the application content, and adjusts the level of detail of the document to be generated. The generation unit can provide the user with an optimal document by adjusting the level of detail of the document to be generated based on the importance of the application content at the time of generation. For example, in the case of important application content, a document including detailed information is generated. In addition, in the case of general application content, a document including standard information is generated. Furthermore, in the case of simple application content, a document including concise information is generated. This allows the generation unit to generate a document with a level of detail according to the importance of the application content.

[0111] The provision unit can select the optimal provision method by referring to the user's past application history at the time of provision. For example, the provision unit uses a generation AI to analyze the user's past application history. For example, the provision unit analyzes the contents of applications made by the user in the past and selects the optimal provision method. The provision unit can provide the optimal document to the user by selecting the optimal provision method by referring to the user's past application history at the time of provision. For example, the optimal provision method is selected based on procedures that the user frequently performed in the past. In addition, a provision method that avoids congestion is selected from the user's past application history. Furthermore, the user's past application history is analyzed and the most efficient provision method is selected. This allows the provision unit to select the optimal provision method based on the past application history.

[0112] The support unit can provide the optimal support method by considering the user's device information when providing support. For example, the support unit uses a generation AI to analyze the user's device information. For example, the support unit analyzes information such as the user's device type, OS, and browser, and provides the optimal support method. The support unit can provide the optimal support to the user by providing the optimal support method by considering the user's device information when providing support. For example, if the user is using a smartphone, a support method that matches the screen size is provided. Also, if the user is using a tablet, a support method optimized for a large screen is provided. Furthermore, if the user is using a personal computer, a detailed support method is provided. This allows the support unit to provide the optimal support method based on the device information.

[0113] The reduction department can select the optimal reduction method by referring to past operational efficiency data when making reductions. For example, the reduction department uses a generation AI to analyze past operational efficiency data. For example, the reduction department analyzes data such as past work hours, resource usage, and quality of deliverables to select the optimal reduction method. The reduction department can reduce the operating costs of the public institution by selecting the optimal reduction method by referring to past operational efficiency data when making reductions. For example, the most effective cost reduction method is selected based on past operational efficiency data. In addition, a reduction method for eliminating waste is selected from the past operational efficiency data. Furthermore, the past operational efficiency data is analyzed to propose the optimal reduction method. This allows the reduction department to select the optimal reduction method based on past operational efficiency data.

[0114] The reception unit can estimate the user's emotions and customize the interface for procedures in the metaverse space based on the estimated user's emotions. For example, the reception unit estimates the user's emotions using a generation AI. For example, the reception unit analyzes the user's facial expressions and voice to estimate the emotions. The reception unit customizes the interface for procedures in the metaverse space based on the estimated user's emotions. For example, if the user is stressed, a simple and intuitive interface is provided to minimize the procedure steps. Also, if the user is relaxed, detailed options are provided and a customizable procedure method is suggested. Furthermore, if the user is in a hurry, voice input is prioritized so that the procedure can be completed quickly. This allows the reception unit to provide an interface according to the user's emotions.

[0115] The generation unit can estimate the user's emotions and adjust the expression method of the document to be generated based on the estimated user's emotions. For example, the generation unit estimates the user's emotions using a generation AI. For example, the generation unit analyzes the user's facial expressions and voice to estimate the emotions. The generation unit adjusts the expression method of the document to be generated based on the estimated user's emotions. For example, when the user is relaxed, the generation unit generates a document using polite and detailed expressions. When the user is in a hurry, the generation unit generates a document using concise expressions that focus on the main points. Furthermore, when the user is stressed, the generation unit generates a document using simple and intuitive expressions. This allows the generation unit to generate a document using an expression method according to the user's emotions.

[0116] The providing unit can estimate the user's emotions and adjust the display method of the document to be provided based on the estimated user's emotions. For example, the providing unit estimates the user's emotions using a generation AI. For example, the providing unit analyzes the user's facial expressions and voice to estimate the emotions. The providing unit adjusts the display method of the document to be provided based on the estimated user's emotions. For example, when the user is nervous, a simple and highly visible display method is provided. Also, when the user is relaxed, a display method including detailed information is provided. Furthermore, when the user is in a hurry, a display method that focuses on the main points is provided. This allows the providing unit to provide documents in a display method according to the user's emotions.

[0117] The support unit can estimate the user's emotions and adjust the content of the tutorial based on the estimated user's emotions. For example, the support unit estimates the user's emotions using a generation AI. For example, the support unit analyzes the user's facial expressions and voice to estimate the emotions. The support unit adjusts the content of the tutorial based on the estimated user's emotions. For example, if the user is nervous, the support unit provides a tutorial in a calm voice to give a sense of security. Also, if the user is relaxed, the support unit provides a tutorial including detailed explanations to deepen understanding. Furthermore, if the user is in a hurry, the support unit provides a concise tutorial that covers the main points to quickly proceed with the procedure. This allows the support unit to provide a tutorial according to the user's emotions.

[0118] The reduction unit can estimate the user's emotions and adjust the cost reduction method based on the estimated user's emotions. For example, the reduction unit estimates the user's emotions using a generation AI. For example, the reduction unit analyzes the user's facial expressions and voice to estimate the emotions. The reduction unit adjusts the cost reduction method based on the estimated user's emotions. For example, when the user is stressed, the reduction unit proposes a simple and intuitive cost reduction method. Also, when the user is relaxed, the reduction unit proposes a detailed cost reduction method. Furthermore, when the user is in a hurry, the reduction unit proposes a cost reduction method that can be quickly executed. This allows the reduction unit to provide a cost reduction method according to the user's emotions.

[0119] The process flow of the second embodiment will be briefly described below.

[0120] Step 1: The reception unit accepts procedures in the metaverse space. For example, a user can access the metaverse space and carry out procedures such as issuing a resident's card or submitting a change of address notification. The reception unit uses a generation AI to analyze the user's input and transmits the necessary documents and information to the generation unit. Step 2: The generation unit uses generation AI to analyze the information received from the reception unit and generate the necessary documents and information. For example, the generation unit automatically generates documents such as application forms and certificates based on the user's input. The generation unit can also use generation AI to analyze the user's input using natural language processing technology and extract the necessary information. Step 3: The provision unit provides the documents and information generated by the generation unit to the public institution. For example, the provision unit sends the generated documents to the public institution's system and completes the procedure. Using the generation AI, the provision unit can automatically suggest corrections and encourage the user to resubmit documents if there are any defects in the documents submitted by the user. The provision unit also automates tasks performed by public institution employees, improving work efficiency.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0122] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0123] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, support unit, reduction unit, and utilization unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and the user can access the metaverse space and perform procedures. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the contents of the user's input and generates necessary documents and information. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides the generated documents and information to public institutions. The support unit is realized, for example, by the control unit 46A of the smart device 14, and provides tutorials and guidelines in the metaverse space. The reduction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and optimizes the business process of the public institution and reduces costs. The utilization unit is realized, for example, by the control unit 46A of the smart device 14, and simplifies the user's procedures and reduces the effort and work. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 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.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] 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.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0132] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] 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.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0135] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0139] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, support unit, reduction unit, and utilization unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and the user can access the metaverse space to perform procedures. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's input contents and generates necessary documents and information. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides the generated documents and information to public institutions. The support unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides tutorials and guidelines in the metaverse space. The reduction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and optimizes the business process of the public institution and reduces costs. The utilization unit is realized, for example, by the control unit 46A of the smart glasses 214, and simplifies the user's procedures and reduces the effort and work. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] 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.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0148] 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.

[0149] 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.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0151] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0155] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, support unit, reduction unit, and utilization unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and the user can access the metaverse space and perform procedures. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the contents of the user's input and generates necessary documents and information. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides the generated documents and information to the public institution. The support unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides tutorials and guidelines in the metaverse space. The reduction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and optimizes the business process of the public institution and reduces costs. The utilization unit is realized, for example, by the control unit 46A of the headset type terminal 314, and simplifies the user's procedures and reduces the effort and work. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0160] 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.

[0161] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0163] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0164] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0165] 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.

[0166] 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.

[0167] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0168] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0169] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0170] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0171] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0172] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0173] Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, support unit, reduction unit, and utilization unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and the user can access the metaverse space and perform procedures. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the contents of the user's input and generates necessary documents and information. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides the generated documents and information to public institutions. The support unit is realized, for example, by the control unit 46A of the robot 414, and provides tutorials and guidelines in the metaverse space. The reduction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and optimizes the business process of the public institution and reduces costs. The utilization unit is realized, for example, by the control unit 46A of the robot 414, and simplifies the user's procedures and reduces the effort and work. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0174] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0175] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0176] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0177] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0178] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0179] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0181] 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.

[0182] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0183] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0184] 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.

[0185] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0186] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0187] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0188] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0189] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0190] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0191] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0192] (Appendix 1) A reception unit that receives procedures in the metaverse space; a generating unit that analyzes the information received by the receiving unit and generates necessary documents and information; a providing unit that provides documents and information generated by the generating unit to public institutions. A system characterized by: (Appendix 2) The generation unit is Generative AI analyzes user input and generates necessary documents and information 2. The system of claim 1, (Appendix 3) The generation unit is Generative AI uses natural language processing to analyze user input and extract the necessary information 2. The system of claim 1, (Appendix 4) The providing unit is If the document submitted by the user is found to be incomplete, the generative AI will automatically suggest corrections and prompt the user to resubmit the document. 2. The system of claim 1, (Appendix 5) The providing unit is Generative AI automates tasks performed by public sector employees and improves work efficiency 2. The system of claim 1, (Appendix 6) Establish a support department that will provide tutorials and guidelines within the metaverse space and help improve the digital literacy of citizens. 2. The system of claim 1, (Appendix 7) Equipping a cost reduction department that reduces costs by improving business efficiency 2. The system of claim 1, (Appendix 8) Establish a department that will make effective use of people's time by reducing the amount of work and hassle that people have to do. 2. The system of claim 1, (Appendix 9) The reception unit is To estimate a user's emotion and customize a procedural interface in a metaverse space based on the estimated user's emotion. 2. The system of claim 1, (Appendix 10) The reception unit is Analyze the user's past application history and suggest the optimal procedure 2. The system of claim 1, (Appendix 11) The reception unit is Filtering at checkout based on the user's current life situation and interests 2. The system of claim 1, (Appendix 12) The reception unit is Estimate the user's emotions and determine the reception priority based on the estimated user's emotions 2. The system of claim 1, (Appendix 13) The reception unit is When accepting applications, the system takes into account the user's geographic location information to prioritize applications that are more relevant to the user. 2. The system of claim 1, (Appendix 14) The reception unit is Upon acceptance, analyze the user's social media activity and accept relevant applications. 2. The system of claim 1, (Appendix 15) The generation unit is Inferring a user's emotions and adjusting the presentation of a document based on the estimated user's emotions 2. The system of claim 1, (Appendix 16) The generation unit is When generating documents, adjust the level of detail based on the importance of the application. 2. The system of claim 1, (Appendix 17) The generation unit is During generation, different generation algorithms are applied depending on the category of the application. 2. The system of claim 1, (Appendix 18) The generation unit is The user's emotions are estimated, and the length of the document to be generated is adjusted based on the estimated user's emotions. 2. The system of claim 1, (Appendix 19) The generation unit is At generation time, prioritize generated documents based on when the application was submitted 2. The system of claim 1, (Appendix 20) The generation unit is At generation time, adjust the order of generated documents based on the relevance of the application 2. The system of claim 1, (Appendix 21) The providing unit is To estimate a user's emotion and adjust a display method of a document to be provided based on the estimated user's emotion. 2. The system of claim 1, (Appendix 22) The providing unit is When providing the service, the optimal method of providing the service is selected by referring to the user's past application history. 2. The system of claim 1, (Appendix 23) The providing unit is At the time of delivery, customize the means of delivery of documents based on the user's current living situation 2. The system of claim 1, (Appendix 24) The providing unit is Infer user sentiment and prioritize documents to provide based on the estimated user sentiment 2. The system of claim 1, (Appendix 25) The providing unit is When providing information, the optimal method of providing the information is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 26) The providing unit is At the time of delivery, the system analyzes the user's social media activity to suggest a method of delivery of documents. 2. The system of claim 1, (Appendix 27) The support unit includes: Estimate user emotions and adjust tutorial content based on the estimated user emotions 2. The system of claim 1, (Appendix 28) The support unit includes: When providing assistance, the system provides the most appropriate assistance method by referring to the user's past digital literacy history. 2. The system of claim 1, (Appendix 29) The support unit includes: Estimating a user's emotions and determining the priority of assistance based on the estimated user's emotions 2. The system of claim 1, (Appendix 30) The support unit includes: When providing assistance, provide the most appropriate assistance method by taking into account the user's device information 2. The system of claim 1, (Appendix 31) The reduction section is Inferring user sentiment and adjusting cost reduction methods based on the estimated user sentiment 2. The system of claim 1, (Appendix 32) The reduction section is When making reductions, select the optimal reduction method by referring to past operational efficiency data. 2. The system of claim 1, (Appendix 33) The reduction section is Estimate user sentiment and determine reduction priorities based on the estimated user sentiment 2. The system of claim 1, (Appendix 34) The reduction section is When reducing operations, select the most appropriate reduction method, taking into account the geographical distribution of operations. 2. The system of claim 1, (Appendix 35) The utilization section includes: To estimate a user's feelings and adjust a method of effectively using time based on the estimated user's feelings 2. The system of claim 1, (Appendix 36) The utilization section includes: When using the system, the system provides optimal usage methods by referring to the user's past time management history. 2. The system of claim 1, (Appendix 37) The utilization section includes: To estimate a user's feelings and determine priorities for effective use of time based on the estimated user's feelings 2. The system of claim 1, (Appendix 38) The utilization section includes: When using the service, provide the most optimal usage method by taking into account the user's device information. 2. The system of claim 1, [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit that receives procedures in the metaverse space; a generating unit that analyzes the information received by the receiving unit and generates necessary documents and information; a providing unit that provides documents and information generated by the generating unit to public institutions. A system characterized by:

2. The generation unit is Analyze user input using generation AI and generate necessary documents and information 2. The system of claim 1 .

3. The generation unit is Generative AI uses natural language processing to analyze user input and extract the necessary information 2. The system of claim 1 .

4. The providing unit is If the document submitted by the user is found to be incomplete, the generation AI will automatically suggest corrections and prompt the user to resubmit the document.

2. The system of claim 1 .

5. The providing unit is Generative AI automates tasks performed by public sector employees and improves work efficiency 2. The system of claim 1 .

6. Establish a support department that will provide tutorials and guidelines within the metaverse space and help improve the digital literacy of citizens.

2. The system of claim 1 .

7. Equipping a cost reduction department that reduces costs by improving business efficiency 2. The system of claim 1 .

8. Establish a department that will make effective use of people's time by reducing the amount of work and hassle that people have to do.

2. The system of claim 1 .

9. The reception unit is To estimate a user's emotion and customize a procedural interface in a metaverse space based on the estimated user's emotion.

2. The system of claim 1 .

10. The reception unit is Analyze the user's past application history and suggest the optimal procedure 2. The system of claim 1 .

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