system

Abstract

The system according to the embodiment aims to simplify administrative procedures and improve user convenience. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives information from a user. The analysis unit analyzes the information received by the reception unit. The generation unit generates necessary documents or procedures based on the information analyzed by the analysis unit. The provision unit provides the user with the documents or procedures generated by the generation unit.

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 the problem that administrative procedures such as pension application and tax payment procedures are complicated and time-consuming for users.

[0005] The system according to the embodiment aims to simplify administrative procedures and improve user convenience. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives information from a user. The analysis unit analyzes the information received by the reception unit. The generation unit generates necessary documents or procedures based on the information analyzed by the analysis unit. The provision unit provides the user with the documents or procedures generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can simplify administrative procedures and improve user convenience. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The administrative service support system according to an embodiment of the present invention provides a one-stop service that simplifies pension receipt, hometown tax payment, tax return filing, and payment procedures, such as property tax and automobile tax, by linking these procedures with an electronic payment system. This system supports the creation of necessary documents and procedures and operates as a front-desk administrative service support service in cooperation with public institutions such as the national and local governments. For example, a user inputs information for pension receipt and tax payment procedures. The electronic payment system then analyzes the input information and automatically generates the necessary documents and procedures. The generated documents and procedures are provided to the user, who can use them to receive and pay pensions. This service simplifies pension receipt and tax payment procedures and reduces the burden on the user. Furthermore, by linking with public institutions such as the national and local governments, the efficiency of administrative services can be improved. For example, for pension receipt procedures, the system automatically generates the necessary documents, allowing users to easily submit them. For hometown tax payment procedures, the system easily selects donation recipients and makes donation payments. For tax return filing, the system automatically generates and submits tax returns by simply entering the required information. Even when paying property tax or automobile tax, users can simply enter the necessary information and a payment slip will be automatically generated, allowing them to make the payment. In this way, by linking with the electronic payment system, a one-stop service can be provided that simplifies the procedures for receiving pensions and paying taxes, reducing the burden on users. In this way, the administrative service support system can simplify the procedures for receiving pensions and paying taxes, reducing the burden on users.

[0029] The administrative service support system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives information from a user. The information from the user includes, but is not limited to, information regarding pension benefits, hometown tax donations, tax returns, and property tax and automobile tax payments. The reception unit receives information, for example, through a web form. The reception unit can also receive information through a smartphone app. The reception unit can also receive information through voice input. For example, the reception unit converts information input by the user's voice into text data using voice recognition technology and accepts the text data. The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the information using, for example, data mining technology. The analysis unit can also analyze the information using statistical analysis technology. The analysis unit can also analyze the information using a machine learning algorithm. For example, the analysis unit identifies necessary documents and procedures based on the information input by the user. The generation unit generates necessary documents and procedures based on the information analyzed by the analysis unit. The generation unit, for example, automatically generates an application form. The generation unit can also automatically generate a report. Furthermore, the generation unit can automatically generate an electronic procedure. For example, the generation unit automatically generates necessary documents and procedures based on information input by a user. The provision unit provides the documents and procedures generated by the generation unit to the user. For example, the provision unit sends the generated documents to the user by email. The provision unit can also provide the generated documents to the user through a web portal. Furthermore, the provision unit can print and mail the generated documents. For example, the provision unit provides the generated documents and procedures in a manner desired by the user. As a result, the administrative service support system according to the embodiment can efficiently accept and analyze user information, and generate and provide necessary documents and procedures.

[0030] The reception unit can receive information for receiving a pension or paying taxes. The reception unit, for example, receives information related to receiving a pension. For example, a user can input information necessary for receiving a pension. The reception unit can also receive information related to hometown tax donations. For example, a user can input information related to selecting a donation recipient or paying a donation. The reception unit can also receive information related to tax returns. For example, the user can receive information for automatically generating a tax return simply by inputting the necessary information. The reception unit can also receive information related to payment of fixed asset tax and automobile tax. For example, a user can input information necessary for payment procedures. This allows the reception unit to efficiently receive information necessary for receiving a pension or paying taxes.

[0031] The analysis unit can analyze the information received by the reception unit. The analysis unit analyzes the information using, for example, data mining technology. For example, the analysis unit identifies necessary documents and procedures based on the information input by the user. The analysis unit can also analyze the information using statistical analysis technology. For example, the analysis unit builds a statistical model for identifying necessary documents and procedures based on the information input by the user. Furthermore, the analysis unit can analyze the information using a machine learning algorithm. For example, the analysis unit builds a machine learning model for identifying necessary documents and procedures based on the information input by the user. This allows the analysis unit to efficiently analyze the received information.

[0032] The generation unit can generate necessary documents and procedures based on the information analyzed by the analysis unit. The generation unit, for example, automatically generates an application form. For example, the generation unit automatically generates an application form required for pension application procedures based on information input by a user. The generation unit can also automatically generate reports. For example, the generation unit automatically generates a report on hometown tax donations based on information input by a user. Furthermore, the generation unit can also automatically generate electronic procedures. For example, the generation unit automatically generates electronic procedures required for tax returns based on information input by a user. This allows the generation unit to efficiently generate necessary documents and procedures.

[0033] The providing unit can provide the documents and procedures generated by the generating unit to the user. For example, the providing unit sends the generated documents to the user by email. For example, the providing unit sends the generated documents to an email address desired by the user. The providing unit can also provide the generated documents to the user through a web portal. For example, the providing unit uploads the generated documents to a web portal to which the user has logged in, allowing the user to download them. Furthermore, the providing unit can print and mail the generated documents. For example, the providing unit mails the generated documents to an address desired by the user. This allows the providing unit to efficiently provide the generated documents and procedures to the user.

[0034] The providing unit can provide support for the user to receive or pay benefits. For example, the providing unit provides support for the user when applying for a pension. For example, the providing unit guides the user through the procedures for submitting necessary documents. The providing unit can also provide support for the user when making hometown tax donations. For example, the providing unit guides the user through the procedures for selecting a donation recipient and making a donation. The providing unit can also provide support for the user when filing a tax return. For example, the providing unit guides the user through the procedures for submitting a tax return. The providing unit can also provide support for the user when paying fixed asset tax or automobile tax. For example, the providing unit guides the user through the procedures for submitting a payment slip and making payment. In this way, the providing unit can provide support for the user when receiving or paying benefits.

[0035] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit automatically displays information that the user has frequently input in the past as candidates. For example, the reception unit automatically displays input candidates based on information that the user has input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit can suggest the optimal input method based on input methods that the user has used in the past. The reception unit can also predict and suggest information that will be used in a specific time period based on the user's past input history. For example, the reception unit can suggest the optimal input method based on information that the user has input in a specific time period in the past. In this way, the reception unit can improve input efficiency by suggesting the optimal input method based on the user's past input history.

[0036] The reception unit can customize input items based on the user's current living situation and areas of interest when receiving information. For example, if the user is a pension recipient, the reception unit preferentially displays input items related to pensions. For example, the reception unit automatically displays required items when the user enters information about receiving a pension. Furthermore, if the user is interested in hometown tax donations, the reception unit can also add input items related to selecting a donation destination. For example, the reception unit automatically displays related information when the user selects a donation destination. Furthermore, the reception unit can automatically display related input items when the user is filing a tax return. For example, the reception unit automatically displays related items when the user enters information required for filing a tax return. In this way, the reception unit can improve input efficiency by providing input items according to the user's living situation and areas of interest.

[0037] When receiving information, the reception unit can prioritize receiving highly relevant information taking into account the user's geographical location information. For example, if the user lives in a specific area, the reception unit can prioritize displaying tax payment information related to that area. For example, the reception unit can prioritize displaying information on property tax and automobile tax related to the area where the user lives. In addition, if the user is traveling, the reception unit can prioritize displaying tax payment procedures based on the user's current location. For example, the reception unit can prioritize displaying tax payment procedures required while the user is traveling, allowing the user to respond quickly. In addition, if the user is planning to move, the reception unit can prioritize displaying information related to the user's new address. For example, the reception unit can prioritize displaying tax payment procedures related to the user's new address, allowing the user to complete the procedures smoothly. In this way, the reception unit can improve input efficiency by preferentially receiving highly relevant information based on the user's geographical location information.

[0038] The reception unit can analyze the user's social media activity when receiving information and accept related information. For example, if the user posts about pensions on social media, the reception unit can prioritize displaying pension-related input fields. For example, if the user posts about pensions on social media, the reception unit can prioritize displaying input fields related to pension receipt based on the information. Furthermore, if the user posts about hometown tax donations, the reception unit can also add input fields related to selecting a donation destination. For example, if the user posts about hometown tax donations on social media, the reception unit can add input fields related to selecting a donation destination based on the information. Furthermore, if the user posts about tax returns, the reception unit can automatically display related input fields. For example, if the user posts about tax returns on social media, the reception unit can automatically display related input fields based on the information. In this way, the reception unit can improve input efficiency by accepting related information based on the user's social media activity.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis of information regarding pension benefits to provide accurate results. For example, the analysis unit performs a detailed analysis based on information regarding pension benefits entered by a user to provide accurate results. The analysis unit can also analyze information regarding hometown tax donations with a focus on selecting a donation recipient. For example, the analysis unit analyzes information regarding hometown tax donations entered by a user with a focus on selecting a donation recipient. The analysis unit can also analyze information regarding tax returns with a focus on tax calculation. For example, the analysis unit analyzes information regarding tax returns entered by a user with a focus on tax calculation. In this way, the analysis unit can improve the efficiency of the analysis by adjusting the level of detail of the analysis based on the importance of the information.

[0040] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit analyzes information regarding pension benefits by applying a pension calculation algorithm. For example, the analysis unit analyzes information regarding pension benefits input by the user by applying a pension calculation algorithm. The analysis unit can also analyze information regarding hometown tax payments by applying a donation calculation algorithm. For example, the analysis unit analyzes information regarding hometown tax payments input by the user by applying a donation calculation algorithm. The analysis unit can also analyze information regarding final tax returns by applying a tax calculation algorithm. For example, the analysis unit analyzes information regarding final tax returns input by the user by applying a tax calculation algorithm. In this way, the analysis unit can improve the accuracy of the analysis by applying different analysis algorithms depending on the category of information.

[0041] During analysis, the analysis unit can determine the priority of analysis based on the time of information submission. For example, when the deadline for filing a tax return is approaching, the analysis unit prioritizes analysis of information related to the tax return. For example, the analysis unit prioritizes analysis of information with an upcoming submission deadline based on information related to the tax return entered by the user. The analysis unit can also prioritize analysis of information related to pensions when the deadline for applying for pension benefits is approaching. For example, the analysis unit prioritizes analysis of information with an upcoming application deadline based on information related to pension benefits entered by the user. The analysis unit can also prioritize analysis of information related to tax payment when the deadline for paying fixed asset tax is approaching. For example, the analysis unit prioritizes analysis of information with an upcoming payment deadline based on information related to fixed asset tax entered by the user. In this way, the analysis unit can improve the efficiency of analysis by determining the priority of analysis based on the time of information submission.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, if information regarding pension benefits and information regarding tax payments are related, the analysis unit prioritizes analysis of highly related information. For example, the analysis unit prioritizes analysis of highly related information based on the information regarding pension benefits and information regarding tax payments entered by the user. In addition, the analysis unit can also prioritize analysis of highly related information if information regarding hometown tax payments and information regarding tax returns are related. For example, the analysis unit prioritizes analysis of highly related information based on the information regarding hometown tax payments and information regarding tax returns entered by the user. In addition, the analysis unit can also prioritize analysis of highly related information if information regarding fixed asset tax and information regarding automobile tax are related. For example, the analysis unit prioritizes analysis of highly related information based on the information regarding fixed asset tax and information regarding automobile tax entered by the user. In this way, the analysis unit can improve analysis efficiency by adjusting the order of analysis based on the relevance of the information.

[0043] The generation unit can adjust the level of detail of documents and procedures to be generated based on the importance of the information during generation. For example, the generation unit generates detailed documents related to pension benefits and provides accurate information. For example, the generation unit generates detailed documents and provides accurate information based on information related to pension benefits input by the user. The generation unit can also generate documents related to hometown tax donations with an emphasis on selecting a donation recipient. For example, the generation unit generates documents that focus on selecting a donation recipient based on information related to hometown tax donations input by the user. The generation unit can also generate documents related to tax returns with an emphasis on tax calculation. For example, the generation unit generates documents that focus on tax calculation based on information related to tax returns input by the user. In this way, the generation unit can improve generation efficiency by adjusting the level of detail of documents and procedures to be generated based on the importance of the information.

[0044] The generation unit can apply different generation algorithms depending on the category of information during generation. For example, the generation unit generates documents related to pension benefits by applying a pension calculation algorithm. For example, the generation unit generates documents by applying a pension calculation algorithm based on information related to pension benefits input by a user. The generation unit can also generate documents related to hometown tax payments by applying a donation calculation algorithm. For example, the generation unit generates documents by applying a donation calculation algorithm based on information related to hometown tax payments input by a user. The generation unit can also generate documents related to tax returns by applying a tax calculation algorithm. For example, the generation unit generates documents by applying a tax calculation algorithm based on information related to tax returns input by a user. In this way, the generation unit can improve the accuracy of generation by applying different generation algorithms depending on the category of information.

[0045] At the time of generation, the generation unit can determine the priority of documents and procedures to be generated based on the timing of information submission. For example, when the deadline for filing a tax return is approaching, the generation unit prioritizes generating documents related to the tax return. For example, the generation unit prioritizes generating documents with upcoming submission deadlines based on information about the tax return entered by the user. Furthermore, when the deadline for applying for pension benefits is approaching, the generation unit can also prioritize generating documents related to pensions. For example, the generation unit prioritizes generating documents with upcoming application deadlines based on information about pension benefits entered by the user. Furthermore, when the deadline for paying fixed asset tax is approaching, the generation unit can also prioritize generating documents related to tax payment. For example, the generation unit prioritizes generating documents with upcoming payment deadlines based on information about fixed asset tax entered by the user. In this way, the generation unit can improve generation efficiency by determining the priority of documents and procedures to be generated based on the timing of information submission.

[0046] The generation unit can adjust the order of documents and procedures to be generated based on the relevance of information during generation. For example, when documents related to pension benefits and documents related to tax payments are related, the generation unit prioritizes generating highly related documents. For example, the generation unit prioritizes generating highly related documents based on information related to pension benefits and information related to tax payments input by the user. The generation unit can also prioritize generating highly related documents when documents related to hometown tax payments and documents related to tax returns are related. For example, the generation unit prioritizes generating highly related documents based on information related to hometown tax payments and information related to tax returns input by the user. The generation unit can also prioritize generating highly related documents when documents related to fixed asset tax and documents related to automobile tax are related. For example, the generation unit prioritizes generating highly related documents based on information related to fixed asset tax and information related to automobile tax input by the user. In this way, the generation unit can improve generation efficiency by adjusting the order of documents and procedures to be generated based on the relevance of information.

[0047] When providing the display method, the providing unit can select the optimal display method by referring to the user's past operation history. For example, the providing unit preferentially provides the display method that the user has used in the past. For example, the providing unit selects the optimal display method based on the display method that the user has used in the past. The providing unit can also automatically apply a display layout that the user has previously preferred. For example, the providing unit selects the optimal display method based on the display layout that the user has previously preferred. The providing unit can also suggest the most efficient display method based on the user's past operation history. For example, the providing unit suggests the most efficient display method based on the user's past operation history. In this way, the providing unit can improve display efficiency by selecting the optimal display method based on the user's past operation history.

[0048] The providing unit can customize the display method of documents and procedures to be provided based on the user's current living situation when providing the information. For example, if the user is a pension recipient, the providing unit prioritizes displaying information related to pensions. For example, the providing unit automatically displays required items when the user enters information about receiving a pension. Furthermore, if the user is interested in hometown tax donations, the providing unit can also add information regarding selection of a donation destination. For example, the providing unit automatically displays related information when the user selects a donation destination. Furthermore, the providing unit can automatically display related information when the user is filing a tax return. For example, the providing unit automatically displays related items when the user enters information required for filing a tax return. In this way, the providing unit can improve input efficiency by providing input items according to the user's living situation and areas of interest.

[0049] The providing unit can select the optimal display method by taking into consideration the user's geographical location information when providing the information. For example, if the user lives in a specific area, the providing unit can prioritize displaying information related to that area. For example, the providing unit can prioritize displaying information on property tax and automobile tax related to the area where the user lives. In addition, if the user is traveling, the providing unit can prioritize displaying information based on the user's current location. For example, the providing unit can prioritize displaying tax payment procedures required by the user while traveling, allowing the user to respond quickly. In addition, if the user is planning to move, the providing unit can prioritize displaying information related to the user's new address. For example, the providing unit can prioritize displaying tax payment procedures related to the user's new address, allowing the user to complete the procedures smoothly. In this way, the providing unit can improve display efficiency by selecting the optimal display method based on the user's geographical location information.

[0050] When providing the information, the providing unit can analyze the user's social media activity and adjust the display method of the documents and procedures to be provided. For example, if the user posts about pensions on social media, the providing unit prioritizes displaying pension-related information. For example, if the user posts about pensions on social media, the providing unit prioritizes displaying information about receiving pensions based on that information. In addition, if the user posts about hometown tax donations, the providing unit can add information about selecting a donation destination. For example, if the user posts about hometown tax donations on social media, the providing unit adds information about selecting a donation destination based on that information. In addition, the providing unit can automatically display related information if the user posts about filing a tax return. For example, if the user posts about filing a tax return on social media, the providing unit automatically displays related information based on that information. In this way, the providing unit can improve display efficiency by adjusting the display method of the documents and procedures to be provided based on the user's social media activity.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit automatically displays information that the user has frequently input in the past as candidates. For example, the reception unit automatically displays input candidates based on information that the user has input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit can suggest the optimal input method based on input methods that the user has used in the past. The reception unit can also predict and suggest information that will be used in a specific time period based on the user's past input history. For example, the reception unit can suggest the optimal input method based on information that the user has input in a specific time period in the past. In this way, the reception unit can improve input efficiency by suggesting the optimal input method based on the user's past input history.

[0053] The generation unit can adjust the level of detail of documents and procedures to be generated based on the importance of the information during generation. For example, documents related to pension benefits are generated in detail to provide accurate information. For example, the generation unit generates detailed documents and provides accurate information based on information related to pension benefits input by the user. The generation unit can also generate documents related to hometown tax donations with an emphasis on selecting a donation recipient. For example, the generation unit generates documents that focus on selecting a donation recipient based on information related to hometown tax donations input by the user. The generation unit can also generate documents related to tax returns with an emphasis on tax calculation. For example, the generation unit generates documents that focus on tax calculation based on information related to tax returns input by the user. In this way, the generation unit can improve generation efficiency by adjusting the level of detail of documents and procedures to be generated based on the importance of the information.

[0054] When receiving information, the reception unit can prioritize receiving highly relevant information taking into account the user's geographical location information. For example, if the user lives in a specific area, the reception unit can prioritize displaying tax payment information related to that area. For example, the reception unit can prioritize displaying information on property tax and automobile tax related to the area where the user lives. In addition, if the user is traveling, the reception unit can prioritize displaying tax payment procedures based on the user's current location. For example, the reception unit can prioritize displaying tax payment procedures required while the user is traveling, allowing the user to respond quickly. In addition, if the user is planning to move, the reception unit can prioritize displaying information related to the user's new address. For example, the reception unit can prioritize displaying tax payment procedures related to the user's new address, allowing the user to complete the procedures smoothly. In this way, the reception unit can improve input efficiency by preferentially receiving highly relevant information based on the user's geographical location information.

[0055] During analysis, the analysis unit can determine the priority of analysis based on the time of information submission. For example, if the deadline for filing a tax return is approaching, the analysis unit prioritizes analysis of information related to the tax return. For example, the analysis unit prioritizes analysis of information with an upcoming submission deadline based on information related to the tax return entered by the user. In addition, if the deadline for applying for pension benefits is approaching, the analysis unit can also prioritize analysis of information related to pensions. For example, the analysis unit prioritizes analysis of information with an upcoming application deadline based on information related to pension benefits entered by the user. In addition, if the deadline for paying fixed asset tax is approaching, the analysis unit can also prioritize analysis of information related to tax payment. For example, the analysis unit prioritizes analysis of information with an upcoming payment deadline based on information related to fixed asset tax entered by the user. In this way, the analysis unit can improve the efficiency of analysis by determining the priority of analysis based on the time of information submission.

[0056] When providing the display method, the providing unit can select the optimal display method by referring to the user's past operation history. For example, the providing unit preferentially provides a display method that the user has used in the past. For example, the providing unit selects the optimal display method based on a display method that the user has used in the past. The providing unit can also automatically apply a display layout that the user has previously preferred. For example, the providing unit selects the optimal display method based on a display layout that the user has previously preferred. The providing unit can also suggest the most efficient display method based on the user's past operation history. For example, the providing unit suggests the most efficient display method based on the user's past operation history. In this way, the providing unit can improve display efficiency by selecting the optimal display method based on the user's past operation history.

[0057] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, information regarding pension benefits is analyzed by applying a pension calculation algorithm. For example, the analysis unit analyzes by applying a pension calculation algorithm based on information regarding pension benefits input by the user. The analysis unit can also analyze information regarding hometown tax payments by applying a donation calculation algorithm. For example, the analysis unit analyzes by applying a donation calculation algorithm based on information regarding hometown tax payments input by the user. The analysis unit can also analyze information regarding tax returns by applying a tax calculation algorithm. For example, the analysis unit analyzes by applying a tax calculation algorithm based on information regarding tax returns input by the user. In this way, the analysis unit can improve the accuracy of the analysis by applying different analysis algorithms depending on the category of information.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The reception unit accepts information from the user. This information includes information about receiving pension benefits, hometown tax donations, tax returns, and property tax and automobile tax payments. The reception unit can accept information via a web form, a smartphone app, or voice input. In the case of voice input, the information is converted into text data using voice recognition technology. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the information using data mining technology, statistical analysis technology, and machine learning algorithms to identify the necessary documents and procedures. Step 3: The generation unit generates the necessary documents and procedures based on the information analyzed by the analysis unit. The generation unit can automatically generate applications, reports, and electronic procedures. Step 4: The providing unit provides the documents and procedures generated by the generating unit to the user. The providing unit can provide the documents and procedures by email, a web portal, or by printing and mailing.

[0060] (Example 2) The administrative service support system according to an embodiment of the present invention provides a one-stop service that simplifies pension receipt, hometown tax payment, tax return filing, and payment procedures, such as property tax and automobile tax, by linking these procedures with an electronic payment system. This system supports the creation of necessary documents and procedures and operates as a front-desk administrative service support service in cooperation with public institutions such as the national and local governments. For example, a user inputs information for pension receipt and tax payment procedures. The electronic payment system then analyzes the input information and automatically generates the necessary documents and procedures. The generated documents and procedures are provided to the user, who can use them to receive and pay pensions. This service simplifies pension receipt and tax payment procedures and reduces the burden on the user. Furthermore, by linking with public institutions such as the national and local governments, the efficiency of administrative services can be improved. For example, for pension receipt procedures, the system automatically generates the necessary documents, allowing users to easily submit them. For hometown tax payment procedures, the system easily selects donation recipients and makes donation payments. For tax return filing, the system automatically generates and submits tax returns by simply entering the required information. Even when paying property tax or automobile tax, users can simply enter the necessary information and a payment slip will be automatically generated, allowing them to make the payment. In this way, by linking with the electronic payment system, a one-stop service can be provided that simplifies the procedures for receiving pensions and paying taxes, reducing the burden on users. In this way, the administrative service support system can simplify the procedures for receiving pensions and paying taxes, reducing the burden on users.

[0061] The administrative service support system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives information from a user. The information from the user includes, but is not limited to, information regarding pension benefits, hometown tax donations, tax returns, and property tax and automobile tax payments. The reception unit receives information, for example, through a web form. The reception unit can also receive information through a smartphone app. The reception unit can also receive information through voice input. For example, the reception unit converts information input by the user's voice into text data using voice recognition technology and accepts the text data. The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the information using, for example, data mining technology. The analysis unit can also analyze the information using statistical analysis technology. The analysis unit can also analyze the information using a machine learning algorithm. For example, the analysis unit identifies necessary documents and procedures based on the information input by the user. The generation unit generates necessary documents and procedures based on the information analyzed by the analysis unit. The generation unit, for example, automatically generates an application form. The generation unit can also automatically generate a report. Furthermore, the generation unit can automatically generate an electronic procedure. For example, the generation unit automatically generates necessary documents and procedures based on information input by a user. The provision unit provides the documents and procedures generated by the generation unit to the user. For example, the provision unit sends the generated documents to the user by email. The provision unit can also provide the generated documents to the user through a web portal. Furthermore, the provision unit can print and mail the generated documents. For example, the provision unit provides the generated documents and procedures in a manner desired by the user. As a result, the administrative service support system according to the embodiment can efficiently accept and analyze user information, and generate and provide necessary documents and procedures.

[0062] The reception unit can receive information for receiving a pension or paying taxes. The reception unit, for example, receives information related to receiving a pension. For example, a user can input information necessary for receiving a pension. The reception unit can also receive information related to hometown tax donations. For example, a user can input information related to selecting a donation recipient or paying a donation. The reception unit can also receive information related to tax returns. For example, the user can receive information for automatically generating a tax return simply by inputting the necessary information. The reception unit can also receive information related to payment of fixed asset tax and automobile tax. For example, a user can input information necessary for payment procedures. This allows the reception unit to efficiently receive information necessary for receiving a pension or paying taxes.

[0063] The analysis unit can analyze the information received by the reception unit. The analysis unit analyzes the information using, for example, data mining technology. For example, the analysis unit identifies necessary documents and procedures based on the information input by the user. The analysis unit can also analyze the information using statistical analysis technology. For example, the analysis unit builds a statistical model for identifying necessary documents and procedures based on the information input by the user. Furthermore, the analysis unit can analyze the information using a machine learning algorithm. For example, the analysis unit builds a machine learning model for identifying necessary documents and procedures based on the information input by the user. This allows the analysis unit to efficiently analyze the received information.

[0064] The generation unit can generate necessary documents and procedures based on the information analyzed by the analysis unit. The generation unit, for example, automatically generates an application form. For example, the generation unit automatically generates an application form required for pension application procedures based on information input by a user. The generation unit can also automatically generate reports. For example, the generation unit automatically generates a report on hometown tax donations based on information input by a user. Furthermore, the generation unit can also automatically generate electronic procedures. For example, the generation unit automatically generates electronic procedures required for tax returns based on information input by a user. This allows the generation unit to efficiently generate necessary documents and procedures.

[0065] The providing unit can provide the documents and procedures generated by the generating unit to the user. For example, the providing unit sends the generated documents to the user by email. For example, the providing unit sends the generated documents to an email address desired by the user. The providing unit can also provide the generated documents to the user through a web portal. For example, the providing unit uploads the generated documents to a web portal to which the user has logged in, allowing the user to download them. Furthermore, the providing unit can print and mail the generated documents. For example, the providing unit mails the generated documents to an address desired by the user. This allows the providing unit to efficiently provide the generated documents and procedures to the user.

[0066] The providing unit can provide support for the user to receive or pay benefits. For example, the providing unit provides support for the user when applying for a pension. For example, the providing unit guides the user through the procedures for submitting necessary documents. The providing unit can also provide support for the user when making hometown tax donations. For example, the providing unit guides the user through the procedures for selecting a donation recipient and making a donation. The providing unit can also provide support for the user when filing a tax return. For example, the providing unit guides the user through the procedures for submitting a tax return. The providing unit can also provide support for the user when paying fixed asset tax or automobile tax. For example, the providing unit guides the user through the procedures for submitting a payment slip and making payment. In this way, the providing unit can provide support for the user when receiving or paying benefits.

[0067] The reception unit can estimate the user's emotions and adjust the information input interface based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit provides a simple interface and minimizes input procedures. For example, the reception unit reduces the number of items the user needs to input and allows the user to input only the minimum amount of information necessary. Furthermore, when the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. For example, the reception unit increases the number of items the user needs to input and allows the user to input more detailed information. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input to enable the user to input information quickly. For example, the reception unit can convert information input by voice using voice recognition technology to text data, thereby simplifying the input procedures. In this way, the reception unit can reduce the burden on the user by adjusting the information input interface according to the user's emotions.

[0068] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit automatically displays information that the user has frequently input in the past as candidates. For example, the reception unit automatically displays input candidates based on information that the user has input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit can suggest the optimal input method based on input methods that the user has used in the past. The reception unit can also predict and suggest information that will be used in a specific time period based on the user's past input history. For example, the reception unit can suggest the optimal input method based on information that the user has input in a specific time period in the past. In this way, the reception unit can improve input efficiency by suggesting the optimal input method based on the user's past input history.

[0069] The reception unit can customize input items based on the user's current living situation and areas of interest when receiving information. For example, if the user is a pension recipient, the reception unit preferentially displays input items related to pensions. For example, the reception unit automatically displays required items when the user enters information about receiving a pension. Furthermore, if the user is interested in hometown tax donations, the reception unit can also add input items related to selecting a donation destination. For example, the reception unit automatically displays related information when the user selects a donation destination. Furthermore, the reception unit can automatically display related input items when the user is filing a tax return. For example, the reception unit automatically displays related items when the user enters information required for filing a tax return. In this way, the reception unit can improve input efficiency by providing input items according to the user's living situation and areas of interest.

[0070] The reception unit can estimate the user's emotions and determine the priority of input based on the estimated user's emotions. For example, when the user is feeling stressed, the reception unit prioritizes displaying the most important input items. For example, the reception unit prioritizes displaying the most important information that the user needs to input, and postpones other items. Furthermore, when the user is relaxed, the reception unit can sequentially display detailed input items. For example, the reception unit sequentially displays detailed information that the user needs to input, thereby reducing the effort required for input. Furthermore, when the user is in a hurry, the reception unit can prioritize displaying items that can be input most quickly. For example, the reception unit prioritizes displaying information that the user needs to input quickly, thereby improving input efficiency. In this way, the reception unit can improve input efficiency by determining the priority of input according to the user's emotions.

[0071] When receiving information, the reception unit can prioritize receiving highly relevant information taking into account the user's geographical location information. For example, if the user lives in a specific area, the reception unit can prioritize displaying tax payment information related to that area. For example, the reception unit can prioritize displaying information on property tax and automobile tax related to the area where the user lives. In addition, if the user is traveling, the reception unit can prioritize displaying tax payment procedures based on the user's current location. For example, the reception unit can prioritize displaying tax payment procedures required while the user is traveling, allowing the user to respond quickly. In addition, if the user is planning to move, the reception unit can prioritize displaying information related to the user's new address. For example, the reception unit can prioritize displaying tax payment procedures related to the user's new address, allowing the user to complete the procedures smoothly. In this way, the reception unit can improve input efficiency by preferentially receiving highly relevant information based on the user's geographical location information.

[0072] The reception unit can analyze the user's social media activity when receiving information and accept related information. For example, if the user posts about pensions on social media, the reception unit can prioritize displaying pension-related input fields. For example, if the user posts about pensions on social media, the reception unit can prioritize displaying input fields related to pension receipt based on the information. Furthermore, if the user posts about hometown tax donations, the reception unit can also add input fields related to selecting a donation destination. For example, if the user posts about hometown tax donations on social media, the reception unit can add input fields related to selecting a donation destination based on the information. Furthermore, if the user posts about tax returns, the reception unit can automatically display related input fields. For example, if the user posts about tax returns on social media, the reception unit can automatically display related input fields based on the information. In this way, the reception unit can improve input efficiency by accepting related information based on the user's social media activity.

[0073] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can simplify the analysis algorithm and provide results quickly. For example, the analysis unit can simplify the algorithm for identifying necessary documents and procedures based on information input by the user and provide results quickly. Furthermore, if the user is relaxed, the analysis unit can perform a detailed analysis and provide highly accurate results. For example, the analysis unit can fine-tune the algorithm for identifying necessary documents and procedures based on information input by the user and provide results with high accuracy. Furthermore, if the user is in a hurry, the analysis unit can prioritize the analysis of the most important information. For example, the analysis unit can adjust the algorithm for identifying the most important documents and procedures based on information input by the user and provide results quickly. In this way, the analysis unit can improve the efficiency of the analysis by adjusting the analysis algorithm according to the user's emotions.

[0074] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis of information regarding pension benefits to provide accurate results. For example, the analysis unit performs a detailed analysis based on information regarding pension benefits entered by a user to provide accurate results. The analysis unit can also analyze information regarding hometown tax donations with a focus on selecting a donation recipient. For example, the analysis unit analyzes information regarding hometown tax donations entered by a user with a focus on selecting a donation recipient. The analysis unit can also analyze information regarding tax returns with a focus on tax calculation. For example, the analysis unit analyzes information regarding tax returns entered by a user with a focus on tax calculation. In this way, the analysis unit can improve the efficiency of the analysis by adjusting the level of detail of the analysis based on the importance of the information.

[0075] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit analyzes information regarding pension benefits by applying a pension calculation algorithm. For example, the analysis unit analyzes information regarding pension benefits input by the user by applying a pension calculation algorithm. The analysis unit can also analyze information regarding hometown tax payments by applying a donation calculation algorithm. For example, the analysis unit analyzes information regarding hometown tax payments input by the user by applying a donation calculation algorithm. The analysis unit can also analyze information regarding final tax returns by applying a tax calculation algorithm. For example, the analysis unit analyzes information regarding final tax returns input by the user by applying a tax calculation algorithm. In this way, the analysis unit can improve the accuracy of the analysis by applying different analysis algorithms depending on the category of information.

[0076] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, when the user is feeling stressed, the analysis unit prioritizes analysis of the most important information. For example, the analysis unit adjusts an algorithm to identify the most important documents and procedures based on the information input by the user and quickly provides results. The analysis unit can also perform a detailed analysis to provide highly accurate results when the user is relaxed. For example, the analysis unit can perform a detailed analysis based on the information input by the user and provide highly accurate results. The analysis unit can also perform a simplified analysis to quickly provide results when the user is in a hurry. For example, the analysis unit can perform a simplified analysis based on the information input by the user and quickly provide results. In this way, the analysis unit can improve analysis efficiency by determining the priority of analysis according to the user's emotions.

[0077] During analysis, the analysis unit can determine the priority of analysis based on the time of information submission. For example, when the deadline for filing a tax return is approaching, the analysis unit prioritizes analysis of information related to the tax return. For example, the analysis unit prioritizes analysis of information with an upcoming submission deadline based on information related to the tax return entered by the user. The analysis unit can also prioritize analysis of information related to pensions when the deadline for applying for pension benefits is approaching. For example, the analysis unit prioritizes analysis of information with an upcoming application deadline based on information related to pension benefits entered by the user. The analysis unit can also prioritize analysis of information related to tax payment when the deadline for paying fixed asset tax is approaching. For example, the analysis unit prioritizes analysis of information with an upcoming payment deadline based on information related to fixed asset tax entered by the user. In this way, the analysis unit can improve the efficiency of analysis by determining the priority of analysis based on the time of information submission.

[0078] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, if information regarding pension benefits and information regarding tax payments are related, the analysis unit prioritizes analysis of highly related information. For example, the analysis unit prioritizes analysis of highly related information based on the information regarding pension benefits and information regarding tax payments entered by the user. In addition, the analysis unit can also prioritize analysis of highly related information if information regarding hometown tax payments and information regarding tax returns are related. For example, the analysis unit prioritizes analysis of highly related information based on the information regarding hometown tax payments and information regarding tax returns entered by the user. In addition, the analysis unit can also prioritize analysis of highly related information if information regarding fixed asset tax and information regarding automobile tax are related. For example, the analysis unit prioritizes analysis of highly related information based on the information regarding fixed asset tax and information regarding automobile tax entered by the user. In this way, the analysis unit can improve analysis efficiency by adjusting the order of analysis based on the relevance of the information.

[0079] The generation unit can estimate the user's emotions and adjust the expression method of the generated documents and procedures based on the estimated user's emotions. For example, when the user is feeling stressed, the generation unit generates a simple and easy-to-understand document. For example, the generation unit generates a simple and easy-to-understand document based on information input by the user. The generation unit can also generate a document including detailed information when the user is relaxed. For example, the generation unit generates a document including detailed information based on information input by the user. The generation unit can also generate a document that can be processed quickly when the user is in a hurry. For example, the generation unit generates a document that can be processed quickly based on information input by the user. In this way, the generation unit can reduce the burden on the user by adjusting the expression method of the generated documents and procedures based on the user's emotions.

[0080] The generation unit can adjust the level of detail of documents and procedures to be generated based on the importance of the information during generation. For example, the generation unit generates detailed documents related to pension benefits and provides accurate information. For example, the generation unit generates detailed documents and provides accurate information based on information related to pension benefits input by the user. The generation unit can also generate documents related to hometown tax donations with an emphasis on selecting a donation recipient. For example, the generation unit generates documents that focus on selecting a donation recipient based on information related to hometown tax donations input by the user. The generation unit can also generate documents related to tax returns with an emphasis on tax calculation. For example, the generation unit generates documents that focus on tax calculation based on information related to tax returns input by the user. In this way, the generation unit can improve generation efficiency by adjusting the level of detail of documents and procedures to be generated based on the importance of the information.

[0081] The generation unit can apply different generation algorithms depending on the category of information during generation. For example, the generation unit generates documents related to pension benefits by applying a pension calculation algorithm. For example, the generation unit generates documents by applying a pension calculation algorithm based on information related to pension benefits input by a user. The generation unit can also generate documents related to hometown tax payments by applying a donation calculation algorithm. For example, the generation unit generates documents by applying a donation calculation algorithm based on information related to hometown tax payments input by a user. The generation unit can also generate documents related to tax returns by applying a tax calculation algorithm. For example, the generation unit generates documents by applying a tax calculation algorithm based on information related to tax returns input by a user. In this way, the generation unit can improve the accuracy of generation by applying different generation algorithms depending on the category of information.

[0082] The generation unit can estimate the user's emotions and determine the priority of documents and procedures to be generated based on the estimated user's emotions. For example, when the user is feeling stressed, the generation unit prioritizes generating the most important documents. For example, the generation unit prioritizes generating the most important documents based on information input by the user. The generation unit can also sequentially generate detailed documents when the user is relaxed. For example, the generation unit sequentially generates detailed documents based on information input by the user. The generation unit can also prioritize generating documents that can be processed quickly when the user is in a hurry. For example, the generation unit prioritizes generating documents that can be processed quickly based on information input by the user. In this way, the generation unit can improve generation efficiency by determining the priority of documents and procedures to be generated according to the user's emotions.

[0083] At the time of generation, the generation unit can determine the priority of documents and procedures to be generated based on the timing of information submission. For example, when the deadline for filing a tax return is approaching, the generation unit prioritizes generating documents related to the tax return. For example, the generation unit prioritizes generating documents with upcoming submission deadlines based on information about the tax return entered by the user. Furthermore, when the deadline for applying for pension benefits is approaching, the generation unit can also prioritize generating documents related to pensions. For example, the generation unit prioritizes generating documents with upcoming application deadlines based on information about pension benefits entered by the user. Furthermore, when the deadline for paying fixed asset tax is approaching, the generation unit can also prioritize generating documents related to tax payment. For example, the generation unit prioritizes generating documents with upcoming payment deadlines based on information about fixed asset tax entered by the user. In this way, the generation unit can improve generation efficiency by determining the priority of documents and procedures to be generated based on the timing of information submission.

[0084] The generation unit can adjust the order of documents and procedures to be generated based on the relevance of information during generation. For example, when documents related to pension benefits and documents related to tax payments are related, the generation unit prioritizes generating highly related documents. For example, the generation unit prioritizes generating highly related documents based on information related to pension benefits and information related to tax payments input by the user. The generation unit can also prioritize generating highly related documents when documents related to hometown tax payments and documents related to tax returns are related. For example, the generation unit prioritizes generating highly related documents based on information related to hometown tax payments and information related to tax returns input by the user. The generation unit can also prioritize generating highly related documents when documents related to fixed asset tax and documents related to automobile tax are related. For example, the generation unit prioritizes generating highly related documents based on information related to fixed asset tax and information related to automobile tax input by the user. In this way, the generation unit can improve generation efficiency by adjusting the order of documents and procedures to be generated based on the relevance of information.

[0085] The providing unit can estimate the user's emotions and adjust the display method of documents and procedures to be provided based on the estimated user's emotions. For example, when the user is feeling stressed, the providing unit provides a simple, highly visible display method. For example, the providing unit provides a simple, highly visible display method based on information input by the user. Furthermore, when the user is relaxed, the providing unit can also provide a display method including detailed information. For example, the providing unit provides a display method including detailed information based on information input by the user. Furthermore, when the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. For example, the providing unit provides a display method that focuses on the main points based on information input by the user. In this way, the providing unit can reduce the burden on the user by adjusting the display method of documents and procedures to be provided according to the user's emotions.

[0086] When providing the display method, the providing unit can select the optimal display method by referring to the user's past operation history. For example, the providing unit preferentially provides the display method that the user has used in the past. For example, the providing unit selects the optimal display method based on the display method that the user has used in the past. The providing unit can also automatically apply a display layout that the user has previously preferred. For example, the providing unit selects the optimal display method based on the display layout that the user has previously preferred. The providing unit can also suggest the most efficient display method based on the user's past operation history. For example, the providing unit suggests the most efficient display method based on the user's past operation history. In this way, the providing unit can improve display efficiency by selecting the optimal display method based on the user's past operation history.

[0087] The providing unit can customize the display method of documents and procedures to be provided based on the user's current living situation when providing the information. For example, if the user is a pension recipient, the providing unit prioritizes displaying information related to pensions. For example, the providing unit automatically displays required items when the user enters information about receiving a pension. Furthermore, if the user is interested in hometown tax donations, the providing unit can also add information regarding selection of a donation destination. For example, the providing unit automatically displays related information when the user selects a donation destination. Furthermore, the providing unit can automatically display related information when the user is filing a tax return. For example, the providing unit automatically displays related items when the user enters information required for filing a tax return. In this way, the providing unit can improve input efficiency by providing input items according to the user's living situation and areas of interest.

[0088] The providing unit can estimate the user's emotions and determine the priority of documents and procedures to be provided based on the estimated user's emotions. For example, when the user is feeling stressed, the providing unit prioritizes providing the most important documents. For example, the providing unit prioritizes providing the most important documents based on information input by the user. The providing unit can also sequentially provide detailed documents when the user is relaxed. For example, the providing unit sequentially provides detailed documents based on information input by the user. The providing unit can also prioritize providing documents that can be processed quickly when the user is in a hurry. For example, the providing unit prioritizes providing documents that can be processed quickly based on information input by the user. In this way, the providing unit can improve the efficiency of provision by determining the priority of documents and procedures to be provided according to the user's emotions.

[0089] The providing unit can select the optimal display method by taking into consideration the user's geographical location information when providing the information. For example, if the user lives in a specific area, the providing unit can prioritize displaying information related to that area. For example, the providing unit can prioritize displaying information on property tax and automobile tax related to the area where the user lives. In addition, if the user is traveling, the providing unit can prioritize displaying information based on the user's current location. For example, the providing unit can prioritize displaying tax payment procedures required by the user while traveling, allowing the user to respond quickly. In addition, if the user is planning to move, the providing unit can prioritize displaying information related to the user's new address. For example, the providing unit can prioritize displaying tax payment procedures related to the user's new address, allowing the user to complete the procedures smoothly. In this way, the providing unit can improve display efficiency by selecting the optimal display method based on the user's geographical location information.

[0090] When providing the information, the providing unit can analyze the user's social media activity and adjust the display method of the documents and procedures to be provided. For example, if the user posts about pensions on social media, the providing unit prioritizes displaying pension-related information. For example, if the user posts about pensions on social media, the providing unit prioritizes displaying information about receiving pensions based on that information. In addition, if the user posts about hometown tax donations, the providing unit can add information about selecting a donation destination. For example, if the user posts about hometown tax donations on social media, the providing unit adds information about selecting a donation destination based on that information. In addition, the providing unit can automatically display related information if the user posts about filing a tax return. For example, if the user posts about filing a tax return on social media, the providing unit automatically displays related information based on that information. In this way, the providing unit can improve display efficiency by adjusting the display method of the documents and procedures to be provided based on the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision 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 receives information from a user using the reception device 38 of the smart device 14. The analysis unit analyzes the information received by the specific processing unit 290 of the data processing device 12. The generation unit generates documents and procedures required by the specific processing unit 290 of the data processing device 12. The provision unit provides the generated documents and procedures to the user using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, generation unit, and provision 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 receives information from a user using the microphone 238 of the smart glasses 214. The analysis unit analyzes the information received by the specific processing unit 290 of the data processing device 12. The generation unit generates documents and procedures required by the specific processing unit 290 of the data processing device 12. The provision unit provides the generated documents and procedures to the user using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision 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 receives information from the user using the microphone 238 of the headset type terminal 314. The analysis unit analyzes the information received by the specific processing unit 290 of the data processing device 12. The generation unit generates documents and procedures required by the specific processing unit 290 of the data processing device 12. The provision unit provides the generated documents and procedures to the user using the display 343 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives information from a user using the microphone 238 of the robot 414. The analysis unit analyzes the information received by the specific processing unit 290 of the data processing device 12. The generation unit generates documents and procedures required by the specific processing unit 290 of the data processing device 12. The provision unit provides the generated documents and procedures to the user using the speaker 240 of the robot 414.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit automatically displays information that the user has frequently input in the past as candidates. For example, the reception unit automatically displays input candidates based on information that the user has input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit can suggest the optimal input method based on input methods that the user has used in the past. The reception unit can also predict and suggest information that will be used in a specific time period based on the user's past input history. For example, the reception unit can suggest the optimal input method based on information that the user has input in a specific time period in the past. In this way, the reception unit can improve input efficiency by suggesting the optimal input method based on the user's past input history.

[0093] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can simplify the analysis algorithm and provide results quickly. For example, the analysis unit can simplify the algorithm for identifying necessary documents and procedures based on information input by the user and provide results quickly. The analysis unit can also perform a detailed analysis and provide highly accurate results if the user is relaxed. For example, the analysis unit can fine-tune the algorithm for identifying necessary documents and procedures based on information input by the user and provide results quickly. The analysis unit can also prioritize analysis of the most important information if the user is in a hurry. For example, the analysis unit can adjust the algorithm for identifying the most important documents and procedures based on information input by the user and provide results quickly. In this way, the analysis unit can improve analysis efficiency by adjusting the analysis algorithm according to the user's emotions.

[0094] The generation unit can adjust the level of detail of documents and procedures to be generated based on the importance of the information during generation. For example, documents related to pension benefits are generated in detail to provide accurate information. For example, the generation unit generates detailed documents and provides accurate information based on information related to pension benefits input by the user. The generation unit can also generate documents related to hometown tax donations with an emphasis on selecting a donation recipient. For example, the generation unit generates documents that focus on selecting a donation recipient based on information related to hometown tax donations input by the user. The generation unit can also generate documents related to tax returns with an emphasis on tax calculation. For example, the generation unit generates documents that focus on tax calculation based on information related to tax returns input by the user. In this way, the generation unit can improve generation efficiency by adjusting the level of detail of documents and procedures to be generated based on the importance of the information.

[0095] The providing unit can estimate the user's emotions and adjust the display method of documents and procedures to be provided based on the estimated user's emotions. For example, when the user is feeling stressed, the providing unit provides a simple, highly visible display method. For example, the providing unit provides a simple, highly visible display method based on information input by the user. Furthermore, when the user is relaxed, the providing unit can also provide a display method including detailed information. For example, the providing unit provides a display method including detailed information based on information input by the user. Furthermore, when the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. For example, the providing unit provides a display method that focuses on the main points based on information input by the user. In this way, the providing unit can reduce the burden on the user by adjusting the display method of documents and procedures to be provided according to the user's emotions.

[0096] When receiving information, the reception unit can prioritize receiving highly relevant information taking into account the user's geographical location information. For example, if the user lives in a specific area, the reception unit can prioritize displaying tax payment information related to that area. For example, the reception unit can prioritize displaying information on property tax and automobile tax related to the area where the user lives. In addition, if the user is traveling, the reception unit can prioritize displaying tax payment procedures based on the user's current location. For example, the reception unit can prioritize displaying tax payment procedures required while the user is traveling, allowing the user to respond quickly. In addition, if the user is planning to move, the reception unit can prioritize displaying information related to the user's new address. For example, the reception unit can prioritize displaying tax payment procedures related to the user's new address, allowing the user to complete the procedures smoothly. In this way, the reception unit can improve input efficiency by preferentially receiving highly relevant information based on the user's geographical location information.

[0097] During analysis, the analysis unit can determine the priority of analysis based on the time of information submission. For example, if the deadline for filing a tax return is approaching, the analysis unit prioritizes analysis of information related to the tax return. For example, the analysis unit prioritizes analysis of information with an upcoming submission deadline based on information related to the tax return entered by the user. In addition, if the deadline for applying for pension benefits is approaching, the analysis unit can also prioritize analysis of information related to pensions. For example, the analysis unit prioritizes analysis of information with an upcoming application deadline based on information related to pension benefits entered by the user. In addition, if the deadline for paying fixed asset tax is approaching, the analysis unit can also prioritize analysis of information related to tax payment. For example, the analysis unit prioritizes analysis of information with an upcoming payment deadline based on information related to fixed asset tax entered by the user. In this way, the analysis unit can improve the efficiency of analysis by determining the priority of analysis based on the time of information submission.

[0098] The generation unit can estimate the user's emotions and adjust the expression method of the generated documents and procedures based on the estimated user's emotions. For example, when the user is feeling stressed, the generation unit generates a simple and easy-to-understand document. For example, the generation unit generates a simple and easy-to-understand document based on information input by the user. The generation unit can also generate a document including detailed information when the user is relaxed. For example, the generation unit generates a document including detailed information based on information input by the user. The generation unit can also generate a document that can be processed quickly when the user is in a hurry. For example, the generation unit generates a document that can be processed quickly based on information input by the user. In this way, the generation unit can reduce the burden on the user by adjusting the expression method of the generated documents and procedures based on the user's emotions.

[0099] When providing the display method, the providing unit can select the optimal display method by referring to the user's past operation history. For example, the providing unit preferentially provides a display method that the user has used in the past. For example, the providing unit selects the optimal display method based on a display method that the user has used in the past. The providing unit can also automatically apply a display layout that the user has previously preferred. For example, the providing unit selects the optimal display method based on a display layout that the user has previously preferred. The providing unit can also suggest the most efficient display method based on the user's past operation history. For example, the providing unit suggests the most efficient display method based on the user's past operation history. In this way, the providing unit can improve display efficiency by selecting the optimal display method based on the user's past operation history.

[0100] The reception unit can estimate the user's emotions and adjust the information input interface based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit provides a simple interface and minimizes input procedures. For example, the reception unit reduces the number of items the user needs to input and allows the user to input only the minimum amount of information necessary. Furthermore, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. For example, the reception unit increases the number of items the user needs to input and allows the user to input more detailed information. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input to enable the user to input information quickly. For example, the reception unit converts information input by voice using voice recognition technology to text data, thereby simplifying the input procedures. In this way, the reception unit can reduce the burden on the user by adjusting the information input interface according to the user's emotions.

[0101] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, information regarding pension benefits is analyzed by applying a pension calculation algorithm. For example, the analysis unit analyzes by applying a pension calculation algorithm based on information regarding pension benefits input by the user. The analysis unit can also analyze information regarding hometown tax payments by applying a donation calculation algorithm. For example, the analysis unit analyzes by applying a donation calculation algorithm based on information regarding hometown tax payments input by the user. The analysis unit can also analyze information regarding tax returns by applying a tax calculation algorithm. For example, the analysis unit analyzes by applying a tax calculation algorithm based on information regarding tax returns input by the user. In this way, the analysis unit can improve the accuracy of the analysis by applying different analysis algorithms depending on the category of information.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The reception unit accepts information from the user. This information includes information about receiving pension benefits, hometown tax donations, tax returns, and property tax and automobile tax payments. The reception unit can accept information via a web form, a smartphone app, or voice input. In the case of voice input, the information is converted into text data using voice recognition technology. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the information using data mining technology, statistical analysis technology, and machine learning algorithms to identify the necessary documents and procedures. Step 3: The generation unit generates the necessary documents and procedures based on the information analyzed by the analysis unit. The generation unit can automatically generate applications, reports, and electronic procedures. Step 4: The providing unit provides the documents and procedures generated by the generating unit to the user. The providing unit can provide the documents and procedures by email, a web portal, or by printing and mailing.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] 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.

[0120] 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.

[0121] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0135] 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.

[0136] 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.

[0137] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0153] 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.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 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 information from a user; an analysis unit that analyzes the information received by the reception unit; a generation unit that generates necessary documents or procedures based on the information analyzed by the analysis unit; a providing unit that provides the document or procedure generated by the generating unit to a user. A system characterized by:

2. The reception unit Accepting information for pension or tax payment procedures 2. The system of claim 1.

3. The analysis unit Analyzing the information received by the reception unit 2. The system of claim 1.

4. The generation unit Generate necessary documents and procedures based on the information analyzed by the analysis unit 2. The system of claim 1.

5. The providing unit Providing the documents and procedures generated by the generating unit to the user 2. The system of claim 1.

6. The providing unit Providing support for users to receive or pay benefits 2. The system of claim 1.

7. The reception unit Analyzes the user's facial expressions and voice to estimate the user's emotions, and adjusts the information input interface based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyzes the user's past input history and suggests the optimal input method 2. The system of claim 1.

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