system

Abstract

The system according to this embodiment aims to efficiently collect and analyze information on subsidies and grants, propose the optimal combination, and automate the application process. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and an automation unit. The collection unit collects subsidy information. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes the optimal combination of subsidies and grants based on the analysis results obtained by the analysis unit. The automation unit automates the application process.

Description

Technical Field

[0004] ,

[0006] , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to collect information on subsidies and grants and find an optimal combination, and the application procedures are also complicated.

[0005] The system according to the embodiment aims to efficiently collect and analyze information on subsidies and grants, propose an optimal combination, and automate the application procedures.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and an automation unit. The collection unit collects subsidy information. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes the optimal combination of subsidies and grants based on the analysis results obtained by the analysis unit. The automation unit automates the application process. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently collect and analyze information on subsidies and grants, propose the optimal combination, and automate the application process. [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 a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a 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 AI ​​agent system according to an embodiment of the present invention is a system that autonomously collects the latest policy information and proposes the optimal combination of subsidies and grants to the user. This system collects and analyzes agricultural subsidy information in real time and provides automation of application procedures and optimal utilization methods. It also supports the application process for grants and the preparation of necessary documents, enabling users to receive subsidies without hassle. For example, the AI ​​agent system collects information from government and local government websites, agricultural news sites, etc. When a new subsidy program is announced, it collects that information and adds it to the database. Next, the AI ​​analyzes the collected information. The AI ​​analyzes the collected subsidy information and proposes the optimal combination of subsidies and grants to the user. For example, if the user is a young farmer, it will prioritize proposing subsidies and grants for young farmers. Furthermore, it automates the application procedure. The AI ​​automatically creates the application documents required by the user and supports the preparation of necessary documents. For example, it automatically inputs the necessary information into the application document format, enabling users to complete the application procedure without hassle. It also provides the optimal way to utilize subsidies. Based on the user's business plan and schedule, the AI ​​proposes effective ways to utilize subsidies. For example, it provides specific advice on when and how to use subsidies. Finally, it supports the grant application process and the preparation of necessary documents. The AI ​​explains the grant application process in detail and lists the necessary documents. This allows users to receive subsidies with minimal effort. This system makes it easier for farmers and individuals / organizations who want to start farming to access the latest subsidy and grant information and reduces the burden of application procedures. Furthermore, by suggesting the best way to utilize subsidies, it promotes business success and strengthens cooperation with local communities. For example, young farmers can use subsidies to introduce new agricultural technologies, which can lead to increased efficiency and profitability in agriculture. In this way, the AI ​​agent system allows users to receive subsidies with minimal effort.

[0029] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and an automation unit. The collection unit collects subsidy information. The collection unit collects information from, for example, the official websites of the government and local authorities, and agricultural news sites. The collection unit collects information and adds it to the database when, for example, a new subsidy program is announced. The collection unit can also collect information periodically using, for example, web scraping technology. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the collected subsidy information and proposes the optimal combination of subsidies and grants to the user. The analysis unit, for example, if the user is a young farmer, will prioritize proposing subsidies and grants for young farmers. The analysis unit can also propose the optimal combination of subsidies and grants based on, for example, the user's business scale and application conditions. The proposal unit proposes the optimal combination of subsidies and grants based on the analysis results obtained by the analysis unit. The proposal unit proposes effective ways to utilize subsidies based on, for example, the user's business plan and schedule. The proposal unit provides specific advice, for example, on when and how subsidies should be used. The proposal unit can also propose combinations of multiple subsidies and grants according to the user's needs. The automation unit automates the application process. The automation unit can, for example, automatically create the application documents required by the user and support the preparation of necessary documents. The automation unit can, for example, automatically input the necessary information into the application document format, allowing the user to complete the application process without hassle. The automation unit can also, for example, explain the grant application process in detail and list the necessary documents. As a result, the AI ​​agent system according to the embodiment can efficiently collect, analyze, propose, and automate the application process for subsidy information. Some or all of the above-described processes in the collection unit, analysis unit, proposal unit, and automation unit may be performed using AI, for example, or not using AI. For example, the collection unit can input information obtained from the official websites of the government or local authorities into a generating AI and have the generating AI organize and classify the information. The analysis unit can input the information collected by the collection unit into a generating AI and have the generating AI analyze the information.The proposal department inputs the analysis results obtained by the analysis department into the generation AI, which can then propose the optimal combination of subsidies and grants. The automation department inputs the information necessary for the application process into the generation AI, which can then create application documents and list the required documents.

[0030] The data collection unit collects subsidy information. For example, it collects information from government and local government websites, and agricultural news sites. Specifically, the unit uses web scraping technology to periodically obtain information from these sites. Web scraping technology is a technique that analyzes the HTML structure of web pages and extracts necessary information. For example, when a new subsidy program is announced, the unit collects that information and adds it to the database. The unit organizes the collected information and stores it in the database. The database includes detailed information such as the name of the subsidy, the provider, the target recipients, the application period, and the application conditions. The unit inputs the collected information into a generative AI, which then organizes and classifies the information. The generative AI uses natural language processing technology to analyze the collected information and classify it into categories. For example, the generative AI can determine whether the target recipients of a subsidy are young farmers and prioritize organizing subsidy information for young farmers. This allows the unit to efficiently and accurately collect subsidy information and store it in the database. Furthermore, the data collection unit can share the collected information with other systems and departments. For example, the data collection unit can provide the collected information to the analysis unit, which can then use it as foundational data for analyzing the information. This allows the data collection unit to streamline the subsidy information collection process and improve the overall system performance.

[0031] The analysis department analyzes the information collected by the collection department. For example, the analysis department analyzes the collected subsidy information and proposes the optimal combination of subsidies and grants to the user. Specifically, the analysis department inputs the collected subsidy information into a generating AI and has the generating AI perform the information analysis. The generating AI uses machine learning algorithms to analyze the collected information and identify the optimal combination of subsidies and grants to meet the user's needs. For example, if the user is a young farmer, the generating AI will prioritize proposing subsidies and grants for young farmers. The generating AI can also propose the optimal combination of subsidies and grants based on the user's business scale and application conditions. Based on the analysis results obtained by the generating AI, the analysis department proposes the optimal combination of subsidies and grants to the user. Furthermore, the analysis department can also utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past subsidy application data, it can predict subsidy usage trends in specific regions and time periods and formulate future countermeasures. In addition, the analysis department can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis department to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and security of the entire system.

[0032] The Proposal Department proposes the optimal combination of subsidies and grants based on the analysis results obtained by the Analysis Department. For example, the Proposal Department proposes effective ways to utilize subsidies based on the user's business plan and schedule. Specifically, the Proposal Department proposes the optimal combination of subsidies and grants to the user based on the analysis results obtained by the Generative AI. The Generative AI analyzes the user's business plan and schedule and provides specific advice on when and how to use the subsidies. For example, the Generative AI proposes the application timing and usage method of subsidies based on the user's business plan. The Proposal Department can also propose combinations of multiple subsidies and grants according to the user's needs. For example, if a user applies for multiple subsidies simultaneously, the Proposal Department considers the application and usage conditions of each subsidy and proposes the optimal combination. Furthermore, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. For example, the Proposal Department revises its proposals based on user feedback to make more effective proposals. The Proposal Department can also understand the user's needs and requests through communication and make proposals accordingly. This allows the proposal department to suggest the optimal combination of subsidies and grants to users and support the effective use of subsidies.

[0033] The Automation Department automates the application process. For example, the Automation Department can automatically create the application documents required by the user and support the preparation of necessary documents. Specifically, the Automation Department inputs the information necessary for the application process into a Generating AI, which can then create the application documents and list the necessary documents. The Generating AI automatically inputs the necessary information into the application document format, allowing users to complete the application process without hassle. For example, based on the user's information, the Generating AI automatically inputs the necessary information into each field of the application document, completing the application document. The Automation Department provides the application documents created by the Generating AI to the user, supporting them in completing the application process smoothly. Furthermore, the Automation Department can also explain the grant application process in detail and list the necessary documents. For example, the Automation Department explains each step of the application process to the user and provides details of the necessary documents and procedures. In this way, the Automation Department can support users in completing the application process smoothly and improve the efficiency of the application process. In addition, the Automation Department can monitor the progress of the application process in real time and provide users with timely information. For example, the automation unit sends notifications to the user and provides instructions for proceeding to the next step when each step of the application process is completed. This allows the automation unit to manage the entire flow of the application process and support the user in completing the application process smoothly.

[0034] The data collection unit can collect information from government and local government websites and agricultural news sites. For example, the data collection unit can collect the latest subsidy information from government and local government websites. The data collection unit can also collect information on new subsidy programs from agricultural news sites. The data collection unit can also collect information periodically using web scraping techniques, for example. This allows the collection unit to collect subsidy information from reliable sources. Reliable sources include, but are not limited to, government and local government websites and agricultural news sites. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input information obtained from government and local government websites into a generating AI and have the generating AI organize and classify the information.

[0035] The analysis unit can analyze the collected subsidy information and propose the optimal combination of subsidies and grants to the user. For example, if the user is a young farmer, the analysis unit will prioritize proposing subsidies and grants for young farmers. The analysis unit can also propose the optimal combination of subsidies and grants based on the user's business scale and application conditions. This allows the analysis unit to propose the optimal combination of subsidies and grants to the user. The optimal combination of subsidies and grants includes, but is not limited to, the user's needs, business scale, and application conditions. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the information collected by the collection unit into a generating AI and have the generating AI perform the analysis of the information.

[0036] The automation unit can automatically input the necessary information into the application form format and perform the application procedure. For example, the automation unit can automatically create the application forms required by the user and support the preparation of necessary documents. For example, the automation unit can automatically input the necessary information into the application form format, allowing the user to perform the application procedure without effort. The automation unit can also, for example, retrieve the necessary information into the application form format from a database and input it automatically. This reduces the effort required from the user by automating the application procedure. Methods of automatic input include, but are not limited to, OCR technology and database lookup. Some or all of the above processes in the automation unit may be performed using, for example, AI, or not using AI. For example, the automation unit can input the necessary information for the application procedure into a generating AI and have the generating AI create the application forms and list the necessary documents.

[0037] The proposal department can propose effective ways to utilize subsidies based on the user's business plan and schedule. For example, the proposal department can propose effective ways to utilize subsidies based on the user's business plan and schedule. The proposal department can also provide specific advice on when and how to use subsidies. The proposal department can also propose effective ways to utilize subsidies according to the user's needs. By proposing effective ways to utilize subsidies, the success of the business can be promoted. Effective ways of utilization include, but are not limited to, business success rates and efficient use of funds. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the analysis results obtained by the analysis department into a generation AI and have the generation AI propose the optimal combination of subsidies and grants.

[0038] The automated unit can provide a detailed explanation of the grant application process and list the required documents. For example, the automated unit can provide a step-by-step explanation of the grant application process, allowing users to proceed with the process without hassle. The automated unit can also list the required documents, supporting users in preparing all necessary documents. This supports the grant application process and streamlines the preparation of required documents. Methods of providing detailed explanations include, but are not limited to, step-by-step guides and FAQs. Some or all of the above processes in the automated unit may be performed using AI, for example, or not. For example, the automated unit can input information about the grant application process into a generating AI, which can then perform detailed explanations and list the required documents.

[0039] The data collection unit can analyze the user's past subsidy application history and select the optimal data collection method. For example, the data collection unit can prioritize collecting relevant new subsidy information based on the types of subsidies the user has applied for in the past. For example, the data collection unit can also analyze patterns of successful applications the user has made in the past and collect data on subsidies that meet similar criteria. For example, the data collection unit can re-collect information on subsidies that the user previously abandoned applying for and suggest areas for improvement. This allows the optimal data collection method to be selected by analyzing past application history. The optimal data collection method includes, but is not limited to, data collection frequency and criteria for selecting information sources. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's past subsidy application history into a generating AI and have the generating AI select the optimal data collection method.

[0040] The data collection unit can filter subsidy information based on the user's current business status and areas of interest. For example, the data collection unit can filter applicable subsidy information according to the user's business scale. The data collection unit can also prioritize the collection of relevant subsidy information based on the user's areas of interest (e.g., organic farming, smart farming). The data collection unit can also provide optimal subsidy information according to the user's current business status (e.g., new business, expansion business). This enables the provision of information tailored to the user's business status and areas of interest. Filtering methods include, but are not limited to, keyword matching and category classification. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's business status and areas of interest into a generating AI and have the generating AI perform the filtering.

[0041] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting subsidy information. For example, the data collection unit can prioritize the collection of region-specific subsidy information based on the user's location. The data collection unit can also prioritize the provision of subsidy information related to the user's business area. For example, the data collection unit can also collect relevant regional subsidy information by referring to the user's travel history. This enables the provision of information based on the user's geographical location. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant information.

[0042] The data collection unit can analyze the user's social media activity and collect relevant information when collecting subsidy information. For example, the data collection unit can prioritize collecting subsidy information that the user has shown interest in on social media. The data collection unit can also collect subsidy information shared by the user's followers and friends. For example, the data collection unit can provide subsidy information in areas of high interest based on the user's social media activity. This makes it possible to provide information based on the user's social media activity. Social media activity includes, but is not limited to, analyzing post content and analyzing follower counts. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant information.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the subsidy information during the analysis. For example, the analysis unit can perform a detailed analysis of highly important subsidy information and provide it to the user. For example, the analysis unit can perform a concise analysis of less important subsidy information and provide only the key points. The analysis unit can also adjust the level of detail of the analysis in stages according to importance. This enables analysis that is appropriate to the importance of the subsidy information. Importance includes, but is not limited to, the amount of funding and the strictness of the application conditions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input data on the importance of the subsidy information into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the subsidy during the analysis. For example, for subsidies related to agricultural technology, the analysis unit can apply an analysis algorithm that emphasizes technical elements. For example, for subsidies related to environmental protection, the analysis unit can also apply an analysis algorithm that emphasizes environmental impact. For example, for subsidies related to regional development, the analysis unit can also apply an analysis algorithm that emphasizes the impact on the regional economy. This makes it possible to perform analysis according to the category of the subsidy. Categories include, for example, by industry and by application, but are not limited to these examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the category of subsidy into a generating AI and have the generating AI perform the application of different analysis algorithms.

[0045] The analysis department can determine the priority of analysis based on the submission timing of subsidy information during the analysis process. For example, the analysis department may prioritize the analysis of subsidy information with an approaching submission deadline. For example, the analysis department may postpone the analysis of subsidy information with a longer submission deadline. The analysis department may also adjust the priority of analysis in stages according to the submission timing. This makes it possible to determine the priority of analysis according to the submission timing. The submission timing includes, but is not limited to, application deadlines and application start dates. Some or all of the above processing in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input data on the submission timing of subsidy information into a generating AI and have the generating AI perform the determination of analysis priorities.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the subsidy information during the analysis process. For example, the analysis unit can prioritize the analysis of subsidy information that is most relevant to the user's business. The analysis unit can also postpone the analysis of less relevant subsidy information. The analysis unit can also adjust the order of analysis in stages according to relevance. This makes it possible to adjust the order of analysis according to relevance. Relevance includes, but is not limited to, the degree of match with the business content and past performance. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the relevance of the subsidy information into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0047] The proposal department can adjust the level of detail in a proposal based on the importance of the subsidy. For example, the proposal department can provide a detailed proposal to the user for high-importance subsidies. For example, the proposal department can provide a concise proposal, offering only the essentials, for low-importance subsidies. The proposal department can also adjust the level of detail in a stepwise manner according to importance. This makes it possible to adjust the level of detail in a proposal according to the importance of the subsidy. Importance includes, but is not limited to, the amount of funding and the strictness of the application conditions. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input data on the importance of the subsidy into a generating AI and have the generating AI perform the adjustment of the level of detail in the proposal.

[0048] The proposal unit can apply different proposal algorithms depending on the category of the subsidy when making a proposal. For example, for subsidies related to agricultural technology, the proposal unit can apply a proposal algorithm that emphasizes technical elements. For example, for subsidies related to environmental protection, the proposal unit can also apply a proposal algorithm that emphasizes environmental impact. For example, for subsidies related to regional development, the proposal unit can also apply a proposal algorithm that emphasizes the impact on the regional economy. This makes it possible to make proposals that are tailored to the category of the subsidy. Categories include, for example, by industry or by application, but are not limited to these examples. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input data on the category of the subsidy into a generating AI and have the generating AI apply different proposal algorithms.

[0049] The proposal department can determine the priority of proposals based on the timing of grant submissions. For example, the proposal department can prioritize proposals for grants with approaching submission deadlines. For example, the proposal department can postpone proposals for grants with longer submission deadlines. The proposal department can also adjust the priority of proposals in stages according to the submission timing. This makes it possible to determine the priority of proposals according to the submission timing. The submission timing includes, but is not limited to, the application deadline and the start date of applications. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input data on the submission timing of grants into a generating AI and have the generating AI perform the determination of proposal priorities.

[0050] The proposal department can adjust the order of proposals based on the relevance of the subsidies when submitting them. For example, the proposal department will prioritize proposing subsidies that are most relevant to the user's business. The proposal department can also postpone proposing less relevant subsidies. The proposal department can also adjust the order of proposals in stages according to relevance. This makes it possible to adjust the order of proposals according to relevance. Relevance includes, but is not limited to, the degree of match with the business content and past performance. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input data on the relevance of subsidies into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0051] The automation unit can analyze the user's past application behavior during automation to select the optimal automation method. For example, the automation unit can propose a similar automation method based on patterns of successful applications in the user's past. The automation unit can also analyze the reasons why the user abandoned an application in the past and provide an automation method that reflects improvements. The automation unit can also propose the optimal automation procedure by referring to the user's past application behavior. This makes it possible to select the optimal automation method based on past application behavior. The optimal automation method includes, but is not limited to, work efficiency and error rate. Some or all of the above processing in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input data on the user's past application behavior into a generating AI and have the generating AI select the optimal automation method.

[0052] The automation unit can customize the automation methods based on the user's current business situation during automation. For example, the automation unit can provide appropriate automation methods according to the user's business scale. The automation unit can also propose the optimal automation procedure based on the user's business content. The automation unit can also customize the automation methods according to the user's current business situation (e.g., new business, expansion business). This makes it possible to customize the automation methods according to the current business situation. Customization includes, but is not limited to, the user's needs and business scale. Some or all of the above processing in the automation unit may be performed using, for example, AI, or not using AI. For example, the automation unit can input data about the user's current business situation into a generating AI and have the generating AI perform the customization of the automation methods.

[0053] The automation unit can select the optimal automation method during automation, taking into account the user's geographical location information. For example, the automation unit can provide region-specific automation methods based on the user's location. The automation unit can also, for example, prioritize providing automation methods related to the user's business area. The automation unit can also, for example, provide automation methods for relevant regions by referring to the user's travel history. This makes it possible to select the optimal automation method based on geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the automation unit may be performed using, for example, AI, or without AI. For example, the automation unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal automation method.

[0054] The automation unit can analyze the user's social media activity and propose automation methods during the automation process. For example, the automation unit can prioritize providing automation methods that the user has shown interest in on social media. The automation unit can also provide automation methods shared by the user's followers or friends. For example, the automation unit can provide automation methods in areas of high interest based on the user's social media activity. This makes it possible to propose automation methods based on social media activity. Social media activity includes, but is not limited to, analyzing post content and follower counts. Some or all of the above processing in the automation unit may be performed using, for example, AI, or without AI. For example, the automation unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of automation methods.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The data collection unit can analyze a user's past subsidy application history and select the optimal collection method. For example, it can prioritize collecting relevant new subsidy information based on the types of subsidies the user has applied for in the past. It can also analyze patterns of successful past applications and collect subsidy information that meets similar criteria. It can even recollect information on subsidies that the user previously abandoned and suggest improvements. In this way, the optimal collection method can be selected by analyzing past application history. The optimal collection method includes, but is not limited to, collection frequency and criteria for selecting information sources.

[0057] The data collection unit can filter subsidy information based on the user's current business status and areas of interest. For example, it can filter applicable subsidy information according to the user's business scale. It can also prioritize the collection of relevant subsidy information based on the user's areas of interest (e.g., organic farming, smart farming). It can also provide the most suitable subsidy information according to the user's current business status (e.g., new business, expansion business). This enables the provision of information tailored to the user's business status and areas of interest. Filtering methods include, but are not limited to, keyword matching and category classification.

[0058] The analysis department can adjust the level of detail in its analysis based on the importance of the subsidy information. For example, it can perform a detailed analysis on highly important subsidy information and provide it to the user. For less important subsidy information, it can perform a concise analysis and provide only the key points. It can also adjust the level of detail in the analysis in stages according to importance. This makes it possible to perform analysis according to the importance of the subsidy information. Importance includes, but is not limited to, the amount of funding and the strictness of the application conditions.

[0059] The proposal department can determine the priority of proposals based on the timing of the grant submissions. For example, proposals for grants with approaching submission deadlines can be given priority. Proposals for grants with longer submission deadlines can be submitted later. The priority of proposals can also be adjusted in stages according to the submission timing. This makes it possible to determine the priority of proposals according to the submission timing. The submission timing includes, but is not limited to, application deadlines and application start dates.

[0060] The automation unit can select the optimal automation method during automation, taking into account the user's geographical location information. For example, it can provide region-specific automation methods based on the user's location. It can also prioritize providing automation methods related to the user's business area. It can also provide automation methods for relevant regions by referring to the user's travel history. This makes it possible to select the optimal automation method based on geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The collection team collects subsidy information. The collection team collects information from sources such as government and local government websites and agricultural news sites. When new subsidy programs are announced, the collection team quickly gathers the information and adds it to the database. The collection team can also collect information regularly using web scraping techniques. Step 2: The analysis department analyzes the information collected by the data collection department. The analysis department analyzes the collected subsidy information and proposes the most suitable combination of subsidies and grants to the user. If the user is a young farmer, the analysis department will prioritize proposing subsidies and grants for young farmers. The analysis department can also propose the most suitable combination of subsidies and grants based on the user's business scale and application requirements. Step 3: The proposal department proposes the optimal combination of subsidies and grants based on the analysis results obtained by the analysis department. The proposal department proposes effective ways to utilize the subsidies based on the user's business plan and schedule. The proposal department provides specific advice on when and how to use the subsidies. Depending on the user's needs, the proposal department can also propose combinations of multiple subsidies and grants. Step 4: The automation department automates the application process. The automation department automatically creates the application documents required by the user and supports the preparation of necessary documents. The automation department automatically enters the necessary information into the application document format, allowing the user to complete the application process effortlessly. The automation department can also provide a detailed explanation of the grant application process and list the required documents.

[0063] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that autonomously collects the latest policy information and proposes the optimal combination of subsidies and grants to the user. This system collects and analyzes agricultural subsidy information in real time and provides automation of application procedures and optimal utilization methods. It also supports the application process for grants and the preparation of necessary documents, enabling users to receive subsidies without hassle. For example, the AI ​​agent system collects information from government and local government websites, agricultural news sites, etc. When a new subsidy program is announced, it collects that information and adds it to the database. Next, the AI ​​analyzes the collected information. The AI ​​analyzes the collected subsidy information and proposes the optimal combination of subsidies and grants to the user. For example, if the user is a young farmer, it will prioritize proposing subsidies and grants for young farmers. Furthermore, it automates the application procedure. The AI ​​automatically creates the application documents required by the user and supports the preparation of necessary documents. For example, it automatically inputs the necessary information into the application document format, enabling users to complete the application procedure without hassle. It also provides the optimal way to utilize subsidies. Based on the user's business plan and schedule, the AI ​​proposes effective ways to utilize subsidies. For example, it provides specific advice on when and how to use subsidies. Finally, it supports the grant application process and the preparation of necessary documents. The AI ​​explains the grant application process in detail and lists the necessary documents. This allows users to receive subsidies with minimal effort. This system makes it easier for farmers and individuals / organizations who want to start farming to access the latest subsidy and grant information and reduces the burden of application procedures. Furthermore, by suggesting the best way to utilize subsidies, it promotes business success and strengthens cooperation with local communities. For example, young farmers can use subsidies to introduce new agricultural technologies, which can lead to increased efficiency and profitability in agriculture. In this way, the AI ​​agent system allows users to receive subsidies with minimal effort.

[0064] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and an automation unit. The collection unit collects subsidy information. The collection unit collects information from, for example, the official websites of the government and local authorities, and agricultural news sites. The collection unit collects information and adds it to the database when, for example, a new subsidy program is announced. The collection unit can also collect information periodically using, for example, web scraping technology. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the collected subsidy information and proposes the optimal combination of subsidies and grants to the user. The analysis unit, for example, if the user is a young farmer, will prioritize proposing subsidies and grants for young farmers. The analysis unit can also propose the optimal combination of subsidies and grants based on, for example, the user's business scale and application conditions. The proposal unit proposes the optimal combination of subsidies and grants based on the analysis results obtained by the analysis unit. The proposal unit proposes effective ways to utilize subsidies based on, for example, the user's business plan and schedule. The proposal unit provides specific advice, for example, on when and how subsidies should be used. The proposal unit can also propose combinations of multiple subsidies and grants according to the user's needs. The automation unit automates the application process. The automation unit can, for example, automatically create the application documents required by the user and support the preparation of necessary documents. The automation unit can, for example, automatically input the necessary information into the application document format, allowing the user to complete the application process without hassle. The automation unit can also, for example, explain the grant application process in detail and list the necessary documents. As a result, the AI ​​agent system according to the embodiment can efficiently collect, analyze, propose, and automate the application process for subsidy information. Some or all of the above-described processes in the collection unit, analysis unit, proposal unit, and automation unit may be performed using AI, for example, or not using AI. For example, the collection unit can input information obtained from the official websites of the government or local authorities into a generating AI and have the generating AI organize and classify the information. The analysis unit can input the information collected by the collection unit into a generating AI and have the generating AI analyze the information.The proposal department inputs the analysis results obtained by the analysis department into the generation AI, which can then propose the optimal combination of subsidies and grants. The automation department inputs the information necessary for the application process into the generation AI, which can then create application documents and list the required documents.

[0065] The data collection unit collects subsidy information. For example, it collects information from government and local government websites, and agricultural news sites. Specifically, the unit uses web scraping technology to periodically obtain information from these sites. Web scraping technology is a technique that analyzes the HTML structure of web pages and extracts necessary information. For example, when a new subsidy program is announced, the unit collects that information and adds it to the database. The unit organizes the collected information and stores it in the database. The database includes detailed information such as the name of the subsidy, the provider, the target recipients, the application period, and the application conditions. The unit inputs the collected information into a generative AI, which then organizes and classifies the information. The generative AI uses natural language processing technology to analyze the collected information and classify it into categories. For example, the generative AI can determine whether the target recipients of a subsidy are young farmers and prioritize organizing subsidy information for young farmers. This allows the unit to efficiently and accurately collect subsidy information and store it in the database. Furthermore, the data collection unit can share the collected information with other systems and departments. For example, the data collection unit can provide the collected information to the analysis unit, which can then use it as foundational data for analyzing the information. This allows the data collection unit to streamline the subsidy information collection process and improve the overall system performance.

[0066] The analysis department analyzes the information collected by the collection department. For example, the analysis department analyzes the collected subsidy information and proposes the optimal combination of subsidies and grants to the user. Specifically, the analysis department inputs the collected subsidy information into a generating AI and has the generating AI perform the information analysis. The generating AI uses machine learning algorithms to analyze the collected information and identify the optimal combination of subsidies and grants to meet the user's needs. For example, if the user is a young farmer, the generating AI will prioritize proposing subsidies and grants for young farmers. The generating AI can also propose the optimal combination of subsidies and grants based on the user's business scale and application conditions. Based on the analysis results obtained by the generating AI, the analysis department proposes the optimal combination of subsidies and grants to the user. Furthermore, the analysis department can also utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past subsidy application data, it can predict subsidy usage trends in specific regions and time periods and formulate future countermeasures. In addition, the analysis department can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis department to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and security of the entire system.

[0067] The Proposal Department proposes the optimal combination of subsidies and grants based on the analysis results obtained by the Analysis Department. For example, the Proposal Department proposes effective ways to utilize subsidies based on the user's business plan and schedule. Specifically, the Proposal Department proposes the optimal combination of subsidies and grants to the user based on the analysis results obtained by the Generative AI. The Generative AI analyzes the user's business plan and schedule and provides specific advice on when and how to use the subsidies. For example, the Generative AI proposes the application timing and usage method of subsidies based on the user's business plan. The Proposal Department can also propose combinations of multiple subsidies and grants according to the user's needs. For example, if a user applies for multiple subsidies simultaneously, the Proposal Department considers the application and usage conditions of each subsidy and proposes the optimal combination. Furthermore, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. For example, the Proposal Department revises its proposals based on user feedback to make more effective proposals. The Proposal Department can also understand the user's needs and requests through communication and make proposals accordingly. This allows the proposal department to suggest the optimal combination of subsidies and grants to users and support the effective use of subsidies.

[0068] The Automation Department automates the application process. For example, the Automation Department can automatically create the application documents required by the user and support the preparation of necessary documents. Specifically, the Automation Department inputs the information necessary for the application process into a Generating AI, which can then create the application documents and list the necessary documents. The Generating AI automatically inputs the necessary information into the application document format, allowing users to complete the application process without hassle. For example, based on the user's information, the Generating AI automatically inputs the necessary information into each field of the application document, completing the application document. The Automation Department provides the application documents created by the Generating AI to the user, supporting them in completing the application process smoothly. Furthermore, the Automation Department can also explain the grant application process in detail and list the necessary documents. For example, the Automation Department explains each step of the application process to the user and provides details of the necessary documents and procedures. In this way, the Automation Department can support users in completing the application process smoothly and improve the efficiency of the application process. In addition, the Automation Department can monitor the progress of the application process in real time and provide users with timely information. For example, the automation unit sends notifications to the user and provides instructions for proceeding to the next step when each step of the application process is completed. This allows the automation unit to manage the entire flow of the application process and support the user in completing the application process smoothly.

[0069] The data collection unit can collect information from government and local government websites and agricultural news sites. For example, the data collection unit can collect the latest subsidy information from government and local government websites. The data collection unit can also collect information on new subsidy programs from agricultural news sites. The data collection unit can also collect information periodically using web scraping techniques, for example. This allows the collection unit to collect subsidy information from reliable sources. Reliable sources include, but are not limited to, government and local government websites and agricultural news sites. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input information obtained from government and local government websites into a generating AI and have the generating AI organize and classify the information.

[0070] The analysis unit can analyze the collected subsidy information and propose the optimal combination of subsidies and grants to the user. For example, if the user is a young farmer, the analysis unit will prioritize proposing subsidies and grants for young farmers. The analysis unit can also propose the optimal combination of subsidies and grants based on the user's business scale and application conditions. This allows the analysis unit to propose the optimal combination of subsidies and grants to the user. The optimal combination of subsidies and grants includes, but is not limited to, the user's needs, business scale, and application conditions. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the information collected by the collection unit into a generating AI and have the generating AI perform the analysis of the information.

[0071] The automation unit can automatically input the necessary information into the application form format and perform the application procedure. For example, the automation unit can automatically create the application forms required by the user and support the preparation of necessary documents. For example, the automation unit can automatically input the necessary information into the application form format, allowing the user to perform the application procedure without effort. The automation unit can also, for example, retrieve the necessary information into the application form format from a database and input it automatically. This reduces the effort required from the user by automating the application procedure. Methods of automatic input include, but are not limited to, OCR technology and database lookup. Some or all of the above processes in the automation unit may be performed using, for example, AI, or not using AI. For example, the automation unit can input the necessary information for the application procedure into a generating AI and have the generating AI create the application forms and list the necessary documents.

[0072] The proposal department can propose effective ways to utilize subsidies based on the user's business plan and schedule. For example, the proposal department can propose effective ways to utilize subsidies based on the user's business plan and schedule. The proposal department can also provide specific advice on when and how to use subsidies. The proposal department can also propose effective ways to utilize subsidies according to the user's needs. By proposing effective ways to utilize subsidies, the success of the business can be promoted. Effective ways of utilization include, but are not limited to, business success rates and efficient use of funds. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the analysis results obtained by the analysis department into a generation AI and have the generation AI propose the optimal combination of subsidies and grants.

[0073] The automated unit can provide a detailed explanation of the grant application process and list the required documents. For example, the automated unit can provide a step-by-step explanation of the grant application process, allowing users to proceed with the process without hassle. The automated unit can also list the required documents, supporting users in preparing all necessary documents. This supports the grant application process and streamlines the preparation of required documents. Methods of providing detailed explanations include, but are not limited to, step-by-step guides and FAQs. Some or all of the above processes in the automated unit may be performed using AI, for example, or not. For example, the automated unit can input information about the grant application process into a generating AI, which can then perform detailed explanations and list the required documents.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of subsidy information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the collection frequency and collect only important information. If the user is relaxed, for example, the data collection unit can increase the collection frequency and provide more detailed information. If the user is in a hurry, for example, the data collection unit can immediately collect the latest subsidy information and provide it quickly. This allows for more appropriate information to be provided by adjusting the collection timing according to the user's emotions. Methods for estimating emotions include, but are not limited to, facial recognition and text analysis. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0075] The data collection unit can analyze the user's past subsidy application history and select the optimal data collection method. For example, the data collection unit can prioritize collecting relevant new subsidy information based on the types of subsidies the user has applied for in the past. For example, the data collection unit can also analyze patterns of successful applications the user has made in the past and collect data on subsidies that meet similar criteria. For example, the data collection unit can re-collect information on subsidies that the user previously abandoned applying for and suggest areas for improvement. This allows the optimal data collection method to be selected by analyzing past application history. The optimal data collection method includes, but is not limited to, data collection frequency and criteria for selecting information sources. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's past subsidy application history into a generating AI and have the generating AI select the optimal data collection method.

[0076] The data collection unit can filter subsidy information based on the user's current business status and areas of interest. For example, the data collection unit can filter applicable subsidy information according to the user's business scale. The data collection unit can also prioritize the collection of relevant subsidy information based on the user's areas of interest (e.g., organic farming, smart farming). The data collection unit can also provide optimal subsidy information according to the user's current business status (e.g., new business, expansion business). This enables the provision of information tailored to the user's business status and areas of interest. Filtering methods include, but are not limited to, keyword matching and category classification. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's business status and areas of interest into a generating AI and have the generating AI perform the filtering.

[0077] The data collection unit can estimate the user's emotions and determine the priority of subsidy information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize providing only the most important subsidy information. If the user is relaxed, the data collection unit may also provide detailed subsidy information and broaden the options. If the user is in a hurry, the data collection unit may also prioritize providing subsidy information that can be applied for immediately. This allows for more appropriate information to be provided by prioritizing information according to the user's emotions. Methods for determining priorities include, but are not limited to, importance, urgency, and relevance. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0078] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting subsidy information. For example, the data collection unit can prioritize the collection of region-specific subsidy information based on the user's location. The data collection unit can also prioritize the provision of subsidy information related to the user's business area. For example, the data collection unit can also collect relevant regional subsidy information by referring to the user's travel history. This enables the provision of information based on the user's geographical location. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant information.

[0079] The data collection unit can analyze the user's social media activity and collect relevant information when collecting subsidy information. For example, the data collection unit can prioritize collecting subsidy information that the user has shown interest in on social media. The data collection unit can also collect subsidy information shared by the user's followers and friends. For example, the data collection unit can provide subsidy information in areas of high interest based on the user's social media activity. This makes it possible to provide information based on the user's social media activity. Social media activity includes, but is not limited to, analyzing post content and analyzing follower counts. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant information.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide simple and intuitive analysis results. If the user is relaxed, the analysis unit can provide detailed analysis results and broaden the range of options. If the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. This makes it possible to provide analysis results that are tailored to the user's emotions. Methods for estimating emotions include, but are not limited to, facial recognition and text analysis. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the subsidy information during the analysis. For example, the analysis unit can perform a detailed analysis of highly important subsidy information and provide it to the user. For example, the analysis unit can perform a concise analysis of less important subsidy information and provide only the key points. The analysis unit can also adjust the level of detail of the analysis in stages according to importance. This enables analysis that is appropriate to the importance of the subsidy information. Importance includes, but is not limited to, the amount of funding and the strictness of the application conditions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input data on the importance of the subsidy information into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of the subsidy during the analysis. For example, for subsidies related to agricultural technology, the analysis unit can apply an analysis algorithm that emphasizes technical elements. For example, for subsidies related to environmental protection, the analysis unit can also apply an analysis algorithm that emphasizes environmental impact. For example, for subsidies related to regional development, the analysis unit can also apply an analysis algorithm that emphasizes the impact on the regional economy. This makes it possible to perform analysis according to the category of the subsidy. Categories include, for example, by industry and by application, but are not limited to these examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the category of subsidy into a generating AI and have the generating AI perform the application of different analysis algorithms.

[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 stressed, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. This makes it possible to provide a display method that is appropriate to the user's emotions. Methods for estimating emotions include, but are not limited to, facial recognition and text analysis. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0084] The analysis department can determine the priority of analysis based on the submission timing of subsidy information during the analysis process. For example, the analysis department may prioritize the analysis of subsidy information with an approaching submission deadline. For example, the analysis department may postpone the analysis of subsidy information with a longer submission deadline. The analysis department may also adjust the priority of analysis in stages according to the submission timing. This makes it possible to determine the priority of analysis according to the submission timing. The submission timing includes, but is not limited to, application deadlines and application start dates. Some or all of the above processing in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input data on the submission timing of subsidy information into a generating AI and have the generating AI perform the determination of analysis priorities.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the subsidy information during the analysis process. For example, the analysis unit can prioritize the analysis of subsidy information that is most relevant to the user's business. The analysis unit can also postpone the analysis of less relevant subsidy information. The analysis unit can also adjust the order of analysis in stages according to relevance. This makes it possible to adjust the order of analysis according to relevance. Relevance includes, but is not limited to, the degree of match with the business content and past performance. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the relevance of the subsidy information into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0086] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can offer simple and intuitive suggestions. If the user is relaxed, the suggestion unit can offer more detailed suggestions and broaden the range of options. If the user is in a hurry, the suggestion unit can offer concise and to-the-point suggestions. This makes it possible to provide suggestions in a way that suits the user's emotions. Methods for estimating emotions include, but are not limited to, facial recognition and text analysis. Some or all of the processing described above in the suggestion unit may be performed using, for example, AI, or not. For example, the suggestion unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0087] The proposal department can adjust the level of detail in a proposal based on the importance of the subsidy. For example, the proposal department can provide a detailed proposal to the user for high-importance subsidies. For example, the proposal department can provide a concise proposal, offering only the essentials, for low-importance subsidies. The proposal department can also adjust the level of detail in a stepwise manner according to importance. This makes it possible to adjust the level of detail in a proposal according to the importance of the subsidy. Importance includes, but is not limited to, the amount of funding and the strictness of the application conditions. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input data on the importance of the subsidy into a generating AI and have the generating AI perform the adjustment of the level of detail in the proposal.

[0088] The proposal unit can apply different proposal algorithms depending on the category of the subsidy when making a proposal. For example, for subsidies related to agricultural technology, the proposal unit can apply a proposal algorithm that emphasizes technical elements. For example, for subsidies related to environmental protection, the proposal unit can also apply a proposal algorithm that emphasizes environmental impact. For example, for subsidies related to regional development, the proposal unit can also apply a proposal algorithm that emphasizes the impact on the regional economy. This makes it possible to make proposals that are tailored to the category of the subsidy. Categories include, for example, by industry or by application, but are not limited to these examples. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input data on the category of the subsidy into a generating AI and have the generating AI apply different proposal algorithms.

[0089] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is stressed, the suggestion unit can provide a short, to-the-point suggestion. If the user is relaxed, the suggestion unit can provide a longer suggestion with more detailed explanations. If the user is in a hurry, the suggestion unit can provide a quick and concise suggestion. This allows for adjustment of the suggestion length according to the user's emotions. Methods for estimating emotions include, but are not limited to, facial recognition and text analysis. Some or all of the processing described above in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0090] The proposal department can determine the priority of proposals based on the timing of grant submissions. For example, the proposal department can prioritize proposals for grants with approaching submission deadlines. For example, the proposal department can postpone proposals for grants with longer submission deadlines. The proposal department can also adjust the priority of proposals in stages according to the submission timing. This makes it possible to determine the priority of proposals according to the submission timing. The submission timing includes, but is not limited to, the application deadline and the start date of applications. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input data on the submission timing of grants into a generating AI and have the generating AI perform the determination of proposal priorities.

[0091] The proposal department can adjust the order of proposals based on the relevance of the subsidies when submitting them. For example, the proposal department will prioritize proposing subsidies that are most relevant to the user's business. The proposal department can also postpone proposing less relevant subsidies. The proposal department can also adjust the order of proposals in stages according to relevance. This makes it possible to adjust the order of proposals according to relevance. Relevance includes, but is not limited to, the degree of match with the business content and past performance. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input data on the relevance of subsidies into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0092] The automation unit can estimate the user's emotions and adjust the automation method based on the estimated user emotions. For example, if the user is stressed, the automation unit can provide a simple and intuitive automation method. If the user is relaxed, for example, the automation unit can also provide detailed automation options and suggest a customizable method. If the user is in a hurry, for example, the automation unit can also provide a method to complete the automation quickly. This makes it possible to provide automation methods that are tailored to the user's emotions. Methods for estimating emotions include, but are not limited to, facial recognition and text analysis. Some or all of the above processing in the automation unit may be performed using, for example, AI, or not using AI. For example, the automation unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0093] The automation unit can analyze the user's past application behavior during automation to select the optimal automation method. For example, the automation unit can propose a similar automation method based on patterns of successful applications in the user's past. The automation unit can also analyze the reasons why the user abandoned an application in the past and provide an automation method that reflects improvements. The automation unit can also propose the optimal automation procedure by referring to the user's past application behavior. This makes it possible to select the optimal automation method based on past application behavior. The optimal automation method includes, but is not limited to, work efficiency and error rate. Some or all of the above processing in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input data on the user's past application behavior into a generating AI and have the generating AI select the optimal automation method.

[0094] The automation unit can customize the automation methods based on the user's current business situation during automation. For example, the automation unit can provide appropriate automation methods according to the user's business scale. The automation unit can also propose the optimal automation procedure based on the user's business content. The automation unit can also customize the automation methods according to the user's current business situation (e.g., new business, expansion business). This makes it possible to customize the automation methods according to the current business situation. Customization includes, but is not limited to, the user's needs and business scale. Some or all of the above processing in the automation unit may be performed using, for example, AI, or not using AI. For example, the automation unit can input data about the user's current business situation into a generating AI and have the generating AI perform the customization of the automation methods.

[0095] The automation unit can estimate the user's emotions and determine the priority of automation based on the estimated emotions. For example, if the user is stressed, the automation unit will prioritize executing the most important automation tasks. If the user is relaxed, the automation unit may also execute detailed automation tasks step by step. If the user is in a hurry, the automation unit may also prioritize executing automation tasks that can be completed quickly. This makes it possible to determine the priority of automation according to the user's emotions. Methods for estimating emotions include, but are not limited to, facial recognition and text analysis. Some or all of the above processing in the automation unit may be performed using, for example, AI, or not using AI. For example, the automation unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0096] The automation unit can select the optimal automation method during automation, taking into account the user's geographical location information. For example, the automation unit can provide region-specific automation methods based on the user's location. The automation unit can also, for example, prioritize providing automation methods related to the user's business area. The automation unit can also, for example, provide automation methods for relevant regions by referring to the user's travel history. This makes it possible to select the optimal automation method based on geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the automation unit may be performed using, for example, AI, or without AI. For example, the automation unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal automation method.

[0097] The automation unit can analyze the user's social media activity and propose automation methods during the automation process. For example, the automation unit can prioritize providing automation methods that the user has shown interest in on social media. The automation unit can also provide automation methods shared by the user's followers or friends. For example, the automation unit can provide automation methods in areas of high interest based on the user's social media activity. This makes it possible to propose automation methods based on social media activity. Social media activity includes, but is not limited to, analyzing post content and follower counts. Some or all of the above processing in the automation unit may be performed using, for example, AI, or without AI. For example, the automation unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of automation methods.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The data collection unit can estimate the user's emotions and prioritize the subsidy information to collect based on those emotions. For example, if the user is stressed, only the most important subsidy information can be prioritized. If the user is relaxed, detailed subsidy information can be provided, expanding their options. If the user is in a hurry, subsidy information that can be applied for immediately can be prioritized. This allows for more appropriate information to be provided by prioritizing information according to the user's emotions. Methods for estimating emotions include, but are not limited to, facial recognition and text analysis.

[0100] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those estimated emotions. For example, if the user is stressed, it can provide simple and intuitive analysis results. If the user is relaxed, it can provide detailed analysis results and broaden the range of options. If the user is in a hurry, it can provide concise analysis results that get straight to the point. This makes it possible to provide analysis results that are tailored to the user's emotions. Methods for estimating emotions include, but are not limited to, facial recognition and text analysis.

[0101] The suggestion function can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, it can offer simple and intuitive suggestions. If the user is relaxed, it can offer more detailed suggestions and broaden the range of options. If the user is in a hurry, it can offer concise suggestions that get straight to the point. This makes it possible to provide suggestions that are tailored to the user's emotions. Methods for estimating emotions include, but are not limited to, facial recognition and text analysis.

[0102] The automation unit can estimate the user's emotions and adjust the automation method based on those emotions. For example, if the user is stressed, it can provide a simple and intuitive automation method. If the user is relaxed, it can provide detailed automation options and suggest a customizable method. If the user is in a hurry, it can provide a method to complete the automation quickly. This makes it possible to provide automation methods that are tailored to the user's emotions. Methods for estimating emotions include, but are not limited to, facial recognition and text analysis.

[0103] The data collection unit can estimate the user's emotions and prioritize the subsidy information to collect based on those emotions. For example, if the user is stressed, only the most important subsidy information can be prioritized. If the user is relaxed, detailed subsidy information can be provided, expanding their options. If the user is in a hurry, subsidy information that can be applied for immediately can be prioritized. This allows for more appropriate information to be provided by prioritizing information according to the user's emotions. Methods for estimating emotions include, but are not limited to, facial recognition and text analysis.

[0104] The data collection unit can analyze a user's past subsidy application history and select the optimal collection method. For example, it can prioritize collecting relevant new subsidy information based on the types of subsidies the user has applied for in the past. It can also analyze patterns of successful past applications and collect subsidy information that meets similar criteria. It can even recollect information on subsidies that the user previously abandoned and suggest improvements. In this way, the optimal collection method can be selected by analyzing past application history. The optimal collection method includes, but is not limited to, collection frequency and criteria for selecting information sources.

[0105] The data collection unit can filter subsidy information based on the user's current business status and areas of interest. For example, it can filter applicable subsidy information according to the user's business scale. It can also prioritize the collection of relevant subsidy information based on the user's areas of interest (e.g., organic farming, smart farming). It can also provide the most suitable subsidy information according to the user's current business status (e.g., new business, expansion business). This enables the provision of information tailored to the user's business status and areas of interest. Filtering methods include, but are not limited to, keyword matching and category classification.

[0106] The analysis department can adjust the level of detail in its analysis based on the importance of the subsidy information. For example, it can perform a detailed analysis on highly important subsidy information and provide it to the user. For less important subsidy information, it can perform a concise analysis and provide only the key points. It can also adjust the level of detail in the analysis in stages according to importance. This makes it possible to perform analysis according to the importance of the subsidy information. Importance includes, but is not limited to, the amount of funding and the strictness of the application conditions.

[0107] The proposal department can determine the priority of proposals based on the timing of the grant submissions. For example, proposals for grants with approaching submission deadlines can be given priority. Proposals for grants with longer submission deadlines can be submitted later. The priority of proposals can also be adjusted in stages according to the submission timing. This makes it possible to determine the priority of proposals according to the submission timing. The submission timing includes, but is not limited to, application deadlines and application start dates.

[0108] The automation unit can select the optimal automation method during automation, taking into account the user's geographical location information. For example, it can provide region-specific automation methods based on the user's location. It can also prioritize providing automation methods related to the user's business area. It can also provide automation methods for relevant regions by referring to the user's travel history. This makes it possible to select the optimal automation method based on geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The collection team collects subsidy information. The collection team collects information from sources such as government and local government websites and agricultural news sites. When new subsidy programs are announced, the collection team quickly gathers the information and adds it to the database. The collection team can also collect information regularly using web scraping techniques. Step 2: The analysis department analyzes the information collected by the data collection department. The analysis department analyzes the collected subsidy information and proposes the most suitable combination of subsidies and grants to the user. If the user is a young farmer, the analysis department will prioritize proposing subsidies and grants for young farmers. The analysis department can also propose the most suitable combination of subsidies and grants based on the user's business scale and application requirements. Step 3: The proposal department proposes the optimal combination of subsidies and grants based on the analysis results obtained by the analysis department. The proposal department proposes effective ways to utilize the subsidies based on the user's business plan and schedule. The proposal department provides specific advice on when and how to use the subsidies. Depending on the user's needs, the proposal department can also propose combinations of multiple subsidies and grants. Step 4: The automation department automates the application process. The automation department automatically creates the application documents required by the user and supports the preparation of necessary documents. The automation department automatically enters the necessary information into the application document format, allowing the user to complete the application process effortlessly. The automation department can also provide a detailed explanation of the grant application process and list the required documents.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and automation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14 and collects information from government and local government websites and agricultural news sites. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected subsidy information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal combination of subsidies and grants to the user. The automation unit is implemented by the control unit 46A of the smart device 14 and supports the creation of application documents and the preparation of necessary documents. 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.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and automation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and collects information from government and local government websites and agricultural news sites. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected subsidy information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal combination of subsidies and grants to the user. The automation unit is implemented by the control unit 46A of the smart glasses 214 and supports the creation of application documents and the preparation of necessary documents. 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.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and automation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 and collects information from government and local government websites and agricultural news sites. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected subsidy information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal combination of subsidies and grants to the user. The automation unit is implemented by the control unit 46A of the headset terminal 314 and supports the creation of application documents and the preparation of necessary documents. 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.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and automation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the robot 414 and collects information from government and local government websites and agricultural news sites. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected subsidy information. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal combination of subsidies and grants to the user. The automation unit is implemented by, for example, the control unit 46A of the robot 414 and supports the creation of application documents and the preparation of necessary documents. 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A collection department that collects information on subsidies, An analysis unit analyzes the information collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes the optimal combination of subsidies and grants. It includes an automation unit that automates the application process. A system characterized by the following features. (Note 2) The aforementioned collection unit is Gather information from government and local government websites, and agricultural news sites. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is We analyze the collected subsidy information and propose the optimal combination of subsidies and grants to the user. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned automation unit, The application form automatically fills in the necessary information and completes the application process. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We propose effective ways to utilize subsidies based on the user's business plan and schedule. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned automation unit, Explain the grant application process in detail and list the required documents. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates user sentiment and adjusts the timing of subsidy information collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past subsidy application history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting subsidy information, filtering is performed based on the user's current business situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates user sentiment and prioritizes the collection of subsidy information based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting subsidy information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting subsidy information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During the analysis, adjust the level of detail based on the importance of the subsidy information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During the analysis, different analytical algorithms are applied depending on the category of the subsidy. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During the analysis, prioritize the analysis based on when the subsidy information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the subsidy information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When submitting a proposal, adjust the level of detail based on the importance of the grant. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When submitting a proposal, a different proposal algorithm is applied depending on the grant category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting proposals, prioritize them based on the timing of grant submissions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When submitting proposals, adjust the order of proposals based on their relevance to the subsidies. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned automation unit, It estimates the user's emotions and adjusts the automation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned automation unit, During automation, the system analyzes the user's past application behavior to select the optimal automation method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned automation unit, During automation, the automation methods are customized based on the user's current business situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned automation unit, It estimates user emotions and determines automation priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned automation unit, When automating processes, the system selects the optimal automation method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned automation unit, During automation, we analyze users' social media activity and suggest automation methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 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 collection department that collects information on subsidies, An analysis unit analyzes the information collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes the optimal combination of subsidies and grants. It includes an automation unit that automates the application process. A system characterized by the following features.

2. The aforementioned collection unit is Gather information from government and local government websites, and agricultural news sites. The system according to feature 1.

3. The aforementioned analysis unit is We analyze the collected subsidy information and propose the most suitable combination of subsidies and grants to the user. The system according to feature 1.

4. The aforementioned automation unit, The application form automatically fills in the necessary information and completes the application process. The system according to feature 1.

5. The aforementioned proposal section is, We propose effective ways to utilize subsidies based on the user's business plan and schedule. The system according to feature 1.

6. The aforementioned automation unit, Explain the grant application process in detail and list the required documents. The system according to feature 1.

7. The aforementioned collection unit is The system estimates user sentiment and adjusts the timing of subsidy information collection based on the estimated user sentiment. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past subsidy application history and select the optimal data collection method. The system according to feature 1.

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