system

Abstract

The system according to this embodiment aims to facilitate the necessary procedures after the death of a relative. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, a management unit, a provision unit, and a support unit. The reception unit receives information from the user. The analysis unit analyzes the information received by the reception unit and lists the necessary procedures. The generation unit automatically generates document templates based on the procedures listed by the analysis unit. The management unit manages the progress of the procedures based on the document templates generated by the generation unit. The provision unit provides information on how to contact relevant organizations and legal advice based on the progress managed by the management unit. The support unit provides emotional support based on the legal advice provided by the provision unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to smoothly proceed with the complicated procedures required after the death of a relative, which imposes a great burden on the bereaved family.

[0005] The system according to the embodiment aims to smoothly proceed with the procedures required after the death of a relative.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, a management unit, a provision unit, and a support unit. The reception unit receives information from users. The analysis unit analyzes the information received by the reception unit and lists the necessary procedures. The generation unit automatically generates document templates based on the procedures listed by the analysis unit. The management unit manages the progress of the procedures based on the document templates generated by the generation unit. The provision unit provides information on how to contact relevant organizations and legal advice based on the progress managed by the management unit. The support unit provides emotional support based on the legal advice provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can smoothly carry out the necessary procedures after the death of a relative. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The generative AI agent system according to an embodiment of the present invention is a system for smoothly navigating the complex procedures required after the death of a relative. This generative AI agent system aims to reduce the burden of various procedures faced by bereaved family members and related parties. The generative AI agent system provides clear guidance on the necessary steps, from filing the death certificate to inheritance, and supports document preparation and schedule management. The generative AI agent system also provides information on how to contact relevant organizations and legal advice, and supports access to experts as needed. Furthermore, the generative AI agent system also considers emotional support and provides information on mental health care. For example, the generative AI agent system is accessed by the user to begin the procedures required after the death of a relative. The user inputs information regarding the filing of the death certificate and inheritance. The generative AI agent system analyzes the input information and lists the necessary procedures. For example, this includes stopping pension payments, changing names on electricity, gas, and water accounts, canceling credit cards, changing names on mobile phone and internet provider accounts, surrendering driver's licenses, returning My Number cards, changing names on bank accounts (inheritance) or closing accounts, changing names on stocks (inheritance), changing names on real estate (inheritance), and canceling life insurance and non-life insurance contracts. Next, the generating AI agent system automatically generates templates of the documents required for each procedure and provides them to the user. The user fills out the documents according to the guidance of the generating AI agent system and proceeds with the necessary procedures. The generating AI agent system manages the progress of the procedures on a schedule and sends reminders to the user. Furthermore, the generating AI agent system provides information on how to contact relevant organizations and legal advice. For example, the generating AI agent system guides the user on which organizations to contact and how, and provides necessary legal information. The generating AI agent system also supports access to professionals (lawyers, tax accountants, administrative scriveners, etc.) as needed. Finally, the generating AI agent system also provides emotional support. For example, a generative AI agent system provides information about mental health care and supports users in reducing their emotional burden.This allows users to spend their precious time with peace of mind. It also enables the AI ​​agent system to smoothly handle the necessary procedures after the death of a family member.

[0029] The generation AI agent system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, a management unit, a provision unit, and a support unit. The reception unit receives information from the user. Information from the user includes, but is not limited to, personal information, procedural information, and legal information. For example, the reception unit receives information on death notifications entered by the user. The reception unit can also receive information on inheritance. Furthermore, the reception unit can also receive information on other procedures provided by the user. For example, the reception unit receives information on pension payment suspension entered by the user. The analysis unit analyzes the information received by the reception unit and lists the necessary procedures. For example, the analysis unit lists the necessary procedures based on the information entered by the user. The analysis unit can also list procedures using information classification methods and analysis algorithms. For example, the analysis unit classifies the information entered by the user and extracts the information necessary for each procedure. The generation unit automatically generates document templates based on the procedures listed by the analysis unit. The generation unit generates document templates based on information entered by the user, for example. The generation unit can also automatically generate templates for documents required for a procedure. For example, the generation unit generates a death certificate template based on information entered by the user. The management unit manages the progress of the procedure based on the document templates generated by the generation unit. The management unit manages the progress of the procedure based on information entered by the user, for example. The management unit can also schedule the progress of the procedure and send reminders to the user. For example, the management unit manages the progress of the pension suspension procedure based on information entered by the user. The provision unit provides information on how to contact relevant organizations and legal advice based on the progress managed by the management unit. The provision unit provides information on how to contact relevant organizations based on information entered by the user, for example. The provision unit can also provide legal advice. For example, the provision unit provides legal advice regarding the pension suspension procedure based on information entered by the user. The support unit provides emotional support based on the legal advice provided by the provision unit.The support unit provides emotional support, for example, based on information entered by the user. The support unit can also provide information related to mental health care. For instance, based on information entered by the user, the support unit provides emotional support regarding the procedure for suspending pension payments. This allows the generative AI agent system according to the embodiment to efficiently receive and analyze user information, generate documents, manage progress, and provide legal advice and emotional support.

[0030] The reception department receives information from users. This information includes, but is not limited to, personal information, procedural information, and legal information. For example, the reception department receives death notification information entered by users. It can also receive information regarding inheritance. Furthermore, the reception department can receive information regarding other procedures provided by users. For example, the reception department receives pension payment suspension information entered by users. The reception department provides multiple input methods to securely and efficiently receive information provided by users. For example, information can be received through web forms, mobile apps, telephone, and mail. This allows users to provide information in the way that is most convenient for them. Furthermore, the reception department has a function to automatically classify the received information and distribute it to the appropriate department. For example, death notification information is distributed to the legal procedures department, and pension payment suspension information is distributed to the pension management department. This ensures that information is processed quickly and accurately. The reception department also has a checking function to verify the accuracy of the information provided by users. For example, the system automatically checks whether the entered information is correct and whether all necessary information has been provided, and notifies the user if any information is missing. This prevents delays in the process and ensures smooth progress. Furthermore, the reception department implements security measures such as information encryption and access control to protect user privacy. This prevents unauthorized access to users' personal information and ensures its secure management.

[0031] The analysis unit analyzes the information received by the reception unit and lists the necessary procedures. For example, the analysis unit lists the necessary procedures based on the information entered by the user. The analysis unit can also list procedures using information classification methods and analysis algorithms. For example, the analysis unit classifies the information entered by the user and extracts the information necessary for each procedure. The analysis unit uses AI to analyze the information and determine the priority of procedures. For example, it uses natural language processing technology to analyze the text information entered by the user and extract keywords and phrases related to the procedures. This allows the necessary procedures to be automatically listed and presented to the user. Furthermore, the analysis unit can use past data and statistical information to predict the progress and success rate of procedures. For example, it can predict the time required for a procedure and the success rate based on data from users who have performed similar procedures in the past and provide this information to the user. This allows the user to understand the progress of the procedure and take appropriate measures. The analysis unit can also automatically extract the documents and information necessary for a procedure and provide them to the user. For example, it can list the documents and information necessary for a death certificate procedure and notify the user. This allows users to prepare necessary documents in advance, ensuring a smoother process. Furthermore, the analysis unit monitors the progress of the process in real time and can respond immediately if any anomalies occur. For example, if the process is behind schedule or necessary information is missing, the system can notify the user and take appropriate measures. This streamlines the process and improves user satisfaction.

[0032] The generation unit automatically generates document templates based on the procedures listed by the analysis unit. For example, the generation unit generates document templates based on information entered by the user. The generation unit can also automatically generate templates for documents required for each procedure. For example, the generation unit generates a death certificate template based on information entered by the user. The generation unit utilizes AI to generate the optimal document template based on the information provided by the user. For example, it uses natural language generation technology to analyze the information entered by the user and create documents with appropriate wording and format. This allows the user to prepare the necessary documents without any effort. Furthermore, the generation unit can automatically customize the format and content of the documents required for each procedure. For example, even if the required document format differs depending on the region or institution, the generation unit generates a corresponding template. This saves the user the trouble of preparing different documents for each region or institution. In addition, the generation unit has a function to review and modify the content of the generated document templates before providing them to the user. For example, the user can review the content of the generated documents and make corrections as needed. This ensures that the user is satisfied with the content of the documents and that the procedures proceed smoothly. Furthermore, the generation unit can securely store templates of the generated documents and reuse them as needed. For example, if a user performs a similar procedure again, they can reuse a previously generated template to improve the efficiency of the procedure. This allows the generation unit to support the user's procedures efficiently and effectively.

[0033] The management department manages the progress of procedures based on document templates generated by the generation department. For example, the management department manages the progress of procedures based on information entered by the user. The management department can also schedule the progress of procedures and send reminders to users. For example, the management department manages the progress of pension benefit suspension procedures based on information entered by the user. The management department monitors the progress of procedures in real time and notifies the user as needed. For example, if the progress of the procedure is delayed or necessary documents are missing, the management department can send a reminder to the user and take appropriate measures. This makes the process smoother and improves user satisfaction. Furthermore, the management department has a function to visualize the progress of procedures. For example, it can display the progress of procedures in graphs and charts so that users can grasp the progress at a glance. This allows users to intuitively understand the progress of procedures and take necessary measures quickly. The management department also has a function to suggest the next action to take to the user according to the progress of the procedure. For example, if a procedure is progressing smoothly, the system will suggest the next steps to take, supporting the user to ensure a smooth process. This allows users to efficiently manage the procedure and reduce the time it takes to complete it. Furthermore, the management department can accumulate data on the progress of procedures and use it to improve procedures in the future. For instance, by analyzing the data on the progress of procedures, bottlenecks and areas for improvement can be identified. This can improve the efficiency of procedures and further increase user satisfaction.

[0034] The Service Department provides information on how to contact relevant organizations and legal advice based on the progress managed by the Management Department. For example, the Service Department will guide users on how to contact relevant organizations based on the information they have entered. The Service Department can also provide legal advice. For example, the Service Department will provide legal advice on the procedure for suspending pension payments based on the information the user has entered. The Service Department will provide detailed information on how to contact relevant organizations to help users proceed with the procedure smoothly. For example, they will provide contact information, methods of contact, and information required when contacting relevant organizations. This will allow users to communicate with relevant organizations smoothly. Furthermore, the Service Department can incorporate expert opinions when providing legal advice. For example, they will provide users with appropriate advice based on the opinions of lawyers and consultants with legal expertise. This will allow users to proceed with legal procedures with peace of mind. The Service Department will also provide advice on potential problems and risks that users may face while proceeding with the procedure. For example, they will guide users on potential legal troubles that may arise during the procedure and measures to take against delays in the procedure. This will allow users to proceed with the procedure smoothly and respond quickly if problems arise. Furthermore, the service provider can also provide users with the necessary documents and information to proceed with the procedures. For example, they can provide document formats and examples for submission to relevant institutions, as well as required attachments. This allows users to obtain all the necessary information to smoothly proceed with the procedures in one place. The service provider can support users in smoothly completing the procedures and improve the success rate of the procedures.

[0035] The support department provides emotional support based on the legal advice provided by the service provider. For example, the support department provides emotional support based on information entered by the user. The support department can also provide information on mental health care. For example, the support department provides emotional support regarding the pension suspension procedure based on information entered by the user. The support department provides emotional support to alleviate the stress and anxiety the user feels while going through the procedure. For example, it provides appropriate advice and words of encouragement for any anxieties or questions the user may have while going through the procedure. This allows the user to proceed with the procedure with peace of mind. Furthermore, the support department provides information on mental health care necessary for the user as they go through the procedure. For example, it provides information on stress management, relaxation methods, and counseling services. This allows the user to reduce the stress they feel while going through the procedure and maintain their mental health. The support department also addresses any emotional issues the user may face while going through the procedure. For example, it provides appropriate support for emotions such as sadness, anger, and anxiety felt during the process. This reduces the emotional burden the user feels while going through the procedure and allows them to proceed smoothly. Furthermore, the support department continuously provides the necessary assistance to users as they proceed with the process. For example, they periodically review the support provided based on the progress of the process and provide additional support as needed. This ensures that users always receive appropriate support as they go through the process, thereby improving the success rate of the process.

[0036] The reception unit can analyze the user's past procedure history and select the most suitable method for receiving information. For example, the reception unit can prioritize suggesting procedures the user has used in the past. The reception unit can also automatically display procedures the user has frequently performed in the past as candidates. Furthermore, the reception unit can predict and suggest procedures to be performed at specific times based on the user's past procedure history. This improves user convenience by providing the most suitable method for receiving information based on past procedure history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's past procedure history data into a generating AI and have the generating AI select the most suitable method for receiving information.

[0037] The reception unit can filter information upon receipt based on the user's current situation and areas of interest. For example, the reception unit can prioritize displaying information related to the procedure the user is currently facing. It can also filter and provide relevant procedural information based on the user's areas of interest. Furthermore, the reception unit can prioritize suggesting necessary procedures according to the user's current situation. This improves user convenience by providing relevant information based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's current situation data into a generating AI and have the generating AI perform the filtering.

[0038] The reception desk can prioritize receiving highly relevant information by considering the user's geographical location when receiving information. For example, the reception desk can prioritize displaying information about the nearest relevant organization based on the user's current location. The reception desk can also provide region-specific procedural information based on the user's geographical location. Furthermore, the reception desk can suggest the most suitable procedure based on the user's location. This improves user convenience by providing highly relevant information based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI select highly relevant information.

[0039] The reception desk can analyze the user's social media activity and receive relevant information upon receiving the information. For example, the reception desk can suggest procedures relevant to the user's current situation based on the user's social media posts. The reception desk can also analyze the user's social media activity and provide information on procedures of interest. Furthermore, the reception desk can suggest relevant procedures considering the user's social media friendships. This improves user convenience by providing relevant information based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI select relevant information.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit can analyze information about important procedures in detail and provide it to the user. It can also analyze information about lower-priority procedures concisely and provide it to the user. Furthermore, the analysis unit can adjust the level of detail of the analysis in stages according to the importance of the information. This supports user understanding by providing an analysis level that matches the importance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0041] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a legal analysis algorithm to information about legal procedures. It can also apply a financial analysis algorithm to information about financial procedures. Furthermore, it can apply an emotional analysis algorithm to information about emotional support. This improves the accuracy of the analysis by applying an analysis algorithm appropriate to the category of information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0042] The analysis unit can determine the priority of analysis based on the submission timing of the information during the analysis process. For example, the analysis unit will prioritize the analysis of information related to procedures with approaching submission deadlines. It can also postpone the analysis of information related to procedures with ample time for submission. Furthermore, the analysis unit can adjust the priority of analysis in stages based on the submission timing. This improves user convenience by providing analysis priorities based on the submission timing of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information submission timing data into a generating AI and have the generating AI determine the analysis priority.

[0043] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant information and provide it to the user. It can also postpone the analysis of less relevant information. Furthermore, the analysis unit can adjust the order of analysis in stages based on the relevance of the information. This improves user convenience by providing an analysis order based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0044] The generation unit can adjust the level of detail in a document template based on the importance of the procedure when generating the template. For example, the generation unit can generate detailed document templates for important procedures. It can also generate concise document templates for lower-priority procedures. Furthermore, the generation unit can adjust the level of detail in the template in stages according to the importance of the procedure. This supports user understanding by providing a level of detail in the template that corresponds to the importance of the procedure. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input procedure importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the template.

[0045] The generation unit can apply different template generation algorithms depending on the category of the procedure when generating document templates. For example, the generation unit can apply a legal template generation algorithm to document templates related to legal procedures. It can also apply a financial template generation algorithm to document templates related to financial procedures. Furthermore, it can apply an emotional template generation algorithm to document templates related to emotional support. This improves the accuracy of the templates by applying a template generation algorithm according to the category of the procedure. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input procedure category data into a generation AI and have the generation AI execute the application of the template generation algorithm.

[0046] The generation unit can determine the priority of document templates based on the submission deadline for each procedure when generating them. For example, the generation unit can prioritize the generation of templates for procedures with approaching submission deadlines. It can also postpone the generation of templates for procedures with ample time for submission. Furthermore, the generation unit can adjust the priority of templates in stages based on the submission deadline. This improves user convenience by providing template priorities based on the submission deadline for each procedure. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input procedure submission deadline data into a generation AI and have the generation AI determine the priority of templates.

[0047] The generation unit can adjust the order of templates based on the relevance of procedures when generating document templates. For example, the generation unit can prioritize the generation of templates related to highly relevant procedures. It can also postpone the generation of templates related to less relevant procedures. Furthermore, the generation unit can adjust the order of templates in stages based on the relevance of procedures. This improves user convenience by providing a template order based on the relevance of procedures. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input procedure relevance data into a generation AI and have the generation AI perform the adjustment of the template order.

[0048] The management department can select the optimal management method by referring to past progress data when managing the progress of procedures. For example, the management department can propose the optimal management method based on the progress data of procedures previously performed by the user. The management department can also predict the time required for the procedure to proceed based on past progress data and notify the user. Furthermore, the management department can analyze past progress data and propose methods to streamline the procedure. In this way, the procedure is made more efficient by providing the optimal management method based on past progress data. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input past progress data into a generating AI and have the generating AI select the optimal management method.

[0049] The management department can apply different management methods to each category of procedure when managing the progress of procedures. For example, the management department can manage the progress of legal procedures by applying legal management methods. It can also manage the progress of financial procedures by applying financial management methods. Furthermore, it can manage the progress of emotional support by applying emotional management methods. This streamlines the progress of procedures by applying management methods appropriate to the category of procedure. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input procedure category data into a generating AI and have the generating AI apply the management methods.

[0050] The management department can analyze changes in the progress of procedures based on the submission date when managing the progress of procedures. For example, the management department can prioritize the management of procedures with approaching submission deadlines. It can also postpone the management of procedures with ample time before the submission deadline. Furthermore, the management department can analyze changes in progress in stages based on the submission date. This improves user convenience by providing information on changes in progress based on the submission date of procedures. Some or all of the above processing in the management department may be performed using AI, for example, or not. For example, the management department can input procedure submission date data into a generating AI and have the generating AI perform an analysis of changes in progress.

[0051] The management department can analyze the progress of a procedure by referring to relevant market data when managing the progress of the procedure. For example, the management department can predict the progress of the procedure based on the relevant market data and notify the user. The management department can also refer to the relevant market data to suggest ways to streamline the progress of the procedure. Furthermore, the management department can analyze the relevant market data and optimize the progress of the procedure. This streamlines the progress of the procedure by providing an analysis of the procedure's progress based on relevant market data. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input relevant market data into a generating AI and have the generating AI perform the progress analysis.

[0052] The service provider can select the optimal advice method by referring to past advice data when providing legal advice. For example, the service provider can propose the optimal advice method based on the legal advice the user has received in the past. The service provider can also select the most suitable advice method for the user from past advice data. Furthermore, the service provider can analyze past advice data and optimize the method of providing legal advice. This supports the user's understanding by providing the optimal advice method based on past advice data. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input past advice data into a generating AI and have the generating AI select the optimal advice method.

[0053] The service provider can apply different advice methods depending on the category of the procedure when providing legal advice. For example, the service provider can apply a legal advice method to advice on legal procedures. It can also apply a financial advice method to advice on financial procedures. Furthermore, it can apply an emotional advice method to advice on emotional support. This improves the accuracy of the advice by applying an advice method appropriate to the category of the procedure. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input procedure category data into a generating AI and have the generating AI apply the advice method.

[0054] The service provider can adjust the importance of legal advice based on the filing date of the procedure. For example, the service provider can prioritize advice for procedures with approaching filing deadlines. Conversely, it can postpone advice for procedures with ample time before filing. Furthermore, the service provider can adjust the importance of advice in stages based on the filing date. This improves user convenience by providing advice importance based on the filing date of the procedure. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input procedure filing date data into a generating AI and have the generating AI perform the adjustment of advice importance.

[0055] The service provider can improve the accuracy of its legal advice by referring to relevant literature on the procedure. For example, the service provider can provide accurate advice on the procedure based on relevant literature. The service provider can also supplement the content of the advice by referring to relevant literature. Furthermore, the service provider can analyze relevant literature to improve the accuracy of the advice. This supports user understanding by providing accurate advice based on relevant literature on the procedure. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input relevant literature data into a generating AI and have the generating AI perform the improvement of the accuracy of the advice.

[0056] The support unit can select the optimal support method when providing emotional support by referring to past support data. For example, the support unit can propose the optimal support method based on the emotional support the user has received in the past. The support unit can also select the most suitable support method for the user from past support data. Furthermore, the support unit can analyze past support data and optimize the method of providing emotional support. This reduces the burden on the user by providing the optimal support method based on past support data. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input past support data into a generating AI and have the generating AI select the optimal support method.

[0057] The support unit can customize the means of support based on the user's current situation when providing emotional support. For example, the support unit can provide the optimal means of support based on the user's current emotional state. The support unit can also customize the means of support according to the user's current situation. Furthermore, the support unit can analyze the user's current situation and propose the optimal means of support. This reduces the burden on the user by providing means of support that are appropriate to the user's current situation. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's current situation data into a generating AI and have the generating AI perform the customization of the means of support.

[0058] The support unit can select the optimal support method when providing emotional support, taking into account the user's geographical location. For example, the support unit can provide information on the nearest support organization based on the user's current location. The support unit can also provide region-specific support information based on the user's geographical location. Furthermore, the support unit can suggest the optimal support method based on the user's location. This reduces the user's burden by providing the optimal support method based on the user's geographical location. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal support method.

[0059] The support unit can analyze a user's social media activity and suggest support measures when providing emotional support. For example, the support unit can suggest support relevant to the current situation based on the user's social media posts. The support unit can also analyze a user's social media activity and provide support information of interest. Furthermore, the support unit can consider the user's social media friendships and suggest relevant support. This reduces the user's burden by providing the most appropriate support measures based on the user's social media activity. Some or all of the above processing in the support unit may be performed using AI, for example, or not. For example, the support unit can input the user's social media data into a generating AI and have the generating AI execute the suggestion of support measures.

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

[0061] The analysis unit can analyze a user's past procedure history and select the optimal method for receiving information. For example, it can prioritize suggesting procedures the user has used in the past. It can also automatically display procedures the user has frequently performed in the past as candidates. Furthermore, it can predict and suggest procedures to be performed during specific time periods based on the user's past procedure history. In this way, by providing the optimal method for receiving information based on past procedure history, it improves user convenience.

[0062] The management department can select the optimal management method by referring to past progress data when managing the progress of procedures. For example, it can propose the optimal management method based on the progress data of procedures previously performed by the user. It can also predict the time required for the procedure to proceed based on past progress data and notify the user. Furthermore, it can analyze past progress data and propose methods to streamline the procedure. In this way, the procedure is made more efficient by providing the optimal management method based on past progress data.

[0063] The reception desk can prioritize receiving highly relevant information by considering the user's geographical location when receiving information. For example, it can prioritize displaying information about the nearest relevant organization based on the user's current location. It can also provide region-specific procedural information based on the user's geographical location. Furthermore, it can suggest the most suitable procedure based on the user's location. In this way, by providing highly relevant information based on the user's geographical location, user convenience is improved.

[0064] The analysis unit can adjust the level of detail in the analysis based on the importance of the information. For example, information about important procedures can be analyzed in detail and provided to the user. Conversely, information about lower-priority procedures can be analyzed concisely and provided to the user. Furthermore, the level of detail in the analysis can be adjusted in stages according to the importance of the information. This supports user understanding by providing an analysis level appropriate to the importance of the information.

[0065] The service provider can adjust the importance of legal advice based on the filing date of the procedure. For example, it can prioritize advice on procedures with approaching deadlines, while delaying advice on procedures with ample time before filing. Furthermore, it can adjust the importance of advice in stages based on the filing date. This improves user convenience by providing advice tailored to the filing date of the procedure.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception desk receives information from users. This information includes personal information, procedural information, and legal information. For example, it receives information such as death certificates, inheritance information, and pension payment suspension information entered by the user. Step 2: The analysis unit analyzes the information received by the reception unit and lists the necessary procedures. Based on the information entered by the user, the analysis unit lists the procedures using information classification methods and analysis algorithms. For example, it classifies the information entered by the user and extracts the information necessary for each procedure. Step 3: The generation unit automatically generates document templates based on the procedures listed by the analysis unit. The generation unit automatically generates the necessary document templates for the procedures based on the information entered by the user. For example, it generates a death certificate template. Step 4: The management department manages the progress of the procedure based on the document templates generated by the generation department. The management department can also schedule the progress of the procedure based on the information entered by the user and send reminders to the user. For example, it can manage the progress of the procedure for suspending pension payments. Step 5: The service provider will provide information on how to contact relevant organizations and legal advice based on the progress managed by the administration department. Based on the information entered by the user, the service provider will guide the user on how to contact relevant organizations and provide legal advice. For example, they will provide legal advice regarding the procedure for suspending pension payments. Step 6: The support department provides emotional support based on the legal advice provided by the service provider. The support department provides information on emotional support and mental health care based on the information entered by the user. For example, they provide emotional support regarding the procedure for suspending pension payments.

[0068] (Example of form 2) The generative AI agent system according to an embodiment of the present invention is a system for smoothly navigating the complex procedures required after the death of a relative. This generative AI agent system aims to reduce the burden of various procedures faced by bereaved family members and related parties. The generative AI agent system provides clear guidance on the necessary steps, from filing the death certificate to inheritance, and supports document preparation and schedule management. The generative AI agent system also provides information on how to contact relevant organizations and legal advice, and supports access to experts as needed. Furthermore, the generative AI agent system also considers emotional support and provides information on mental health care. For example, the generative AI agent system is accessed by the user to begin the procedures required after the death of a relative. The user inputs information regarding the filing of the death certificate and inheritance. The generative AI agent system analyzes the input information and lists the necessary procedures. For example, this includes stopping pension payments, changing names on electricity, gas, and water accounts, canceling credit cards, changing names on mobile phone and internet provider accounts, surrendering driver's licenses, returning My Number cards, changing names on bank accounts (inheritance) or closing accounts, changing names on stocks (inheritance), changing names on real estate (inheritance), and canceling life insurance and non-life insurance contracts. Next, the generating AI agent system automatically generates templates of the documents required for each procedure and provides them to the user. The user fills out the documents according to the guidance of the generating AI agent system and proceeds with the necessary procedures. The generating AI agent system manages the progress of the procedures on a schedule and sends reminders to the user. Furthermore, the generating AI agent system provides information on how to contact relevant organizations and legal advice. For example, the generating AI agent system guides the user on which organizations to contact and how, and provides necessary legal information. The generating AI agent system also supports access to professionals (lawyers, tax accountants, administrative scriveners, etc.) as needed. Finally, the generating AI agent system also provides emotional support. For example, a generative AI agent system provides information about mental health care and supports users in reducing their emotional burden.This allows users to spend their precious time with peace of mind. It also enables the AI ​​agent system to smoothly handle the necessary procedures after the death of a family member.

[0069] The generation AI agent system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, a management unit, a provision unit, and a support unit. The reception unit receives information from the user. Information from the user includes, but is not limited to, personal information, procedural information, and legal information. For example, the reception unit receives information on death notifications entered by the user. The reception unit can also receive information on inheritance. Furthermore, the reception unit can also receive information on other procedures provided by the user. For example, the reception unit receives information on pension payment suspension entered by the user. The analysis unit analyzes the information received by the reception unit and lists the necessary procedures. For example, the analysis unit lists the necessary procedures based on the information entered by the user. The analysis unit can also list procedures using information classification methods and analysis algorithms. For example, the analysis unit classifies the information entered by the user and extracts the information necessary for each procedure. The generation unit automatically generates document templates based on the procedures listed by the analysis unit. The generation unit generates document templates based on information entered by the user, for example. The generation unit can also automatically generate templates for documents required for a procedure. For example, the generation unit generates a death certificate template based on information entered by the user. The management unit manages the progress of the procedure based on the document templates generated by the generation unit. The management unit manages the progress of the procedure based on information entered by the user, for example. The management unit can also schedule the progress of the procedure and send reminders to the user. For example, the management unit manages the progress of the pension suspension procedure based on information entered by the user. The provision unit provides information on how to contact relevant organizations and legal advice based on the progress managed by the management unit. The provision unit provides information on how to contact relevant organizations based on information entered by the user, for example. The provision unit can also provide legal advice. For example, the provision unit provides legal advice regarding the pension suspension procedure based on information entered by the user. The support unit provides emotional support based on the legal advice provided by the provision unit.The support unit provides emotional support, for example, based on information entered by the user. The support unit can also provide information related to mental health care. For instance, based on information entered by the user, the support unit provides emotional support regarding the procedure for suspending pension payments. This allows the generative AI agent system according to the embodiment to efficiently receive and analyze user information, generate documents, manage progress, and provide legal advice and emotional support.

[0070] The reception department receives information from users. This information includes, but is not limited to, personal information, procedural information, and legal information. For example, the reception department receives death notification information entered by users. It can also receive information regarding inheritance. Furthermore, the reception department can receive information regarding other procedures provided by users. For example, the reception department receives pension payment suspension information entered by users. The reception department provides multiple input methods to securely and efficiently receive information provided by users. For example, information can be received through web forms, mobile apps, telephone, and mail. This allows users to provide information in the way that is most convenient for them. Furthermore, the reception department has a function to automatically classify the received information and distribute it to the appropriate department. For example, death notification information is distributed to the legal procedures department, and pension payment suspension information is distributed to the pension management department. This ensures that information is processed quickly and accurately. The reception department also has a checking function to verify the accuracy of the information provided by users. For example, the system automatically checks whether the entered information is correct and whether all necessary information has been provided, and notifies the user if any information is missing. This prevents delays in the process and ensures smooth progress. Furthermore, the reception department implements security measures such as information encryption and access control to protect user privacy. This prevents unauthorized access to users' personal information and ensures its secure management.

[0071] The analysis unit analyzes the information received by the reception unit and lists the necessary procedures. For example, the analysis unit lists the necessary procedures based on the information entered by the user. The analysis unit can also list procedures using information classification methods and analysis algorithms. For example, the analysis unit classifies the information entered by the user and extracts the information necessary for each procedure. The analysis unit uses AI to analyze the information and determine the priority of procedures. For example, it uses natural language processing technology to analyze the text information entered by the user and extract keywords and phrases related to the procedures. This allows the necessary procedures to be automatically listed and presented to the user. Furthermore, the analysis unit can use past data and statistical information to predict the progress and success rate of procedures. For example, it can predict the time required for a procedure and the success rate based on data from users who have performed similar procedures in the past and provide this information to the user. This allows the user to understand the progress of the procedure and take appropriate measures. The analysis unit can also automatically extract the documents and information necessary for a procedure and provide them to the user. For example, it can list the documents and information necessary for a death certificate procedure and notify the user. This allows users to prepare necessary documents in advance, ensuring a smoother process. Furthermore, the analysis unit monitors the progress of the process in real time and can respond immediately if any anomalies occur. For example, if the process is behind schedule or necessary information is missing, the system can notify the user and take appropriate measures. This streamlines the process and improves user satisfaction.

[0072] The generation unit automatically generates document templates based on the procedures listed by the analysis unit. For example, the generation unit generates document templates based on information entered by the user. The generation unit can also automatically generate templates for documents required for each procedure. For example, the generation unit generates a death certificate template based on information entered by the user. The generation unit utilizes AI to generate the optimal document template based on the information provided by the user. For example, it uses natural language generation technology to analyze the information entered by the user and create documents with appropriate wording and format. This allows the user to prepare the necessary documents without any effort. Furthermore, the generation unit can automatically customize the format and content of the documents required for each procedure. For example, even if the required document format differs depending on the region or institution, the generation unit generates a corresponding template. This saves the user the trouble of preparing different documents for each region or institution. In addition, the generation unit has a function to review and modify the content of the generated document templates before providing them to the user. For example, the user can review the content of the generated documents and make corrections as needed. This ensures that the user is satisfied with the content of the documents and that the procedures proceed smoothly. Furthermore, the generation unit can securely store templates of the generated documents and reuse them as needed. For example, if a user performs a similar procedure again, they can reuse a previously generated template to improve the efficiency of the procedure. This allows the generation unit to support the user's procedures efficiently and effectively.

[0073] The management department manages the progress of procedures based on document templates generated by the generation department. For example, the management department manages the progress of procedures based on information entered by the user. The management department can also schedule the progress of procedures and send reminders to users. For example, the management department manages the progress of pension benefit suspension procedures based on information entered by the user. The management department monitors the progress of procedures in real time and notifies the user as needed. For example, if the progress of the procedure is delayed or necessary documents are missing, the management department can send a reminder to the user and take appropriate measures. This makes the process smoother and improves user satisfaction. Furthermore, the management department has a function to visualize the progress of procedures. For example, it can display the progress of procedures in graphs and charts so that users can grasp the progress at a glance. This allows users to intuitively understand the progress of procedures and take necessary measures quickly. The management department also has a function to suggest the next action to take to the user according to the progress of the procedure. For example, if a procedure is progressing smoothly, the system will suggest the next steps to take, supporting the user to ensure a smooth process. This allows users to efficiently manage the procedure and reduce the time it takes to complete it. Furthermore, the management department can accumulate data on the progress of procedures and use it to improve procedures in the future. For instance, by analyzing the data on the progress of procedures, bottlenecks and areas for improvement can be identified. This can improve the efficiency of procedures and further increase user satisfaction.

[0074] The Service Department provides information on how to contact relevant organizations and legal advice based on the progress managed by the Management Department. For example, the Service Department will guide users on how to contact relevant organizations based on the information they have entered. The Service Department can also provide legal advice. For example, the Service Department will provide legal advice on the procedure for suspending pension payments based on the information the user has entered. The Service Department will provide detailed information on how to contact relevant organizations to help users proceed with the procedure smoothly. For example, they will provide contact information, methods of contact, and information required when contacting relevant organizations. This will allow users to communicate with relevant organizations smoothly. Furthermore, the Service Department can incorporate expert opinions when providing legal advice. For example, they will provide users with appropriate advice based on the opinions of lawyers and consultants with legal expertise. This will allow users to proceed with legal procedures with peace of mind. The Service Department will also provide advice on potential problems and risks that users may face while proceeding with the procedure. For example, they will guide users on potential legal troubles that may arise during the procedure and measures to take against delays in the procedure. This will allow users to proceed with the procedure smoothly and respond quickly if problems arise. Furthermore, the service provider can also provide users with the necessary documents and information to proceed with the procedures. For example, they can provide document formats and examples for submission to relevant institutions, as well as required attachments. This allows users to obtain all the necessary information to smoothly proceed with the procedures in one place. The service provider can support users in smoothly completing the procedures and improve the success rate of the procedures.

[0075] The support department provides emotional support based on the legal advice provided by the service provider. For example, the support department provides emotional support based on information entered by the user. The support department can also provide information on mental health care. For example, the support department provides emotional support regarding the pension suspension procedure based on information entered by the user. The support department provides emotional support to alleviate the stress and anxiety the user feels while going through the procedure. For example, it provides appropriate advice and words of encouragement for any anxieties or questions the user may have while going through the procedure. This allows the user to proceed with the procedure with peace of mind. Furthermore, the support department provides information on mental health care necessary for the user as they go through the procedure. For example, it provides information on stress management, relaxation methods, and counseling services. This allows the user to reduce the stress they feel while going through the procedure and maintain their mental health. The support department also addresses any emotional issues the user may face while going through the procedure. For example, it provides appropriate support for emotions such as sadness, anger, and anxiety felt during the process. This reduces the emotional burden the user feels while going through the procedure and allows them to proceed smoothly. Furthermore, the support department continuously provides the necessary assistance to users as they proceed with the process. For example, they periodically review the support provided based on the progress of the process and provide additional support as needed. This ensures that users always receive appropriate support as they go through the process, thereby improving the success rate of the process.

[0076] The reception desk can estimate the user's emotions and adjust the information processing method based on the estimated emotions. For example, if the user is sad, the reception desk can provide guidance in a gentle tone and proceed with the procedure slowly. If the user is anxious, the reception desk can also provide concise guidance to expedite the process. Furthermore, if the user is confused, the reception desk can provide detailed step-by-step guidance to support the process. This reduces the user's burden by providing an information processing method that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0077] The reception unit can analyze the user's past procedure history and select the most suitable method for receiving information. For example, the reception unit can prioritize suggesting procedures the user has used in the past. The reception unit can also automatically display procedures the user has frequently performed in the past as candidates. Furthermore, the reception unit can predict and suggest procedures to be performed at specific times based on the user's past procedure history. This improves user convenience by providing the most suitable method for receiving information based on past procedure history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's past procedure history data into a generating AI and have the generating AI select the most suitable method for receiving information.

[0078] The reception unit can filter information upon receipt based on the user's current situation and areas of interest. For example, the reception unit can prioritize displaying information related to the procedure the user is currently facing. It can also filter and provide relevant procedural information based on the user's areas of interest. Furthermore, the reception unit can prioritize suggesting necessary procedures according to the user's current situation. This improves user convenience by providing relevant information based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's current situation data into a generating AI and have the generating AI perform the filtering.

[0079] The reception desk can estimate the user's emotions and prioritize the information it receives based on those emotions. For example, if the user is sad, the reception desk will prioritize guiding them through important procedures to alleviate their emotional burden. If the user is anxious, the reception desk can also prioritize procedures that can be completed quickly. Furthermore, if the user is confused, the reception desk can prioritize procedures that are easy to understand. This reduces the user's burden by prioritizing information according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0080] The reception desk can prioritize receiving highly relevant information by considering the user's geographical location when receiving information. For example, the reception desk can prioritize displaying information about the nearest relevant organization based on the user's current location. The reception desk can also provide region-specific procedural information based on the user's geographical location. Furthermore, the reception desk can suggest the most suitable procedure based on the user's location. This improves user convenience by providing highly relevant information based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI select highly relevant information.

[0081] The reception desk can analyze the user's social media activity and receive relevant information upon receiving the information. For example, the reception desk can suggest procedures relevant to the user's current situation based on the user's social media posts. The reception desk can also analyze the user's social media activity and provide information on procedures of interest. Furthermore, the reception desk can suggest relevant procedures considering the user's social media friendships. This improves user convenience by providing relevant information based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI select relevant information.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is sad, the analysis unit will provide analysis results in a gentle tone. If the user is anxious, the analysis unit can also provide concise analysis results for quick understanding. Furthermore, if the user is confused, the analysis unit can provide detailed analysis results to support understanding. This supports user understanding by providing analysis presentation methods tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit can analyze information about important procedures in detail and provide it to the user. It can also analyze information about lower-priority procedures concisely and provide it to the user. Furthermore, the analysis unit can adjust the level of detail of the analysis in stages according to the importance of the information. This supports user understanding by providing an analysis level that matches the importance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0084] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a legal analysis algorithm to information about legal procedures. It can also apply a financial analysis algorithm to information about financial procedures. Furthermore, it can apply an emotional analysis algorithm to information about emotional support. This improves the accuracy of the analysis by applying an analysis algorithm appropriate to the category of information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is sad, the analysis unit provides a short, concise analysis result. If the user is anxious, the analysis unit can also provide a concise analysis result for quick understanding. Furthermore, if the user is confused, the analysis unit can provide a detailed analysis result to support understanding. In this way, the analysis unit supports user understanding by providing an analysis length appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0086] The analysis unit can determine the priority of analysis based on the submission timing of the information during the analysis process. For example, the analysis unit will prioritize the analysis of information related to procedures with approaching submission deadlines. It can also postpone the analysis of information related to procedures with ample time for submission. Furthermore, the analysis unit can adjust the priority of analysis in stages based on the submission timing. This improves user convenience by providing analysis priorities based on the submission timing of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information submission timing data into a generating AI and have the generating AI determine the analysis priority.

[0087] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant information and provide it to the user. It can also postpone the analysis of less relevant information. Furthermore, the analysis unit can adjust the order of analysis in stages based on the relevance of the information. This improves user convenience by providing an analysis order based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0088] The generation unit can estimate the user's emotions and adjust the presentation of the document template based on the estimated emotions. For example, if the user is sad, the generation unit can provide a document template in a gentle tone. It can also provide a concise document template for quick understanding if the user is anxious. Furthermore, if the user is confused, the generation unit can provide a detailed document template to support understanding. This supports user understanding by providing a document template presentation that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not. For example, the generation unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0089] The generation unit can adjust the level of detail in a document template based on the importance of the procedure when generating the template. For example, the generation unit can generate detailed document templates for important procedures. It can also generate concise document templates for lower-priority procedures. Furthermore, the generation unit can adjust the level of detail in the template in stages according to the importance of the procedure. This supports user understanding by providing a level of detail in the template that corresponds to the importance of the procedure. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input procedure importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the template.

[0090] The generation unit can apply different template generation algorithms depending on the category of the procedure when generating document templates. For example, the generation unit can apply a legal template generation algorithm to document templates related to legal procedures. It can also apply a financial template generation algorithm to document templates related to financial procedures. Furthermore, it can apply an emotional template generation algorithm to document templates related to emotional support. This improves the accuracy of the templates by applying a template generation algorithm according to the category of the procedure. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input procedure category data into a generation AI and have the generation AI execute the application of the template generation algorithm.

[0091] The generation unit can estimate the user's emotions and adjust the template length based on the estimated emotions. For example, if the user is sad, the generation unit can provide a short, concise template. If the user is anxious, the generation unit can also provide a concise template for quick understanding. Furthermore, if the user is confused, the generation unit can provide a detailed template to support understanding. This supports user understanding by providing template lengths appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0092] The generation unit can determine the priority of document templates based on the submission deadline for each procedure when generating them. For example, the generation unit can prioritize the generation of templates for procedures with approaching submission deadlines. It can also postpone the generation of templates for procedures with ample time for submission. Furthermore, the generation unit can adjust the priority of templates in stages based on the submission deadline. This improves user convenience by providing template priorities based on the submission deadline for each procedure. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input procedure submission deadline data into a generation AI and have the generation AI determine the priority of templates.

[0093] The generation unit can adjust the order of templates based on the relevance of procedures when generating document templates. For example, the generation unit can prioritize the generation of templates related to highly relevant procedures. It can also postpone the generation of templates related to less relevant procedures. Furthermore, the generation unit can adjust the order of templates in stages based on the relevance of procedures. This improves user convenience by providing a template order based on the relevance of procedures. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input procedure relevance data into a generation AI and have the generation AI perform the adjustment of the template order.

[0094] The management unit can estimate the user's emotions and adjust the method of managing the progress of the procedure based on the estimated user emotions. For example, if the user is sad, the management unit can provide progress notifications in a gentle tone. If the user is anxious, the management unit can also provide concise progress notifications for quick understanding. Furthermore, if the user is confused, the management unit can provide detailed progress notifications to support understanding. This reduces the user's burden by providing a method of managing the progress of the procedure that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI, or not. For example, the management unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0095] The management department can select the optimal management method by referring to past progress data when managing the progress of procedures. For example, the management department can propose the optimal management method based on the progress data of procedures previously performed by the user. The management department can also predict the time required for the procedure to proceed based on past progress data and notify the user. Furthermore, the management department can analyze past progress data and propose methods to streamline the procedure. In this way, the procedure is made more efficient by providing the optimal management method based on past progress data. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input past progress data into a generating AI and have the generating AI select the optimal management method.

[0096] The management department can apply different management methods to each category of procedure when managing the progress of procedures. For example, the management department can manage the progress of legal procedures by applying legal management methods. It can also manage the progress of financial procedures by applying financial management methods. Furthermore, it can manage the progress of emotional support by applying emotional management methods. This streamlines the progress of procedures by applying management methods appropriate to the category of procedure. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input procedure category data into a generating AI and have the generating AI apply the management methods.

[0097] The management unit can estimate the user's emotions and prioritize the progress based on the estimated emotions. For example, if the user is sad, the management unit can prioritize important procedures to alleviate their emotional burden. Similarly, if the user is anxious, the management unit can prioritize procedures that can be completed quickly. Furthermore, if the user is confused, the management unit can prioritize procedures that are easy to understand. This reduces the user's burden by providing progress priorities that correspond to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI or not. For example, the management unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0098] The management department can analyze changes in the progress of procedures based on the submission date when managing the progress of procedures. For example, the management department can prioritize the management of procedures with approaching submission deadlines. It can also postpone the management of procedures with ample time before the submission deadline. Furthermore, the management department can analyze changes in progress in stages based on the submission date. This improves user convenience by providing information on changes in progress based on the submission date of procedures. Some or all of the above processing in the management department may be performed using AI, for example, or not. For example, the management department can input procedure submission date data into a generating AI and have the generating AI perform an analysis of changes in progress.

[0099] The management department can analyze the progress of a procedure by referring to relevant market data when managing the progress of the procedure. For example, the management department can predict the progress of the procedure based on the relevant market data and notify the user. The management department can also refer to the relevant market data to suggest ways to streamline the progress of the procedure. Furthermore, the management department can analyze the relevant market data and optimize the progress of the procedure. This streamlines the progress of the procedure by providing an analysis of the procedure's progress based on relevant market data. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input relevant market data into a generating AI and have the generating AI perform the progress analysis.

[0100] The service provider can estimate the user's emotions and adjust the way legal advice is delivered based on those estimated emotions. For example, if the user is sad, the service provider can provide legal advice in a gentle tone. If the user is anxious, the service provider can also provide concise legal advice that is easy to understand quickly. Furthermore, if the user is confused, the service provider can provide detailed legal advice to support understanding. This supports user understanding by providing legal advice tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0101] The service provider can select the optimal advice method by referring to past advice data when providing legal advice. For example, the service provider can propose the optimal advice method based on the legal advice the user has received in the past. The service provider can also select the most suitable advice method for the user from past advice data. Furthermore, the service provider can analyze past advice data and optimize the method of providing legal advice. This supports the user's understanding by providing the optimal advice method based on past advice data. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input past advice data into a generating AI and have the generating AI select the optimal advice method.

[0102] The service provider can apply different advice methods depending on the category of the procedure when providing legal advice. For example, the service provider can apply a legal advice method to advice on legal procedures. It can also apply a financial advice method to advice on financial procedures. Furthermore, it can apply an emotional advice method to advice on emotional support. This improves the accuracy of the advice by applying an advice method appropriate to the category of the procedure. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input procedure category data into a generating AI and have the generating AI apply the advice method.

[0103] The service provider can estimate the user's emotions and prioritize advice based on those emotions. For example, if the user is sad, the service provider will prioritize important advice to alleviate their emotional burden. If the user is anxious, the service provider can also prioritize advice that can be completed quickly. Furthermore, if the user is confused, the service provider can prioritize easy-to-understand advice. This reduces the user's burden by prioritizing advice according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0104] The service provider can adjust the importance of legal advice based on the filing date of the procedure. For example, the service provider can prioritize advice for procedures with approaching filing deadlines. Conversely, it can postpone advice for procedures with ample time before filing. Furthermore, the service provider can adjust the importance of advice in stages based on the filing date. This improves user convenience by providing advice importance based on the filing date of the procedure. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input procedure filing date data into a generating AI and have the generating AI perform the adjustment of advice importance.

[0105] The service provider can improve the accuracy of its legal advice by referring to relevant literature on the procedure. For example, the service provider can provide accurate advice on the procedure based on relevant literature. The service provider can also supplement the content of the advice by referring to relevant literature. Furthermore, the service provider can analyze relevant literature to improve the accuracy of the advice. This supports user understanding by providing accurate advice based on relevant literature on the procedure. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input relevant literature data into a generating AI and have the generating AI perform the improvement of the accuracy of the advice.

[0106] The support unit can estimate the user's emotions and adjust the method of providing emotional support based on the estimated emotions. For example, if the user is sad, the support unit can provide emotional support in a gentle tone. If the user is anxious, the support unit can also provide concise emotional support to facilitate quick understanding. Furthermore, if the user is confused, the support unit can provide detailed emotional support to aid understanding. This reduces the user's burden by providing emotional support tailored to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the support unit may be performed using AI or not. For example, the support unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0107] The support unit can select the optimal support method when providing emotional support by referring to past support data. For example, the support unit can propose the optimal support method based on the emotional support the user has received in the past. The support unit can also select the most suitable support method for the user from past support data. Furthermore, the support unit can analyze past support data and optimize the method of providing emotional support. This reduces the burden on the user by providing the optimal support method based on past support data. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input past support data into a generating AI and have the generating AI select the optimal support method.

[0108] The support unit can customize the means of support based on the user's current situation when providing emotional support. For example, the support unit can provide the optimal means of support based on the user's current emotional state. The support unit can also customize the means of support according to the user's current situation. Furthermore, the support unit can analyze the user's current situation and propose the optimal means of support. This reduces the burden on the user by providing means of support that are appropriate to the user's current situation. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's current situation data into a generating AI and have the generating AI perform the customization of the means of support.

[0109] The support unit can estimate the user's emotions and determine the priority of support based on the estimated emotions. For example, if the user is sad, the support unit can prioritize providing important support to alleviate their emotional burden. If the user is anxious, the support unit can also prioritize providing support that can be completed quickly. Furthermore, if the user is confused, the support unit can prioritize providing support that is easy to understand. This reduces the user's burden by providing support prioritization according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, or not. For example, the support unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0110] The support unit can select the optimal support method when providing emotional support, taking into account the user's geographical location. For example, the support unit can provide information on the nearest support organization based on the user's current location. The support unit can also provide region-specific support information based on the user's geographical location. Furthermore, the support unit can suggest the optimal support method based on the user's location. This reduces the user's burden by providing the optimal support method based on the user's geographical location. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal support method.

[0111] The support unit can analyze a user's social media activity and suggest support measures when providing emotional support. For example, the support unit can suggest support relevant to the current situation based on the user's social media posts. The support unit can also analyze a user's social media activity and provide support information of interest. Furthermore, the support unit can consider the user's social media friendships and suggest relevant support. This reduces the user's burden by providing the most appropriate support measures based on the user's social media activity. Some or all of the above processing in the support unit may be performed using AI, for example, or not. For example, the support unit can input the user's social media data into a generating AI and have the generating AI execute the suggestion of support measures.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The reception desk can estimate the user's emotions and adjust how information is received based on those estimates. For example, if the user is sad, it can provide guidance in a gentle tone and proceed slowly. If the user is anxious, it can provide concise guidance to expedite the process. Furthermore, if the user is confused, it can provide detailed step-by-step guidance to support the process. This reduces the user's burden by providing information reception methods that are tailored to their emotions.

[0114] The analysis unit can analyze a user's past procedure history and select the optimal method for receiving information. For example, it can prioritize suggesting procedures the user has used in the past. It can also automatically display procedures the user has frequently performed in the past as candidates. Furthermore, it can predict and suggest procedures to be performed during specific time periods based on the user's past procedure history. In this way, by providing the optimal method for receiving information based on past procedure history, it improves user convenience.

[0115] The service provider can estimate the user's emotions and adjust how legal advice is delivered based on those emotions. For example, if the user is sad, legal advice can be provided in a gentle tone. If the user is anxious, concise legal advice can be provided for quick understanding. Furthermore, if the user is confused, detailed legal advice can be provided to support their understanding. In this way, the service provides legal advice tailored to the user's emotions, thereby supporting their understanding.

[0116] The management department can select the optimal management method by referring to past progress data when managing the progress of procedures. For example, it can propose the optimal management method based on the progress data of procedures previously performed by the user. It can also predict the time required for the procedure to proceed based on past progress data and notify the user. Furthermore, it can analyze past progress data and propose methods to streamline the procedure. In this way, the procedure is made more efficient by providing the optimal management method based on past progress data.

[0117] The support unit can estimate the user's emotions and adjust the way emotional support is provided based on those estimates. For example, if the user is sad, it can provide emotional support in a gentle tone. If the user is anxious, it can provide concise emotional support to help them understand quickly. Furthermore, if the user is confused, it can provide detailed emotional support to help them understand. This reduces the user's burden by providing emotional support tailored to their emotions.

[0118] The reception desk can prioritize receiving highly relevant information by considering the user's geographical location when receiving information. For example, it can prioritize displaying information about the nearest relevant organization based on the user's current location. It can also provide region-specific procedural information based on the user's geographical location. Furthermore, it can suggest the most suitable procedure based on the user's location. In this way, by providing highly relevant information based on the user's geographical location, user convenience is improved.

[0119] The analysis unit can adjust the level of detail in the analysis based on the importance of the information. For example, information about important procedures can be analyzed in detail and provided to the user. Conversely, information about lower-priority procedures can be analyzed concisely and provided to the user. Furthermore, the level of detail in the analysis can be adjusted in stages according to the importance of the information. This supports user understanding by providing an analysis level appropriate to the importance of the information.

[0120] The generation unit can estimate the user's emotions and adjust the presentation of the document template based on those emotions. For example, if the user is sad, it can provide a document template in a gentle tone. If the user is anxious, it can provide a concise document template for quick understanding. Furthermore, if the user is confused, it can provide a detailed document template to support understanding. In this way, by providing document templates that are presented in a way that suits the user's emotions, it supports the user's understanding.

[0121] The service provider can adjust the importance of legal advice based on the filing date of the procedure. For example, it can prioritize advice on procedures with approaching deadlines, while delaying advice on procedures with ample time before filing. Furthermore, it can adjust the importance of advice in stages based on the filing date. This improves user convenience by providing advice tailored to the filing date of the procedure.

[0122] The management department can estimate the user's emotions and adjust the way the process progress is managed based on those emotions. For example, if the user is sad, it can provide progress notifications in a gentle tone. If the user is anxious, it can provide concise progress notifications for quick understanding. Furthermore, if the user is confused, it can provide detailed progress notifications to support understanding. This reduces the user's burden by providing a process progress management method that is tailored to the user's emotions.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The reception desk receives information from users. This information includes personal information, procedural information, and legal information. For example, it receives information such as death certificates, inheritance information, and pension payment suspension information entered by the user. Step 2: The analysis unit analyzes the information received by the reception unit and lists the necessary procedures. Based on the information entered by the user, the analysis unit lists the procedures using information classification methods and analysis algorithms. For example, it classifies the information entered by the user and extracts the information necessary for each procedure. Step 3: The generation unit automatically generates document templates based on the procedures listed by the analysis unit. The generation unit automatically generates the necessary document templates for the procedures based on the information entered by the user. For example, it generates a death certificate template. Step 4: The management department manages the progress of the procedure based on the document templates generated by the generation department. The management department can also schedule the progress of the procedure based on the information entered by the user and send reminders to the user. For example, it can manage the progress of the procedure for suspending pension payments. Step 5: The service provider will provide information on how to contact relevant organizations and legal advice based on the progress managed by the administration department. Based on the information entered by the user, the service provider will guide the user on how to contact relevant organizations and provide legal advice. For example, they will provide legal advice regarding the procedure for suspending pension payments. Step 6: The support department provides emotional support based on the legal advice provided by the service provider. The support department provides information on emotional support and mental health care based on the information entered by the user. For example, they provide emotional support regarding the procedure for suspending pension payments.

[0125] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0126] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0127] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0128] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, management unit, provision unit, and support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives information from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received information and lists the necessary procedures. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates document templates based on the listed procedures. The management unit is implemented by the control unit 46A of the smart device 14 and manages the progress of the procedures. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides information on how to contact relevant organizations and legal advice. The support unit is implemented by the control unit 46A of the smart device 14 and provides emotional support. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] As shown in Figure 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.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, management unit, provision unit, and support unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives information from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received information and lists the necessary procedures. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates document templates based on the listed procedures. The management unit is implemented by the control unit 46A of the smart glasses 214 and manages the progress of the procedures. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides information on how to contact relevant organizations and legal advice. The support unit is implemented by the control unit 46A of the smart glasses 214 and provides emotional support. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, management unit, provision unit, and support unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives information from the user. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the received information and lists the necessary procedures. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically generates document templates based on the listed procedures. The management unit is implemented by, for example, the control unit 46A of the headset terminal 314 and manages the progress of the procedures. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides information on how to contact relevant organizations and legal advice. The support unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides emotional support. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] As shown in Figure 7, the 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.

[0163] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0164] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0165] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0167] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0168] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0169] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0170] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0171] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0172] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0173] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0174] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0175] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0176] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0177] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, management unit, provision unit, and support unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives information from the user. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the received information and lists the necessary procedures. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically generates document templates based on the listed procedures. The management unit is implemented by, for example, the control unit 46A of the robot 414 and manages the progress of the procedures. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides information on how to contact relevant organizations and legal advice. The support unit is implemented by, for example, the control unit 46A of the robot 414 and provides emotional support. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0178] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0179] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0180] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0181] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0182] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0183] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0184] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0185] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0186] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0187] 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.

[0188] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0189] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0190] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0191] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0192] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0193] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0194] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0195] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0196] (Note 1) A reception desk that receives information from users, An analysis unit analyzes the information received by the reception unit and lists the necessary procedures, A generation unit that automatically generates document templates based on the procedures listed by the analysis unit, A management unit manages the progress of procedures based on the document templates generated by the generation unit, Based on the progress managed by the aforementioned management department, the provision department provides information on how to contact relevant organizations and legal advice. The system includes a support unit that provides emotional support based on legal advice provided by the aforementioned provision unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is It estimates the user's emotions and adjusts how information is received based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is Analyze the user's past transaction history and select the most suitable method for receiving information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When receiving information, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and determines the priority of information to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When receiving information, the system prioritizes receiving highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When receiving information, the system analyzes the user's social media activity and collects relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is It estimates the user's emotions and adjusts the way the document template is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating document templates, adjust the level of detail in the template based on the importance of the procedure. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating document templates, different template generation algorithms are applied depending on the category of the procedure. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is It estimates the user's emotions and adjusts the template length based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating document templates, prioritize templates based on the submission deadline for the procedure. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating document templates, adjust the order of templates based on the relevance of the procedures. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned management department, The system estimates the user's emotions and adjusts the management of the procedure's progress based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned management department, When managing the progress of a procedure, refer to past progress data to select the most suitable management method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned management department, When managing the progress of procedures, different management methods should be applied to each category of procedure. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned management department, It estimates the user's emotions and determines the priority of the progress based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned management department, When managing the progress of a procedure, analyze changes in the progress based on when the procedure was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned management department, When managing the progress of a procedure, analyze the progress by referring to relevant market data for that procedure. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, We estimate the user's emotions and adjust how legal advice is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing legal advice, we refer to past advice data to select the most appropriate advice method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing legal advice, we apply different advice methods depending on the category of the procedure. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing legal advice, we adjust the importance of the advice based on when the proceedings are filed. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing legal advice, we refer to relevant procedural literature to improve the accuracy of the advice. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned support unit is It estimates the user's emotions and adjusts how emotional support is provided based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned support unit is When providing emotional support, refer to past support data to select the most appropriate support method. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned support unit is When providing emotional support, customize the support methods based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned support unit is The system estimates the user's emotions and determines support priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned support unit is When providing emotional support, the optimal support method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned support unit is When providing emotional support, we analyze the user's social media activity and suggest support methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives information from users, An analysis unit analyzes the information received by the reception unit and lists the necessary procedures, A generation unit that automatically generates document templates based on the procedures listed by the analysis unit, A management unit manages the progress of procedures based on the document templates generated by the generation unit, Based on the progress managed by the aforementioned management department, the provision department provides information on how to contact relevant organizations and legal advice. The system includes a support unit that provides emotional support based on legal advice provided by the aforementioned provision unit. A system characterized by the following features.

2. The aforementioned reception unit is It estimates the user's emotions and adjusts how information is received based on those estimated emotions. The system according to feature 1.

3. The aforementioned reception unit is Analyze the user's past transaction history and select the most suitable method for receiving information. The system according to feature 1.

4. The aforementioned reception unit is When receiving information, filtering is performed based on the user's current situation and areas of interest. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and determines the priority of information to accept based on the estimated user emotions. The system according to feature 1.

6. The aforementioned reception unit is When receiving information, the system prioritizes receiving highly relevant information, taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned reception unit is When receiving information, the system analyzes the user's social media activity and collects relevant information. The system according to feature 1.

8. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system according to feature 1.

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