System

Abstract

An object of a system according to an embodiment is to enable a user to easily acquire an application form of a local government and accurately input the application form.SOLUTION: A system includes an application form acquisition unit and an application form input following unit. The application form acquisition unit acquires an appropriate application form from the website of the corresponding local government based on the content that the user wants to apply for. The application form input following unit follows when the user inputs necessary information to the application form acquired by the application form acquiring unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is cumbersome and time-consuming for users to obtain application forms from local governments and enter information accurately.

[0005] The system according to the embodiment aims to enable users to easily obtain and accurately fill out application forms issued by local governments. [Means for solving the problem]

[0006] The system according to the embodiment includes an application form acquisition unit and an application form input support unit. The application form acquisition unit acquires an appropriate application form from the website of the relevant local government based on the content of the application that the user wishes to apply for. The application form input support unit supports the user when entering the required information into the application form acquired by the application form acquisition unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable users to easily obtain and accurately fill out local government application forms. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's 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 read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The application form acquisition and follow-up system according to an embodiment of the present invention is a system that acquires an appropriate application form from the local government website based on the content of the application that the user wishes to apply for, and the generation AI follows up when the user enters the necessary information. This simplifies the application procedure and reduces the burden on the user.

[0029] An application acquisition and follow-up system according to an embodiment includes an application acquisition unit and an application input follow-up unit. The application acquisition unit acquires an appropriate application form from the website of the relevant local government based on the content of the user's application. For example, if a user inputs "I would like to obtain a copy of my resident registration card in Tokyo," the generation AI downloads an application form for a copy of the resident registration card from the official website of Tokyo. Similarly, if a user inputs "I would like to apply for child care support grants," the generation AI downloads an application form for child care support grants from the website of the relevant local government. The application input follow-up unit assists the user in entering the necessary information into the application form acquired by the application acquisition unit. For example, if the user is unsure of what to fill in a specific field on the application form, the generation AI suggests an appropriate input method for that field. Furthermore, if the user provides basic information such as name and address, the generation AI automatically enters that information into the appropriate fields on the application form. Furthermore, the generation AI provides instructions on how to submit the application online and address information for submitting by mail. This allows the application acquisition and follow-up system according to an embodiment to simplify the application process and reduce the burden on users.

[0030] The application form acquisition unit can refer to past application data and prioritize the acquisition of the most frequently used application forms. For example, the generation AI analyzes the past application database to identify the application forms most frequently used in a particular local government. For example, it prioritizes the acquisition of application forms such as copies of resident registration certificates and moving-out notifications. This allows the most frequently used application forms to be prioritized by referring to past application data.

[0031] The application form acquisition unit monitors updates on the local government website in real time, making it possible to always obtain the latest application forms. For example, the generation AI monitors the RSS feed and update notifications on the local government website, and automatically acquires the latest application forms when they are published. For example, if a new format of application form is published, it is immediately acquired. This makes it possible to always obtain the latest application forms by monitoring updates on the local government website in real time.

[0032] The application form acquisition unit can simultaneously acquire application forms from other local governments and provide comparative information to the user. For example, the generation AI acquires the same type of application form from the websites of multiple local governments and provides comparative information to the user. For example, application forms for copies of resident registration certificates from Tokyo and Osaka prefectures can be acquired simultaneously. This allows application forms from other local governments to be acquired simultaneously and comparative information to be provided to the user.

[0033] The application form acquisition unit can simultaneously acquire related laws and guidelines and provide them to the user. For example, when the generation AI acquires an application form, the application form acquisition unit simultaneously acquires related laws and guidelines and provides them to the user. For example, it acquires laws and guidelines related to applying for a copy of a resident registration card. This allows related laws and guidelines to be simultaneously acquired and provided to the user.

[0034] When acquiring an application form based on the purpose of the application, the application form acquisition unit can refer to past success cases and prioritize acquisition of application forms with the highest success rate. For example, the application form acquisition unit uses a generation AI to analyze past application data and identify applications with a high success rate. For example, it prioritizes acquisition of application forms with many past success cases. In this way, by referring to past success cases, it is possible to prioritize acquisition of application forms with the highest success rate.

[0035] When acquiring an application form based on the purpose of application, the application form acquisition unit can simultaneously acquire information on related subsidies and grants and provide it to the user. For example, the generation AI simultaneously acquires information on subsidies and grants related to the purpose of application and provides it to the user. For example, it provides subsidy information related to an application for child care support. This allows information on related subsidies and grants to be simultaneously acquired and provided to the user.

[0036] When acquiring an application form based on the purpose of application, the application form acquisition unit can simultaneously acquire other related application forms and provide them to the user. For example, the generation AI simultaneously acquires other application forms related to the purpose of application and provides them to the user. For example, it acquires other support grant applications related to an application for child care support grants. This allows other related application forms to be simultaneously acquired and provided to the user.

[0037] When acquiring an application form based on the purpose of application, the application form acquisition unit can simultaneously acquire related FAQs and support information and provide them to the user. For example, the generation AI can simultaneously acquire FAQs and support information related to the purpose of application and provide them to the user. For example, it can provide frequently asked questions about applying for child care support grants. This allows related FAQs and support information to be simultaneously acquired and provided to the user.

[0038] The application form input follow-up unit can refer to past input data and suggest the most common input method. For example, the application form input follow-up unit uses a generation AI to analyze past input data and identify the most common input method. For example, it makes suggestions based on methods that many users have used in the past. This makes it possible to suggest the most common input method by referring to past input data.

[0039] The application form input follow-up unit can detect input errors in real time and make correction suggestions. For example, the generation AI analyzes the user's input content in real time and detects input errors. For example, it detects typos and format inconsistencies. This makes it possible to detect input errors in real time and make correction suggestions.

[0040] The application form input follow-up unit can refer to input examples from other users and suggest the most appropriate input method. For example, the application form input follow-up unit uses a generation AI to analyze input examples from other users and identify the most appropriate input method. For example, it makes suggestions based on methods that many users have used in the past. This allows the most appropriate input method to be suggested by referring to input examples from other users.

[0041] The application form input follow-up unit can refer to relevant laws and guidelines and suggest an appropriate input method. For example, the generation AI analyzes relevant laws and guidelines and identifies an appropriate input method. For example, it suggests a correct input method based on laws and regulations. This makes it possible to suggest an appropriate input method by referring to relevant laws and guidelines.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The application acquisition follow-up system can also be equipped with a history reference unit that references the user's past application history and automatically suggests similar application forms. For example, if a user has previously applied for a copy of their resident registration card, the system will preferentially suggest a similar application form for the next application. It can also suggest new related application forms based on the content of past applications. This allows users to utilize their past application history to complete application procedures more quickly and efficiently.

[0044] The application acquisition follow-up system can also be equipped with a content analysis unit that analyzes the user's input and automatically suggests related application forms based on the input. For example, if a user inputs "moving," the system will suggest related application forms such as a moving-out notification, a moving-in notification, and a copy of the resident registration card all at once. Also, if a user inputs "childcare support," the system will suggest an application form for childcare support grants and a nursery school application, etc. This allows the user to obtain multiple related application forms with a single input.

[0045] The application form acquisition follow-up system may further include a geographic information unit that acquires the user's geographic information and preferentially suggests application forms specific to the region. For example, if the user lives in Tokyo, application forms for Tokyo will be preferentially suggested. Also, if the user plans to move, application forms related to the new address will be suggested. This allows the user to quickly acquire application forms specific to the region.

[0046] The application acquisition follow-up system can also be equipped with a success rate reference unit that references the user's past application results and prioritizes suggesting applications with a high success rate. For example, it can prioritize suggesting applications that many users have successfully submitted in the past, and postpone applications with a low success rate. It can also provide advice for success based on past application results. This makes it easier for users to select applications with a high success rate.

[0047] The application acquisition and follow-up system can also be equipped with a law suggestion unit that analyzes the user's input and automatically suggests relevant laws and guidelines based on the input. For example, if the user inputs "copy of resident registration," the system will suggest laws and guidelines related to resident registration. Also, if the user inputs "childcare support grant," the system will suggest laws and guidelines related to childcare support. This allows the user to easily refer to relevant laws and guidelines.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The application form acquisition unit acquires the appropriate application form from the relevant local government website based on the content of the user's application. For example, if the user inputs "I would like to obtain a copy of my resident registration card in Tokyo," the generation AI downloads the application form for a copy of the resident registration card from the official Tokyo Metropolitan Government website. Similarly, if the user inputs "I would like to apply for child care support grants," the generation AI downloads the application form for child care support grants from the relevant local government website. Step 2: The application form input support unit supports the user when entering the required information into the application form acquired by the application form acquisition unit. For example, if the user does not know what to fill in a specific field on the application form, the generation AI will suggest the appropriate input method for that field. Also, if the user provides basic information such as name and address, the generation AI will automatically enter that information into the corresponding fields on the application form. Furthermore, the generation AI will provide instructions on how to submit the application online and address information for submitting by mail.

[0050] (Example 2) The application form acquisition and follow-up system according to an embodiment of the present invention is a system that acquires an appropriate application form from the local government website based on the content of the application that the user wishes to apply for, and the generation AI follows up when the user enters the necessary information. This simplifies the application procedure and reduces the burden on the user.

[0051] An application acquisition and follow-up system according to an embodiment includes an application acquisition unit and an application input follow-up unit. The application acquisition unit acquires an appropriate application form from the website of the relevant local government based on the content of the user's application. For example, if a user inputs "I would like to obtain a copy of my resident registration card in Tokyo," the generation AI downloads an application form for a copy of the resident registration card from the official website of Tokyo. Similarly, if a user inputs "I would like to apply for child care support grants," the generation AI downloads an application form for child care support grants from the website of the relevant local government. The application input follow-up unit assists the user in entering the necessary information into the application form acquired by the application acquisition unit. For example, if the user is unsure of what to fill in a specific field on the application form, the generation AI suggests an appropriate input method for that field. Furthermore, if the user provides basic information such as name and address, the generation AI automatically enters that information into the appropriate fields on the application form. Furthermore, the generation AI provides instructions on how to submit the application online and address information for submitting by mail. This allows the application acquisition and follow-up system according to an embodiment to simplify the application process and reduce the burden on users.

[0052] The application form acquisition unit can refer to past application data and prioritize the acquisition of the most frequently used application forms. For example, the generation AI analyzes the past application database to identify the application forms most frequently used in a particular local government. For example, it prioritizes the acquisition of application forms such as copies of resident registration certificates and moving-out notifications. This allows the most frequently used application forms to be prioritized by referring to past application data.

[0053] The application form acquisition unit monitors updates on the local government website in real time, making it possible to always obtain the latest application forms. For example, the generation AI monitors the RSS feed and update notifications on the local government website, and automatically acquires the latest application forms when they are published. For example, if a new format of application form is published, it is immediately acquired. This makes it possible to always obtain the latest application forms by monitoring updates on the local government website in real time.

[0054] The application form acquisition unit estimates the user's stress level, and if stress is high, it can prioritize acquiring simple application forms. For example, the generation AI of the application form acquisition unit analyzes the user's facial expression and voice when entering data to estimate the stress level. For example, if the user is feeling anxious, it prioritizes acquiring simple application forms. This makes it possible to estimate the user's stress level, and if stress is high, it prioritizes acquiring simple application forms.

[0055] The application form acquisition unit can simultaneously acquire application forms from other local governments and provide comparative information to the user. For example, the generation AI acquires the same type of application form from the websites of multiple local governments and provides comparative information to the user. For example, application forms for copies of resident registration certificates from Tokyo and Osaka prefectures can be acquired simultaneously. This allows application forms from other local governments to be acquired simultaneously and comparative information to be provided to the user.

[0056] The application form acquisition unit can simultaneously acquire related laws and guidelines and provide them to the user. For example, when the generation AI acquires an application form, the application form acquisition unit simultaneously acquires related laws and guidelines and provides them to the user. For example, it acquires laws and guidelines related to applying for a copy of a resident registration card. This allows related laws and guidelines to be simultaneously acquired and provided to the user.

[0057] The application form acquisition unit can analyze the user's emotions and provide an interface for eliciting positive emotions. For example, the application form acquisition unit provides an interface for eliciting positive emotions by having the generation AI analyze the user's facial expressions and voice. For example, it displays a message that makes the user smile. This makes it possible to analyze the user's emotions and provide an interface for eliciting positive emotions.

[0058] When acquiring an application form based on the purpose of the application, the application form acquisition unit can refer to past success cases and prioritize acquisition of application forms with the highest success rate. For example, the application form acquisition unit uses a generation AI to analyze past application data and identify applications with a high success rate. For example, it prioritizes acquisition of application forms with many past success cases. In this way, by referring to past success cases, it is possible to prioritize acquisition of application forms with the highest success rate.

[0059] When acquiring an application form based on the purpose of application, the application form acquisition unit can simultaneously acquire information on related subsidies and grants and provide it to the user. For example, the generation AI simultaneously acquires information on subsidies and grants related to the purpose of application and provides it to the user. For example, it provides subsidy information related to an application for child care support. This allows information on related subsidies and grants to be simultaneously acquired and provided to the user.

[0060] The application form acquisition unit can analyze the user's emotions and make suggestions to bring out positive emotions. For example, the generation AI analyzes the user's facial expressions and voice and makes suggestions to bring out positive emotions. For example, it displays a message that helps the user relax. This makes it possible to analyze the user's emotions and make suggestions to bring out positive emotions.

[0061] When acquiring an application form based on the purpose of application, the application form acquisition unit can simultaneously acquire other related application forms and provide them to the user. For example, the generation AI simultaneously acquires other application forms related to the purpose of application and provides them to the user. For example, it acquires other support grant applications related to an application for child care support grants. This allows other related application forms to be simultaneously acquired and provided to the user.

[0062] When acquiring an application form based on the purpose of application, the application form acquisition unit can simultaneously acquire related FAQs and support information and provide them to the user. For example, the generation AI can simultaneously acquire FAQs and support information related to the purpose of application and provide them to the user. For example, it can provide frequently asked questions about applying for child care support grants. This allows related FAQs and support information to be simultaneously acquired and provided to the user.

[0063] The application form acquisition unit can analyze the user's emotions and provide an interface for eliciting positive emotions. For example, the application form acquisition unit provides an interface for eliciting positive emotions by having the generation AI analyze the user's facial expressions and voice. For example, it displays a message that makes the user smile. This makes it possible to analyze the user's emotions and provide an interface for eliciting positive emotions.

[0064] The application form input follow-up unit can refer to past input data and suggest the most common input method. For example, the application form input follow-up unit uses a generation AI to analyze past input data and identify the most common input method. For example, it makes suggestions based on methods that many users have used in the past. This makes it possible to suggest the most common input method by referring to past input data.

[0065] The application form input follow-up unit can detect input errors in real time and make correction suggestions. For example, the generation AI analyzes the user's input content in real time and detects input errors. For example, it detects typos and format inconsistencies. This makes it possible to detect input errors in real time and make correction suggestions.

[0066] The application form input follow-up unit can estimate the user's stress level and suggest a simple input method if the user's stress level is high. For example, the generation AI analyzes the user's facial expressions and voice to estimate the stress level. For example, if the user is feeling anxious, a simple input method will be suggested. This makes it possible to estimate the user's stress level and suggest a simple input method if the user's stress level is high.

[0067] The application form input follow-up unit can refer to input examples from other users and suggest the most appropriate input method. For example, the application form input follow-up unit uses a generation AI to analyze input examples from other users and identify the most appropriate input method. For example, it makes suggestions based on methods that many users have used in the past. This allows the most appropriate input method to be suggested by referring to input examples from other users.

[0068] The application form input follow-up unit can refer to relevant laws and guidelines and suggest an appropriate input method. For example, the generation AI analyzes relevant laws and guidelines and identifies an appropriate input method. For example, it suggests a correct input method based on laws and regulations. This makes it possible to suggest an appropriate input method by referring to relevant laws and guidelines.

[0069] The application form input follow-up unit can analyze the user's emotions and provide an interface for eliciting positive emotions. For example, the application form input follow-up unit uses a generation AI to analyze the user's facial expressions and voice and provide an interface for eliciting positive emotions. For example, it can display a message that makes the user smile. This makes it possible to analyze the user's emotions and provide an interface for eliciting positive emotions.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The application acquisition follow-up system can also be equipped with a history reference unit that references the user's past application history and automatically suggests similar application forms. For example, if a user has previously applied for a copy of their resident registration card, the system will preferentially suggest a similar application form for the next application. It can also suggest new related application forms based on the content of past applications. This allows users to utilize their past application history to complete application procedures more quickly and efficiently.

[0072] The application form acquisition follow-up system may further include an emotion adjustment unit that estimates the user's emotion and adjusts the order in which application forms are acquired based on the estimated emotion. For example, if the user is feeling stressed, simple application forms are prioritized, and if the user is relaxed, detailed application forms are acquired. Also, if the user is in a hurry, application forms that can be acquired quickly are prioritized. This makes it possible to acquire optimal application forms according to the user's emotion.

[0073] The application acquisition follow-up system can also be equipped with a content analysis unit that analyzes the user's input and automatically suggests related application forms based on the input. For example, if a user inputs "moving," the system will suggest related application forms such as a moving-out notification, a moving-in notification, and a copy of the resident registration card all at once. Also, if a user inputs "childcare support," the system will suggest an application form for childcare support grants and a nursery school application, etc. This allows the user to obtain multiple related application forms with a single input.

[0074] The application form acquisition follow-up system may further include an emotion input support unit that estimates the user's emotions and provides support for inputting the application form based on the estimated emotions. For example, if the user is feeling anxious, detailed input guides may be provided, and if the user is feeling confident, simplified guides may be provided. In addition, if the user is tired, the function for automating input may be enhanced. This enables optimal input support according to the user's emotions.

[0075] The application form acquisition follow-up system may further include a geographic information unit that acquires the user's geographic information and preferentially suggests application forms specific to the region. For example, if the user lives in Tokyo, application forms for Tokyo will be preferentially suggested. Also, if the user plans to move, application forms related to the new address will be suggested. This allows the user to quickly acquire application forms specific to the region.

[0076] The application acquisition follow-up system may further include an emotion submission support unit that estimates the user's emotion and suggests a method for submitting the application based on the estimated emotion. For example, if the user is feeling stressed, the system may preferentially suggest an online submission method, and if the user is relaxed, the system may suggest a submission method by mail. Also, if the user is in a hurry, the system may suggest the quickest submission method. This allows the system to provide the optimal submission method according to the user's emotion.

[0077] The application acquisition follow-up system can also be equipped with a success rate reference unit that references the user's past application results and prioritizes suggesting applications with a high success rate. For example, it can prioritize suggesting applications that many users have successfully submitted in the past, and postpone applications with a low success rate. It can also provide advice for success based on past application results. This makes it easier for users to select applications with a high success rate.

[0078] The application acquisition follow-up system can also include an emotion deadline management unit that estimates the user's emotions and manages application submission deadlines based on the estimated emotions. For example, if the user is feeling anxious, priority is given to managing applications with approaching deadlines, while if the user is relaxed, applications with ample time to complete are managed. Furthermore, if the user is feeling stressed, frequent deadline reminders are sent. This allows for optimal submission deadline management based on the user's emotions.

[0079] The application acquisition and follow-up system can also be equipped with a law suggestion unit that analyzes the user's input and automatically suggests relevant laws and guidelines based on the input. For example, if the user inputs "copy of resident registration," the system will suggest laws and guidelines related to resident registration. Also, if the user inputs "childcare support grant," the system will suggest laws and guidelines related to childcare support. This allows the user to easily refer to relevant laws and guidelines.

[0080] The application form acquisition follow-up system may further include an emotion format adjustment unit that estimates the user's emotion and adjusts the application form format based on the estimated emotion. For example, if the user is feeling stressed, a simplified format may be provided, and if the user is relaxed, a detailed format may be provided. Also, if the user is feeling impatient, adjustments may be made, such as reducing the number of input items. In this way, the optimal application form format is provided according to the user's emotion.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The application form acquisition unit acquires the appropriate application form from the relevant local government website based on the content of the user's application. For example, if the user inputs "I would like to obtain a copy of my resident registration card in Tokyo," the generation AI downloads the application form for a copy of the resident registration card from the official Tokyo Metropolitan Government website. Similarly, if the user inputs "I would like to apply for child care support grants," the generation AI downloads the application form for child care support grants from the relevant local government website. Step 2: The application form input support unit supports the user when entering the required information into the application form acquired by the application form acquisition unit. For example, if the user does not know what to fill in a specific field on the application form, the generation AI will suggest the appropriate input method for that field. Also, if the user provides basic information such as name and address, the generation AI will automatically enter that information into the corresponding fields on the application form. Furthermore, the generation AI will provide instructions on how to submit the application online and address information for submitting by mail.

[0083] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] 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.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] 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.

[0095] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] 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.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0109] 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.

[0110] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] 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.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0124] 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.

[0125] 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.

[0126] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0133] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0134] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0135] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0138] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0139] 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.

[0140] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0141] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0142] 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.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0145] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0146] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0147] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 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. an application form acquisition unit that acquires an appropriate application form from the website of the relevant local government based on the content of the application that the user wishes to apply for; an application form input follow-up unit that follows up when a user inputs necessary information into the application form acquired by the application form acquisition unit. A system characterized by:

2. The application form acquisition unit Monitor the local government's website for updates in real time to keep up to date with the latest application forms.

2. The system of claim 1.

3. The application form acquisition unit Application forms from other local governments are also obtained at the same time, and comparative information is provided to the user.

2. The system of claim 1.

4. The application form input follow-up unit Refers to past input data and suggests the most common input methods 2. The system of claim 1.

5. The application form acquisition unit The stress level of the user is estimated, and if the stress level is high, simple application forms are preferentially acquired.

2. The system of claim 1.

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