System

Abstract

To provide a system according to an embodiment, capable of effectively collecting a document that is required for a visa application and correcting a document to be submitted.SOLUTION: A system according to an embodiment comprises a reception unit, a guide unit, an upload unit and an analysis unit. The reception unit receives basic information on a user. The guide unit analyzes the information received by the reception unit to perform a guide of a government office. The upload unit uploads a document collected by the user. The analysis unit analyzes the document uploaded by the upload unit, and indicates a correction point.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, collecting documents required for visa applications and correcting the documents to be submitted required time and effort, making it difficult to carry out the process efficiently.

[0005] The system according to the embodiment aims to efficiently collect documents required for visa applications and to efficiently correct the documents to be submitted. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a guidance unit, an upload unit, and an analysis unit. The reception unit receives basic information of a user. The guidance unit analyzes the information received by the reception unit and provides guidance to government offices. The upload unit uploads documents collected by the user. The analysis unit analyzes the documents uploaded by the upload unit and points out corrections. Effect of the Invention

[0007] The system according to the embodiment can efficiently collect documents required for visa applications and correct the documents to be submitted. [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 visa application support system according to the embodiment of the present invention is a system that supports a user from collecting documents related to a visa application to correcting documents to be submitted. When a user inputs basic information such as a name and address, the system guides the user on which department to take to the local government office in the user's area to obtain the necessary documents. Furthermore, when the user uploads the documents he or she has collected to the system, the system corrects the documents and points out the necessary corrections. For example, when a user inputs information such as "Taro Yamada, Shinjuku Ward, Tokyo," the system provides specific guidance such as "Go to the family registry department of the Shinjuku Ward Office and bring your ID to obtain a resident's certificate." Furthermore, when the user uploads the documents he or she has collected to the system, the system gives specific instructions such as "This document requires a signature." This mechanism allows the user to apply for a visa at a lower cost than the agency fee, and allows the user to easily apply without spending time on collecting documents required for the procedure and writing the documents by himself or herself. For example, the user only needs to collect the documents required for a visa application and upload them to the system, and the system automatically corrects the documents and points out the necessary corrections, allowing the user to prepare accurate documents in a short time. This allows the visa application support system to assist users in everything from collecting documents related to visa applications to correcting submitted documents.

[0029] The visa application support system according to the embodiment includes a reception unit, a guidance unit, an upload unit, and an analysis unit. The reception unit receives basic information of a user. The basic information of a user includes, for example, a name, an address, and a contact address, but is not limited to such examples. The reception unit receives information such as "Taro Yamada, Shinjuku-ku, Tokyo" input by the user. The guidance unit analyzes the information received by the reception unit and provides guidance to the government office. The guidance unit provides guidance, for example, on which department of the government office in the area where the user lives and what to take to obtain the necessary documents. For example, the guidance unit provides specific guidance such as "Go to the family registry department of Shinjuku Ward Office and bring your ID to obtain a resident's certificate." The upload unit uploads documents collected by the user. For example, the upload unit accepts documents collected by the user to be uploaded to the system. The analysis unit analyzes the documents uploaded by the upload unit and points out corrections. For example, the analysis unit provides specific guidance such as "This document requires a signature" for the uploaded documents. As a result, the visa application support system according to the embodiment can accept basic information from the user, guide the government office, upload documents, analyze, and point out corrections. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the uploaded documents to a generation AI and have the generation AI analyze the documents and point out corrections.

[0030] The reception unit receives basic information of a user. The basic information of a user includes, for example, a name, an address, and contact information, but is not limited to such examples. The reception unit receives information such as "Taro Yamada, Shinjuku-ku, Tokyo" input by the user. Specifically, the reception unit stores the information input by the user in a database and makes it available for subsequent processing. The information input by the user is collected through a web form or a mobile application, and also has a validation function for checking the accuracy of the input contents. For example, the address input field has an auto-complete function for a zip code, and when a user inputs a zip code, the corresponding address is automatically displayed. In addition, the contact input field performs a format check for a phone number and a domain check for an email address to prevent input errors. Furthermore, the reception unit encrypts and stores the information input by the user, ensuring thorough privacy protection. This makes it possible to minimize the risk of the user's personal information being leaked to a third party. The reception unit also has a function for checking the information input by the user in real time and prompting the user to correct it if necessary. For example, if the name input field contains special characters or if inaccurate information is entered in the address input field, an error message is displayed to prompt the user to correct it. In this way, the reception unit assists the user in inputting accurate and complete information, and ensures that subsequent processing proceeds smoothly.

[0031] The guidance unit analyzes the information received by the reception unit and provides guidance on the government office. For example, the guidance unit provides guidance on which department of the government office in the area where the user lives and what to bring to obtain the necessary documents. Specifically, the guidance unit searches the database for information on the relevant government office based on the user's address information, and generates a list of necessary procedures and documents to bring. For example, if the user lives in Shinjuku Ward, the guidance unit provides information on the family registry department of the Shinjuku Ward Office and provides specific guidance such as that a resident's certificate can be obtained by bringing an ID card. The guidance unit also provides information on the business hours, contact information, map information, etc. of the government office, and supports the user to complete the procedures smoothly. Furthermore, the guidance unit can provide guidance customized according to the user's situation. For example, if the user is a foreign national, the necessary documents and procedures are different, so the guidance unit adjusts the guidance content taking that into consideration. In addition, if the user can complete the procedures online, the guidance unit also provides guidance on how to do so. For example, for documents that can be applied for online, the link to the application form and the steps of the necessary procedures are explained in detail. This enables the guidance section to assist the user in efficiently completing procedures at government offices, and to smoothly advance the visa application process.

[0032] The upload unit uploads documents collected by the user. For example, the upload unit accepts documents collected by the user to be uploaded to the system. Specifically, the upload unit accepts documents scanned by the user and image files taken with a smartphone, and stores these files in the system. The upload unit checks the file format and size to ensure that the documents are uploaded in an appropriate format. For example, the upload unit accepts files in PDF format and JPEG format, and encourages compression if the file size is too large. In addition, the upload unit provides a preview function for documents uploaded by the user, allowing the user to check the uploaded contents. This allows the user to check that the uploaded documents are accurate and re-upload them as necessary. In addition, the upload unit uses OCR (optical character recognition) technology to extract text data from the uploaded documents, making the processing in the analysis unit more efficient. For example, important information such as names, addresses, and dates are automatically extracted from the uploaded documents and stored in a database. This allows the upload unit to efficiently import documents collected by the user into the system, and smoothly proceed with subsequent analysis processing.

[0033] The analysis unit analyzes the documents uploaded by the upload unit and points out corrections. For example, the analysis unit gives specific instructions such as "This document requires a signature" to the uploaded document. Specifically, the analysis unit uses AI to analyze the contents of the uploaded document and identify necessary corrections and missing information. For example, AI analyzes the layout and text content of the document and displays a warning if the signature field is blank or if the required stamp is not affixed. Furthermore, the analysis unit checks whether the contents of the document meet the requirements for the visa application and points out missing or incorrect information. For example, a warning is displayed if the information written on the application form does not match the information on the passport or if the required attached documents are missing. The analysis unit guides the user to specific correction methods and additional procedures to assist in correcting the document. For example, it provides specific instructions such as "This document requires a signature. Please add a signature and re-upload." This allows the analysis unit to ensure the accuracy and completeness of the documents submitted by the user and to smoothly proceed with the visa application process. Furthermore, the analysis unit improves the accuracy of the analysis by having the AI ​​learn based on past data and similar application documents. This enables the analysis unit to always provide highly accurate analysis based on the latest information, and effectively support the user's visa application.

[0034] The upload unit can provide an interface that allows a user to upload documents. The interface includes, for example, usability, design elements, and the like, but is not limited to such examples. The upload unit can provide, for example, an intuitive interface that allows a user to easily upload documents. For example, the upload unit can provide an interface with a drag-and-drop function. The upload unit can also display a file selection dialog so that a user can select and upload a file. Furthermore, the upload unit can also support uploading from a mobile device such as a smartphone or tablet. For example, the upload unit can take a picture of a document using a camera of a mobile device and upload the image. This allows a user to easily upload a document. A part or all of the above-mentioned processing in the upload unit may be performed, for example, using AI, or may be performed without using AI. For example, the upload unit can input a user's operation history into AI and have AI design an optimal interface.

[0035] The analysis unit can analyze the contents of the document using AI and automatically indicate when necessary information is missing. AI includes, but is not limited to, technologies such as machine learning and deep learning. The analysis unit can analyze the contents of the document using, for example, a machine learning algorithm. For example, the analysis unit uses a machine learning model that takes text data of the document as input and outputs whether necessary information is missing. The analysis unit can also analyze the contents of the document using deep learning technology. For example, the analysis unit uses a deep learning model that takes image data of the document as input and outputs whether necessary information is missing. Furthermore, the analysis unit can analyze the contents of the document using natural language processing technology. For example, the analysis unit analyzes text data of the document and automatically indicates when necessary information is missing. This makes it possible to automatically indicate when necessary information is missing. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the contents of the document to the generation AI and have the generation AI execute an analysis of whether necessary information is missing.

[0036] The analysis unit can automatically start the analysis immediately after uploading the document and provide feedback quickly. "Quickly" includes, but is not limited to, a time range such as within a few seconds or within a few minutes. The analysis unit can automatically start the analysis immediately after uploading the document, for example. For example, the analysis unit starts the analysis process as soon as the document is uploaded and provides feedback within a few seconds. The analysis unit can also provide feedback quickly based on the contents of the document. For example, the analysis unit analyzes the contents of the document and points out necessary corrections within a few seconds. Furthermore, the analysis unit can provide feedback in real time. For example, the analysis unit points out corrections in real time while analyzing the contents of the document. This makes it possible to provide feedback quickly immediately after uploading the document. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the contents of the document to the generation AI and cause the generation AI to perform quick feedback.

[0037] The guidance unit can provide detailed guidance on what to take to which department of the local government office in the user's area to obtain the necessary documents. Details include, for example, the depth of the information to be provided and the means to provide it, but are not limited to such examples. The guidance unit provides, for example, guidance on what to take to which department of the local government office in the user's area to obtain the necessary documents. For example, the guidance unit provides specific guidance such as "Go to the family registry department of Shinjuku Ward Office and bring your ID to obtain a resident's certificate." The guidance unit can also provide information such as the business hours and congestion status of the government office. For example, the guidance unit provides information on the business hours of the government office and suggests the best time for the user to visit. Furthermore, the guidance unit can provide information such as a map of the government office and how to access it. For example, the guidance unit displays the location of the government office on a map and provides information on the nearest transportation. This allows the user to provide specific guidance for obtaining the necessary documents. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the guidance unit can input user input information to the generation AI and have the generation AI execute optimal guidance.

[0038] The reception unit can provide an auto-completion function to analyze the user's past input history and reduce the effort of input. The auto-completion function includes, for example, a prediction algorithm, completion accuracy, and the like, but is not limited to such examples. The reception unit, for example, automatically displays a name or address previously input by the user as a candidate. The reception unit can also preferentially suggest an input method (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest basic information to be used in a specific time period from the user's past input history. For example, the reception unit displays optimal input candidates based on information previously input by the user. This makes it possible to provide an auto-completion function based on the user's past input history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history to AI and cause AI to generate optimal input candidates.

[0039] The reception unit can suggest necessary additional information in real time based on the user's input contents. Real time includes, for example, a time range such as immediately or within a few seconds, but is not limited to such examples. For example, the reception unit displays a message prompting the user to input an address when the user inputs a name. In addition, the reception unit can also suggest the input of a phone number or email address when the user inputs an address. Furthermore, the reception unit can suggest additional information required for a visa application in real time when the user inputs basic information. For example, the reception unit displays a message such as "Please enter your address next" when the user inputs a name. This makes it possible to suggest necessary additional information in real time based on the user's input contents. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's input contents to AI and have the AI ​​execute the suggestion of necessary additional information.

[0040] The reception unit can automatically add region-specific input items based on the geographical location information of the user. The geographical location information includes, for example, GPS data, IP address, and the like, but is not limited to such examples. For example, if the user lives in Tokyo, the reception unit automatically adds input items specific to Tokyo. In addition, if the user lives in Osaka, the reception unit can also automatically add input items specific to Osaka. Furthermore, if the user lives in Hokkaido, the reception unit can also automatically add input items specific to Hokkaido. This makes it possible to add region-specific input items based on the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the user to AI and cause the AI ​​to execute the addition of region-specific input items.

[0041] The reception unit can analyze the user's social media activity and automatically reflect related information in the input form. The social media activity includes, for example, the content of posts, the number of likes, and the like, but is not limited to such examples. The reception unit, for example, automatically reflects address information published by the user on social media in the input form. The reception unit can also automatically reflect name information published by the user on social media in the input form. Furthermore, the reception unit can automatically reflect contact information published by the user on social media in the input form. This makes it possible to reflect related information in the input form based on the user's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into AI and cause AI to reflect the related information.

[0042] The guidance unit can analyze the past guidance history and select the optimal guidance method. The optimal includes, but is not limited to, criteria such as user satisfaction and efficiency. The guidance unit, for example, proposes the optimal guidance method based on the guidance method used by the user in the past. The guidance unit can also propose the most efficient guidance method from the user's past guidance history. Furthermore, the guidance unit can analyze the user's past guidance history and propose a guidance method that avoids congestion. This makes it possible to select the optimal guidance method based on the past guidance history. A part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can input the past guidance history into AI and cause the AI ​​to select the optimal guidance method.

[0043] The guidance unit can provide the business hours and congestion status of the government office in real time based on the user's input. Real time includes, for example, a time range such as immediately or within a few seconds, but is not limited to such examples. For example, the guidance unit provides real-time business hours when the user inputs a plan to go to the government office. In addition, the guidance unit can also provide real-time congestion status when the user inputs a plan to go to the government office. Furthermore, the guidance unit can suggest the optimal visiting time when the user inputs a plan to go to the government office. This makes it possible to provide the business hours and congestion status of the government office in real time based on the user's input. Part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can input the user's input content to AI and cause the AI ​​to provide real-time information.

[0044] The guidance unit can automatically guide the user to the nearest government office in consideration of the geographical location information of the user. The geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. For example, if the user lives in Shinjuku Ward, Tokyo, the guidance unit automatically guides the user to Shinjuku Ward Office. In addition, if the user lives in Osaka City, Osaka Prefecture, the guidance unit can also automatically guide the user to Osaka City Hall. Furthermore, if the user lives in Sapporo City, Hokkaido, the guidance unit can also automatically guide the user to Sapporo City Hall. This makes it possible to guide the user to the nearest government office based on the user's geographical location information. A part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can input the user's geographical location information into AI and cause the AI ​​to perform guidance of the nearest government office.

[0045] The guidance unit can analyze the user's social media activity and provide information on related government offices. Social media activity includes, for example, the content of posts, the number of likes, and the like, but is not limited to such examples. For example, the guidance unit guides the user to the nearest government office based on address information published by the user on social media. The guidance unit can also guide the user to necessary documents based on name information published by the user on social media. Furthermore, the guidance unit can also guide the user to the contact information of the government office based on contact information published by the user on social media. This makes it possible to provide information on related government offices based on the user's social media activity. Part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can input the user's social media data into AI and have the AI ​​provide information on related government offices.

[0046] The upload unit can analyze the user's past upload history and propose an optimal upload method. The optimal includes, but is not limited to, criteria such as user satisfaction and efficiency. The upload unit can propose an optimal upload method, for example, based on the format of documents previously uploaded by the user. The upload unit can also propose the most efficient upload method from the user's past upload history. Furthermore, the upload unit can analyze the user's past upload history and propose the most appropriate file format. This makes it possible to propose an optimal upload method based on the user's past upload history. A part or all of the above-mentioned processing in the upload unit may be performed, for example, using AI, or may be performed without using AI. For example, the upload unit can input the past upload history into AI and cause the AI ​​to execute a proposal for an optimal upload method.

[0047] The upload unit can provide an automatic file format and size conversion function at the time of uploading. The automatic conversion includes, for example, convertible file formats, conversion accuracy, and the like, but is not limited to such examples. For example, when the format of the document uploaded by the user is different, the upload unit automatically converts it to an appropriate format. In addition, when the size of the document uploaded by the user is large, the upload unit can automatically compress and upload it. Furthermore, when the format of the document uploaded by the user is inappropriate, the upload unit can automatically convert it to an appropriate format and upload it. This makes it possible to provide an automatic file format and size conversion function. A part or all of the above-mentioned processing in the upload unit may be performed, for example, using AI, or may be performed without using AI. For example, the upload unit can input the uploaded file to AI and have the AI ​​execute conversion to an appropriate format and size.

[0048] The upload unit can automatically upload region-specific documents in consideration of the geographical location information of the user. The geographical location information includes, for example, GPS data, IP address, and the like, but is not limited to such examples. For example, if the user lives in Tokyo, the upload unit automatically uploads documents specific to Tokyo. In addition, if the user lives in Osaka, the upload unit can also automatically upload documents specific to Osaka. Furthermore, if the user lives in Hokkaido, the upload unit can also automatically upload documents specific to Hokkaido. This makes it possible to automatically upload region-specific documents based on the geographical location information of the user. A part or all of the above-mentioned processing in the upload unit may be performed, for example, using AI, or may be performed without using AI. For example, the upload unit can input the geographical location information of the user to AI and cause the AI ​​to execute uploading of region-specific documents.

[0049] The upload unit can analyze the social media activity of the user and automatically upload related documents. The social media activity includes, for example, the content of posts, the number of likes, and the like, but is not limited to such examples. The upload unit automatically uploads related documents based on, for example, address information published by the user on social media. The upload unit can also automatically upload related documents based on name information published by the user on social media. Furthermore, the upload unit can automatically upload related documents based on contact information published by the user on social media. This makes it possible to automatically upload related documents based on the social media activity of the user. A part or all of the above-mentioned processing in the upload unit may be performed, for example, using AI, or may be performed without using AI. For example, the upload unit can input the social media data of the user into AI and cause AI to execute uploading of related documents.

[0050] The analysis unit can analyze the past analysis history and select the optimal analysis algorithm. The optimal includes, but is not limited to, criteria such as user satisfaction and efficiency. The analysis unit selects the optimal analysis algorithm, for example, based on the analysis history of documents previously uploaded by the user. The analysis unit can also select the most efficient analysis algorithm from the user's past analysis history. Furthermore, the analysis unit can analyze the user's past analysis history and select the most accurate analysis algorithm. This makes it possible to select the optimal analysis algorithm based on the past analysis history. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the past analysis history into AI and cause the AI ​​to select the optimal analysis algorithm.

[0051] The analysis unit can point out necessary corrections in real time based on the contents of the document. Real time includes, for example, time ranges such as immediately and within a few seconds, but is not limited to such examples. For example, when a document uploaded by a user requires a signature, the analysis unit points out the need for a signature in real time. In addition, when a document uploaded by a user has an incompleteness, the analysis unit can also point out the incompleteness in real time. Furthermore, when a document uploaded by a user requires additional information, the analysis unit can also point out the need for additional information in real time. This makes it possible to point out necessary corrections in real time based on the contents of the document. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the uploaded document into AI and have the AI ​​execute the pointing out of necessary corrections.

[0052] The analysis unit can automatically point out region-specific correction points in consideration of the user's geographical location information. The geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. For example, if the user lives in Tokyo, the analysis unit automatically points out Tokyo-specific correction points. In addition, if the user lives in Osaka, the analysis unit can also automatically point out Osaka-specific correction points. Furthermore, if the user lives in Hokkaido, the analysis unit can automatically point out Hokkaido-specific correction points. This makes it possible to automatically point out region-specific correction points based on the user's geographical location information. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical location information to AI and cause the AI ​​to point out region-specific correction points.

[0053] The analysis unit can analyze the user's social media activity and automatically point out relevant corrections. Social media activity includes, for example, the content of posts, the number of likes, and the like, but is not limited to such examples. The analysis unit automatically points out relevant corrections, for example, based on address information published by the user on social media. The analysis unit can also automatically point out relevant corrections based on name information published by the user on social media. Furthermore, the analysis unit can automatically point out relevant corrections based on contact information published by the user on social media. In this way, it is possible to automatically point out relevant corrections based on the user's social media activity. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's social media data into AI and have the AI ​​execute the pointing out of relevant corrections.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The reception unit can automatically generate templates for documents required for visa applications based on the contents of the user's input. For example, when the user inputs his / her name and address, the reception unit generates templates for the necessary documents based on that information and provides them to the user. The reception unit can also automatically fill in parts of the documents based on the information input by the user. Furthermore, the reception unit can optimize the format of the documents based on the information input by the user and provide them in a format that meets the requirements of the submission destination. This allows the user to prepare the necessary documents without any hassle.

[0056] The analysis unit can analyze the contents of documents uploaded by a user and automatically complete necessary information if it is missing. For example, if an address is missing from a document uploaded by a user, the analysis unit can complete the address information from past input history or other documents. The analysis unit can also automatically suggest corrections if there are deficiencies in the document uploaded by the user. Furthermore, if the format of the document uploaded by the user is inappropriate, the analysis unit can convert it into an appropriate format and provide it. This allows the user to prepare accurate and complete documents.

[0057] The guidance unit can provide detailed guidance on the procedure for obtaining documents necessary for a visa application based on the contents of the user's input. For example, when the user inputs his / her name and address, the guidance unit can use that information to guide the user as to which government office to go to obtain the necessary documents. The guidance unit can also provide real-time information on the opening hours and congestion status of government offices, enabling the user to visit at the optimal time. Furthermore, the guidance unit can display the location of the government office on a map and provide guidance on the nearest means of transportation. This allows the user to efficiently obtain the necessary documents.

[0058] When a user uploads documents, the upload unit can automatically classify the contents of the documents and organize them into appropriate folders. For example, when a user uploads multiple documents, the upload unit analyzes the contents and classifies them into documents required for visa applications. In addition, when the documents uploaded by the user are in a different format, the upload unit can automatically convert them into an appropriate format and organize them. Furthermore, the upload unit can point out necessary corrections based on the contents of the documents uploaded by the user and present suggested corrections. This allows the user to manage documents efficiently.

[0059] The analysis unit can analyze the contents of documents uploaded by the user and automatically extract information required for the visa application. For example, the analysis unit can extract basic information such as name and address from documents uploaded by the user and automatically reflect it in documents required for the visa application. The analysis unit can also suggest required additional information based on the contents of documents uploaded by the user. Furthermore, the analysis unit can analyze the contents of documents uploaded by the user and provide them in a format that meets the requirements of the submission destination. This allows the user to prepare accurate and complete documents.

[0060] The process flow of the first embodiment will be briefly described below.

[0061] Step 1: The reception unit receives basic information of a user. The basic information of a user includes a name, an address, a contact address, etc. For example, the reception unit receives information such as "Taro Yamada, Shinjuku-ku, Tokyo" input by the user. Step 2: The information section analyzes the information received by the reception section and provides guidance to the government office. For example, it provides guidance on what to take to which department in the government office in the area where the user lives in order to obtain the necessary documents. Specifically, it provides guidance such as "Go to the family registry section of Shinjuku Ward Office and bring your ID to obtain a resident registration card." Step 3: The upload unit uploads documents collected by the user, for example, accepting documents collected by the user to be uploaded to the system. Step 4: The analysis unit analyzes the document uploaded by the upload unit and points out corrections. For example, the analysis unit gives specific instructions such as "This document requires a signature" to the uploaded document. The analysis unit may use AI to perform the process, and a generation AI may be used to analyze the document and point out corrections.

[0062] (Example 2) The visa application support system according to the embodiment of the present invention is a system that supports a user from collecting documents related to a visa application to correcting documents to be submitted. When a user inputs basic information such as a name and address, the system guides the user on which department to take to the local government office in the user's area to obtain the necessary documents. Furthermore, when the user uploads the documents he or she has collected to the system, the system corrects the documents and points out the necessary corrections. For example, when a user inputs information such as "Taro Yamada, Shinjuku Ward, Tokyo," the system provides specific guidance such as "Go to the family registry department of the Shinjuku Ward Office and bring your ID to obtain a resident's certificate." Furthermore, when the user uploads the documents he or she has collected to the system, the system gives specific instructions such as "This document requires a signature." This mechanism allows the user to apply for a visa at a lower cost than the agency fee, and allows the user to easily apply without spending time on collecting documents required for the procedure and writing the documents by himself or herself. For example, the user only needs to collect the documents required for a visa application and upload them to the system, and the system automatically corrects the documents and points out the necessary corrections, allowing the user to prepare accurate documents in a short time. This allows the visa application support system to assist users in everything from collecting documents related to visa applications to correcting submitted documents.

[0063] The visa application support system according to the embodiment includes a reception unit, a guidance unit, an upload unit, and an analysis unit. The reception unit receives basic information of a user. The basic information of a user includes, for example, a name, an address, and a contact address, but is not limited to such examples. The reception unit receives information such as "Taro Yamada, Shinjuku-ku, Tokyo" input by the user. The guidance unit analyzes the information received by the reception unit and provides guidance to the government office. The guidance unit provides guidance, for example, on which department of the government office in the area where the user lives and what to take to obtain the necessary documents. For example, the guidance unit provides specific guidance such as "Go to the family registry department of Shinjuku Ward Office and bring your ID to obtain a resident's certificate." The upload unit uploads documents collected by the user. For example, the upload unit accepts documents collected by the user to be uploaded to the system. The analysis unit analyzes the documents uploaded by the upload unit and points out corrections. For example, the analysis unit provides specific guidance such as "This document requires a signature" for the uploaded documents. As a result, the visa application support system according to the embodiment can accept basic information from the user, guide the government office, upload documents, analyze, and point out corrections. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the uploaded documents to a generation AI and have the generation AI analyze the documents and point out corrections.

[0064] The reception unit receives basic information of a user. The basic information of a user includes, for example, a name, an address, and contact information, but is not limited to such examples. The reception unit receives information such as "Taro Yamada, Shinjuku-ku, Tokyo" input by the user. Specifically, the reception unit stores the information input by the user in a database and makes it available for subsequent processing. The information input by the user is collected through a web form or a mobile application, and also has a validation function for checking the accuracy of the input contents. For example, the address input field has an auto-complete function for a zip code, and when a user inputs a zip code, the corresponding address is automatically displayed. In addition, the contact input field performs a format check for a phone number and a domain check for an email address to prevent input errors. Furthermore, the reception unit encrypts and stores the information input by the user, ensuring thorough privacy protection. This makes it possible to minimize the risk of the user's personal information being leaked to a third party. The reception unit also has a function for checking the information input by the user in real time and prompting the user to correct it if necessary. For example, if the name input field contains special characters or if inaccurate information is entered in the address input field, an error message is displayed to prompt the user to correct it. In this way, the reception unit assists the user in inputting accurate and complete information, and ensures that subsequent processing proceeds smoothly.

[0065] The guidance unit analyzes the information received by the reception unit and provides guidance on the government office. For example, the guidance unit provides guidance on which department of the government office in the area where the user lives and what to bring to obtain the necessary documents. Specifically, the guidance unit searches the database for information on the relevant government office based on the user's address information, and generates a list of necessary procedures and documents to bring. For example, if the user lives in Shinjuku Ward, the guidance unit provides information on the family registry department of the Shinjuku Ward Office and provides specific guidance such as that a resident's certificate can be obtained by bringing an ID card. The guidance unit also provides information on the business hours, contact information, map information, etc. of the government office, and supports the user to complete the procedures smoothly. Furthermore, the guidance unit can provide guidance customized according to the user's situation. For example, if the user is a foreign national, the necessary documents and procedures are different, so the guidance unit adjusts the guidance content taking that into consideration. In addition, if the user can complete the procedures online, the guidance unit also provides guidance on how to do so. For example, for documents that can be applied for online, the link to the application form and the steps of the necessary procedures are explained in detail. This enables the guidance section to assist the user in efficiently completing procedures at government offices, and to smoothly advance the visa application process.

[0066] The upload unit uploads documents collected by the user. For example, the upload unit accepts documents collected by the user to be uploaded to the system. Specifically, the upload unit accepts documents scanned by the user and image files taken with a smartphone, and stores these files in the system. The upload unit checks the file format and size to ensure that the documents are uploaded in an appropriate format. For example, the upload unit accepts files in PDF format and JPEG format, and encourages compression if the file size is too large. In addition, the upload unit provides a preview function for documents uploaded by the user, allowing the user to check the uploaded contents. This allows the user to check that the uploaded documents are accurate and re-upload them as necessary. In addition, the upload unit uses OCR (optical character recognition) technology to extract text data from the uploaded documents, making the processing in the analysis unit more efficient. For example, important information such as names, addresses, and dates are automatically extracted from the uploaded documents and stored in a database. This allows the upload unit to efficiently import documents collected by the user into the system, and smoothly proceed with subsequent analysis processing.

[0067] The analysis unit analyzes the documents uploaded by the upload unit and points out corrections. For example, the analysis unit gives specific instructions such as "This document requires a signature" to the uploaded document. Specifically, the analysis unit uses AI to analyze the contents of the uploaded document and identify necessary corrections and missing information. For example, AI analyzes the layout and text content of the document and displays a warning if the signature field is blank or if the required stamp is not affixed. Furthermore, the analysis unit checks whether the contents of the document meet the requirements for the visa application and points out missing or incorrect information. For example, a warning is displayed if the information written on the application form does not match the information on the passport or if the required attached documents are missing. The analysis unit guides the user to specific correction methods and additional procedures to assist in correcting the document. For example, it provides specific instructions such as "This document requires a signature. Please add a signature and re-upload." This allows the analysis unit to ensure the accuracy and completeness of the documents submitted by the user and to smoothly proceed with the visa application process. Furthermore, the analysis unit improves the accuracy of the analysis by having the AI ​​learn based on past data and similar application documents. This enables the analysis unit to always provide highly accurate analysis based on the latest information, and effectively support the user's visa application.

[0068] The upload unit can provide an interface that allows a user to upload documents. The interface includes, for example, usability, design elements, and the like, but is not limited to such examples. The upload unit can provide, for example, an intuitive interface that allows a user to easily upload documents. For example, the upload unit can provide an interface with a drag-and-drop function. The upload unit can also display a file selection dialog so that a user can select and upload a file. Furthermore, the upload unit can also support uploading from a mobile device such as a smartphone or tablet. For example, the upload unit can take a picture of a document using a camera of a mobile device and upload the image. This allows a user to easily upload a document. A part or all of the above-mentioned processing in the upload unit may be performed, for example, using AI, or may be performed without using AI. For example, the upload unit can input a user's operation history into AI and have AI design an optimal interface.

[0069] The analysis unit can analyze the contents of the document using AI and automatically indicate when necessary information is missing. AI includes, but is not limited to, technologies such as machine learning and deep learning. The analysis unit can analyze the contents of the document using, for example, a machine learning algorithm. For example, the analysis unit uses a machine learning model that takes text data of the document as input and outputs whether necessary information is missing. The analysis unit can also analyze the contents of the document using deep learning technology. For example, the analysis unit uses a deep learning model that takes image data of the document as input and outputs whether necessary information is missing. Furthermore, the analysis unit can analyze the contents of the document using natural language processing technology. For example, the analysis unit analyzes text data of the document and automatically indicates when necessary information is missing. This makes it possible to automatically indicate when necessary information is missing. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the contents of the document to the generation AI and have the generation AI execute an analysis of whether necessary information is missing.

[0070] The analysis unit can automatically start the analysis immediately after uploading the document and provide feedback quickly. "Quickly" includes, but is not limited to, a time range such as within a few seconds or within a few minutes. The analysis unit can automatically start the analysis immediately after uploading the document, for example. For example, the analysis unit starts the analysis process as soon as the document is uploaded and provides feedback within a few seconds. The analysis unit can also provide feedback quickly based on the contents of the document. For example, the analysis unit analyzes the contents of the document and points out necessary corrections within a few seconds. Furthermore, the analysis unit can provide feedback in real time. For example, the analysis unit points out corrections in real time while analyzing the contents of the document. This makes it possible to provide feedback quickly immediately after uploading the document. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the contents of the document to the generation AI and cause the generation AI to perform quick feedback.

[0071] The guidance unit can provide detailed guidance on what to take to which department of the local government office in the user's area to obtain the necessary documents. Details include, for example, the depth of the information to be provided and the means to provide it, but are not limited to such examples. The guidance unit provides, for example, guidance on what to take to which department of the local government office in the user's area to obtain the necessary documents. For example, the guidance unit provides specific guidance such as "Go to the family registry department of Shinjuku Ward Office and bring your ID to obtain a resident's certificate." The guidance unit can also provide information such as the business hours and congestion status of the government office. For example, the guidance unit provides information on the business hours of the government office and suggests the best time for the user to visit. Furthermore, the guidance unit can provide information such as a map of the government office and how to access it. For example, the guidance unit displays the location of the government office on a map and provides information on the nearest transportation. This allows the user to provide specific guidance for obtaining the necessary documents. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the guidance unit can input user input information to the generation AI and have the generation AI execute optimal guidance.

[0072] The reception unit can estimate the user's emotion and dynamically change the design of the input form based on the estimated user's emotion. The emotion estimation is performed using technologies such as facial expression recognition and voice analysis. The dynamic change includes, for example, a change in real time, a trigger of the change, and the like, but is not limited to such examples. For example, when the user is stressed, the reception unit provides a simple and intuitive interface and minimizes the input procedure. In addition, when the user is relaxed, the reception unit can also provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input and enable basic information to be input quickly. This makes it possible to change the design of the input form 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. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI estimate emotions and change the design of the input form.

[0073] The reception unit can provide an auto-completion function to analyze the user's past input history and reduce the effort of input. The auto-completion function includes, for example, a prediction algorithm, completion accuracy, and the like, but is not limited to such examples. The reception unit, for example, automatically displays a name or address previously input by the user as a candidate. The reception unit can also preferentially suggest an input method (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest basic information to be used in a specific time period from the user's past input history. For example, the reception unit displays optimal input candidates based on information previously input by the user. This makes it possible to provide an auto-completion function based on the user's past input history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history to AI and cause AI to generate optimal input candidates.

[0074] The reception unit can suggest necessary additional information in real time based on the user's input contents. Real time includes, for example, a time range such as immediately or within a few seconds, but is not limited to such examples. For example, the reception unit displays a message prompting the user to input an address when the user inputs a name. In addition, the reception unit can also suggest the input of a phone number or email address when the user inputs an address. Furthermore, the reception unit can suggest additional information required for a visa application in real time when the user inputs basic information. For example, the reception unit displays a message such as "Please enter your address next" when the user inputs a name. This makes it possible to suggest necessary additional information in real time based on the user's input contents. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's input contents to AI and have the AI ​​execute the suggestion of necessary additional information.

[0075] The reception unit can estimate the user's emotion and determine the priority of inputs based on the estimated user's emotion. The estimation of emotion is performed using technologies such as facial expression recognition and voice analysis. The determination of the priority includes, for example, criteria such as importance and urgency, but is not limited to such examples. For example, when the user is nervous, the reception unit displays the simplest input items in order. Furthermore, when the user is relaxed, the reception unit can also display detailed input items with priority. Furthermore, when the user is in a hurry, the reception unit can also display the most important input items with priority. This makes it possible to determine the priority of inputs 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. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's facial expression data to the generation AI and cause the generation AI to execute the estimation of emotion and the determination of the priority of inputs.

[0076] The reception unit can automatically add region-specific input items based on the geographical location information of the user. The geographical location information includes, for example, GPS data, IP address, and the like, but is not limited to such examples. For example, if the user lives in Tokyo, the reception unit automatically adds input items specific to Tokyo. In addition, if the user lives in Osaka, the reception unit can also automatically add input items specific to Osaka. Furthermore, if the user lives in Hokkaido, the reception unit can also automatically add input items specific to Hokkaido. This makes it possible to add region-specific input items based on the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the user to AI and cause the AI ​​to execute the addition of region-specific input items.

[0077] The reception unit can analyze the user's social media activity and automatically reflect related information in the input form. The social media activity includes, for example, the content of posts, the number of likes, and the like, but is not limited to such examples. The reception unit, for example, automatically reflects address information published by the user on social media in the input form. The reception unit can also automatically reflect name information published by the user on social media in the input form. Furthermore, the reception unit can automatically reflect contact information published by the user on social media in the input form. This makes it possible to reflect related information in the input form based on the user's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into AI and cause AI to reflect the related information.

[0078] The guidance unit can estimate the user's emotion and adjust the way of expressing guidance based on the estimated emotion of the user. The estimation of emotion is performed using technologies such as facial expression recognition and voice analysis. The adjustment of the way of expression includes, for example, but is not limited to, the wording and the display format. For example, when the user is nervous, the guidance unit uses a concise and easy-to-understand expression. Furthermore, when the user is relaxed, the guidance unit can also use an expression including a detailed explanation. Furthermore, when the user is in a hurry, the guidance unit can also use an expression that focuses on the main points. This makes it possible to adjust the way of expressing guidance according to the emotion of the user. 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. A part or all of the above-mentioned processing in the guidance unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the guidance unit can input the user's facial expression data to the generation AI and cause the generation AI to estimate the emotion and adjust the way of expressing guidance.

[0079] The guidance unit can analyze the past guidance history and select the optimal guidance method. The optimal includes, but is not limited to, criteria such as user satisfaction and efficiency. The guidance unit, for example, proposes the optimal guidance method based on the guidance method used by the user in the past. The guidance unit can also propose the most efficient guidance method from the user's past guidance history. Furthermore, the guidance unit can analyze the user's past guidance history and propose a guidance method that avoids congestion. This makes it possible to select the optimal guidance method based on the past guidance history. A part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can input the past guidance history into AI and cause the AI ​​to select the optimal guidance method.

[0080] The guidance unit can provide the business hours and congestion status of the government office in real time based on the user's input. Real time includes, for example, a time range such as immediately or within a few seconds, but is not limited to such examples. For example, the guidance unit provides real-time business hours when the user inputs a plan to go to the government office. In addition, the guidance unit can also provide real-time congestion status when the user inputs a plan to go to the government office. Furthermore, the guidance unit can suggest the optimal visiting time when the user inputs a plan to go to the government office. This makes it possible to provide the business hours and congestion status of the government office in real time based on the user's input. Part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can input the user's input content to AI and cause the AI ​​to provide real-time information.

[0081] The guidance unit can estimate the user's emotion and determine the priority of guidance based on the estimated user's emotion. The estimation of emotion is performed using technologies such as facial expression recognition and voice analysis. The determination of the priority includes, for example, criteria such as importance and urgency, but is not limited to such examples. For example, when the user is nervous, the guidance unit preferentially displays the most important guidance. In addition, when the user is relaxed, the guidance unit can also preferentially display detailed guidance. Furthermore, when the user is in a hurry, the guidance unit can also preferentially display the simplest guidance. This makes it possible to determine the priority of guidance 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. A part or all of the above-mentioned processing in the guidance unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the guidance unit can input the user's facial expression data to the generation AI and cause the generation AI to execute the estimation of emotion and the determination of the priority of guidance.

[0082] The guidance unit can automatically guide the user to the nearest government office in consideration of the geographical location information of the user. The geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. For example, if the user lives in Shinjuku Ward, Tokyo, the guidance unit automatically guides the user to Shinjuku Ward Office. In addition, if the user lives in Osaka City, Osaka Prefecture, the guidance unit can also automatically guide the user to Osaka City Hall. Furthermore, if the user lives in Sapporo City, Hokkaido, the guidance unit can also automatically guide the user to Sapporo City Hall. This makes it possible to guide the user to the nearest government office based on the user's geographical location information. A part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can input the user's geographical location information into AI and cause the AI ​​to perform guidance of the nearest government office.

[0083] The guidance unit can analyze the user's social media activity and provide information on related government offices. Social media activity includes, for example, the content of posts, the number of likes, and the like, but is not limited to such examples. For example, the guidance unit guides the user to the nearest government office based on address information published by the user on social media. The guidance unit can also guide the user to necessary documents based on name information published by the user on social media. Furthermore, the guidance unit can also guide the user to the contact information of the government office based on contact information published by the user on social media. This makes it possible to provide information on related government offices based on the user's social media activity. Part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can input the user's social media data into AI and have the AI ​​provide information on related government offices.

[0084] The upload unit can estimate the user's emotion and dynamically change the design of the upload interface based on the estimated user's emotion. The emotion estimation is performed using technologies such as facial expression recognition and voice analysis. The dynamic change includes, for example, a change in real time, a trigger of the change, and the like, but is not limited to such examples. For example, when the user is stressed, the upload unit provides a simple and intuitive interface and minimizes the upload procedure. In addition, when the user is relaxed, the upload unit can also provide detailed upload options and suggest a customizable upload method. Furthermore, when the user is in a hurry, the upload unit can prioritize voice input and enable documents to be uploaded quickly. This makes it possible to change the design of the upload 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, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the upload unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the upload unit can input the user's facial expression data into the generation AI and have the generation AI estimate emotions and change the design of the upload interface.

[0085] The upload unit can analyze the user's past upload history and propose an optimal upload method. The optimal includes, but is not limited to, criteria such as user satisfaction and efficiency. The upload unit can propose an optimal upload method, for example, based on the format of documents previously uploaded by the user. The upload unit can also propose the most efficient upload method from the user's past upload history. Furthermore, the upload unit can analyze the user's past upload history and propose the most appropriate file format. This makes it possible to propose an optimal upload method based on the user's past upload history. A part or all of the above-mentioned processing in the upload unit may be performed, for example, using AI, or may be performed without using AI. For example, the upload unit can input the past upload history into AI and cause the AI ​​to execute a proposal for an optimal upload method.

[0086] The upload unit can provide an automatic file format and size conversion function at the time of uploading. The automatic conversion includes, for example, convertible file formats, conversion accuracy, and the like, but is not limited to such examples. For example, when the format of the document uploaded by the user is different, the upload unit automatically converts it to an appropriate format. In addition, when the size of the document uploaded by the user is large, the upload unit can automatically compress and upload it. Furthermore, when the format of the document uploaded by the user is inappropriate, the upload unit can automatically convert it to an appropriate format and upload it. This makes it possible to provide an automatic file format and size conversion function. A part or all of the above-mentioned processing in the upload unit may be performed, for example, using AI, or may be performed without using AI. For example, the upload unit can input the uploaded file to AI and have the AI ​​execute conversion to an appropriate format and size.

[0087] The upload unit can estimate the user's emotion and determine the priority of uploading based on the estimated user's emotion. The estimation of emotion is performed using technologies such as facial expression recognition and voice analysis. The determination of the priority includes, for example, criteria such as importance and urgency, but is not limited to such examples. For example, when the user is nervous, the upload unit displays the upload items in order starting from the simplest. Furthermore, when the user is relaxed, the upload unit can also preferentially display detailed upload items. Furthermore, when the user is in a hurry, the upload unit can also preferentially display the most important upload items. This makes it possible to determine the priority of uploading 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. A part or all of the above-mentioned processing in the upload unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the upload unit can input the user's facial expression data to the generation AI and cause the generation AI to execute the estimation of emotion and the determination of the priority of uploading.

[0088] The upload unit can automatically upload region-specific documents in consideration of the geographical location information of the user. The geographical location information includes, for example, GPS data, IP address, and the like, but is not limited to such examples. For example, if the user lives in Tokyo, the upload unit automatically uploads documents specific to Tokyo. In addition, if the user lives in Osaka, the upload unit can also automatically upload documents specific to Osaka. Furthermore, if the user lives in Hokkaido, the upload unit can also automatically upload documents specific to Hokkaido. This makes it possible to automatically upload region-specific documents based on the geographical location information of the user. A part or all of the above-mentioned processing in the upload unit may be performed, for example, using AI, or may be performed without using AI. For example, the upload unit can input the geographical location information of the user to AI and cause the AI ​​to execute uploading of region-specific documents.

[0089] The upload unit can analyze the social media activity of the user and automatically upload related documents. The social media activity includes, for example, the content of posts, the number of likes, and the like, but is not limited to such examples. The upload unit automatically uploads related documents based on, for example, address information published by the user on social media. The upload unit can also automatically upload related documents based on name information published by the user on social media. Furthermore, the upload unit can automatically upload related documents based on contact information published by the user on social media. This makes it possible to automatically upload related documents based on the social media activity of the user. A part or all of the above-mentioned processing in the upload unit may be performed, for example, using AI, or may be performed without using AI. For example, the upload unit can input the social media data of the user into AI and cause AI to execute uploading of related documents.

[0090] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The emotion estimation is performed using technologies such as facial expression recognition and voice analysis. The adjustment of the display method includes, for example, a display format and a display content, but is not limited to such examples. For example, when the user is nervous, the analysis unit provides a display method that is simple and highly visible. In addition, when the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, when the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. This makes it possible to adjust the display method of the analysis result 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. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate emotions and adjust the way the analysis results are displayed.

[0091] The analysis unit can analyze the past analysis history and select the optimal analysis algorithm. The optimal includes, but is not limited to, criteria such as user satisfaction and efficiency. The analysis unit selects the optimal analysis algorithm, for example, based on the analysis history of documents previously uploaded by the user. The analysis unit can also select the most efficient analysis algorithm from the user's past analysis history. Furthermore, the analysis unit can analyze the user's past analysis history and select the most accurate analysis algorithm. This makes it possible to select the optimal analysis algorithm based on the past analysis history. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the past analysis history into AI and cause the AI ​​to select the optimal analysis algorithm.

[0092] The analysis unit can point out necessary corrections in real time based on the contents of the document. Real time includes, for example, time ranges such as immediately and within a few seconds, but is not limited to such examples. For example, when a document uploaded by a user requires a signature, the analysis unit points out the need for a signature in real time. In addition, when a document uploaded by a user has an incompleteness, the analysis unit can also point out the incompleteness in real time. Furthermore, when a document uploaded by a user requires additional information, the analysis unit can also point out the need for additional information in real time. This makes it possible to point out necessary corrections in real time based on the contents of the document. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the uploaded document into AI and have the AI ​​execute the pointing out of necessary corrections.

[0093] The analysis unit can estimate the user's emotion and determine the priority of the analysis results based on the estimated user's emotion. The emotion estimation is performed using technologies such as facial expression recognition and voice analysis. The priority determination includes, for example, criteria such as importance and urgency, but is not limited to such examples. For example, when the user is nervous, the analysis unit preferentially displays the most important analysis results. In addition, when the user is relaxed, the analysis unit can also preferentially display detailed analysis results. Furthermore, when the user is in a hurry, the analysis unit can also preferentially display the most concise analysis results. This makes it possible to determine the priority of the analysis results 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. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input a user's facial expression data into the generation AI and have the generation AI estimate emotions and determine the priority of the analysis results.

[0094] The analysis unit can automatically point out region-specific correction points in consideration of the user's geographical location information. The geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. For example, if the user lives in Tokyo, the analysis unit automatically points out Tokyo-specific correction points. In addition, if the user lives in Osaka, the analysis unit can also automatically point out Osaka-specific correction points. Furthermore, if the user lives in Hokkaido, the analysis unit can automatically point out Hokkaido-specific correction points. This makes it possible to automatically point out region-specific correction points based on the user's geographical location information. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical location information to AI and cause the AI ​​to point out region-specific correction points.

[0095] The analysis unit can analyze the user's social media activity and automatically point out relevant corrections. Social media activity includes, for example, the content of posts, the number of likes, and the like, but is not limited to such examples. The analysis unit automatically points out relevant corrections, for example, based on address information published by the user on social media. The analysis unit can also automatically point out relevant corrections based on name information published by the user on social media. Furthermore, the analysis unit can automatically point out relevant corrections based on contact information published by the user on social media. In this way, it is possible to automatically point out relevant corrections based on the user's social media activity. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's social media data into AI and have the AI ​​execute the pointing out of relevant corrections.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The reception unit can automatically generate templates for documents required for visa applications based on the contents of the user's input. For example, when the user inputs his / her name and address, the reception unit generates templates for the necessary documents based on that information and provides them to the user. The reception unit can also automatically fill in parts of the documents based on the information input by the user. Furthermore, the reception unit can optimize the format of the documents based on the information input by the user and provide them in a format that meets the requirements of the submission destination. This allows the user to prepare the necessary documents without any hassle.

[0098] The analysis unit can analyze the contents of documents uploaded by a user and automatically complete necessary information if it is missing. For example, if an address is missing from a document uploaded by a user, the analysis unit can complete the address information from past input history or other documents. The analysis unit can also automatically suggest corrections if there are deficiencies in the document uploaded by the user. Furthermore, if the format of the document uploaded by the user is inappropriate, the analysis unit can convert it into an appropriate format and provide it. This allows the user to prepare accurate and complete documents.

[0099] The guidance unit can provide detailed guidance on the procedure for obtaining documents necessary for a visa application based on the contents of the user's input. For example, when the user inputs his / her name and address, the guidance unit can use that information to guide the user as to which government office to go to obtain the necessary documents. The guidance unit can also provide real-time information on the opening hours and congestion status of government offices, enabling the user to visit at the optimal time. Furthermore, the guidance unit can display the location of the government office on a map and provide guidance on the nearest means of transportation. This allows the user to efficiently obtain the necessary documents.

[0100] When a user uploads documents, the upload unit can automatically classify the contents of the documents and organize them into appropriate folders. For example, when a user uploads multiple documents, the upload unit analyzes the contents and classifies them into documents required for visa applications. In addition, when the documents uploaded by the user are in a different format, the upload unit can automatically convert them into an appropriate format and organize them. Furthermore, the upload unit can point out necessary corrections based on the contents of the documents uploaded by the user and present suggested corrections. This allows the user to manage documents efficiently.

[0101] The analysis unit can analyze the contents of documents uploaded by the user and automatically extract information required for the visa application. For example, the analysis unit can extract basic information such as name and address from documents uploaded by the user and automatically reflect it in documents required for the visa application. The analysis unit can also suggest required additional information based on the contents of documents uploaded by the user. Furthermore, the analysis unit can analyze the contents of documents uploaded by the user and provide them in a format that meets the requirements of the submission destination. This allows the user to prepare accurate and complete documents.

[0102] The reception unit can estimate the user's emotion and dynamically change the design of the input form based on the estimated user's emotion. For example, when the user is stressed, a simple and intuitive interface is provided to minimize the input procedure. In addition, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input to enable the user to quickly input basic information. This makes it possible to change the design of the input form according to the user's emotion.

[0103] The guidance unit can estimate the user's emotions and adjust the way in which guidance is expressed based on the estimated user's emotions. For example, if the user is nervous, a concise and easy-to-understand expression is used. Also, if the user is relaxed, the guidance unit can use an expression including a detailed explanation. Furthermore, if the user is in a hurry, the guidance unit can use an expression that focuses on the main points. In this way, the way in which guidance is expressed can be adjusted according to the user's emotions.

[0104] The upload unit can estimate the user's emotion and dynamically change the design of the upload interface based on the estimated user's emotion. For example, when the user is stressed, a simple and intuitive interface is provided to minimize the upload procedure. In addition, when the user is relaxed, the upload unit can provide detailed upload options and suggest a customizable upload method. Furthermore, when the user is in a hurry, the upload unit can prioritize voice input to enable documents to be uploaded quickly. In this way, the design of the upload interface can be changed according to the user's emotion.

[0105] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible display method is provided. In addition, the analysis unit can also provide a display method including detailed information if the user is relaxed. Furthermore, the analysis unit can also provide a display method that focuses on the main points if the user is in a hurry. This makes it possible to adjust the display method of the analysis results according to the user's emotions.

[0106] The analysis unit can estimate the user's emotions and determine the priority order of the analysis results based on the estimated user's emotions. For example, if the user is nervous, the most important analysis results are displayed with priority. The analysis unit can also display detailed analysis results with priority when the user is relaxed. Furthermore, the analysis unit can display the simplest analysis results with priority when the user is in a hurry. This makes it possible to determine the priority order of the analysis results according to the user's emotions.

[0107] The process flow of the second embodiment will be briefly described below.

[0108] Step 1: The reception unit receives basic information of a user. The basic information of a user includes a name, an address, a contact address, etc. For example, the reception unit receives information such as "Taro Yamada, Shinjuku-ku, Tokyo" input by the user. Step 2: The information section analyzes the information received by the reception section and provides guidance to the government office. For example, it provides guidance on what to take to which department in the government office in the area where the user lives in order to obtain the necessary documents. Specifically, it provides guidance such as "Go to the family registry section of Shinjuku Ward Office and bring your ID to obtain a resident registration card." Step 3: The upload unit uploads documents collected by the user, for example, accepting documents collected by the user to be uploaded to the system. Step 4: The analysis unit analyzes the document uploaded by the upload unit and points out corrections. For example, the analysis unit gives specific instructions such as "This document requires a signature" to the uploaded document. The analysis unit may use AI to perform the process, and a generation AI may be used to analyze the document and point out corrections.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the elements including the above-mentioned reception unit, guidance unit, upload unit, and analysis 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 receives basic information of the user. The guidance unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides guidance to government offices in the area where the user lives. The upload unit is realized, for example, by the control unit 46A of the smart device 14 and uploads documents collected by the user to the system. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded documents and points out corrections. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements including the above-mentioned reception unit, guidance unit, upload unit, and analysis 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 receives basic information of the user. The guidance unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides guidance to government offices in the area where the user lives. The upload unit is realized, for example, by the control unit 46A of the smart glasses 214 and uploads documents collected by the user to the system. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded documents and points out corrections. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements including the above-mentioned reception unit, guidance unit, upload unit, and analysis 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 receives basic information of the user. The guidance unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides guidance to government offices in the area where the user lives. The upload unit is realized, for example, by the control unit 46A of the headset type terminal 314 and uploads documents collected by the user to the system. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded documents and points out corrections. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements including the above-mentioned reception unit, guidance unit, upload unit, and analysis 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 receives basic information of the user. The guidance unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides guidance to government offices in the area where the user lives. The upload unit is realized, for example, by the control unit 46A of the robot 414 and uploads documents collected by the user to the system. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded documents and points out corrections. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Appendix 1) A reception unit that receives basic information of a user; a guidance unit that analyzes the information received by the reception unit and provides guidance about the government office; an uploading section for uploading documents collected by a user; An analysis unit that analyzes the document uploaded by the upload unit and indicates corrections. A system characterized by: (Appendix 2) The upload unit is Provide an interface for users to upload documents 2. The system of claim 1, (Appendix 3) The analysis unit is Uses AI to analyze the contents of documents and automatically point out when necessary information is missing 2. The system of claim 1, (Appendix 4) The analysis unit is Automatic analysis begins immediately after uploading documents to provide immediate feedback 2. The system of claim 1, (Appendix 5) The guide unit is Provide detailed information on which department to take to the local government office in the user's area to obtain the necessary documents 2. The system of claim 1, (Appendix 6) The reception unit is Estimate user emotions and dynamically change the design of the input form based on the estimated user emotions 2. The system of claim 1, (Appendix 7) The reception unit is Analyze the user's past input history and provide auto-completion to reduce input effort 2. The system of claim 1, (Appendix 8) The reception unit is Suggest additional information needed in real time based on user input 2. The system of claim 1, (Appendix 9) The reception unit is Estimate the user's emotions and prioritize inputs based on the estimated user emotions. 2. The system of claim 1, (Appendix 10) The reception unit is Automatically populate region-specific fields based on the user's geographic location 2. The system of claim 1, (Appendix 11) The reception unit is Analyze users' social media activity and automatically populate forms with relevant information 2. The system of claim 1, (Appendix 12) The guide unit is The system estimates the user's emotions and adjusts the way the guidance is presented based on the estimated user's emotions. 2. The system of claim 1, (Appendix 13) The guide unit is Analyze past guidance history and select the optimal guidance method 2. The system of claim 1, (Appendix 14) The guide unit is Provides real-time information on government office opening hours and congestion status based on user input 2. The system of claim 1, (Appendix 15) The guide unit is To estimate a user's emotion and determine a priority of guidance based on the estimated user's emotion. 2. The system of claim 1, (Appendix 16) The guide unit is Taking into account the user's geographic location, automatically guide them to the nearest government office 2. The system of claim 1, (Appendix 17) The guide unit is Analyze users' social media activity and provide relevant government information 2. The system of claim 1, (Appendix 18) The upload unit is Estimate user emotions and dynamically change the design of the upload interface based on the estimated user emotions. 2. The system of claim 1, (Appendix 19) The upload unit is Analyze users' past upload history and suggest the best upload method 2. The system of claim 1, (Appendix 20) The upload unit is Provide automatic file format and size conversion when uploading 2. The system of claim 1, (Appendix 21) The upload unit is Infer user sentiment and prioritize uploads based on the inferred sentiment 2. The system of claim 1, (Appendix 22) The upload unit is Considers the user's geographic location and automatically uploads region-specific documents 2. The system of claim 1, (Appendix 23) The upload unit is Analyze users' social media activity and automatically upload relevant documents 2. The system of claim 1, (Appendix 24) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 25) The analysis unit is Analyze past analysis history and select the optimal analysis algorithm 2. The system of claim 1, (Appendix 26) The analysis unit is Based on the content of the document, we will suggest necessary revisions in real time 2. The system of claim 1, (Appendix 27) The analysis unit is Infer user emotions and prioritize analysis results based on the estimated user emotions. 2. The system of claim 1, (Appendix 28) The analysis unit is Considers the user's geographic location to automatically suggest localized fixes 2. The system of claim 1, (Appendix 29) The analysis unit is Analyze your social media activity and automatically suggest relevant changes 2. The system of claim 1, [Explanation of symbols]

[0181] 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 basic information of a user; a guidance unit that analyzes the information received by the reception unit and provides guidance about the government office; an uploading section for uploading documents collected by a user; An analysis unit that analyzes the document uploaded by the upload unit and indicates corrections. A system characterized by:

2. The upload unit is Provide an interface for users to upload documents 2. The system of claim 1 .

3. The analysis unit is Use AI to analyze the contents of documents and automatically point out when necessary information is missing 2. The system of claim 1 .

4. The analysis unit is Automatic analysis begins immediately after uploading documents to provide immediate feedback 2. The system of claim 1 .

5. The guide portion is Provide detailed information on which department to take to the local government office in the user's area to obtain the necessary documents 2. The system of claim 1 .

6. The reception unit is Estimate user emotions and dynamically change the design of the input form based on the estimated user emotions 2. The system of claim 1 .

7. The reception unit is Analyze the user's past input history and provide auto-completion to reduce input effort 2. The system of claim 1 .

8. The reception unit is Suggest additional information needed in real time based on user input 2. The system of claim 1 .

9. The reception unit is Estimate the user's emotions and prioritize inputs based on the estimated user emotions.

2. The system of claim 1 .

10. The reception unit is Automatically populate region-specific fields based on the user's geographic location 2. The system of claim 1 .

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