system

Abstract

The system according to this embodiment aims to allow users to easily identify and apply for subsidies and allowances that apply to them. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, an application unit, and a notification unit. The reception unit receives data input from the user. The analysis unit analyzes the data received by the reception unit and identifies the subsidies and allowances applicable to the user. The application unit automatically submits an application for the subsidies and allowances identified by the analysis unit. The notification unit notifies the user of the application status and conditions for the subsidies and allowances applied for by the application unit.

Description

Technical Field

[0001] The technology disclosed herein relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is 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 the process for a user to identify and apply for subsidies and allowances applicable to themselves is complicated and time-consuming and labor-intensive.

[0005] The system according to the embodiment aims to enable a user to easily identify and apply for subsidies and allowances applicable to themselves.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, an application unit, and a notification unit. The reception unit receives data input from the user. The analysis unit analyzes the data received by the reception unit and identifies the subsidies or allowances applicable to the user. The application unit automatically submits an application for the subsidies or allowances identified by the analysis unit. The notification unit notifies the user of the application status and conditions for the subsidies or allowances applied for by the application unit. [Effects of the Invention]

[0007] The system according to this embodiment allows users to easily identify and apply for subsidies and allowances that are applicable to them. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable 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 receiving 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 receiving 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 automated subsidy and allowance application system according to an embodiment of the present invention is a system that utilizes a generating AI to automate the search for and application of subsidies and allowances that can be received from the government. In this system, the user inputs the data necessary for the application, the generating AI analyzes the input data to identify the subsidies and allowances applicable to the user, automatically applies for the identified subsidies and allowances, and notifies the user of the application status and conditions. This eliminates the need for the user to perform tedious search and application work. In addition, it is possible to ask questions and make application reservations regarding subsidies and allowances related to future events (e.g., marriage, retirement, starting a business). When the user inputs a question about a future event, the generating AI provides an answer to that question and makes an application reservation if necessary. Furthermore, it employs a business model in which the provider receives a margin as a performance-based fee when a grant is awarded. This allows the user to receive subsidies and allowances without hassle, and the service provider can receive compensation according to the results. The specific service flow is as follows: First, the user inputs their own information and presents the conditions for receiving subsidies and allowances. Next, the generating AI identifies the most suitable subsidies and allowances based on that information and automatically submits the application with simple consent and data entry. The generating AI notifies the user of the application status and conditions, and a fee is charged when the subsidy is deposited. This system allows users to easily find and apply for subsidies and allowances that apply to them. It also allows for Q&A and application reservations in preparation for future events, improving user convenience. Furthermore, the performance-based business model ensures that service providers can also earn stable revenue. In this way, the automated subsidy and allowance application system reduces the burden on users and allows them to receive subsidies and allowances efficiently.

[0029] The automated application system for subsidies and allowances according to this embodiment comprises a reception unit, an analysis unit, an application unit, and a notification unit. The reception unit receives data input from the user. User data input includes, but is not limited to, text input, multiple-choice input, voice input, etc. The reception unit provides, for example, an interface for the user to input their own information. The analysis unit uses a generation AI to analyze the data received by the reception unit and identify the subsidies and allowances applicable to the user. The generation AI analyzes the data using, for example, natural language processing or machine learning algorithms. The analysis unit identifies the most suitable subsidies and allowances based on the user's input data. The application unit uses the generation AI to automatically apply for the subsidies and allowances identified by the analysis unit. The application unit performs automatic applications with, for example, simple acceptance or data input. The application unit is designed so that, for example, the application is completed simply by the user clicking an acceptance button. The notification unit notifies the user of the application status and conditions of the subsidies and allowances applied for by the application unit. The notification unit notifies the user, for example, by email, SMS, or app. The notification unit also enables the user to check the application status in real time. As a result, the automated application system for subsidies and allowances according to the embodiment reduces the burden on the user and allows them to receive subsidies and allowances efficiently. Some or all of the above-described processes in the analysis unit and application unit are performed using a generation AI. For example, the analysis unit inputs user input data into the generation AI and causes the generation AI to identify subsidies and allowances. The application unit causes the generation AI to execute the application for the identified subsidies and allowances.

[0030] The reception desk accepts data input from users. This data input includes, but is not limited to, text input, multiple-choice input, and voice input. The reception desk provides an interface for users to input their information. Specifically, it provides an interface that users can easily access through a web browser or mobile application. For text input, users can enter the necessary information using a keyboard; for multiple-choice input, they can select options using dropdown menus or radio buttons; and for voice input, speech recognition technology can convert what the user says into text and accept it as input data. Furthermore, the reception desk has a function to check the integrity of the data entered by users, verifying that there is no missing or incorrect information. For example, if required fields are not entered or the input format is incorrect, it can display an error message to the user and prompt them to re-enter the information. This allows the reception desk to support users in entering accurate and complete information, ensuring that subsequent analysis and application processes proceed smoothly. The reception desk also implements security measures to safely protect user input data. For example, input data is encrypted, and measures are in place to prevent unauthorized external access and data leakage. This allows the reception desk to efficiently collect data while protecting user privacy.

[0031] The analysis unit uses generative AI to analyze data received by the reception unit and identify applicable subsidies and allowances for users. The generative AI analyzes data using, for example, natural language processing and machine learning algorithms. Specifically, it receives user input data and performs text analysis to understand the user's situation and needs. For example, it generates a list of applicable subsidies and allowances based on information such as the user's occupation, income, family structure, and place of residence. The generative AI learns from past data and statistics to build a model for identifying the most suitable subsidies and allowances. This model can be compared with the user's input data to quickly identify the most appropriate subsidies and allowances. Furthermore, the generative AI automatically checks the application requirements for subsidies and allowances based on the user's input data to determine whether the user meets the eligibility requirements. For example, it checks whether the user meets conditions such as being within a specific income range or residing in a specific region. This allows the analysis unit to propose the most suitable subsidies and allowances to users and streamline the application process. Additionally, the analysis unit can continuously learn from user input data to improve analysis accuracy. For example, if new subsidies or allowances are added, or if application requirements are changed, the generation AI model can be updated to perform analysis based on the latest information. This allows the analysis unit to always provide highly accurate analysis based on the latest information and propose the most suitable subsidies and allowances to users.

[0032] The application department uses a generation AI to automatically submit applications for subsidies and allowances identified by the analysis department. The application department automates the application process, for example, by simply requiring user approval or data entry. Specifically, it is designed so that the application is completed with just a click of an approval button. Based on the data entered by the user, the application department automatically generates the necessary application documents and submits them online. The generation AI automatically fills in the application document format and required information, eliminating the need for manual entry by the user. For example, it automatically creates and submits subsidy and allowance application documents based on the personal and income information entered by the user. Furthermore, the application department monitors the progress of the application process in real time and can request additional information from the user as needed. For example, if there are deficiencies in the application documents or if additional supporting documents are required, the user will be notified, allowing for a quick response. In addition, the application department notifies the user of the progress of the application process in real time, allowing the user to check the progress of their application. This allows the application department to streamline the application process, making it easier for users to apply for subsidies and allowances without hassle. Furthermore, the application department has implemented measures to ensure the security of application data. For example, application data is encrypted, and measures are in place to prevent unauthorized access and data leaks from external sources. This allows the application department to efficiently manage the application process while protecting user privacy.

[0033] The notification unit informs users of the application status and conditions for subsidies and allowances applied for by the application unit. The notification unit notifies users through methods such as email notifications, SMS notifications, and app notifications. Specifically, to allow users to check the progress of the subsidies and allowances they have applied for in real time, the notification unit will notify them of application acceptance, review progress, and results in a timely manner. For example, when an application is accepted, an acceptance notification will be sent to the user to inform them that the review is in progress. When the review is completed and the results are available, a result notification will be sent to inform the user whether or not they have been approved for the subsidy or allowance. Furthermore, the notification unit can also provide a dedicated dashboard where users can check the status of their applications. This dashboard allows users to see a list of the subsidies and allowances they have applied for and their progress at a glance, enabling them to take necessary actions quickly. For example, if additional documents need to be submitted or the application needs to be corrected, a notification will be sent to the dashboard so that the user can respond quickly. In this way, the notification unit can ensure that users are always aware of the progress of the application process, improving the transparency and efficiency of the application. Furthermore, the notification system can collect user feedback and continuously improve notification content and methods. For example, it can implement a function that allows users to provide feedback on notification content and collect data to improve the accuracy and timing of notifications. This enables the notification system to provide users with more appropriate and effective notifications, improving the overall user experience of the application process.

[0034] The reception unit can input user information. The reception unit provides an interface for users to input information such as their name, address, and contact information. The reception unit can store the information entered by the user in a database. The reception unit can also send the information entered by the user to the analysis unit. This ensures that user information is entered accurately. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the information entered by the user into the AI ​​to verify the accuracy of the information.

[0035] The analysis unit can analyze the conditions for subsidies and allowances using generative AI. For example, the analysis unit uses generative AI to analyze user input data and identify the most suitable subsidies and allowances. The generative AI analyzes data using, for example, natural language processing and machine learning algorithms. The analysis unit inputs user input data into the generative AI and analyzes the conditions for subsidies and allowances. This allows for accurate analysis of the conditions for subsidies and allowances. Some or all of the above-described processes in the analysis unit are performed using generative AI. For example, the analysis unit can input user input data into the generative AI and analyze the conditions for subsidies and allowances.

[0036] The application department can automatically submit applications using a generation AI, requiring only simple consent and data entry. For example, the application department is designed so that the user can complete the application simply by clicking an consent button, using the generation AI. The application department automatically submits applications based on data entered by the user. This enables automated applications with simple consent and data entry. Some or all of the above-mentioned processes in the application department are performed using the generation AI. For example, the application department can input user data into the generation AI and have the generation AI execute the application.

[0037] The notification unit can provide Q&A and application status notifications. For example, when a user enters a question, the notification unit can generate an answer using AI. The notification unit can also enable users to check their application status in real time. This makes it possible to provide Q&A and application status notifications. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input a user's question into AI and generate an answer.

[0038] The analysis unit can provide answers to questions about future events and, if necessary, allow users to make application reservations. For example, when a user inputs a question about a future event, the generation AI will provide an answer to that question. For example, when a user inputs a question about an event such as marriage, retirement, or starting a business, the generation AI will provide an answer to that question and, if necessary, allow users to make application reservations. This makes it possible to get answers to questions about future events and to make application reservations. Some or all of the above processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input a user's question into the generation AI and generate an answer.

[0039] The notification unit can notify users of the fee when the subsidy is deposited. For example, the notification unit will notify the user of the fee when the subsidy is deposited. The notification unit will notify the user of the fee by means of, for example, email notification, SMS notification, or app notification. This makes it possible to notify users of the fee when the subsidy is deposited. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input the subsidy deposit information into AI and generate a fee notification.

[0040] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display data that the user has frequently entered in the past as a suggestion. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest data to be used during a specific time period based on the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history. 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 past input history into an AI and have the AI ​​generate the optimal input method.

[0041] The reception unit can automatically filter input items based on the user's current life situation and areas of interest during data entry. For example, the reception unit can prioritize displaying relevant input items based on the user's current occupation and family structure. For example, the reception unit can suggest relevant input items based on the user's areas of interest (e.g., education, healthcare). For example, the reception unit can automatically select necessary input items based on the user's life situation (e.g., marriage, retirement). This enables filtering of input items based on the user's life situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input data on the user's life situation and areas of interest into an AI and have the generation AI perform the filtering of input items.

[0042] The reception desk can prioritize displaying input fields that are highly relevant to the user's geographical location during data entry. For example, if the user lives in a specific region, the reception desk will prioritize displaying input fields related to subsidies and allowances in that region. For example, if the user is traveling, the reception desk will suggest relevant input fields based on the user's current location. For example, if the user is planning to move, the reception desk will prioritize displaying input fields related to the new address. This enables the priority display of input fields based on the user's geographical location. 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 geographical location into an AI and have the AI ​​generate highly relevant input fields.

[0043] The reception unit can analyze the user's social media activity and suggest relevant input fields when data is entered. For example, the reception unit can suggest relevant input fields based on information the user has shared on social media. For example, the reception unit can identify areas of interest from the user's social media activity and prioritize the display of relevant input fields. For example, the reception unit can analyze the content of the user's social media posts and automatically select the necessary input fields. This makes it possible to suggest input fields based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's social media activity into an AI and have the AI ​​generate relevant input fields.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the subsidies and allowances during the analysis. For example, the analysis unit performs a detailed analysis on subsidies and allowances of high importance and provides it to the user. For example, the analysis unit provides a simplified analysis result on subsidies and allowances of low importance. For example, the analysis unit determines the priority of the analysis and adjusts the level of detail according to the importance of the subsidies and allowances. This makes it possible to adjust the level of detail of the analysis based on the importance of the subsidies and allowances. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the importance data of subsidies and allowances into a generation AI and have the generation AI perform the analysis with a specified level of detail.

[0045] The analysis unit can apply different analysis algorithms depending on the category of subsidies and allowances during analysis. For example, the analysis unit applies an education-specific analysis algorithm to education-related subsidies and allowances. For example, the analysis unit applies a medical-specific analysis algorithm to medical-related subsidies and allowances. For example, the analysis unit applies a startup-specific analysis algorithm to startup-related subsidies and allowances. This makes it possible to apply analysis algorithms according to the category of subsidies and allowances. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input subsidy and allowance category data into the generation AI and have the generation AI execute the application of analysis algorithms.

[0046] The analysis unit can determine the priority of analysis based on the submission timing of subsidies and allowances during the analysis process. For example, the analysis unit will prioritize the analysis of subsidies and allowances with approaching submission deadlines. For example, the analysis unit will postpone the analysis of subsidies and allowances with distant submission deadlines. The analysis unit can dynamically adjust the analysis priority based on the submission timing. This makes it possible to determine the analysis priority based on the submission timing of subsidies and allowances. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the submission timing data of subsidies and allowances into a generation AI and have the generation AI execute the analysis priority.

[0047] The analysis unit can adjust the order of analysis results based on the relevance of subsidies and allowances during the analysis. For example, the analysis unit may prioritize displaying subsidies and allowances that are most relevant to the user's current situation. For example, the analysis unit may prioritize displaying subsidies and allowances that are relevant to the user's future events. For example, the analysis unit can dynamically adjust the order of analysis results based on the relevance of subsidies and allowances. This makes it possible to adjust the order of analysis results based on the relevance of subsidies and allowances. Some or all of the above processing in the analysis unit may be performed using a generating AI, or not. For example, the analysis unit can input the relevance data of subsidies and allowances into a generating AI and have the generating AI execute the order of analysis results.

[0048] The application department can adjust the level of detail of an application based on the importance of the subsidy or allowance. For example, the application department can submit a detailed application for high-importance subsidies or allowances, and a simplified application for low-importance subsidies or allowances. The application department can also determine the priority of applications and adjust the level of detail based on the importance of the subsidies or allowances. This makes it possible to adjust the level of detail of applications based on the importance of the subsidies or allowances. Some or all of the above processing in the application department may be performed using a generation AI, or not. For example, the application department can input the importance data of the subsidies or allowances into a generation AI and have the generation AI determine the level of detail of the application.

[0049] The application department can apply different application algorithms depending on the category of the subsidy or allowance when an application is submitted. For example, the application department can apply an education-specific application algorithm for education-related subsidies and allowances. For example, the application department can apply a medical-specific application algorithm for medical-related subsidies and allowances. For example, the application department can apply a business-specific application algorithm for business-related subsidies and allowances. This makes it possible to apply an application algorithm according to the category of the subsidy or allowance. Some or all of the above processing in the application department may be performed using a generative AI, or it may be performed without using a generative AI. For example, the application department can input the category data of the subsidy or allowance into a generative AI and have the generative AI execute the application of the application algorithm.

[0050] The application department can determine the priority of applications based on the submission deadlines for subsidies and allowances at the time of application. For example, the application department will prioritize applications for subsidies and allowances with approaching deadlines. For example, the application department will postpone applications for subsidies and allowances with distant deadlines. The application department can dynamically adjust the priority of applications based on the submission deadlines. This makes it possible to determine the priority of applications based on the submission deadlines for subsidies and allowances. Some or all of the above processing in the application department may be performed using a generation AI, or it may be performed without a generation AI. For example, the application department can input data on the submission deadlines for subsidies and allowances into a generation AI and have the generation AI determine the priority of applications.

[0051] The application unit can adjust the order of applications based on the relevance of subsidies and allowances at the time of application. For example, the application unit may prioritize applications for subsidies and allowances that are most relevant to the user's current situation. For example, the application unit may prioritize applications for subsidies and allowances that are relevant to the user's future events. For example, the application unit may dynamically adjust the order of applications based on the relevance of subsidies and allowances. This makes it possible to adjust the order of applications based on the relevance of subsidies and allowances. Some or all of the above processing in the application unit may be performed using a generative AI, or not. For example, the application unit may input the relevance data of subsidies and allowances into a generative AI and have the generative AI execute the order of applications.

[0052] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit may prioritize notification methods that the user has previously preferred to receive (email, push notifications, etc.). For example, the notification unit may suggest the optimal notification method for a specific time period based on the user's past notification history. For example, the notification unit may analyze the user's past notification history and select the most effective notification method. This makes it possible to select the optimal notification method based on the user's past notification history. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit may input the user's past notification history into an AI and have the AI ​​generate the optimal notification method.

[0053] The notification unit can apply different notification algorithms depending on the category of the subsidy or allowance when sending notifications. For example, the notification unit applies an education-specific notification algorithm for education-related subsidies and allowances. For example, the notification unit applies a medical-specific notification algorithm for medical-related subsidies and allowances. For example, the notification unit applies a business-specific notification algorithm for business-related subsidies and allowances. This makes it possible to apply notification algorithms according to the category of the subsidy or allowance. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input subsidy and allowance category data into an AI and have the generating AI execute the application of the notification algorithm.

[0054] The notification unit can select the optimal notification method when sending a notification, taking into account the user's geographical location information. For example, if the user lives in a specific region, the notification unit will prioritize displaying notifications related to that region. For example, if the user is traveling, the notification unit will suggest relevant notifications based on the user's current location. For example, if the user is planning to move, the notification unit will prioritize displaying notifications related to the new address. This makes it possible to select the optimal notification method based on the user's geographical location information. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the user's geographical location information into an AI and have the AI ​​generate the optimal notification method.

[0055] The notification unit can analyze the user's social media activity and suggest notification methods when sending a notification. For example, the notification unit can suggest relevant notifications based on information the user has shared on social media. For example, the notification unit can identify areas of interest from the user's social media activity and prioritize displaying relevant notifications. For example, the notification unit can analyze the content of the user's social media posts and automatically select the necessary notifications. This makes it possible to suggest notification methods based on the user's social media activity. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the user's social media activity into an AI and have the AI ​​generate notification methods.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The reception desk can automatically complete input fields by referring to the user's past input history when receiving user input data. For example, it can automatically display addresses and contact information previously entered by the user, saving the user the trouble of re-entering them. Furthermore, the reception desk can prioritize displaying relevant input fields based on the type of subsidy or allowance the user has previously selected. This streamlines the user's input process and reduces the risk of input errors. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input history into the AI ​​and have the AI ​​generate the data to automatically complete the input fields.

[0058] The analysis unit can customize its analysis algorithm based on the user's current lifestyle and areas of interest when analyzing user input data. For example, if a user is looking for education-related subsidies, it can apply an education-specific analysis algorithm to identify the most suitable subsidies. Furthermore, if a user is looking for medical-related subsidies, it can also apply a medical-specific analysis algorithm. This enables highly accurate analysis tailored to the user's needs. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input data on the user's lifestyle and areas of interest into a generative AI and have the generative AI perform the customization of the analysis algorithm.

[0059] The reception desk can prioritize displaying input fields related to relevant subsidies and allowances, taking into account the user's geographical location. For example, if the user lives in a specific region, it can prioritize displaying input fields related to subsidies and allowances in that region. If the user is traveling, it can suggest relevant input fields based on their current location. Furthermore, if the user is planning to move, it can prioritize displaying input fields related to their new address. This enables the prioritization of input fields based on the user's geographical location, improving user convenience. 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 geographical location information into the AI ​​and have the AI ​​generate the input fields to prioritize display.

[0060] The application department can analyze a user's past application history and suggest the most suitable application method. For example, it can automatically display as candidates the types of subsidies and allowances the user has frequently applied for in the past. It can also prioritize suggesting application methods (voice, text, etc.) the user has used in the past. Furthermore, it can predict and suggest the most suitable application method for a specific time period based on the user's past application history. This enables the suggestion of the most suitable application method based on the user's past application history, thereby streamlining the application process. Some or all of the above processes in the application department may be performed using AI or not. For example, the application department can input the user's past application history into an AI and have the AI ​​generate the most suitable application method.

[0061] The notification unit can analyze a user's social media activity and suggest relevant notifications. For example, it can suggest relevant notifications based on information the user has shared on social media. It can identify areas of interest from the user's social media activity and prioritize the display of relevant notifications. Furthermore, it can analyze the content of the user's social media posts and automatically select the necessary notifications. This makes it possible to suggest notification methods based on the user's social media activity, enabling the provision of information tailored to the user's interests. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the user's social media activity into an AI and have the AI ​​generate notification methods.

[0062] The analysis unit can determine the priority of analysis based on the submission timing of subsidies and allowances during the analysis process. For example, subsidies and allowances with approaching submission deadlines will be analyzed first, while those with later deadlines will be analyzed later. Furthermore, the analysis priority can be dynamically adjusted based on the submission timing. This enables the determination of analysis priorities based on the submission timing of subsidies and allowances, helping users complete their applications within the deadline. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the submission timing data of subsidies and allowances into a generation AI and have the generation AI execute the analysis prioritization.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk receives data input from users. User data input includes text input, multiple-choice input, voice input, etc. The reception desk provides an interface for users to input their own information. Step 2: The analysis unit uses a generation AI to analyze the data received by the reception unit and identify the subsidies and allowances applicable to the user. The generation AI analyzes the data using natural language processing and machine learning algorithms. Step 3: The application unit uses generation AI to automatically submit applications for subsidies and allowances identified by the analysis unit. The application unit is designed so that the user can complete the application simply by clicking an acceptance button. Step 4: The notification unit notifies the user of the application status and conditions for subsidies and allowances applied for by the application unit. The notification unit notifies the user via methods such as email, SMS, and app notifications.

[0065] (Example of form 2) The automated subsidy and allowance application system according to an embodiment of the present invention is a system that utilizes a generating AI to automate the search for and application of subsidies and allowances that can be received from the government. In this system, the user inputs the data necessary for the application, the generating AI analyzes the input data to identify the subsidies and allowances applicable to the user, automatically applies for the identified subsidies and allowances, and notifies the user of the application status and conditions. This eliminates the need for the user to perform tedious search and application work. In addition, it is possible to ask questions and make application reservations regarding subsidies and allowances related to future events (e.g., marriage, retirement, starting a business). When the user inputs a question about a future event, the generating AI provides an answer to that question and makes an application reservation if necessary. Furthermore, it employs a business model in which the provider receives a margin as a performance-based fee when a grant is awarded. This allows the user to receive subsidies and allowances without hassle, and the service provider can receive compensation according to the results. The specific service flow is as follows: First, the user inputs their own information and presents the conditions for receiving subsidies and allowances. Next, the generating AI identifies the most suitable subsidies and allowances based on that information and automatically submits the application with simple consent and data entry. The generating AI notifies the user of the application status and conditions, and a fee is charged when the subsidy is deposited. This system allows users to easily find and apply for subsidies and allowances that apply to them. It also allows for Q&A and application reservations in preparation for future events, improving user convenience. Furthermore, the performance-based business model ensures that service providers can also earn stable revenue. In this way, the automated subsidy and allowance application system reduces the burden on users and allows them to receive subsidies and allowances efficiently.

[0066] The automated application system for subsidies and allowances according to this embodiment comprises a reception unit, an analysis unit, an application unit, and a notification unit. The reception unit receives data input from the user. User data input includes, but is not limited to, text input, multiple-choice input, voice input, etc. The reception unit provides, for example, an interface for the user to input their own information. The analysis unit uses a generation AI to analyze the data received by the reception unit and identify the subsidies and allowances applicable to the user. The generation AI analyzes the data using, for example, natural language processing or machine learning algorithms. The analysis unit identifies the most suitable subsidies and allowances based on the user's input data. The application unit uses the generation AI to automatically apply for the subsidies and allowances identified by the analysis unit. The application unit performs automatic applications with, for example, simple acceptance or data input. The application unit is designed so that, for example, the application is completed simply by the user clicking an acceptance button. The notification unit notifies the user of the application status and conditions of the subsidies and allowances applied for by the application unit. The notification unit notifies the user, for example, by email, SMS, or app. The notification unit also enables the user to check the application status in real time. As a result, the automated application system for subsidies and allowances according to the embodiment reduces the burden on the user and allows them to receive subsidies and allowances efficiently. Some or all of the above-described processes in the analysis unit and application unit are performed using a generation AI. For example, the analysis unit inputs user input data into the generation AI and causes the generation AI to identify subsidies and allowances. The application unit causes the generation AI to execute the application for the identified subsidies and allowances.

[0067] The reception desk accepts data input from users. This data input includes, but is not limited to, text input, multiple-choice input, and voice input. The reception desk provides an interface for users to input their information. Specifically, it provides an interface that users can easily access through a web browser or mobile application. For text input, users can enter the necessary information using a keyboard; for multiple-choice input, they can select options using dropdown menus or radio buttons; and for voice input, speech recognition technology can convert what the user says into text and accept it as input data. Furthermore, the reception desk has a function to check the integrity of the data entered by users, verifying that there is no missing or incorrect information. For example, if required fields are not entered or the input format is incorrect, it can display an error message to the user and prompt them to re-enter the information. This allows the reception desk to support users in entering accurate and complete information, ensuring that subsequent analysis and application processes proceed smoothly. The reception desk also implements security measures to safely protect user input data. For example, input data is encrypted, and measures are in place to prevent unauthorized external access and data leakage. This allows the reception desk to efficiently collect data while protecting user privacy.

[0068] The analysis unit uses generative AI to analyze data received by the reception unit and identify applicable subsidies and allowances for users. The generative AI analyzes data using, for example, natural language processing and machine learning algorithms. Specifically, it receives user input data and performs text analysis to understand the user's situation and needs. For example, it generates a list of applicable subsidies and allowances based on information such as the user's occupation, income, family structure, and place of residence. The generative AI learns from past data and statistics to build a model for identifying the most suitable subsidies and allowances. This model can be compared with the user's input data to quickly identify the most appropriate subsidies and allowances. Furthermore, the generative AI automatically checks the application requirements for subsidies and allowances based on the user's input data to determine whether the user meets the eligibility requirements. For example, it checks whether the user meets conditions such as being within a specific income range or residing in a specific region. This allows the analysis unit to propose the most suitable subsidies and allowances to users and streamline the application process. Additionally, the analysis unit can continuously learn from user input data to improve analysis accuracy. For example, if new subsidies or allowances are added, or if application requirements are changed, the generation AI model can be updated to perform analysis based on the latest information. This allows the analysis unit to always provide highly accurate analysis based on the latest information and propose the most suitable subsidies and allowances to users.

[0069] The application department uses a generation AI to automatically submit applications for subsidies and allowances identified by the analysis department. The application department automates the application process, for example, by simply requiring user approval or data entry. Specifically, it is designed so that the application is completed with just a click of an approval button. Based on the data entered by the user, the application department automatically generates the necessary application documents and submits them online. The generation AI automatically fills in the application document format and required information, eliminating the need for manual entry by the user. For example, it automatically creates and submits subsidy and allowance application documents based on the personal and income information entered by the user. Furthermore, the application department monitors the progress of the application process in real time and can request additional information from the user as needed. For example, if there are deficiencies in the application documents or if additional supporting documents are required, the user will be notified, allowing for a quick response. In addition, the application department notifies the user of the progress of the application process in real time, allowing the user to check the progress of their application. This allows the application department to streamline the application process, making it easier for users to apply for subsidies and allowances without hassle. Furthermore, the application department has implemented measures to ensure the security of application data. For example, application data is encrypted, and measures are in place to prevent unauthorized access and data leaks from external sources. This allows the application department to efficiently manage the application process while protecting user privacy.

[0070] The notification unit informs users of the application status and conditions for subsidies and allowances applied for by the application unit. The notification unit notifies users through methods such as email notifications, SMS notifications, and app notifications. Specifically, to allow users to check the progress of the subsidies and allowances they have applied for in real time, the notification unit will notify them of application acceptance, review progress, and results in a timely manner. For example, when an application is accepted, an acceptance notification will be sent to the user to inform them that the review is in progress. When the review is completed and the results are available, a result notification will be sent to inform the user whether or not they have been approved for the subsidy or allowance. Furthermore, the notification unit can also provide a dedicated dashboard where users can check the status of their applications. This dashboard allows users to see a list of the subsidies and allowances they have applied for and their progress at a glance, enabling them to take necessary actions quickly. For example, if additional documents need to be submitted or the application needs to be corrected, a notification will be sent to the dashboard so that the user can respond quickly. In this way, the notification unit can ensure that users are always aware of the progress of the application process, improving the transparency and efficiency of the application. Furthermore, the notification system can collect user feedback and continuously improve notification content and methods. For example, it can implement a function that allows users to provide feedback on notification content and collect data to improve the accuracy and timing of notifications. This enables the notification system to provide users with more appropriate and effective notifications, improving the overall user experience of the application process.

[0071] The reception unit can input user information. The reception unit provides an interface for users to input information such as their name, address, and contact information. The reception unit can store the information entered by the user in a database. The reception unit can also send the information entered by the user to the analysis unit. This ensures that user information is entered accurately. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the information entered by the user into the AI ​​to verify the accuracy of the information.

[0072] The analysis unit can analyze the conditions for subsidies and allowances using generative AI. For example, the analysis unit uses generative AI to analyze user input data and identify the most suitable subsidies and allowances. The generative AI analyzes data using, for example, natural language processing and machine learning algorithms. The analysis unit inputs user input data into the generative AI and analyzes the conditions for subsidies and allowances. This allows for accurate analysis of the conditions for subsidies and allowances. Some or all of the above-described processes in the analysis unit are performed using generative AI. For example, the analysis unit can input user input data into the generative AI and analyze the conditions for subsidies and allowances.

[0073] The application department can automatically submit applications using a generation AI, requiring only simple consent and data entry. For example, the application department is designed so that the user can complete the application simply by clicking an consent button, using the generation AI. The application department automatically submits applications based on data entered by the user. This enables automated applications with simple consent and data entry. Some or all of the above-mentioned processes in the application department are performed using the generation AI. For example, the application department can input user data into the generation AI and have the generation AI execute the application.

[0074] The notification unit can provide Q&A and application status notifications. For example, when a user enters a question, the notification unit can generate an answer using AI. The notification unit can also enable users to check their application status in real time. This makes it possible to provide Q&A and application status notifications. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input a user's question into AI and generate an answer.

[0075] The analysis unit can provide answers to questions about future events and, if necessary, allow users to make application reservations. For example, when a user inputs a question about a future event, the generation AI will provide an answer to that question. For example, when a user inputs a question about an event such as marriage, retirement, or starting a business, the generation AI will provide an answer to that question and, if necessary, allow users to make application reservations. This makes it possible to get answers to questions about future events and to make application reservations. Some or all of the above processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input a user's question into the generation AI and generate an answer.

[0076] The notification unit can notify users of the fee when the subsidy is deposited. For example, the notification unit will notify the user of the fee when the subsidy is deposited. The notification unit will notify the user of the fee by means of, for example, email notification, SMS notification, or app notification. This makes it possible to notify users of the fee when the subsidy is deposited. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input the subsidy deposit information into AI and generate a fee notification.

[0077] The reception desk can estimate the user's emotions and customize the data entry interface based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, for example, the reception desk can provide detailed input options and suggest a customizable input method. If the user is in a hurry, for example, the reception desk can prioritize voice input to allow for quick data entry. This enables the customization of the interface according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, 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 user emotion data into an AI and have the generative AI perform the interface customization.

[0078] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display data that the user has frequently entered in the past as a suggestion. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest data to be used during a specific time period based on the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history. 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 past input history into an AI and have the AI ​​generate the optimal input method.

[0079] The reception unit can automatically filter input items based on the user's current life situation and areas of interest during data entry. For example, the reception unit can prioritize displaying relevant input items based on the user's current occupation and family structure. For example, the reception unit can suggest relevant input items based on the user's areas of interest (e.g., education, healthcare). For example, the reception unit can automatically select necessary input items based on the user's life situation (e.g., marriage, retirement). This enables filtering of input items based on the user's life situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input data on the user's life situation and areas of interest into an AI and have the generation AI perform the filtering of input items.

[0080] The reception desk can estimate the user's emotions and prioritize input data based on those emotions. For example, if the user is stressed, the reception desk will prioritize important data input and postpone other input items. If the user is relaxed, the reception desk will prioritize detailed data input and suggest customizable input methods. If the user is in a hurry, the reception desk will prioritize the most important data input to allow for quick completion. This enables the prioritization of input data based on 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 processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into an AI and have the generative AI prioritize input data.

[0081] The reception desk can prioritize displaying input fields that are highly relevant to the user's geographical location during data entry. For example, if the user lives in a specific region, the reception desk will prioritize displaying input fields related to subsidies and allowances in that region. For example, if the user is traveling, the reception desk will suggest relevant input fields based on the user's current location. For example, if the user is planning to move, the reception desk will prioritize displaying input fields related to the new address. This enables the priority display of input fields based on the user's geographical location. 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 geographical location into an AI and have the AI ​​generate highly relevant input fields.

[0082] The reception unit can analyze the user's social media activity and suggest relevant input fields when data is entered. For example, the reception unit can suggest relevant input fields based on information the user has shared on social media. For example, the reception unit can identify areas of interest from the user's social media activity and prioritize the display of relevant input fields. For example, the reception unit can analyze the content of the user's social media posts and automatically select the necessary input fields. This makes it possible to suggest input fields based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's social media activity into an AI and have the AI ​​generate relevant input fields.

[0083] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a display method that gets straight to the point. This makes it possible to adjust the display method of the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI execute the display method of the analysis results.

[0084] The analysis unit can adjust the level of detail of the analysis based on the importance of the subsidies and allowances during the analysis. For example, the analysis unit performs a detailed analysis on subsidies and allowances of high importance and provides it to the user. For example, the analysis unit provides a simplified analysis result on subsidies and allowances of low importance. For example, the analysis unit determines the priority of the analysis and adjusts the level of detail according to the importance of the subsidies and allowances. This makes it possible to adjust the level of detail of the analysis based on the importance of the subsidies and allowances. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the importance data of subsidies and allowances into a generation AI and have the generation AI perform the analysis with a specified level of detail.

[0085] The analysis unit can apply different analysis algorithms depending on the category of subsidies and allowances during analysis. For example, the analysis unit applies an education-specific analysis algorithm to education-related subsidies and allowances. For example, the analysis unit applies a medical-specific analysis algorithm to medical-related subsidies and allowances. For example, the analysis unit applies a startup-specific analysis algorithm to startup-related subsidies and allowances. This makes it possible to apply analysis algorithms according to the category of subsidies and allowances. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input subsidy and allowance category data into the generation AI and have the generation AI execute the application of analysis algorithms.

[0086] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit will prioritize displaying important analysis results. For example, if the user is relaxed, the analysis unit will prioritize displaying detailed analysis results. For example, if the user is in a hurry, the analysis unit will prioritize displaying the most important analysis results. This makes it possible to determine the priority of analysis results based on 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, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using a generative AI or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI prioritize the analysis results.

[0087] The analysis unit can determine the priority of analysis based on the submission timing of subsidies and allowances during the analysis process. For example, the analysis unit will prioritize the analysis of subsidies and allowances with approaching submission deadlines. For example, the analysis unit will postpone the analysis of subsidies and allowances with distant submission deadlines. The analysis unit can dynamically adjust the analysis priority based on the submission timing. This makes it possible to determine the analysis priority based on the submission timing of subsidies and allowances. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the submission timing data of subsidies and allowances into a generation AI and have the generation AI execute the analysis priority.

[0088] The analysis unit can adjust the order of analysis results based on the relevance of subsidies and allowances during the analysis. For example, the analysis unit may prioritize displaying subsidies and allowances that are most relevant to the user's current situation. For example, the analysis unit may prioritize displaying subsidies and allowances that are relevant to the user's future events. For example, the analysis unit can dynamically adjust the order of analysis results based on the relevance of subsidies and allowances. This makes it possible to adjust the order of analysis results based on the relevance of subsidies and allowances. Some or all of the above processing in the analysis unit may be performed using a generating AI, or not. For example, the analysis unit can input the relevance data of subsidies and allowances into a generating AI and have the generating AI execute the order of analysis results.

[0089] The application unit can estimate the user's emotions and adjust the way the application is presented based on the estimated emotions. For example, if the user is nervous, the application unit provides a simple and easily visible presentation. For example, if the user is relaxed, the application unit provides a presentation that includes detailed information. For example, if the user is in a hurry, the application unit provides a presentation that gets straight to the point. This makes it possible to adjust the way the application is presented based on 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, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the application unit may be performed using a generative AI or not using a generative AI. For example, the application unit can input user emotion data into a generative AI and have the generative AI execute the presentation of the application.

[0090] The application department can adjust the level of detail of an application based on the importance of the subsidy or allowance. For example, the application department can submit a detailed application for high-importance subsidies or allowances, and a simplified application for low-importance subsidies or allowances. The application department can also determine the priority of applications and adjust the level of detail based on the importance of the subsidies or allowances. This makes it possible to adjust the level of detail of applications based on the importance of the subsidies or allowances. Some or all of the above processing in the application department may be performed using a generation AI, or not. For example, the application department can input the importance data of the subsidies or allowances into a generation AI and have the generation AI determine the level of detail of the application.

[0091] The application department can apply different application algorithms depending on the category of the subsidy or allowance when an application is submitted. For example, the application department can apply an education-specific application algorithm for education-related subsidies and allowances. For example, the application department can apply a medical-specific application algorithm for medical-related subsidies and allowances. For example, the application department can apply a business-specific application algorithm for business-related subsidies and allowances. This makes it possible to apply an application algorithm according to the category of the subsidy or allowance. Some or all of the above processing in the application department may be performed using a generative AI, or it may be performed without using a generative AI. For example, the application department can input the category data of the subsidy or allowance into a generative AI and have the generative AI execute the application of the application algorithm.

[0092] The application unit can estimate the user's emotions and adjust the length of the application based on the estimated emotions. For example, if the user is nervous, the application unit will submit a short, to-the-point application. If the user is relaxed, the application unit will submit a longer application with detailed explanations. If the user is in a hurry, the application unit will submit a short application that can be completed quickly. This allows for adjustment of the application length based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the application unit may be performed using or without generative AI. For example, the application unit can input user emotion data into a generative AI and have the generative AI determine the length of the application.

[0093] The application department can determine the priority of applications based on the submission deadlines for subsidies and allowances at the time of application. For example, the application department will prioritize applications for subsidies and allowances with approaching deadlines. For example, the application department will postpone applications for subsidies and allowances with distant deadlines. The application department can dynamically adjust the priority of applications based on the submission deadlines. This makes it possible to determine the priority of applications based on the submission deadlines for subsidies and allowances. Some or all of the above processing in the application department may be performed using a generation AI, or it may be performed without a generation AI. For example, the application department can input data on the submission deadlines for subsidies and allowances into a generation AI and have the generation AI determine the priority of applications.

[0094] The application unit can adjust the order of applications based on the relevance of subsidies and allowances at the time of application. For example, the application unit may prioritize applications for subsidies and allowances that are most relevant to the user's current situation. For example, the application unit may prioritize applications for subsidies and allowances that are relevant to the user's future events. For example, the application unit may dynamically adjust the order of applications based on the relevance of subsidies and allowances. This makes it possible to adjust the order of applications based on the relevance of subsidies and allowances. Some or all of the above processing in the application unit may be performed using a generative AI, or not. For example, the application unit may input the relevance data of subsidies and allowances into a generative AI and have the generative AI execute the order of applications.

[0095] The notification unit can estimate the user's emotions and adjust the way notifications are displayed based on the estimated emotions. For example, if the user is stressed, the notification unit provides a simple and highly visible display method. For example, if the user is relaxed, the notification unit provides a display method that includes detailed information. For example, if the user is in a hurry, the notification unit provides a display method that gets straight to the point. This makes it possible to adjust the way notifications are displayed based on 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, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input user emotion data into an AI and have the generative AI execute the method of displaying notifications.

[0096] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit may prioritize notification methods that the user has previously preferred to receive (email, push notifications, etc.). For example, the notification unit may suggest the optimal notification method for a specific time period based on the user's past notification history. For example, the notification unit may analyze the user's past notification history and select the most effective notification method. This makes it possible to select the optimal notification method based on the user's past notification history. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit may input the user's past notification history into an AI and have the AI ​​generate the optimal notification method.

[0097] The notification unit can apply different notification algorithms depending on the category of the subsidy or allowance when sending notifications. For example, the notification unit applies an education-specific notification algorithm for education-related subsidies and allowances. For example, the notification unit applies a medical-specific notification algorithm for medical-related subsidies and allowances. For example, the notification unit applies a business-specific notification algorithm for business-related subsidies and allowances. This makes it possible to apply notification algorithms according to the category of the subsidy or allowance. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input subsidy and allowance category data into an AI and have the generating AI execute the application of the notification algorithm.

[0098] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit will prioritize important notifications. For example, if the user is relaxed, the notification unit will prioritize detailed notifications. For example, if the user is in a hurry, the notification unit will prioritize the most important notifications. This makes it possible to determine notification priorities based on 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, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input user emotion data into an AI and have the generative AI perform notification prioritization.

[0099] The notification unit can select the optimal notification method when sending a notification, taking into account the user's geographical location information. For example, if the user lives in a specific region, the notification unit will prioritize displaying notifications related to that region. For example, if the user is traveling, the notification unit will suggest relevant notifications based on the user's current location. For example, if the user is planning to move, the notification unit will prioritize displaying notifications related to the new address. This makes it possible to select the optimal notification method based on the user's geographical location information. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the user's geographical location information into an AI and have the AI ​​generate the optimal notification method.

[0100] The notification unit can analyze the user's social media activity and suggest notification methods when sending a notification. For example, the notification unit can suggest relevant notifications based on information the user has shared on social media. For example, the notification unit can identify areas of interest from the user's social media activity and prioritize displaying relevant notifications. For example, the notification unit can analyze the content of the user's social media posts and automatically select the necessary notifications. This makes it possible to suggest notification methods based on the user's social media activity. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the user's social media activity into an AI and have the AI ​​generate notification methods.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The reception desk can automatically complete input fields by referring to the user's past input history when receiving user input data. For example, it can automatically display addresses and contact information previously entered by the user, saving the user the trouble of re-entering them. Furthermore, the reception desk can prioritize displaying relevant input fields based on the type of subsidy or allowance the user has previously selected. This streamlines the user's input process and reduces the risk of input errors. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input history into the AI ​​and have the AI ​​generate the data to automatically complete the input fields.

[0103] The analysis unit can customize its analysis algorithm based on the user's current lifestyle and areas of interest when analyzing user input data. For example, if a user is looking for education-related subsidies, it can apply an education-specific analysis algorithm to identify the most suitable subsidies. Furthermore, if a user is looking for medical-related subsidies, it can also apply a medical-specific analysis algorithm. This enables highly accurate analysis tailored to the user's needs. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input data on the user's lifestyle and areas of interest into a generative AI and have the generative AI perform the customization of the analysis algorithm.

[0104] The application unit can estimate the user's emotions and adjust the application process based on those emotions. For example, if the user is stressed, the application process can be simplified, displaying only the minimum necessary input fields. If the user is relaxed, detailed input options can be provided, and a customizable application method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow for quick completion of the application. This enables a flexible application process that responds to the user's emotions. Emotion estimation can be 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 application unit may be performed using AI or not. For example, the application unit can input user emotion data into an AI and have the generative AI perform adjustments to the application process.

[0105] The notification unit can estimate the user's emotions and customize the notification content based on the estimated emotions. For example, if the user is stressed, it provides a simple and highly visible notification. If the user is relaxed, it provides a notification with detailed information. Furthermore, if the user is in a hurry, it provides a concise notification that gets straight to the point. This ensures that appropriate notification content is provided according to the user's emotions, facilitating user understanding. 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 notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into an AI and have the generative AI customize the notification content.

[0106] The reception desk can prioritize displaying input fields related to relevant subsidies and allowances, taking into account the user's geographical location. For example, if the user lives in a specific region, it can prioritize displaying input fields related to subsidies and allowances in that region. If the user is traveling, it can suggest relevant input fields based on their current location. Furthermore, if the user is planning to move, it can prioritize displaying input fields related to their new address. This enables the prioritization of input fields based on the user's geographical location, improving user convenience. 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 geographical location information into the AI ​​and have the AI ​​generate the input fields to prioritize display.

[0107] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple and highly visible display method is provided. If the user is relaxed, a display method including detailed information is provided. Furthermore, if the user is in a hurry, a display method that gets straight to the point is provided. This allows for adjustment of the display method of the analysis results based on the user's emotions, thereby promoting user understanding. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using the generative AI or not using the generative AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI execute the display method of the analysis results.

[0108] The application department can analyze a user's past application history and suggest the most suitable application method. For example, it can automatically display as candidates the types of subsidies and allowances the user has frequently applied for in the past. It can also prioritize suggesting application methods (voice, text, etc.) the user has used in the past. Furthermore, it can predict and suggest the most suitable application method for a specific time period based on the user's past application history. This enables the suggestion of the most suitable application method based on the user's past application history, thereby streamlining the application process. Some or all of the above processes in the application department may be performed using AI or not. For example, the application department can input the user's past application history into an AI and have the AI ​​generate the most suitable application method.

[0109] The notification unit can analyze a user's social media activity and suggest relevant notifications. For example, it can suggest relevant notifications based on information the user has shared on social media. It can identify areas of interest from the user's social media activity and prioritize the display of relevant notifications. Furthermore, it can analyze the content of the user's social media posts and automatically select the necessary notifications. This makes it possible to suggest notification methods based on the user's social media activity, enabling the provision of information tailored to the user's interests. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the user's social media activity into an AI and have the AI ​​generate notification methods.

[0110] The analysis unit can determine the priority of analysis based on the submission timing of subsidies and allowances during the analysis process. For example, subsidies and allowances with approaching submission deadlines will be analyzed first, while those with later deadlines will be analyzed later. Furthermore, the analysis priority can be dynamically adjusted based on the submission timing. This enables the determination of analysis priorities based on the submission timing of subsidies and allowances, helping users complete their applications within the deadline. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the submission timing data of subsidies and allowances into a generation AI and have the generation AI execute the analysis prioritization.

[0111] The notification unit can estimate the user's emotions and determine notification priorities based on those emotions. For example, if the user is stressed, important notifications will be displayed first. If the user is relaxed, detailed notifications will be displayed first. Furthermore, if the user is in a hurry, the most important notifications will be displayed first. This enables notification prioritization based on the user's emotions, allowing the user to quickly obtain the information they need. 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 notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into an AI and have the generative AI prioritize notifications.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The reception desk receives data input from users. User data input includes text input, multiple-choice input, voice input, etc. The reception desk provides an interface for users to input their own information. Step 2: The analysis unit uses a generation AI to analyze the data received by the reception unit and identify the subsidies and allowances applicable to the user. The generation AI analyzes the data using natural language processing and machine learning algorithms. Step 3: The application unit uses generation AI to automatically submit applications for subsidies and allowances identified by the analysis unit. The application unit is designed so that the user can complete the application simply by clicking an acceptance button. Step 4: The notification unit notifies the user of the application status and conditions for subsidies and allowances applied for by the application unit. The notification unit notifies the user via methods such as email, SMS, and app notifications.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the reception unit, analysis unit, application unit, and notification unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives data input from the user. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the data using generating AI to identify the subsidies and allowances applicable to the user. The application unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and automatically submits an application for the identified subsidies and allowances. The notification unit is implemented by, for example, the output device 40 of the smart device 14 and notifies the user of the application status and conditions. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the reception unit, analysis unit, application unit, and notification unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives data input from the user. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the data using generating AI to identify the subsidies or allowances applicable to the user. The application unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and automatically submits an application for the identified subsidies or allowances. The notification unit is implemented by, for example, the speaker 240 of the smart glasses 214 and notifies the user of the application status and conditions. 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.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the reception unit, analysis unit, application unit, and notification 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 microphone 238 of the headset terminal 314 and receives data input from the user. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the data using generating AI to identify the subsidies and allowances applicable to the user. The application unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and automatically submits an application for the identified subsidies and allowances. The notification unit is implemented by, for example, the display 343 of the headset terminal 314 and notifies the user of the application status and conditions. 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.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] Each of the multiple elements described above, including the reception unit, analysis unit, application unit, and notification 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 microphone 238 of the robot 414 and receives data input from the user. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the data using generating AI to identify the subsidies or allowances applicable to the user. The application unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and automatically submits an application for the identified subsidies or allowances. The notification unit is implemented by, for example, the speaker 240 of the robot 414 and notifies the user of the application status and conditions. 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] (Note 1) A reception area that accepts data input from users, An analysis unit analyzes the data received by the aforementioned reception unit and identifies the subsidies and allowances applicable to the user, An application unit that automatically submits applications for subsidies and allowances identified by the aforementioned analysis unit, The application unit includes a notification unit that notifies the applicant of the application status and conditions of subsidies and allowances applied for by the aforementioned application unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is Enter user information The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The AI ​​generates data to analyze the conditions for subsidies and allowances. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned application department, The AI ​​generates the application automatically with simple consent and data entry. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned notification unit, We will provide Q&A and notifications regarding the application status. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, We will provide answers to questions about future events and, if necessary, allow you to make an application reservation. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned notification unit, We will notify you of the fee when the subsidy is deposited. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and customizes the data entry interface based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is During data entry, input fields are automatically filtered based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is It estimates the user's emotions and prioritizes input data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering data, the system prioritizes displaying the most relevant input fields by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When entering data, the system analyzes the user's social media activity and suggests relevant input fields. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, adjust the level of detail based on the importance of subsidies and allowances. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of subsidies or allowances. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on the timing of the application for subsidies and allowances. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During the analysis, the order of the analysis results will be adjusted based on the relevance of subsidies and allowances. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned application department, The system estimates the user's emotions and adjusts the way the application is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned application department, When applying, adjust the level of detail in your application based on the importance of the subsidy or allowance you are seeking. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned application department, When applying, different application algorithms are applied depending on the category of subsidy or allowance. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned application department, The system estimates the user's emotions and adjusts the length of the application based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned application department, When applying, the priority of applications will be determined based on the submission timing of subsidies and allowances. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned application department, When applying, the order of applications will be adjusted based on the relevance of the subsidies and allowances. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned notification unit, It estimates the user's emotions and adjusts how notifications are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned notification unit, When sending a notification, the system will refer to the user's past notification history to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned notification unit, When sending notifications, different notification algorithms are applied depending on the category of subsidy or allowance. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned notification unit, When sending notifications, the system will select the most suitable notification method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned notification unit, When sending notifications, we analyze the user's social media activity and suggest notification methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0186] 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 area that accepts data input from users, An analysis unit analyzes the data received by the aforementioned reception unit and identifies the subsidies and allowances applicable to the user, An application unit that automatically submits applications for subsidies and allowances identified by the aforementioned analysis unit, The application unit includes a notification unit that notifies the applicant of the application status and conditions of subsidies and allowances applied for by the aforementioned application unit. A system characterized by the following features.

2. The aforementioned reception unit is Enter user information The system according to feature 1.

3. The aforementioned analysis unit, The AI ​​generates data to analyze the conditions for subsidies and allowances. The system according to feature 1.

4. The aforementioned application department, The AI ​​generates the application automatically with simple consent and data entry. The system according to feature 1.

5. The aforementioned notification unit, We will provide Q&A and notifications regarding the application status. The system according to feature 1.

6. The aforementioned analysis unit, We will provide answers to questions about future events and, if necessary, allow you to make an application reservation. The system according to feature 1.

7. The aforementioned notification unit, We will notify you of the fee when the subsidy is deposited. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and customizes the data entry interface based on those estimated emotions. The system according to feature 1.

9. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

10. The aforementioned reception unit is During data entry, input fields are automatically filtered based on the user's current lifestyle and areas of interest. The system according to feature 1.

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