System

Abstract

An object of a system according to an embodiment is to make creation of a real estate registration application form efficient and promote reduction of an owner unknown site.SOLUTION: A system includes a reception part, a generation part, a confirmation part, and an examination part. The reception unit receives information from a user. The generation unit generates a real estate registration application form on the basis of the information received by the reception unit. The confirmation unit confirms and corrects the application form generated by the generation unit. The examination unit examines the application form confirmed and corrected by the confirmation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that the preparation of real estate registration applications is cumbersome and there is a lack of efficient means to prevent the increase in ownerless land.

[0005] The system according to the embodiment aims to improve the efficiency of creating real estate registration applications and promote the reduction of land with unknown owners. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a confirmation unit, and an examination unit. The reception unit receives information from a user. The generation unit generates a real estate registration application form based on the information received by the reception unit. The confirmation unit confirms and modifies the application form generated by the generation unit. The examination unit examines the application form confirmed and modified by the confirmation unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of creating real estate registration applications and promote the reduction of land with unknown owners. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A real estate registration application form automatic generation system according to an embodiment of the present invention is a system in which a generation AI receives information from a user, generates a real estate registration application form, verifies and corrects it, and reviews it. The real estate registration application form automatic generation system receives information from a user, generates a real estate registration application form, verifies and corrects it, and reviews it, thereby enabling quick and accurate inheritance registration. For example, in the real estate registration application form automatic generation system, a user inputs information required for a real estate registration application. For example, the real estate registration application form automatic generation system inputs information such as information about heirs and the contents of an inheritance division agreement. This information is input to the generation AI. Next, the real estate registration application form automatic generation system uses the generation AI to analyze the input information and automatically generate a real estate registration application form. The generation AI generates an accurate application form based on Ministry of Justice regulations. For example, the generation AI automatically fills in necessary fields based on the information about heirs and the contents of the inheritance division agreement. Next, in the real estate registration application form automatic generation system, a user reviews the generated real estate registration application form and makes corrections as necessary. This ensures the accuracy of the application form. Next, the real estate registration application automatic generation system reviews the generated application form based on the Ministry of Justice's review and approval system. The Ministry of Justice then verifies the accuracy of the application form generated by the generation AI and approves it. This allows users to submit their application forms with peace of mind. The real estate registration application automatic generation system also works with the legislature to allow lawyers, administrative scriveners, and tax accountants who have been contracted to prepare inheritance division agreements to act on behalf of others, a role previously reserved exclusively for judicial scriveners, to a certain extent, thereby further reducing the number of unowned land parcels. This allows the real estate registration application automatic generation system to quickly and accurately complete inheritance registrations, resolving the problem of unowned land parcels. This will solve significant problems, such as the construction of temporary housing during disasters, stabilizing rights on land for base station construction, and promoting the use of renewable energy.

[0029] The real estate registration application form automatic generation system according to the embodiment includes a reception unit, a generation unit, a confirmation unit, and an examination unit. The reception unit receives information from a user. The information from the user includes, but is not limited to, information about heirs, property information, and inheritance information. The reception unit receives, for example, information about heirs and the contents of an estate division agreement. The reception unit may also include input guides and check functions. For example, the reception unit may provide input guides to help a user enter information accurately. The reception unit may also include a function to check for errors in the input content. The generation unit uses a generation AI to generate a real estate registration application form based on the information received by the reception unit. The generation unit generates an accurate real estate registration application form based on, for example, Ministry of Justice regulations. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and automatically fills in required fields based on the information about heirs and the contents of the estate division agreement. For example, the generation unit takes information about heirs and the contents of an inheritance division agreement as input and generates a real estate registration application form using a generation AI that generates the application form based on Ministry of Justice regulations. The verification unit allows a user to review the application form generated by the generation unit and make corrections as necessary. The verification unit, for example, provides an interface for a user to review the generated application form and make necessary corrections. For example, the verification unit displays the generated application form and provides an editing function for the user to make corrections. The verification unit can also estimate the user's emotions and adjust the display method of the confirmation screen based on the estimated user emotions. For example, if the user is nervous, the verification unit provides a simple, highly visible display method. The review unit reviews the application form confirmed and corrected by the verification unit. For example, the review unit verifies the accuracy of the application form generated by the generation AI and approves it. The review unit verifies the accuracy of the application form based on Ministry of Justice regulations. For example, the review unit takes the application form generated by the generation AI as input and performs a review using an AI model that verifies the accuracy of the application form based on Ministry of Justice regulations. As a result, the real estate registration application form automatic generation system according to the embodiment accepts information from the user and generates, confirms, and reviews the information, thereby streamlining the creation of real estate registration application forms.

[0030] The reception unit can receive information about the heirs or the contents of the estate division agreement. The information about the heirs includes, for example, name, address, inheritance relationship, etc., but is not limited to these examples. The reception unit, for example, receives the names and addresses of the heirs. The reception unit can also receive information indicating the inheritance relationship. For example, the reception unit receives a family tree or inheritance relationship chart showing the relationship between the heirs. The contents of the estate division agreement include, for example, the division method and the contents of the agreement between the heirs, but are not limited to these examples. The reception unit, for example, receives the division method in the estate division agreement. The reception unit can also receive the contents of the agreement between the heirs. For example, the reception unit receives the division method and the contents of the agreement agreed upon by the heirs. In this way, by receiving the information about the heirs and the contents of the estate division agreement, an application form can be generated based on accurate information.

[0031] The generation unit can generate an accurate real estate registration application form based on the regulations of the Ministry of Justice. The regulations of the Ministry of Justice include, but are not limited to, relevant laws and guidelines. The generation unit generates, for example, a real estate registration application form based on the regulations of the Ministry of Justice. The generation unit uses a generation AI to generate an accurate real estate registration application form based on the regulations of the Ministry of Justice. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, and generates the application form based on the regulations of the Ministry of Justice. For example, the generation unit inputs information about heirs and the contents of an inheritance division agreement based on the regulations of the Ministry of Justice, and generates the application form using a generation AI that generates an accurate real estate registration application form. As a result, by generating the application form based on the regulations of the Ministry of Justice, an accurate application form can be created.

[0032] The confirmation unit allows the user to check the generated application form and make corrections as necessary. For example, the confirmation unit provides an interface for the user to check the generated application form and make necessary corrections. The confirmation unit displays the generated application form and provides an editing function for the user to make corrections. For example, the confirmation unit displays the generated application form and provides an editing function for the user to make corrections. The confirmation unit can also estimate the user's emotions and adjust the display method of the confirmation screen based on the estimated user emotions. For example, if the user is nervous, the confirmation unit provides a simple and highly visible display method. This allows the user to check and correct the application form, ensuring its accuracy.

[0033] The review department can confirm the accuracy of the application form generated by the generation AI and approve it. For example, the review department confirms the accuracy of the application form generated by the generation AI and approves it. The review department confirms the accuracy of the application form based on the regulations of the Ministry of Justice. For example, the review department takes the application form generated by the generation AI as input and performs a review using an AI model that confirms the accuracy of the application form based on the regulations of the Ministry of Justice. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, and generates an application form based on the regulations of the Ministry of Justice. For example, the review department takes the application form generated by the generation AI as input and performs a review using an AI model that confirms the accuracy of the application form based on the regulations of the Ministry of Justice. In this way, by confirming the accuracy of the application form generated by the generation AI and approving it, highly reliable application forms can be provided.

[0034] The reception unit may be provided with an input guide or a check function. For example, the reception unit provides an input guide so that the user can input information accurately. The reception unit may also be provided with a function to check whether there are any errors in the input content. For example, the reception unit provides an input guide so that the user can input information accurately. The reception unit may also be provided with a function to check whether there are any errors in the input content. Thus, by providing the input guide or check function, the user can input information accurately.

[0035] The examination department can have a function to indicate the scope of proxy registration application services provided by lawyers, administrative scriveners, and tax accountants. The examination department, for example, has a function to indicate the scope of proxy registration application services provided by lawyers, administrative scriveners, and tax accountants. By indicating the scope of proxy registration application services provided by lawyers, administrative scriveners, and tax accountants, the examination department clarifies the scope of proxy services. For example, the examination department has a function to indicate the scope of proxy registration application services provided by lawyers, administrative scriveners, and tax accountants. In this way, by indicating the scope of proxy registration application services provided by lawyers, administrative scriveners, and tax accountants, the scope of proxy services is clarified.

[0036] The reception unit can analyze the heir's past registration history and suggest an appropriate input method. For example, the reception unit can prioritize and suggest input methods (voice, text, etc.) that the heir has used in the past. The reception unit can also automatically input specific information from the heir's past registration history. For example, the reception unit determines the priority of input content based on the heir's past registration history. By analyzing the heir's past registration history, the reception unit can suggest the optimal input method and improve input efficiency. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the heir's past registration history data into a generation AI and have the generation AI suggest the optimal input method.

[0037] The reception unit can provide input guidance based on region-specific laws and regulations based on the heir's geographical information. The reception unit can provide input guidance that automatically reflects region-specific laws and regulations based on, for example, the heir's location. The reception unit can also guide the heir on necessary documents and procedures based on the heir's geographical information. For example, the reception unit displays points of caution regarding region-specific laws and regulations depending on the heir's location. By taking the heir's geographical information into consideration, the reception unit can provide input guidance based on region-specific laws and regulations and support accurate information input. Some or all of the above-described processing in the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can input the heir's geographical information data into the generation AI and cause the generation AI to provide input guidance based on region-specific laws and regulations.

[0038] The reception unit can select the optimal input means depending on the heir's input method. For example, if the heir wishes to input by voice, the reception unit provides a voice recognition function. If the heir wishes to input by text, the reception unit can also provide an interface for text input. For example, if the heir wishes to input by image, the reception unit provides an image recognition function. This improves input efficiency by selecting the optimal input means depending on the heir's input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the heir's input method data into the generation AI and have the generation AI select the optimal input means.

[0039] The reception unit can analyze the heir's social media activity and automatically acquire related information. For example, the reception unit can analyze the content of the heir's social media posts and automatically acquire related information. The reception unit can also automatically acquire related locations based on the heir's social media check-in information. For example, the reception unit can automatically acquire related information by referring to the activities of the heir's friends on social media. This allows the heir's social media activity to be analyzed, automatically acquiring related information and improving input efficiency. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the heir's social media activity data into the generation AI and have the generation AI acquire related information.

[0040] The reception unit can customize the input guide by reflecting the heir's past feedback. The reception unit customizes the input guide based on, for example, feedback provided by the heir in the past. The reception unit can also optimize the input procedure by reflecting the heir's past feedback. For example, the reception unit adjusts the priority of input content based on the heir's past feedback. In this way, the input guide is customized by reflecting the heir's past feedback, improving input efficiency. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the heir's past feedback data into the generation AI and have the generation AI customize the input guide.

[0041] The reception unit can filter the input content based on the heir's living situation and areas of interest. The reception unit, for example, filters necessary information based on the heir's living situation. The reception unit can also filter relevant information based on the heir's areas of interest. For example, the reception unit optimizes the input content taking into account the heir's living situation and areas of interest. This improves input efficiency by filtering the input content based on the heir's living situation and areas of interest. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the heir's living situation and area of ​​interest data into the generation AI and have the generation AI filter the input content.

[0042] The generation unit can generate an application form that takes into account regional laws and regulations based on Ministry of Justice regulations. For example, the generation unit automatically generates an application form that reflects regional laws and regulations. The generation unit can also automatically embed required items based on regional laws and regulations. For example, the generation unit reflects points of note regarding regional laws and regulations in the application form. This allows for the creation of an accurate application form by generating an application form that takes into account regional laws and regulations based on Ministry of Justice regulations. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input regional laws and regulations data into a generation AI and cause the generation AI to generate an application form.

[0043] The generation unit can improve the accuracy of the application form by referring to the heir's past registration history. The generation unit, for example, automatically inputs necessary information based on the heir's past registration history. The generation unit can also optimize the content of the application form by referring to the heir's past registration history. For example, the generation unit improves the accuracy of the application form by reflecting the heir's past registration history. In this way, the accuracy of the application form is improved by referring to the heir's past registration history. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the heir's past registration history data into the generation AI and have the generation AI improve the accuracy of the application form.

[0044] The generation unit can automatically generate the necessary legal documents based on the input content of the heir. For example, the generation unit automatically generates the necessary legal documents based on the input content of the heir. The generation unit can also suggest appropriate legal documents based on the input content of the heir. For example, the generation unit optimizes the content of the legal documents by reflecting the input content of the heir. This makes the preparation of application forms more efficient by automatically generating the necessary legal documents based on the input content of the heir. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the input content data of the heir into a generation AI and have the generation AI generate the legal documents.

[0045] When generating an application form, the generation unit can determine the priority of the application form based on the time of submission by the heir. The generation unit determines the priority of the application form based on, for example, the time of submission by the heir. The generation unit can also generate application forms on a priority basis when the submission deadline is approaching. For example, the generation unit optimizes the order in which applications are generated according to the time of submission. This enables efficient creation of application forms in accordance with the submission deadline by determining the priority of applications based on the time of submission by the heir. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the time of submission by the heir into the generation AI and have the generation AI determine the priority of the application forms.

[0046] When generating the application form, the generation unit can improve the accuracy of the application form by referring to literature related to the heir. The generation unit, for example, optimizes the content of the application form by referring to literature related to the heir. The generation unit can also automatically input necessary information based on literature related to the heir. For example, the generation unit improves the accuracy of the application form by reflecting literature related to the heir. In this way, by referring to literature related to the heir, the content of the application form is optimized and its accuracy is improved. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input literature data related to the heir into the generation AI and have the generation AI improve the accuracy of the application form.

[0047] When generating the application form, the generation unit can adjust the use of technical terminology in the application form according to the heir's level of expertise. For example, the generation unit adjusts the use of technical terminology according to the heir's level of expertise. The generation unit can also use detailed technical terminology if the heir has technical expertise. For example, the generation unit uses concise and easy-to-understand terminology if the heir does not have technical expertise. This promotes understanding of the application form by adjusting the use of technical terminology according to the heir's level of expertise. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the heir's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0048] The verification unit can propose an optimal verification procedure by referring to the heir's past verification history. The verification unit, for example, proposes an optimal verification procedure based on the heir's past verification history. The verification unit can also determine the priority of the verification content by referring to the heir's past verification history. For example, the verification unit optimizes the verification procedure by reflecting the heir's past verification history. In this way, by referring to the heir's past verification history, an optimal verification procedure is proposed and the efficiency of the verification work is improved. Some or all of the above-mentioned processing in the verification unit may be performed, for example, using AI, or may be performed without using AI. For example, the verification unit can input the heir's past verification history data into the generation AI and have the generation AI execute the proposed verification procedure.

[0049] The confirmation unit can provide a confirmation procedure based on local laws and regulations, taking into account the heir's geographical information. The confirmation unit can provide a confirmation procedure that automatically reflects local laws and regulations, for example, based on the heir's location. The confirmation unit can also guide the user through the necessary confirmation procedure based on the heir's geographical information. For example, the confirmation unit can display points to note regarding local laws and regulations depending on the heir's location. By taking into account the heir's geographical information, the confirmation unit can provide a confirmation procedure based on local laws and regulations and support accurate confirmation work. Some or all of the above-mentioned processing in the confirmation unit can be performed using, for example, AI, or can be performed without using AI. For example, the confirmation unit can input the heir's geographical information data into the generation AI and have the generation AI provide the confirmation procedure.

[0050] The verification unit can improve the verification procedure by reflecting the heir's feedback. The verification unit can improve the verification procedure, for example, based on feedback previously provided by the heir. The verification unit can also optimize the verification procedure by reflecting the heir's feedback. For example, the verification unit can adjust the priority of the verification content based on the heir's feedback. In this way, the verification procedure can be improved by reflecting the heir's feedback, and the efficiency of the verification work can be improved. Some or all of the above-mentioned processing in the verification unit can be performed, for example, using AI, or can be performed without using AI. For example, the verification unit can input the heir's feedback data into the generation AI and have the generation AI improve the verification procedure.

[0051] During verification, the verification unit can analyze the heir's social media activity and automatically acquire related information. For example, the verification unit can analyze the heir's social media posts and automatically acquire related information. The verification unit can also automatically acquire related locations based on the heir's social media check-in information. For example, the verification unit can automatically acquire related information by referring to the activities of the heir's friends on social media. This allows the heir's social media activity to be analyzed, automatically acquiring related information and improving the efficiency of the verification process. Some or all of the above-mentioned processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit can input the heir's social media activity data into the generation AI and cause the generation AI to acquire related information.

[0052] The confirmation unit can customize the confirmation procedure by reflecting the heir's past feedback during confirmation. The confirmation unit customizes the confirmation procedure based on, for example, feedback provided by the heir in the past. The confirmation unit can also optimize the confirmation procedure by reflecting the heir's past feedback. For example, the confirmation unit adjusts the priority of the confirmation contents based on the heir's past feedback. This customizes the confirmation procedure by reflecting the heir's past feedback, improving the efficiency of the confirmation work. Some or all of the above-mentioned processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can input the heir's past feedback data into the generation AI and have the generation AI customize the confirmation procedure.

[0053] During confirmation, the confirmation unit can filter the confirmation content based on the heir's living situation and areas of interest. The confirmation unit, for example, filters necessary information based on the heir's living situation. The confirmation unit can also filter relevant information based on the heir's areas of interest. For example, the confirmation unit optimizes the confirmation content taking into account the heir's living situation and areas of interest. This improves the efficiency of the confirmation work by filtering the confirmation content based on the heir's living situation and areas of interest. Some or all of the above-mentioned processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can input the heir's living situation and area of ​​interest data into the generation AI and have the generation AI perform filtering of the confirmation content.

[0054] The examination department can provide examination criteria that take into account regional laws and regulations based on Ministry of Justice regulations. For example, the examination department can provide examination criteria that automatically reflect regional laws and regulations. The examination department can also automatically set necessary examination items based on regional laws and regulations. For example, the examination department can reflect points to note regarding regional laws and regulations in the examination criteria. This allows for accurate examination by providing examination criteria that take into account regional laws and regulations based on Ministry of Justice regulations. Some or all of the above-mentioned processing in the examination department can be performed using, for example, AI, or can be performed without using AI. For example, the examination department can input regional laws and regulations data into a generation AI and have the generation AI provide the examination criteria.

[0055] The examination department can improve the accuracy of the examination by referring to the heir's past examination history. For example, the examination department automatically inputs necessary information based on the heir's past examination history. The examination department can also optimize the examination content by referring to the heir's past examination history. For example, the examination department improves the accuracy of the examination by reflecting the heir's past examination history. In this way, the accuracy of the examination is improved by referring to the heir's past examination history. Some or all of the above-mentioned processing in the examination department may be performed, for example, using AI, or may be performed without using AI. For example, the examination department can input the heir's past examination history data into the generation AI and have the generation AI improve the accuracy of the examination.

[0056] The review department can improve the review criteria by reflecting the heirs' feedback. For example, the review department improves the review criteria based on feedback provided by the heirs in the past. The review department can also optimize the review criteria by reflecting the heirs' feedback. For example, the review department adjusts the priority of the review content based on the heirs' feedback. In this way, by reflecting the heirs' feedback, the review criteria are improved and the efficiency of the review work is improved. Some or all of the above-mentioned processing in the review department may be performed, for example, using AI, or may be performed without using AI. For example, the review department can input the heirs' feedback data into the generation AI and have the generation AI improve the review criteria.

[0057] During the review, the review department can analyze the heir's social media activity and automatically obtain relevant information. For example, the review department can analyze the heir's social media posts and automatically obtain relevant information. The review department can also automatically obtain relevant locations based on the heir's social media check-in information. For example, the review department can automatically obtain relevant information by referring to the activities of the heir's friends on social media. In this way, by analyzing the heir's social media activity, relevant information can be automatically obtained and the efficiency of the review work can be improved. Some or all of the above-mentioned processing in the review department can be performed, for example, using AI or without AI. For example, the review department can input the heir's social media activity data into the generation AI and have the generation AI obtain relevant information.

[0058] During the review, the review department can customize the review criteria by reflecting the heir's past feedback. For example, the review department customizes the review criteria based on feedback provided by the heir in the past. The review department can also optimize the review criteria by reflecting the heir's past feedback. For example, the review department adjusts the priority of the review content based on the heir's past feedback. In this way, the review criteria are customized by reflecting the heir's past feedback, improving the efficiency of the review work. Some or all of the above-mentioned processing in the review department may be performed, for example, using AI, or may be performed without using AI. For example, the review department can input the heir's past feedback data into the generation AI and have the generation AI customize the review criteria.

[0059] During the review, the review department can filter the review content based on the heir's living situation and areas of interest. For example, the review department filters necessary information based on the heir's living situation. The review department can also filter relevant information based on the heir's areas of interest. For example, the review department optimizes the review content by taking into account the heir's living situation and areas of interest. This improves the efficiency of the review work by filtering the review content based on the heir's living situation and areas of interest. Some or all of the above-mentioned processing in the review department may be performed using AI, for example, or may be performed without using AI. For example, the review department can input data on the heir's living situation and areas of interest into a generation AI and have the generation AI filter the review content.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The reception unit can translate the user's input content in real time and provide a multilingual input guide. For example, the reception unit can translate information entered by the user in English into Japanese and provide a Japanese input guide. The reception unit can also translate information entered by the user in Chinese into English and provide an English input guide. In this way, the reception unit can provide a multilingual input guide and accommodate users who speak different languages.

[0062] The generation unit can provide relevant legal advice based on the user's input. For example, the generation unit can provide advice on inheritance tax based on inheritance information entered by the user. The generation unit can also provide advice on real estate valuation based on property information entered by the user. In this way, the generation unit can deepen the user's understanding by providing relevant legal advice based on the user's input.

[0063] The reception unit can analyze the user's input content, automatically detect input errors, and suggest corrections. For example, the reception unit can automatically detect an address entered incorrectly by the user and suggest the correct address. The reception unit can also suggest the correct information if there is an error in the information on heirs entered by the user. In this way, the reception unit can improve the accuracy of input by automatically detecting input errors by the user and suggesting corrections.

[0064] The confirmation unit can propose an optimal confirmation procedure based on the user's past confirmation history. For example, the confirmation unit analyzes the history of confirmation work performed by the user in the past and proposes an optimal procedure. The confirmation unit can also adjust the priority of confirmation contents based on the user's past confirmation history. In this way, the confirmation unit improves the efficiency of confirmation work by proposing an optimal confirmation procedure based on the user's past confirmation history.

[0065] The reception unit can automatically generate templates for related legal documents based on the user's input. For example, the reception unit generates a template for an inheritance consent form based on inheritance information entered by the user. The reception unit can also generate a template for a real estate purchase and sale contract based on property information entered by the user. In this way, the reception unit can improve the efficiency of document creation by automatically generating templates for related legal documents based on the user's input.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The reception unit receives information from the user. This information includes information on heirs, property information, inheritance information, etc. The reception unit provides input guidance and checking functions to help users enter information accurately. Step 2: The generation unit generates a real estate registration application form based on the information received by the reception unit. The generation unit uses generation AI to generate an accurate real estate registration application form based on the regulations of the Ministry of Justice. The generation AI uses text generation AI and multimodal generation AI to automatically fill in the necessary fields based on the information of the heirs and the contents of the inheritance division agreement. Step 3: The confirmation unit allows the user to confirm the application form generated by the generation unit and make corrections as necessary. The confirmation unit displays the generated application form and provides an editing function for the user to make corrections. The confirmation unit can also estimate the user's emotions and adjust the display method of the confirmation screen based on the estimated emotions. Step 4: The Review Department reviews the application form that has been confirmed and corrected by the Verification Department. The Review Department verifies the accuracy of the application form generated by the Generation AI and approves it in accordance with the Ministry of Justice regulations. The Review Department uses an AI model to verify the accuracy of the application form.

[0068] (Example 2) A real estate registration application form automatic generation system according to an embodiment of the present invention is a system in which a generation AI receives information from a user, generates a real estate registration application form, verifies and corrects it, and reviews it. The real estate registration application form automatic generation system receives information from a user, generates a real estate registration application form, verifies and corrects it, and reviews it, thereby enabling quick and accurate inheritance registration. For example, in the real estate registration application form automatic generation system, a user inputs information required for a real estate registration application. For example, the real estate registration application form automatic generation system inputs information such as information about heirs and the contents of an inheritance division agreement. This information is input to the generation AI. Next, the real estate registration application form automatic generation system uses the generation AI to analyze the input information and automatically generate a real estate registration application form. The generation AI generates an accurate application form based on Ministry of Justice regulations. For example, the generation AI automatically fills in necessary fields based on the information about heirs and the contents of the inheritance division agreement. Next, in the real estate registration application form automatic generation system, a user reviews the generated real estate registration application form and makes corrections as necessary. This ensures the accuracy of the application form. Next, the real estate registration application automatic generation system reviews the generated application form based on the Ministry of Justice's review and approval system. The Ministry of Justice then verifies the accuracy of the application form generated by the generation AI and approves it. This allows users to submit their application forms with peace of mind. The real estate registration application automatic generation system also works with the legislature to allow lawyers, administrative scriveners, and tax accountants who have been contracted to prepare inheritance division agreements to act on behalf of others, a role previously reserved exclusively for judicial scriveners, to a certain extent, thereby further reducing the number of unowned land parcels. This allows the real estate registration application automatic generation system to quickly and accurately complete inheritance registrations, resolving the problem of unowned land parcels. This will solve significant problems, such as the construction of temporary housing during disasters, stabilizing rights on land for base station construction, and promoting the use of renewable energy.

[0069] The real estate registration application form automatic generation system according to the embodiment includes a reception unit, a generation unit, a confirmation unit, and an examination unit. The reception unit receives information from a user. The information from the user includes, but is not limited to, information about heirs, property information, and inheritance information. The reception unit receives, for example, information about heirs and the contents of an estate division agreement. The reception unit may also include input guides and check functions. For example, the reception unit may provide input guides to help a user enter information accurately. The reception unit may also include a function to check for errors in the input content. The generation unit uses a generation AI to generate a real estate registration application form based on the information received by the reception unit. The generation unit generates an accurate real estate registration application form based on, for example, Ministry of Justice regulations. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and automatically fills in required fields based on the information about heirs and the contents of the estate division agreement. For example, the generation unit takes information about heirs and the contents of an inheritance division agreement as input and generates a real estate registration application form using a generation AI that generates the application form based on Ministry of Justice regulations. The verification unit allows a user to review the application form generated by the generation unit and make corrections as necessary. The verification unit, for example, provides an interface for a user to review the generated application form and make necessary corrections. For example, the verification unit displays the generated application form and provides an editing function for the user to make corrections. The verification unit can also estimate the user's emotions and adjust the display method of the confirmation screen based on the estimated user emotions. For example, if the user is nervous, the verification unit provides a simple, highly visible display method. The review unit reviews the application form confirmed and corrected by the verification unit. For example, the review unit verifies the accuracy of the application form generated by the generation AI and approves it. The review unit verifies the accuracy of the application form based on Ministry of Justice regulations. For example, the review unit takes the application form generated by the generation AI as input and performs a review using an AI model that verifies the accuracy of the application form based on Ministry of Justice regulations. As a result, the real estate registration application form automatic generation system according to the embodiment accepts information from the user and generates, confirms, and reviews the information, thereby streamlining the creation of real estate registration application forms.

[0070] The reception unit can receive information about the heirs or the contents of the estate division agreement. The information about the heirs includes, for example, name, address, inheritance relationship, etc., but is not limited to these examples. The reception unit, for example, receives the names and addresses of the heirs. The reception unit can also receive information indicating the inheritance relationship. For example, the reception unit receives a family tree or inheritance relationship chart showing the relationship between the heirs. The contents of the estate division agreement include, for example, the division method and the contents of the agreement between the heirs, but are not limited to these examples. The reception unit, for example, receives the division method in the estate division agreement. The reception unit can also receive the contents of the agreement between the heirs. For example, the reception unit receives the division method and the contents of the agreement agreed upon by the heirs. In this way, by receiving the information about the heirs and the contents of the estate division agreement, an application form can be generated based on accurate information.

[0071] The generation unit can generate an accurate real estate registration application form based on the regulations of the Ministry of Justice. The regulations of the Ministry of Justice include, but are not limited to, relevant laws and guidelines. The generation unit generates, for example, a real estate registration application form based on the regulations of the Ministry of Justice. The generation unit uses a generation AI to generate an accurate real estate registration application form based on the regulations of the Ministry of Justice. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, and generates the application form based on the regulations of the Ministry of Justice. For example, the generation unit inputs information about heirs and the contents of an inheritance division agreement based on the regulations of the Ministry of Justice, and generates the application form using a generation AI that generates an accurate real estate registration application form. As a result, by generating the application form based on the regulations of the Ministry of Justice, an accurate application form can be created.

[0072] The confirmation unit allows the user to check the generated application form and make corrections as necessary. For example, the confirmation unit provides an interface for the user to check the generated application form and make necessary corrections. The confirmation unit displays the generated application form and provides an editing function for the user to make corrections. For example, the confirmation unit displays the generated application form and provides an editing function for the user to make corrections. The confirmation unit can also estimate the user's emotions and adjust the display method of the confirmation screen based on the estimated user emotions. For example, if the user is nervous, the confirmation unit provides a simple and highly visible display method. This allows the user to check and correct the application form, ensuring its accuracy.

[0073] The review department can confirm the accuracy of the application form generated by the generation AI and approve it. For example, the review department confirms the accuracy of the application form generated by the generation AI and approves it. The review department confirms the accuracy of the application form based on the regulations of the Ministry of Justice. For example, the review department takes the application form generated by the generation AI as input and performs a review using an AI model that confirms the accuracy of the application form based on the regulations of the Ministry of Justice. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, and generates an application form based on the regulations of the Ministry of Justice. For example, the review department takes the application form generated by the generation AI as input and performs a review using an AI model that confirms the accuracy of the application form based on the regulations of the Ministry of Justice. In this way, by confirming the accuracy of the application form generated by the generation AI and approving it, highly reliable application forms can be provided.

[0074] The reception unit may be provided with an input guide or a check function. For example, the reception unit provides an input guide so that the user can input information accurately. The reception unit may also be provided with a function to check whether there are any errors in the input content. For example, the reception unit provides an input guide so that the user can input information accurately. The reception unit may also be provided with a function to check whether there are any errors in the input content. Thus, by providing the input guide or check function, the user can input information accurately.

[0075] The examination department can have a function to indicate the scope of proxy registration application services provided by lawyers, administrative scriveners, and tax accountants. The examination department, for example, has a function to indicate the scope of proxy registration application services provided by lawyers, administrative scriveners, and tax accountants. By indicating the scope of proxy registration application services provided by lawyers, administrative scriveners, and tax accountants, the examination department clarifies the scope of proxy services. For example, the examination department has a function to indicate the scope of proxy registration application services provided by lawyers, administrative scriveners, and tax accountants. In this way, by indicating the scope of proxy registration application services provided by lawyers, administrative scriveners, and tax accountants, the scope of proxy services is clarified.

[0076] The reception unit can estimate the user's emotions and adjust the display method of the input guide based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple and intuitive interface and minimize input steps. If the user is relaxed, the reception unit can also provide detailed input options and suggest customizable input methods. For example, if the user is in a hurry, the reception unit can prioritize voice input to allow the user to input information quickly. This adjusts the display method of the input guide according to the user's emotions, reducing the user's stress and improving input efficiency. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0077] The reception unit can analyze the heir's past registration history and suggest an appropriate input method. For example, the reception unit can prioritize and suggest input methods (voice, text, etc.) that the heir has used in the past. The reception unit can also automatically input specific information from the heir's past registration history. For example, the reception unit determines the priority of input content based on the heir's past registration history. By analyzing the heir's past registration history, the reception unit can suggest the optimal input method and improve input efficiency. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the heir's past registration history data into a generation AI and have the generation AI suggest the optimal input method.

[0078] The reception unit can provide input guidance based on region-specific laws and regulations based on the heir's geographical information. The reception unit can provide input guidance that automatically reflects region-specific laws and regulations based on, for example, the heir's location. The reception unit can also guide the heir on necessary documents and procedures based on the heir's geographical information. For example, the reception unit displays points of caution regarding region-specific laws and regulations depending on the heir's location. By taking the heir's geographical information into consideration, the reception unit can provide input guidance based on region-specific laws and regulations and support accurate information input. Some or all of the above-described processing in the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can input the heir's geographical information data into the generation AI and cause the generation AI to provide input guidance based on region-specific laws and regulations.

[0079] The reception unit can select the optimal input means depending on the heir's input method. For example, if the heir wishes to input by voice, the reception unit provides a voice recognition function. If the heir wishes to input by text, the reception unit can also provide an interface for text input. For example, if the heir wishes to input by image, the reception unit provides an image recognition function. This improves input efficiency by selecting the optimal input means depending on the heir's input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the heir's input method data into the generation AI and have the generation AI select the optimal input means.

[0080] The reception unit can estimate the user's emotions and prioritize input content based on the estimated user emotions. For example, when the user is stressed, the reception unit allows the user to input important information first. When the user is relaxed, the reception unit can also allow the user to input detailed information. For example, when the user is in a hurry, the reception unit allows the user to input minimal information. In this way, the priority of input content is determined according to the user's emotions, allowing important information to be input first. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using an AI, for example, or without an AI. For example, the reception unit may input the user's emotion data into the generation AI and cause the generation AI to perform emotion estimation.

[0081] The reception unit can analyze the heir's social media activity and automatically acquire related information. For example, the reception unit can analyze the content of the heir's social media posts and automatically acquire related information. The reception unit can also automatically acquire related locations based on the heir's social media check-in information. For example, the reception unit can automatically acquire related information by referring to the activities of the heir's friends on social media. This allows the heir's social media activity to be analyzed, automatically acquiring related information and improving input efficiency. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the heir's social media activity data into the generation AI and have the generation AI acquire related information.

[0082] The reception unit can customize the input guide by reflecting the heir's past feedback. The reception unit customizes the input guide based on, for example, feedback provided by the heir in the past. The reception unit can also optimize the input procedure by reflecting the heir's past feedback. For example, the reception unit adjusts the priority of input content based on the heir's past feedback. In this way, the input guide is customized by reflecting the heir's past feedback, improving input efficiency. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the heir's past feedback data into the generation AI and have the generation AI customize the input guide.

[0083] The reception unit can filter the input content based on the heir's living situation and areas of interest. The reception unit, for example, filters necessary information based on the heir's living situation. The reception unit can also filter relevant information based on the heir's areas of interest. For example, the reception unit optimizes the input content taking into account the heir's living situation and areas of interest. This improves input efficiency by filtering the input content based on the heir's living situation and areas of interest. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the heir's living situation and area of ​​interest data into the generation AI and have the generation AI filter the input content.

[0084] The generation unit can estimate the user's emotions and adjust the presentation style of the application form based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates an application form that includes detailed explanations. If the user is in a hurry, the generation unit can also generate a concise application form that focuses on the main points. For example, if the user is stressed, the generation unit generates a simple and intuitive application form. This adjusts the presentation style of the application form according to the user's emotions, thereby reducing the user's stress and facilitating their understanding of the application form. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation style of the application form.

[0085] The generation unit can generate an application form that takes into account regional laws and regulations based on Ministry of Justice regulations. For example, the generation unit automatically generates an application form that reflects regional laws and regulations. The generation unit can also automatically embed required items based on regional laws and regulations. For example, the generation unit reflects points of note regarding regional laws and regulations in the application form. This allows for the creation of an accurate application form by generating an application form that takes into account regional laws and regulations based on Ministry of Justice regulations. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input regional laws and regulations data into a generation AI and cause the generation AI to generate an application form.

[0086] The generation unit can improve the accuracy of the application form by referring to the heir's past registration history. The generation unit, for example, automatically inputs necessary information based on the heir's past registration history. The generation unit can also optimize the content of the application form by referring to the heir's past registration history. For example, the generation unit improves the accuracy of the application form by reflecting the heir's past registration history. In this way, the accuracy of the application form is improved by referring to the heir's past registration history. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the heir's past registration history data into the generation AI and have the generation AI improve the accuracy of the application form.

[0087] The generation unit can automatically generate the necessary legal documents based on the input content of the heir. For example, the generation unit automatically generates the necessary legal documents based on the input content of the heir. The generation unit can also suggest appropriate legal documents based on the input content of the heir. For example, the generation unit optimizes the content of the legal documents by reflecting the input content of the heir. This makes the preparation of application forms more efficient by automatically generating the necessary legal documents based on the input content of the heir. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the input content data of the heir into a generation AI and have the generation AI generate the legal documents.

[0088] The generation unit can estimate the user's emotions and adjust the length of the application form based on the estimated user emotions. For example, if the user is in a hurry, the generation unit generates a short, concise application form. If the user is relaxed, the generation unit can also generate a longer application form with detailed explanations. For example, if the user is stressed, the generation unit generates a simple, intuitive application form. This adjusts the length of the application form according to the user's emotions, thereby reducing the user's stress and facilitating their understanding of the application form. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the application form.

[0089] When generating an application form, the generation unit can determine the priority of the application form based on the time of submission by the heir. The generation unit determines the priority of the application form based on, for example, the time of submission by the heir. The generation unit can also generate application forms on a priority basis when the submission deadline is approaching. For example, the generation unit optimizes the order in which applications are generated according to the time of submission. This enables efficient creation of application forms in accordance with the submission deadline by determining the priority of applications based on the time of submission by the heir. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the time of submission by the heir into the generation AI and have the generation AI determine the priority of the application forms.

[0090] When generating the application form, the generation unit can improve the accuracy of the application form by referring to literature related to the heir. The generation unit, for example, optimizes the content of the application form by referring to literature related to the heir. The generation unit can also automatically input necessary information based on literature related to the heir. For example, the generation unit improves the accuracy of the application form by reflecting literature related to the heir. In this way, by referring to literature related to the heir, the content of the application form is optimized and its accuracy is improved. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input literature data related to the heir into the generation AI and have the generation AI improve the accuracy of the application form.

[0091] When generating the application form, the generation unit can adjust the use of technical terminology in the application form according to the heir's level of expertise. For example, the generation unit adjusts the use of technical terminology according to the heir's level of expertise. The generation unit can also use detailed technical terminology if the heir has technical expertise. For example, the generation unit uses concise and easy-to-understand terminology if the heir does not have technical expertise. This promotes understanding of the application form by adjusting the use of technical terminology according to the heir's level of expertise. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the heir's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0092] The confirmation unit can estimate the user's emotions and adjust the display method of the confirmation screen based on the estimated user emotions. For example, if the user is nervous, the confirmation unit provides a simple, highly visible display method. If the user is relaxed, the confirmation unit can also provide a display method including detailed information. For example, if the user is in a hurry, the confirmation unit provides a display method that focuses on the main points. This adjusts the display method of the confirmation screen according to the user's emotions, thereby reducing the user's stress and improving the efficiency of the confirmation work. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the confirmation unit can be performed using AI, for example, or without AI. For example, the confirmation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method of the confirmation screen.

[0093] The verification unit can propose an optimal verification procedure by referring to the heir's past verification history. The verification unit, for example, proposes an optimal verification procedure based on the heir's past verification history. The verification unit can also determine the priority of the verification content by referring to the heir's past verification history. For example, the verification unit optimizes the verification procedure by reflecting the heir's past verification history. In this way, by referring to the heir's past verification history, an optimal verification procedure is proposed and the efficiency of the verification work is improved. Some or all of the above-mentioned processing in the verification unit may be performed, for example, using AI, or may be performed without using AI. For example, the verification unit can input the heir's past verification history data into the generation AI and have the generation AI execute the proposed verification procedure.

[0094] The confirmation unit can provide a confirmation procedure based on local laws and regulations, taking into account the heir's geographical information. The confirmation unit can provide a confirmation procedure that automatically reflects local laws and regulations, for example, based on the heir's location. The confirmation unit can also guide the user through the necessary confirmation procedure based on the heir's geographical information. For example, the confirmation unit can display points to note regarding local laws and regulations depending on the heir's location. By taking into account the heir's geographical information, the confirmation unit can provide a confirmation procedure based on local laws and regulations and support accurate confirmation work. Some or all of the above-mentioned processing in the confirmation unit can be performed using, for example, AI, or can be performed without using AI. For example, the confirmation unit can input the heir's geographical information data into the generation AI and have the generation AI provide the confirmation procedure.

[0095] The verification unit can improve the verification procedure by reflecting the heir's feedback. The verification unit can improve the verification procedure, for example, based on feedback previously provided by the heir. The verification unit can also optimize the verification procedure by reflecting the heir's feedback. For example, the verification unit can adjust the priority of the verification content based on the heir's feedback. In this way, the verification procedure can be improved by reflecting the heir's feedback, and the efficiency of the verification work can be improved. Some or all of the above-mentioned processing in the verification unit can be performed, for example, using AI, or can be performed without using AI. For example, the verification unit can input the heir's feedback data into the generation AI and have the generation AI improve the verification procedure.

[0096] The confirmation unit can estimate the user's emotions and determine the priority of confirmation contents based on the estimated user emotions. For example, when the user is stressed, the confirmation unit can prioritize important information. When the user is relaxed, the confirmation unit can also prioritize detailed information. For example, when the user is in a hurry, the confirmation unit can prioritize the minimum amount of information. This allows the priority of confirmation contents to be determined according to the user's emotions, thereby allowing important information to be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the confirmation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the confirmation unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of confirmation contents.

[0097] During verification, the verification unit can analyze the heir's social media activity and automatically acquire related information. For example, the verification unit can analyze the heir's social media posts and automatically acquire related information. The verification unit can also automatically acquire related locations based on the heir's social media check-in information. For example, the verification unit can automatically acquire related information by referring to the activities of the heir's friends on social media. This allows the heir's social media activity to be analyzed, automatically acquiring related information and improving the efficiency of the verification process. Some or all of the above-mentioned processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit can input the heir's social media activity data into the generation AI and cause the generation AI to acquire related information.

[0098] The confirmation unit can customize the confirmation procedure by reflecting the heir's past feedback during confirmation. The confirmation unit customizes the confirmation procedure based on, for example, feedback provided by the heir in the past. The confirmation unit can also optimize the confirmation procedure by reflecting the heir's past feedback. For example, the confirmation unit adjusts the priority of the confirmation contents based on the heir's past feedback. This customizes the confirmation procedure by reflecting the heir's past feedback, improving the efficiency of the confirmation work. Some or all of the above-mentioned processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can input the heir's past feedback data into the generation AI and have the generation AI customize the confirmation procedure.

[0099] During confirmation, the confirmation unit can filter the confirmation content based on the heir's living situation and areas of interest. The confirmation unit, for example, filters necessary information based on the heir's living situation. The confirmation unit can also filter relevant information based on the heir's areas of interest. For example, the confirmation unit optimizes the confirmation content taking into account the heir's living situation and areas of interest. This improves the efficiency of the confirmation work by filtering the confirmation content based on the heir's living situation and areas of interest. Some or all of the above-mentioned processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can input the heir's living situation and area of ​​interest data into the generation AI and have the generation AI perform filtering of the confirmation content.

[0100] The review unit can estimate the user's emotions and adjust the review criteria based on the estimated user emotions. For example, if the user is nervous, the review unit can simplify the review criteria and focus on important points. If the user is relaxed, the review unit can provide detailed review criteria to promote overall understanding. For example, if the user is in a hurry, the review unit can set criteria for rapid review. This adjusts the review criteria according to the user's emotions, thereby reducing the user's stress and improving the efficiency of the review process. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the review unit can be performed using AI, for example, or without AI. For example, the review unit can input the user's emotion data into the generation AI and have the generation AI adjust the review criteria.

[0101] The examination department can provide examination criteria that take into account regional laws and regulations based on Ministry of Justice regulations. For example, the examination department can provide examination criteria that automatically reflect regional laws and regulations. The examination department can also automatically set necessary examination items based on regional laws and regulations. For example, the examination department can reflect points to note regarding regional laws and regulations in the examination criteria. This allows for accurate examination by providing examination criteria that take into account regional laws and regulations based on Ministry of Justice regulations. Some or all of the above-mentioned processing in the examination department can be performed using, for example, AI, or can be performed without using AI. For example, the examination department can input regional laws and regulations data into a generation AI and have the generation AI provide the examination criteria.

[0102] The examination department can improve the accuracy of the examination by referring to the heir's past examination history. For example, the examination department automatically inputs necessary information based on the heir's past examination history. The examination department can also optimize the examination content by referring to the heir's past examination history. For example, the examination department improves the accuracy of the examination by reflecting the heir's past examination history. In this way, the accuracy of the examination is improved by referring to the heir's past examination history. Some or all of the above-mentioned processing in the examination department may be performed, for example, using AI, or may be performed without using AI. For example, the examination department can input the heir's past examination history data into the generation AI and have the generation AI improve the accuracy of the examination.

[0103] The review department can improve the review criteria by reflecting the heirs' feedback. For example, the review department improves the review criteria based on feedback provided by the heirs in the past. The review department can also optimize the review criteria by reflecting the heirs' feedback. For example, the review department adjusts the priority of the review content based on the heirs' feedback. In this way, by reflecting the heirs' feedback, the review criteria are improved and the efficiency of the review work is improved. Some or all of the above-mentioned processing in the review department may be performed, for example, using AI, or may be performed without using AI. For example, the review department can input the heirs' feedback data into the generation AI and have the generation AI improve the review criteria.

[0104] The review unit can estimate the user's emotions and prioritize the review content based on the estimated user emotions. For example, if the user is feeling stressed, the review unit can prioritize important information. If the user is relaxed, the review unit can also review detailed information. For example, if the user is in a hurry, the review unit can review minimal information. This allows important information to be prioritized by determining the priority of the review content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the review unit can be performed using AI, for example, or without AI. For example, the review unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the review content.

[0105] During the review, the review department can analyze the heir's social media activity and automatically obtain relevant information. For example, the review department can analyze the heir's social media posts and automatically obtain relevant information. The review department can also automatically obtain relevant locations based on the heir's social media check-in information. For example, the review department can automatically obtain relevant information by referring to the activities of the heir's friends on social media. In this way, by analyzing the heir's social media activity, relevant information can be automatically obtained and the efficiency of the review work can be improved. Some or all of the above-mentioned processing in the review department can be performed, for example, using AI or without AI. For example, the review department can input the heir's social media activity data into the generation AI and have the generation AI obtain relevant information.

[0106] During the review, the review department can customize the review criteria by reflecting the heir's past feedback. For example, the review department customizes the review criteria based on feedback provided by the heir in the past. The review department can also optimize the review criteria by reflecting the heir's past feedback. For example, the review department adjusts the priority of the review content based on the heir's past feedback. In this way, the review criteria are customized by reflecting the heir's past feedback, improving the efficiency of the review work. Some or all of the above-mentioned processing in the review department may be performed, for example, using AI, or may be performed without using AI. For example, the review department can input the heir's past feedback data into the generation AI and have the generation AI customize the review criteria.

[0107] During the review, the review department can filter the review content based on the heir's living situation and areas of interest. For example, the review department filters necessary information based on the heir's living situation. The review department can also filter relevant information based on the heir's areas of interest. For example, the review department optimizes the review content by taking into account the heir's living situation and areas of interest. This improves the efficiency of the review work by filtering the review content based on the heir's living situation and areas of interest. Some or all of the above-mentioned processing in the review department may be performed using AI, for example, or may be performed without using AI. For example, the review department can input data on the heir's living situation and areas of interest into a generation AI and have the generation AI filter the review content. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, confirmation unit, and examination unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by either the smart device 14 or the data processing device 12. For example, information from a user can be received using the reception device 38 of the smart device 14. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a real estate registration application form using a generation AI. The confirmation unit is realized, for example, by the control unit 46A of the smart device 14 and provides an interface for the user to confirm and correct the generated application form. The examination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and verifies the accuracy of the generated application form and approves it. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, confirmation unit, and examination unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by either the smart glasses 214 or the data processing device 12. For example, information from a user can be received using the microphone 238 of the smart glasses 214. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a real estate registration application form using a generation AI. The confirmation unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides an interface for the user to confirm and correct the generated application form. The examination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and verifies the accuracy of the generated application form and approves it. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, confirmation unit, and examination unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by either the headset type terminal 314 or the data processing device 12. For example, information from a user can be received using the microphone 238 of the headset type terminal 314. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a real estate registration application form using a generation AI. The confirmation unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides an interface for the user to confirm and correct the generated application form. The examination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and confirms the accuracy of the generated application form and approves it. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, confirmation unit, and examination unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by either the robot 414 or the data processing device 12. For example, information from a user can be received using the microphone 238 of the robot 414. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a real estate registration application form using a generation AI. The confirmation unit is realized, for example, by the control unit 46A of the robot 414 and provides an interface for the user to confirm and correct the generated application form. The examination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and verifies the accuracy of the generated application form and approves it.

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

[0109] The reception unit can translate the user's input content in real time and provide a multilingual input guide. For example, the reception unit can translate information entered by the user in English into Japanese and provide a Japanese input guide. The reception unit can also translate information entered by the user in Chinese into English and provide an English input guide. In this way, the reception unit can provide a multilingual input guide and accommodate users who speak different languages.

[0110] The generation unit can provide relevant legal advice based on the user's input. For example, the generation unit can provide advice on inheritance tax based on inheritance information entered by the user. The generation unit can also provide advice on real estate valuation based on property information entered by the user. In this way, the generation unit can deepen the user's understanding by providing relevant legal advice based on the user's input.

[0111] The confirmation unit can estimate the user's emotions and visualize the progress of the confirmation work based on the estimated user emotions. For example, if the user is feeling stressed, the confirmation unit can display the progress in simple graphics. If the user is relaxed, the confirmation unit can also display detailed progress. In this way, the confirmation unit can visualize the progress of the confirmation work according to the user's emotions, thereby reducing the user's stress and improving work efficiency.

[0112] The review unit can estimate the user's emotions and adjust the notification method of the review result based on the estimated user's emotions. For example, if the user is nervous, the review unit can provide a notification method that is simple and to the point. If the user is relaxed, the review unit can also provide a notification method that includes a detailed explanation. In this way, the review unit can reduce the user's stress and promote understanding by adjusting the notification method of the review result according to the user's emotions.

[0113] The reception unit can analyze the user's input content, automatically detect input errors, and suggest corrections. For example, the reception unit can automatically detect an address entered incorrectly by the user and suggest the correct address. The reception unit can also suggest the correct information if there is an error in the information on heirs entered by the user. In this way, the reception unit can improve the accuracy of input by automatically detecting input errors by the user and suggesting corrections.

[0114] The generation unit can estimate the user's emotions and adjust the layout of the application form based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit can provide a simple, highly visible layout. If the user is feeling relaxed, the generation unit can also provide a layout including detailed information. In this way, the generation unit can adjust the layout of the application form according to the user's emotions, thereby reducing the user's stress and facilitating their understanding of the application form.

[0115] The confirmation unit can propose an optimal confirmation procedure based on the user's past confirmation history. For example, the confirmation unit analyzes the history of confirmation work performed by the user in the past and proposes an optimal procedure. The confirmation unit can also adjust the priority of confirmation contents based on the user's past confirmation history. In this way, the confirmation unit improves the efficiency of confirmation work by proposing an optimal confirmation procedure based on the user's past confirmation history.

[0116] The review unit can estimate the user's emotions and adjust the feedback method of the review result based on the estimated user's emotions. For example, if the user is nervous, the review unit can provide a feedback method that is simple and to the point. If the user is relaxed, the review unit can also provide a feedback method that includes detailed explanations. In this way, the review unit can adjust the feedback method of the review result according to the user's emotions, thereby reducing stress for the user and promoting understanding.

[0117] The reception unit can automatically generate templates for related legal documents based on the user's input. For example, the reception unit generates a template for an inheritance consent form based on inheritance information entered by the user. The reception unit can also generate a template for a real estate purchase and sale contract based on property information entered by the user. In this way, the reception unit can improve the efficiency of document creation by automatically generating templates for related legal documents based on the user's input.

[0118] The generation unit can estimate the user's emotions and adjust the deadline for submitting the application form based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit can suggest extending the deadline for submission. If the user is feeling relaxed, the generation unit can also maintain the normal deadline for submission. In this way, the generation unit adjusts the deadline for submitting the application form according to the user's emotions, thereby reducing the user's stress and improving the efficiency of the submission process.

[0119] The processing flow of the second embodiment will be briefly explained below.

[0120] Step 1: The reception unit receives information from the user. This information includes information on heirs, property information, inheritance information, etc. The reception unit provides input guidance and checking functions to help users enter information accurately. Step 2: The generation unit generates a real estate registration application form based on the information received by the reception unit. The generation unit uses generation AI to generate an accurate real estate registration application form based on the regulations of the Ministry of Justice. The generation AI uses text generation AI and multimodal generation AI to automatically fill in the necessary fields based on the information of the heirs and the contents of the inheritance division agreement. Step 3: The confirmation unit allows the user to confirm the application form generated by the generation unit and make corrections as necessary. The confirmation unit displays the generated application form and provides an editing function for the user to make corrections. The confirmation unit can also estimate the user's emotions and adjust the display method of the confirmation screen based on the estimated emotions. Step 4: The Review Department reviews the application form that has been confirmed and corrected by the Verification Department. The Review Department verifies the accuracy of the application form generated by the Generation AI and approves it in accordance with the Ministry of Justice regulations. The Review Department uses an AI model to verify the accuracy of the application form.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires 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.

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

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

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

[0126] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

[0130] 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).

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

[0132] 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.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0135] 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.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may 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.

[0140] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

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

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

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0144] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

[0146] 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).

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

[0148] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[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 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.

[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 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.

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

[0155] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may 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.

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

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

[0158] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0162] 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).

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

[0164] 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.

[0165] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0166] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0167] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0168] 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.

[0169] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0170] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires 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.

[0171] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0172] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may 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.

[0173] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0179] 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."

[0180] 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.

[0181] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0182] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a 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.

[0183] 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.

[0184] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0191] 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.

[0192] [Explanation of symbols]

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

Claims

1. a reception unit that receives information from a user; a generation unit that generates a real estate registration application form based on the information received by the reception unit; a confirmation unit that confirms and corrects the application form generated by the generation unit; and an examination unit that examines the application form confirmed and corrected by the confirmation unit. A system characterized by:

2. The reception unit Accept information about heirs or the contents of the inheritance division agreement 2. The system of claim 1.

3. The generation unit Generate accurate real estate registration applications in accordance with Ministry of Justice regulations 2. The system of claim 1.

4. The confirmation unit The user checks the generated application form and makes corrections as necessary.

2. The system of claim 1.

5. The Examination Division: Verify the accuracy of the application form generated by the AI ​​and approve it.

2. The system of claim 1.

6. The reception unit Provide input guide or check function 2. The system of claim 1.

7. The Examination Division: Equipped with a function that indicates the scope of registration application services provided by lawyers, administrative scriveners, and tax accountants 2. The system of claim 1.

8. The reception unit Estimate the user's emotions and adjust the display method of the input guide based on the estimated user emotions.

2. The system of claim 1.

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