system

Abstract

The system according to the embodiment aims to efficiently search for available subsidies and grants and recommend appropriate ones. [Solution] A system according to an embodiment includes a reception unit, a listing unit, and a recommendation unit. The reception unit accepts input from a user of the type of business, the item to be purchased, and the location. The listing unit lists available subsidies and grants based on the information accepted by the reception unit. The recommendation unit recommends appropriate subsidies and grants from among those listed by the listing unit.

Description

[Technical Field]

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

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

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

[0004] Previous technology has made it difficult to efficiently search for available subsidies and grants and find the right one.

[0005] The system according to the embodiment aims to efficiently search for available subsidies and grants and recommend appropriate ones. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a listing unit, and a recommendation unit. The reception unit receives input from a user about the type of business, the item to be purchased, and the location. The listing unit lists available subsidies and grants based on the information received by the reception unit. The recommendation unit recommends appropriate subsidies and grants from among those listed by the listing unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently search for available subsidies and grants and recommend suitable ones. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A subsidy / grant listing system according to an embodiment of the present invention lists currently available national and local government subsidies and grants by inputting the user's industry, desired purchase, and location. This subsidy / grant listing system generates a list of available subsidies and grants by inputting the user's industry, desired purchase, and location. Furthermore, by clicking the "Target Company" button next to a subsidy or grant in the list, a searchable list of eligible companies is displayed. For example, if a user is in the manufacturing industry and wants to purchase a new machine, they would input "manufacturing" as the industry, "machinery" as the desired purchase, and "Chiyoda Ward, Tokyo" as the location. This information is entered into the system. The system then lists available subsidies and grants based on the input information. The system references national and local government databases to search for subsidies and grants that match the input industry, desired purchase, and location. For example, subsidies and grants that match "manufacturing," "machinery," and "Chiyoda Ward, Tokyo" are displayed in a list. The list includes detailed information about each subsidy and grant, such as the name of the subsidy, the provider, the application deadline, the amount, and eligibility requirements. This allows users to see at a glance which subsidies and grants are available. Furthermore, by clicking the "Eligible Companies" button next to a subsidy or grant in the list, a list of eligible companies is displayed, searchable online. The system collects information on companies eligible for subsidies and grants from the web and displays it as a list. This allows users to check whether their company is eligible. This list can also be used as a sales list. For example, users can propose their products or services to companies using the same subsidies and grants. This system allows users to easily find available subsidies and grants and check whether their company is eligible. It can also be used as a sales list, which can help expand their business. This allows the subsidy and grant listing system to list available subsidies and grants based on the information entered by the user and recommend the most suitable ones.

[0029] A subsidy / grant listing system according to an embodiment includes a reception unit, a listing unit, and a recommendation unit. The reception unit accepts input from a user of the user's industry, desired purchase, and location. For example, if a user is in the manufacturing industry and wants to purchase a new machine, the user enters "manufacturing" as the industry, "machinery" as the desired purchase, and "Chiyoda Ward, Tokyo" as the location. This information is input to the reception unit. The listing unit lists available subsidies and grants based on the information received by the reception unit. The listing unit references national and local government databases to search for subsidies and grants that match the input industry, desired purchase, and location. For example, subsidies and grants that match "manufacturing," "machinery," and "Chiyoda Ward, Tokyo" are displayed in a list. The list includes detailed information about each subsidy and grant, such as the name of the subsidy, the provider, the application deadline, the amount, and eligibility criteria. This allows the user to see at a glance which subsidies and grants are available. The recommendation unit recommends the most appropriate subsidies and grants from among those listed by the listing unit. The recommendation unit selects and recommends the most appropriate subsidy or grant based on the information input by the user. For example, the recommendation unit recommends the subsidy or grant that is most suitable for the user's industry, desired purchase, and location. The recommendation unit can also recommend the most appropriate subsidy or grant based on the user's past usage history and evaluations. As a result, the subsidy or grant listing system according to the embodiment can list available subsidies and grants based on the information input by the user and recommend the most appropriate one.

[0030] The listing unit may include a provision unit that provides detailed information about subsidies and grants. The provision unit provides the detailed information about the subsidies and grants. For example, the provision unit displays detailed information such as the name of the subsidy, the provider, the application deadline, the subsidy amount, and the eligibility conditions. This allows the user to see at a glance which subsidies and grants are available. For example, the provision unit displays the name of the subsidy and explains the purpose for which the subsidy is provided. The provision unit also displays the provider of the subsidy and explains the type of organization. The provision unit also displays the application deadline for the subsidy and indicates that the application must be made within that deadline. The provision unit also displays the amount of the subsidy and indicates the amount of the subsidy. The provision unit also displays the eligibility conditions for the subsidy and explains the conditions for receiving the subsidy. This allows the user to check the detailed information by providing detailed information about the subsidies and grants. Some or all of the above-described processing in the provision unit may be performed, for example, using AI or without AI. For example, the provider can input detailed information about the subsidy into the AI, which can then analyze and display the information.

[0031] The provision unit may include a guideline unit that provides guidelines for the application procedure for subsidies and grants. The guideline unit provides guidelines for the application procedure for subsidies and grants. For example, the guideline unit displays the steps of the application procedure and explains how the user should proceed with the application. The guideline unit also displays a list of documents required for the application and indicates which documents the user needs to prepare. The guideline unit further explains how to fill out an application form and indicates how the user should fill it out. For example, the guideline unit displays the steps of the application procedure and explains them in order from the first procedure the user should perform to the last procedure. The guideline unit also displays a list of documents required for the application and indicates which documents the user needs to prepare. The guideline unit also explains how to fill out an application form and indicates how the user should fill it out. This provides guidelines for the application procedure for subsidies and grants, allowing the user to smoothly complete the application procedure. Some or all of the above-mentioned processing in the guideline unit may be performed, for example, using AI or without AI. For example, the guidelines department can input guidelines for application procedures into the AI, which can then analyze and display the information.

[0032] The provision unit may include a document section that provides a list of documents required for applying for a subsidy or grant. The document section provides the list of documents required for applying for a subsidy or grant. For example, the document section displays the types of documents required for the application and indicates which documents the user needs to prepare. The document section also displays the submission destination for each document and indicates where the user should submit the document. The document section also displays the submission deadline for each document and indicates by when the user should submit the document. For example, the document section displays the types of documents required for the application and indicates which documents the user needs to prepare. The document section also displays the submission destination for each document and indicates where the user should submit the document. The document section also displays the submission deadline for each document and indicates by when the user should submit the document. This allows the user to understand the documents required by providing a list of documents required for applying for a subsidy or grant. Some or all of the above-described processing in the document section may be performed, for example, using AI, or may be performed without AI. For example, the document section may input the list of documents required for the application into AI, which may analyze and display the information.

[0033] The listing unit may include a display unit that displays basic information about the companies eligible for the subsidy or grant. The display unit displays the basic information about the companies eligible for the subsidy or grant. For example, the display unit displays basic information such as the name, location, and industry of the eligible company. This allows the user to confirm whether their company is eligible. For example, the display unit displays the name of the eligible company and indicates the industry in which the company belongs. The display unit also displays the location of the eligible company and indicates the region in which the company is located. Furthermore, the display unit also displays the industry of the eligible company and indicates the industry in which the company belongs. This allows the user to confirm the eligible companies by displaying the basic information about the eligible companies. Some or all of the above-described processing in the display unit may be performed, for example, using AI, or may be performed without AI. For example, the display unit may input basic information about the eligible company into AI, which then analyzes and displays the information.

[0034] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit can automatically display as candidates the type of business, desired purchase, and location that the user has frequently input in the past. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the type of business, desired purchase, and location that the user will use during a specific time period based on the user's past input history. This allows the reception unit to suggest the optimal input method by analyzing the user's past input history, thereby improving input efficiency. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's past input history data into a generation AI, which can analyze the data and suggest the optimal input method.

[0035] The reception unit can provide an auto-completion function based on the user's industry and location when inputting data. For example, when the user starts inputting an industry, the reception unit can automatically display related industry candidates. The reception unit can also automatically display prefecture and city / ward / town candidates when the user starts inputting a location. Furthermore, the reception unit can automatically display related product candidates when the user starts inputting an item they want to purchase. This reduces the effort required for input by providing an auto-completion function based on the user's industry and location. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or can be performed without AI. For example, the reception unit can input the user's input data to a generation AI, which can analyze the data and provide the auto-completion function.

[0036] The reception unit can prioritize displaying highly relevant options by taking into account the user's geographical location information when inputting information. For example, if the user is in a specific area, the reception unit can prioritize displaying candidates for industries or items related to that area. The reception unit can also prioritize displaying information on subsidies and grants for that area when the user is in that area. Furthermore, the reception unit can prioritize displaying information on companies in that area when the user is in that area when the user is in that area. This prioritizes displaying highly relevant options by taking the user's geographical location information into account, improving input efficiency. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without AI. For example, the reception unit can input the user's geographical location data into a generation AI, which can analyze the data and prioritize displaying highly relevant options.

[0037] The reception unit can analyze the user's social media activity during input and suggest relevant options. For example, the reception unit can suggest industries frequently mentioned by the user on social media or items the user wants to purchase as candidates. The reception unit can also suggest subsidies and grants related to companies and organizations the user follows on social media. Furthermore, the reception unit can suggest subsidies and grants that the user may be interested in based on the user's social media activity. This allows the reception unit to suggest relevant options by analyzing the user's social media activity, improving input efficiency. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without AI. For example, the reception unit can input the user's social media data into a generation AI, which can analyze the data and suggest relevant options.

[0038] The listing unit can adjust the level of detail of the list based on the importance of the subsidies and grants when listing them. For example, the listing unit can display detailed information for subsidies and grants with high importance. The listing unit can also display concise information for subsidies and grants with low importance. Furthermore, the listing unit can adjust the display order of the list according to importance. By adjusting the level of detail of the list based on the importance of the subsidies and grants, the user can prioritize checking important information. Some or all of the above-mentioned processing in the listing unit may be performed using, for example, AI, or may be performed without AI. For example, the listing unit can input data on the importance of subsidies and grants into a generation AI, which can then analyze the data and adjust the level of detail of the list.

[0039] When listing, the listing unit can apply different listing algorithms depending on the category of subsidy or grant. For example, the listing unit can apply a listing algorithm specialized for the manufacturing industry to subsidies or grants for the manufacturing industry. The listing unit can also apply a listing algorithm specialized for the service industry to subsidies or grants for the service industry. The listing unit can also apply a listing algorithm specialized for agriculture to subsidies or grants for agriculture. This allows users to check highly relevant information by applying a listing algorithm depending on the category of subsidy or grant. Some or all of the above-described processing in the listing unit can be performed using, for example, AI, or without AI. For example, the listing unit can input subsidy or grant category data into a generation AI, which can analyze the data and apply different listing algorithms.

[0040] When listing, the listing unit can determine the priority of the list based on the application deadlines for subsidies and grants. For example, the listing unit can prioritize subsidies and grants with upcoming application deadlines and display them at the top of the list. The listing unit can also display subsidies and grants with more distant application deadlines at the bottom of the list. Furthermore, the listing unit can dynamically adjust the display order of the list according to the application deadlines. This makes it easier for users to understand application deadlines by determining the priority of the list based on the application deadlines for subsidies and grants. Some or all of the above-described processing in the listing unit can be performed, for example, using AI, or can be performed without AI. For example, the listing unit can input application deadline data for subsidies and grants into a generation AI, which can analyze the data and determine the priority of the list.

[0041] The listing unit can adjust the order of the list based on the relevance of the subsidies and grants when listing. For example, the listing unit can display subsidies and grants that are most relevant to the user's industry or location at the top of the list. The listing unit can also display subsidies and grants that are most relevant to the user's desired purchase at the top of the list. Furthermore, the listing unit can dynamically adjust the display order of the list according to the relevance of the subsidies and grants. This allows the user to prioritize viewing highly relevant information by adjusting the order of the list based on the relevance of the subsidies and grants. Some or all of the above-described processing in the listing unit may be performed using, for example, AI, or may be performed without AI. For example, the listing unit can input relevance data of subsidies and grants into a generation AI, which can analyze the data and adjust the order of the list.

[0042] The recommendation unit can adjust the level of detail of the recommendation based on the importance of the subsidy or grant when making a recommendation. For example, the recommendation unit can make recommendations including detailed information for subsidies or grants with high importance. The recommendation unit can also make recommendations including concise information for subsidies or grants with low importance. Furthermore, the recommendation unit can adjust the display order of the recommendations according to their importance. By adjusting the level of detail of the recommendation based on the importance of the subsidy or grant, the user can prioritize checking important information. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input data on the importance of the subsidy or grant into a generation AI, which can then analyze the data and adjust the level of detail of the recommendation.

[0043] The recommendation unit can apply different recommendation algorithms depending on the category of subsidy or grant when making a recommendation. For example, the recommendation unit can apply a recommendation algorithm specialized for the manufacturing industry to subsidies or grants for the manufacturing industry. The recommendation unit can also apply a recommendation algorithm specialized for the service industry to subsidies or grants for the service industry. The recommendation unit can also apply a recommendation algorithm specialized for agriculture to subsidies or grants for agriculture. This allows the user to confirm highly relevant information by applying a recommendation algorithm depending on the category of subsidy or grant. Some or all of the above-described processing in the recommendation unit can be performed using, for example, AI, or without AI. For example, the recommendation unit can input subsidy or grant category data into a generation AI, which can analyze the data and apply different recommendation algorithms.

[0044] The recommendation unit can determine the priority of recommendations based on the application deadlines for subsidies and grants when making recommendations. For example, the recommendation unit prioritizes subsidies and grants with upcoming application deadlines. The recommendation unit can also postpone subsidies and grants with more distant application deadlines. Furthermore, the recommendation unit can dynamically adjust the display order of recommendations according to the application deadlines. This makes it easier for users to understand application deadlines by determining the priority of recommendations based on the application deadlines for subsidies and grants. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without AI. For example, the recommendation unit can input application deadline data for subsidies and grants into a generation AI, which can analyze the data and determine the priority of recommendations.

[0045] The recommendation unit can adjust the order of recommendations based on the relevance of the subsidies and grants when making recommendations. For example, the recommendation unit can prioritize recommending subsidies and grants that are most relevant to the user's industry or location. The recommendation unit can also prioritize recommending subsidies and grants that are most relevant to what the user wants to purchase. Furthermore, the recommendation unit can dynamically adjust the display order of recommendations according to the relevance of the subsidies and grants. This allows the user to prioritize viewing highly relevant information by adjusting the order of recommendations based on the relevance of the subsidies and grants. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without AI. For example, the recommendation unit can input relevance data of subsidies and grants into a generation AI, which can analyze the data and adjust the order of recommendations.

[0046] When providing detailed information, the providing unit can adjust the level of detail of the information based on the importance of the subsidy or grant. For example, the providing unit can display detailed information for subsidies or grants with high importance. The providing unit can also display concise information for subsidies or grants with low importance. Furthermore, the providing unit can adjust the display order of the detailed information according to the importance. This allows the user to prioritize checking important information by adjusting the level of detail of the information based on the importance of the subsidy or grant. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input importance data of the subsidy or grant into the generating AI, which can then analyze the data and adjust the level of detail of the information.

[0047] When providing detailed information, the providing unit can apply different information provision algorithms depending on the category of subsidy or grant. For example, for subsidies and grants for the manufacturing industry, the providing unit can apply an information provision algorithm specialized for the manufacturing industry. For subsidies and grants for the service industry, the providing unit can also apply an information provision algorithm specialized for the service industry. For subsidies and grants for agriculture, the providing unit can also apply an information provision algorithm specialized for agriculture. This allows users to view highly relevant information by applying an information provision algorithm depending on the category of subsidy or grant. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input subsidy and grant category data into a generation AI, which can analyze the data and apply different information provision algorithms.

[0048] When providing detailed information, the providing unit can determine the priority of information based on the application deadline for subsidies and grants. For example, the providing unit can prioritize displaying detailed information about subsidies and grants with an upcoming application deadline. The providing unit can also postpone displaying detailed information about subsidies and grants with a distant application deadline. Furthermore, the providing unit can dynamically adjust the display order of detailed information according to the application deadline. This makes it easier for users to understand application deadlines by determining the priority of information based on the application deadline for subsidies and grants. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input application deadline data for subsidies and grants into a generating AI, which can analyze the data and determine the priority of information.

[0049] When providing detailed information, the providing unit can adjust the order of information based on the relevance of subsidies and grants. For example, the providing unit can prioritize displaying detailed information about subsidies and grants that are most relevant to the user's industry or location. The providing unit can also prioritize displaying detailed information about subsidies and grants that are most relevant to the user's desired purchase. Furthermore, the providing unit can dynamically adjust the display order of detailed information according to the relevance of subsidies and grants. This allows the user to prioritize viewing highly relevant information by adjusting the order of information based on the relevance of subsidies and grants. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input relevance data about subsidies and grants into a generating AI, which can then analyze the data and adjust the order of information.

[0050] When providing guidelines, the guideline unit can adjust the level of detail of the guidelines based on the importance of the subsidies and grants. For example, the guideline unit can display detailed guidelines for subsidies and grants with high importance. The guideline unit can also display concise guidelines for subsidies and grants with low importance. Furthermore, the guideline unit can adjust the display order of the guidelines according to their importance. By adjusting the level of detail of the guidelines based on the importance of the subsidies and grants, the user can prioritize checking important information. Some or all of the above-mentioned processing in the guideline unit may be performed using, for example, AI, or may be performed without using AI. For example, the guideline unit can input data on the importance of the subsidies and grants to a generation AI, which can then analyze the data and adjust the level of detail of the guidelines.

[0051] When providing guidelines, the guideline unit can determine the priority of the guidelines based on the application deadline for the subsidy or grant. For example, the guideline unit can prioritize displaying guidelines for subsidies or grants with an upcoming application deadline. The guideline unit can also postpone guidelines for subsidies or grants with a more distant application deadline. Furthermore, the guideline unit can dynamically adjust the display order of the guidelines according to the application deadline. This makes it easier for users to understand application deadlines by determining the priority of guidelines based on the application deadline for the subsidy or grant. Some or all of the above-mentioned processing in the guideline unit may be performed using, for example, AI, or may be performed without AI. For example, the guideline unit can input application deadline data for subsidies and grants into a generation AI, which can analyze the data to determine the priority of the guidelines.

[0052] When providing a document list, the document unit can adjust the level of detail of the document list based on the importance of the subsidy or grant. For example, the document unit can display a detailed document list for subsidies or grants with high importance. The document unit can also display a concise document list for subsidies or grants with low importance. Furthermore, the document unit can adjust the display order of the document list according to importance. By adjusting the level of detail of the document list based on the importance of the subsidy or grant, the user can prioritize checking important information. Some or all of the above-mentioned processing in the document unit may be performed using, for example, AI, or may be performed without AI. For example, the document unit can input subsidy or grant importance data into a generation AI, which can then analyze the data and adjust the level of detail of the document list.

[0053] When providing a document list, the document unit can determine the priority of the document list based on the application deadline for the subsidy or grant. For example, the document unit can prioritize the display of document lists for subsidies or grants with upcoming application deadlines. The document unit can also postpone the display of document lists for subsidies or grants with distant application deadlines. Furthermore, the document unit can dynamically adjust the display order of the document list according to the application deadline. This makes it easier for users to understand application deadlines by determining the priority of the document list based on the application deadline for the subsidy or grant. Some or all of the above-mentioned processing in the document unit may be performed using, for example, AI, or may be performed without AI. For example, the document unit can input application deadline data for subsidies and grants into a generation AI, which can analyze the data to determine the priority of the document list.

[0054] The display unit can adjust the level of detail of the display based on the importance of the subsidy or grant. For example, the display unit can display detailed information for subsidies or grants with high importance. The display unit can also display concise information for subsidies or grants with low importance. Furthermore, the display unit can adjust the display order according to importance. By adjusting the level of detail of the display based on the importance of the subsidy or grant, the user can prioritize checking important information. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without AI. For example, the display unit can input subsidy or grant importance data into a generation AI, which can analyze the data and adjust the level of detail of the display.

[0055] The display unit can determine the display priority based on the application deadline for subsidies and grants when displaying the information. For example, the display unit can prioritize displaying information about subsidies and grants with upcoming application deadlines. The display unit can also postpone displaying information about subsidies and grants with more distant application deadlines. Furthermore, the display unit can dynamically adjust the display order according to the application deadline. This makes it easier for users to understand application deadlines by determining the display priority based on the application deadline for subsidies and grants. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without AI. For example, the display unit can input application deadline data for subsidies and grants into a generation AI, which can analyze the data and determine the display priority.

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

[0057] The reception unit can display related past success stories based on the user's input. For example, by displaying success stories of companies in the same industry that received similar subsidies, the user can get a concrete image. The reception unit can also provide information based on past success stories, such as what documents were required and how smoothly the procedures went. Furthermore, the reception unit can display detailed information, such as the strategies used by the successful companies and the challenges they overcame. This allows users to refer to specific success stories and increase their motivation to apply for subsidies and grants.

[0058] The listing unit can provide region-specific advice based on the user's industry or location for the list of subsidies and grants. For example, it can display points to note about application procedures in a particular region and advice on how to use region-specific subsidies and grants. The listing unit can also indicate which subsidies and grants are particularly effective based on the region's economic situation and industry trends. Furthermore, the listing unit can provide contact information for local experts and consultants so that the user can consult them directly. This allows the user to obtain region-specific information and make more effective use of subsidies and grants.

[0059] In addition to detailed information about grants and subsidies, the provider can also provide information on related laws, regulations, and guidelines. For example, it can display information about legal requirements and restrictions regarding the use of grants and subsidies. The provider can also explain guidelines and best practices that must be observed when using grants and subsidies. Furthermore, the provider can update information in real time and notify users if there are any changes in laws, regulations, or guidelines. This allows users to avoid legal risks and use grants and subsidies appropriately.

[0060] The Documents section can provide templates for documents required for applying for subsidies and grants. For example, it can make templates for application forms and plans available for download. The Documents section can also display examples of how to fill out each template and important points to note, helping users to create documents accurately. Furthermore, the Documents section has an automatic template generation function that can automatically generate the necessary documents based on the information entered by the user. This saves users the trouble of creating documents and allows them to proceed with the application process efficiently.

[0061] The display section can display basic information about the subsidy / grants-eligible companies, as well as ratings and reviews of the companies. For example, it can display what other users have rated the company and what reviews it has. The display section can also provide information about the company's past performance and success stories. Furthermore, the display section can display the company's contact information and website links so that users can contact them directly. This allows users to obtain detailed information about the target companies and select reliable companies.

[0062] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display as candidates the type of business, items to purchase, and location that the user has frequently input in the past. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the type of business, items to purchase, and location that the user will use during a specific time period based on the user's past input history. This allows the reception unit to suggest the optimal input method by analyzing the user's past input history, thereby improving input efficiency. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's past input history data into a generation AI, which can then analyze the data and suggest the optimal input method.

[0063] The reception unit can provide an auto-completion function based on the user's industry and location when inputting information. For example, when the user starts inputting their industry, it automatically displays related industry candidates. The reception unit can also automatically display prefecture and city / ward / town candidates when the user starts inputting their location. Furthermore, the reception unit can automatically display related product candidates when the user starts inputting what they want to purchase. This reduces the effort required for input by providing an auto-completion function based on the user's industry and location. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data into a generation AI, which then analyzes the data and provides the auto-completion function.

[0064] The reception unit can prioritize displaying highly relevant options by taking into account the user's geographical location information when inputting information. For example, if the user is in a specific area, it can prioritize displaying candidates for industries or items related to that area. The reception unit can also prioritize displaying information on subsidies and grants in that area. Furthermore, the reception unit can prioritize displaying information on companies in that area. This prioritizes displaying highly relevant options by taking the user's geographical location information into account, improving input efficiency. Some or all of the above-described processing in the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can input the user's geographical location data into a generation AI, which can analyze the data and prioritize displaying highly relevant options.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception unit accepts input from the user about the type of business, the item they wish to purchase, and their location. For example, if a user is in the manufacturing industry and wants to purchase a new machine, they would enter "manufacturing" as the type of business, "machinery" as the item they wish to purchase, and "Chiyoda-ku, Tokyo" as the location. This information is entered into the reception unit. Step 2: The listing unit creates a list of available subsidies and grants based on the information received by the reception unit. The listing unit references national and local government databases to search for subsidies and grants that match the input industry, desired purchase, and location. For example, subsidies and grants that match "manufacturing," "machinery," and "Chiyoda Ward, Tokyo" are displayed in the list. The list includes detailed information about each subsidy and grant, such as the name of the subsidy, provider, application deadline, subsidy amount, and eligibility conditions. This allows users to see at a glance which subsidies and grants are available. Step 3: The recommendation unit recommends the most suitable subsidy or grant from among those listed by the listing unit. The recommendation unit selects and recommends the most suitable subsidy or grant based on the information input by the user. For example, the recommendation unit recommends the subsidy or grant that is most suitable for the user's industry, desired purchase, and location. The recommendation unit can also recommend the most suitable subsidy or grant based on the user's past usage history and evaluations.

[0067] (Example 2) A subsidy / grant listing system according to an embodiment of the present invention lists currently available national and local government subsidies and grants by inputting the user's industry, desired purchase, and location. This subsidy / grant listing system generates a list of available subsidies and grants by inputting the user's industry, desired purchase, and location. Furthermore, by clicking the "Target Company" button next to a subsidy or grant in the list, a searchable list of eligible companies is displayed. For example, if a user is in the manufacturing industry and wants to purchase a new machine, they would input "manufacturing" as the industry, "machinery" as the desired purchase, and "Chiyoda Ward, Tokyo" as the location. This information is entered into the system. The system then lists available subsidies and grants based on the input information. The system references national and local government databases to search for subsidies and grants that match the input industry, desired purchase, and location. For example, subsidies and grants that match "manufacturing," "machinery," and "Chiyoda Ward, Tokyo" are displayed in a list. The list includes detailed information about each subsidy and grant, such as the name of the subsidy, the provider, the application deadline, the amount, and eligibility requirements. This allows users to see at a glance which subsidies and grants are available. Furthermore, by clicking the "Eligible Companies" button next to a subsidy or grant in the list, a list of eligible companies is displayed, searchable online. The system collects information on companies eligible for subsidies and grants from the web and displays it as a list. This allows users to check whether their company is eligible. This list can also be used as a sales list. For example, users can propose their products or services to companies using the same subsidies and grants. This system allows users to easily find available subsidies and grants and check whether their company is eligible. It can also be used as a sales list, which can help expand their business. This allows the subsidy and grant listing system to list available subsidies and grants based on the information entered by the user and recommend the most suitable ones.

[0068] A subsidy / grant listing system according to an embodiment includes a reception unit, a listing unit, and a recommendation unit. The reception unit accepts input from a user of the user's industry, desired purchase, and location. For example, if a user is in the manufacturing industry and wants to purchase a new machine, the user enters "manufacturing" as the industry, "machinery" as the desired purchase, and "Chiyoda Ward, Tokyo" as the location. This information is input to the reception unit. The listing unit lists available subsidies and grants based on the information received by the reception unit. The listing unit references national and local government databases to search for subsidies and grants that match the input industry, desired purchase, and location. For example, subsidies and grants that match "manufacturing," "machinery," and "Chiyoda Ward, Tokyo" are displayed in a list. The list includes detailed information about each subsidy and grant, such as the name of the subsidy, the provider, the application deadline, the amount, and eligibility criteria. This allows the user to see at a glance which subsidies and grants are available. The recommendation unit recommends the most appropriate subsidies and grants from among those listed by the listing unit. The recommendation unit selects and recommends the most appropriate subsidy or grant based on the information input by the user. For example, the recommendation unit recommends the subsidy or grant that is most suitable for the user's industry, desired purchase, and location. The recommendation unit can also recommend the most appropriate subsidy or grant based on the user's past usage history and evaluations. As a result, the subsidy or grant listing system according to the embodiment can list available subsidies and grants based on the information input by the user and recommend the most appropriate one.

[0069] The listing unit may include a provision unit that provides detailed information about subsidies and grants. The provision unit provides the detailed information about the subsidies and grants. For example, the provision unit displays detailed information such as the name of the subsidy, the provider, the application deadline, the subsidy amount, and the eligibility conditions. This allows the user to see at a glance which subsidies and grants are available. For example, the provision unit displays the name of the subsidy and explains the purpose for which the subsidy is provided. The provision unit also displays the provider of the subsidy and explains the type of organization. The provision unit also displays the application deadline for the subsidy and indicates that the application must be made within that deadline. The provision unit also displays the amount of the subsidy and indicates the amount of the subsidy. The provision unit also displays the eligibility conditions for the subsidy and explains the conditions for receiving the subsidy. This allows the user to check the detailed information by providing detailed information about the subsidies and grants. Some or all of the above-described processing in the provision unit may be performed, for example, using AI or without AI. For example, the provider can input detailed information about the subsidy into the AI, which can then analyze and display the information.

[0070] The provision unit may include a guideline unit that provides guidelines for the application procedure for subsidies and grants. The guideline unit provides guidelines for the application procedure for subsidies and grants. For example, the guideline unit displays the steps of the application procedure and explains how the user should proceed with the application. The guideline unit also displays a list of documents required for the application and indicates which documents the user needs to prepare. The guideline unit further explains how to fill out an application form and indicates how the user should fill it out. For example, the guideline unit displays the steps of the application procedure and explains them in order from the first procedure the user should perform to the last procedure. The guideline unit also displays a list of documents required for the application and indicates which documents the user needs to prepare. The guideline unit also explains how to fill out an application form and indicates how the user should fill it out. This provides guidelines for the application procedure for subsidies and grants, allowing the user to smoothly complete the application procedure. Some or all of the above-mentioned processing in the guideline unit may be performed, for example, using AI or without AI. For example, the guidelines department can input guidelines for application procedures into the AI, which can then analyze and display the information.

[0071] The provision unit may include a document section that provides a list of documents required for applying for a subsidy or grant. The document section provides the list of documents required for applying for a subsidy or grant. For example, the document section displays the types of documents required for the application and indicates which documents the user needs to prepare. The document section also displays the submission destination for each document and indicates where the user should submit the document. The document section also displays the submission deadline for each document and indicates by when the user should submit the document. For example, the document section displays the types of documents required for the application and indicates which documents the user needs to prepare. The document section also displays the submission destination for each document and indicates where the user should submit the document. The document section also displays the submission deadline for each document and indicates by when the user should submit the document. This allows the user to understand the documents required by providing a list of documents required for applying for a subsidy or grant. Some or all of the above-described processing in the document section may be performed, for example, using AI, or may be performed without AI. For example, the document section may input the list of documents required for the application into AI, which may analyze and display the information.

[0072] The listing unit may include a display unit that displays basic information about the companies eligible for the subsidy or grant. The display unit displays the basic information about the companies eligible for the subsidy or grant. For example, the display unit displays basic information such as the name, location, and industry of the eligible company. This allows the user to confirm whether their company is eligible. For example, the display unit displays the name of the eligible company and indicates the industry in which the company belongs. The display unit also displays the location of the eligible company and indicates the region in which the company is located. Furthermore, the display unit also displays the industry of the eligible company and indicates the industry in which the company belongs. This allows the user to confirm the eligible companies by displaying the basic information about the eligible companies. Some or all of the above-described processing in the display unit may be performed, for example, using AI, or may be performed without AI. For example, the display unit may input basic information about the eligible company into AI, which then analyzes and displays the information.

[0073] The reception unit can estimate the user's emotions and dynamically change the design of the input interface based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple and intuitive interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and enable the user to quickly enter the industry, item to purchase, and location. This improves the user's input experience by changing the design of the input interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, or without AI. For example, the reception unit can input the user's emotion data into a generation AI, which can then analyze the data and dynamically change the design of the input interface.

[0074] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit can automatically display as candidates the type of business, desired purchase, and location that the user has frequently input in the past. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the type of business, desired purchase, and location that the user will use during a specific time period based on the user's past input history. This allows the reception unit to suggest the optimal input method by analyzing the user's past input history, thereby improving input efficiency. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's past input history data into a generation AI, which can analyze the data and suggest the optimal input method.

[0075] The reception unit can provide an auto-completion function based on the user's industry and location when inputting data. For example, when the user starts inputting an industry, the reception unit can automatically display related industry candidates. The reception unit can also automatically display prefecture and city / ward / town candidates when the user starts inputting a location. Furthermore, the reception unit can automatically display related product candidates when the user starts inputting an item they want to purchase. This reduces the effort required for input by providing an auto-completion function based on the user's industry and location. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or can be performed without AI. For example, the reception unit can input the user's input data to a generation AI, which can analyze the data and provide the auto-completion function.

[0076] The reception unit can estimate the user's emotions and adjust the priority of inputs based on the estimated user emotions. For example, when the user is stressed, the reception unit can prioritize displaying the most important input items and deferring other items. Alternatively, when the user is relaxed, the reception unit can display all input items at once to allow the user to freely input. Furthermore, when the user is in a hurry, the reception unit can prompt the user to input the most important input items first and allow the user to complete the details later. This improves the user's input experience by adjusting the priority of inputs according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input the user's emotion data into a generation AI, which can then analyze the data and adjust the priority of inputs.

[0077] The reception unit can prioritize displaying highly relevant options by taking into account the user's geographical location information when inputting information. For example, if the user is in a specific area, the reception unit can prioritize displaying candidates for industries or items related to that area. The reception unit can also prioritize displaying information on subsidies and grants for that area when the user is in that area. Furthermore, the reception unit can prioritize displaying information on companies in that area when the user is in that area when the user is in that area. This prioritizes displaying highly relevant options by taking the user's geographical location information into account, improving input efficiency. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without AI. For example, the reception unit can input the user's geographical location data into a generation AI, which can analyze the data and prioritize displaying highly relevant options.

[0078] The reception unit can analyze the user's social media activity during input and suggest relevant options. For example, the reception unit can suggest industries frequently mentioned by the user on social media or items the user wants to purchase as candidates. The reception unit can also suggest subsidies and grants related to companies and organizations the user follows on social media. Furthermore, the reception unit can suggest subsidies and grants that the user may be interested in based on the user's social media activity. This allows the reception unit to suggest relevant options by analyzing the user's social media activity, improving input efficiency. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without AI. For example, the reception unit can input the user's social media data into a generation AI, which can analyze the data and suggest relevant options.

[0079] The list-creating unit can estimate the user's emotions and adjust the display method of the list based on the estimated user's emotions. For example, when the user is feeling stressed, the list-creating unit displays a simple, highly visible list. Furthermore, when the user is relaxed, the list-creating unit can also display a list containing detailed information. Furthermore, when the user is in a hurry, the list-creating unit can also display a list that focuses on the main points. This improves the user's visibility by adjusting the display method of the list according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the list-creating unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the list-creating unit can input the user's emotion data into the generation AI, which can then analyze the data and adjust the display method of the list.

[0080] The listing unit can adjust the level of detail of the list based on the importance of the subsidies and grants when listing them. For example, the listing unit can display detailed information for subsidies and grants with high importance. The listing unit can also display concise information for subsidies and grants with low importance. Furthermore, the listing unit can adjust the display order of the list according to importance. By adjusting the level of detail of the list based on the importance of the subsidies and grants, the user can prioritize checking important information. Some or all of the above-mentioned processing in the listing unit may be performed using, for example, AI, or may be performed without AI. For example, the listing unit can input data on the importance of subsidies and grants into a generation AI, which can then analyze the data and adjust the level of detail of the list.

[0081] When listing, the listing unit can apply different listing algorithms depending on the category of subsidy or grant. For example, the listing unit can apply a listing algorithm specialized for the manufacturing industry to subsidies or grants for the manufacturing industry. The listing unit can also apply a listing algorithm specialized for the service industry to subsidies or grants for the service industry. The listing unit can also apply a listing algorithm specialized for agriculture to subsidies or grants for agriculture. This allows users to check highly relevant information by applying a listing algorithm depending on the category of subsidy or grant. Some or all of the above-described processing in the listing unit can be performed using, for example, AI, or without AI. For example, the listing unit can input subsidy or grant category data into a generation AI, which can analyze the data and apply different listing algorithms.

[0082] The list-creating unit can estimate the user's emotions and adjust the length of the list based on the estimated user emotions. For example, if the user is feeling stressed, the list-creating unit can display a short, concise list. Furthermore, if the user is relaxed, the list-creating unit can display a longer list including detailed information. Furthermore, if the user is in a hurry, the list-creating unit can display a short list including only the most important information. This improves the user's visibility by adjusting the length of the list according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the list-creating unit can be performed using, for example, an AI, or without an AI. For example, the list-creating unit can input the user's emotion data into the generation AI, which can then analyze the data and adjust the length of the list.

[0083] When listing, the listing unit can determine the priority of the list based on the application deadlines for subsidies and grants. For example, the listing unit can prioritize subsidies and grants with upcoming application deadlines and display them at the top of the list. The listing unit can also display subsidies and grants with more distant application deadlines at the bottom of the list. Furthermore, the listing unit can dynamically adjust the display order of the list according to the application deadlines. This makes it easier for users to understand application deadlines by determining the priority of the list based on the application deadlines for subsidies and grants. Some or all of the above-described processing in the listing unit can be performed, for example, using AI, or can be performed without AI. For example, the listing unit can input application deadline data for subsidies and grants into a generation AI, which can analyze the data and determine the priority of the list.

[0084] The listing unit can adjust the order of the list based on the relevance of the subsidies and grants when listing. For example, the listing unit can display subsidies and grants that are most relevant to the user's industry or location at the top of the list. The listing unit can also display subsidies and grants that are most relevant to the user's desired purchase at the top of the list. Furthermore, the listing unit can dynamically adjust the display order of the list according to the relevance of the subsidies and grants. This allows the user to prioritize viewing highly relevant information by adjusting the order of the list based on the relevance of the subsidies and grants. Some or all of the above-described processing in the listing unit may be performed using, for example, AI, or may be performed without AI. For example, the listing unit can input relevance data of subsidies and grants into a generation AI, which can analyze the data and adjust the order of the list.

[0085] The recommendation unit can estimate the user's emotions and adjust the way recommendations are presented based on the estimated user emotions. For example, if the user is feeling stressed, the recommendation unit can provide a simple, highly visible recommendation. Furthermore, if the user is relaxed, the recommendation unit can provide a recommendation that includes detailed information. Furthermore, if the user is in a hurry, the recommendation unit can provide a recommendation that focuses on the main points. This improves user visibility by adjusting the way recommendations are presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recommendation unit can be performed using, for example, AI, or without AI. For example, the recommendation unit can input the user's emotion data into the generation AI, which can then analyze the data and adjust the way recommendations are presented.

[0086] The recommendation unit can adjust the level of detail of the recommendation based on the importance of the subsidy or grant when making a recommendation. For example, the recommendation unit can make recommendations including detailed information for subsidies or grants with high importance. The recommendation unit can also make recommendations including concise information for subsidies or grants with low importance. Furthermore, the recommendation unit can adjust the display order of the recommendations according to their importance. By adjusting the level of detail of the recommendation based on the importance of the subsidy or grant, the user can prioritize checking important information. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input data on the importance of the subsidy or grant into a generation AI, which can then analyze the data and adjust the level of detail of the recommendation.

[0087] The recommendation unit can apply different recommendation algorithms depending on the category of subsidy or grant when making a recommendation. For example, the recommendation unit can apply a recommendation algorithm specialized for the manufacturing industry to subsidies or grants for the manufacturing industry. The recommendation unit can also apply a recommendation algorithm specialized for the service industry to subsidies or grants for the service industry. The recommendation unit can also apply a recommendation algorithm specialized for agriculture to subsidies or grants for agriculture. This allows the user to confirm highly relevant information by applying a recommendation algorithm depending on the category of subsidy or grant. Some or all of the above-described processing in the recommendation unit can be performed using, for example, AI, or without AI. For example, the recommendation unit can input subsidy or grant category data into a generation AI, which can analyze the data and apply different recommendation algorithms.

[0088] The recommendation unit can estimate the user's emotions and adjust the length of the recommendation based on the estimated user emotions. For example, if the user is feeling stressed, the recommendation unit can provide short, to-the-point recommendations. If the user is relaxed, the recommendation unit can also provide longer recommendations including detailed information. If the user is in a hurry, the recommendation unit can also provide short recommendations including only the most important information. This improves user visibility by adjusting the length of recommendations according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recommendation unit can be performed using, for example, AI, or without AI. For example, the recommendation unit can input the user's emotion data into the generation AI, which can then analyze the data and adjust the length of the recommendation.

[0089] The recommendation unit can determine the priority of recommendations based on the application deadlines for subsidies and grants when making recommendations. For example, the recommendation unit prioritizes subsidies and grants with upcoming application deadlines. The recommendation unit can also postpone subsidies and grants with more distant application deadlines. Furthermore, the recommendation unit can dynamically adjust the display order of recommendations according to the application deadlines. This makes it easier for users to understand application deadlines by determining the priority of recommendations based on the application deadlines for subsidies and grants. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without AI. For example, the recommendation unit can input application deadline data for subsidies and grants into a generation AI, which can analyze the data and determine the priority of recommendations.

[0090] The recommendation unit can adjust the order of recommendations based on the relevance of the subsidies and grants when making recommendations. For example, the recommendation unit can prioritize recommending subsidies and grants that are most relevant to the user's industry or location. The recommendation unit can also prioritize recommending subsidies and grants that are most relevant to what the user wants to purchase. Furthermore, the recommendation unit can dynamically adjust the display order of recommendations according to the relevance of the subsidies and grants. This allows the user to prioritize viewing highly relevant information by adjusting the order of recommendations based on the relevance of the subsidies and grants. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without AI. For example, the recommendation unit can input relevance data of subsidies and grants into a generation AI, which can analyze the data and adjust the order of recommendations.

[0091] The providing unit can estimate the user's emotions and adjust the display method of the detailed information based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can display detailed information in a simple, highly visible manner. Furthermore, if the user is relaxed, the providing unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the providing unit can display detailed information that focuses on the main points. This improves the user's visibility by adjusting the display method of the detailed information according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI, which can then analyze the data and adjust the display method of the detailed information.

[0092] When providing detailed information, the providing unit can adjust the level of detail of the information based on the importance of the subsidy or grant. For example, the providing unit can display detailed information for subsidies or grants with high importance. The providing unit can also display concise information for subsidies or grants with low importance. Furthermore, the providing unit can adjust the display order of the detailed information according to the importance. This allows the user to prioritize checking important information by adjusting the level of detail of the information based on the importance of the subsidy or grant. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input importance data of the subsidy or grant into the generating AI, which can then analyze the data and adjust the level of detail of the information.

[0093] When providing detailed information, the providing unit can apply different information provision algorithms depending on the category of subsidy or grant. For example, for subsidies and grants for the manufacturing industry, the providing unit can apply an information provision algorithm specialized for the manufacturing industry. For subsidies and grants for the service industry, the providing unit can also apply an information provision algorithm specialized for the service industry. For subsidies and grants for agriculture, the providing unit can also apply an information provision algorithm specialized for agriculture. This allows users to view highly relevant information by applying an information provision algorithm depending on the category of subsidy or grant. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input subsidy and grant category data into a generation AI, which can analyze the data and apply different information provision algorithms.

[0094] The providing unit can estimate the user's emotions and adjust the length of the detailed information based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can display short, to-the-point detailed information. Furthermore, if the user is relaxed, the providing unit can display longer, detailed information including detailed information. Furthermore, if the user is in a hurry, the providing unit can display short, detailed information including only the most important information. This improves the user's visibility by adjusting the length of the detailed information according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI, which can then analyze the data and adjust the length of the detailed information.

[0095] When providing detailed information, the providing unit can determine the priority of information based on the application deadline for subsidies and grants. For example, the providing unit can prioritize displaying detailed information about subsidies and grants with an upcoming application deadline. The providing unit can also postpone displaying detailed information about subsidies and grants with a distant application deadline. Furthermore, the providing unit can dynamically adjust the display order of detailed information according to the application deadline. This makes it easier for users to understand application deadlines by determining the priority of information based on the application deadline for subsidies and grants. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input application deadline data for subsidies and grants into a generating AI, which can analyze the data and determine the priority of information.

[0096] When providing detailed information, the providing unit can adjust the order of information based on the relevance of subsidies and grants. For example, the providing unit can prioritize displaying detailed information about subsidies and grants that are most relevant to the user's industry or location. The providing unit can also prioritize displaying detailed information about subsidies and grants that are most relevant to the user's desired purchase. Furthermore, the providing unit can dynamically adjust the display order of detailed information according to the relevance of subsidies and grants. This allows the user to prioritize viewing highly relevant information by adjusting the order of information based on the relevance of subsidies and grants. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input relevance data about subsidies and grants into a generating AI, which can then analyze the data and adjust the order of information.

[0097] The guideline unit can estimate the user's emotions and adjust the guideline display method based on the estimated user's emotions. For example, when the user is feeling stressed, the guideline unit displays simple, highly visible guidelines. Furthermore, when the user is relaxed, the guideline unit can display guidelines containing detailed information. Furthermore, when the user is in a hurry, the guideline unit can display guidelines that focus on the main points. This improves the user's visibility by adjusting the guideline display method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the guideline unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the guideline unit can input the user's emotion data into the generation AI, which then analyzes the data and adjusts the guideline display method.

[0098] When providing guidelines, the guideline unit can adjust the level of detail of the guidelines based on the importance of the subsidies and grants. For example, the guideline unit can display detailed guidelines for subsidies and grants with high importance. The guideline unit can also display concise guidelines for subsidies and grants with low importance. Furthermore, the guideline unit can adjust the display order of the guidelines according to their importance. By adjusting the level of detail of the guidelines based on the importance of the subsidies and grants, the user can prioritize checking important information. Some or all of the above-mentioned processing in the guideline unit may be performed using, for example, AI, or may be performed without using AI. For example, the guideline unit can input data on the importance of the subsidies and grants to a generation AI, which can then analyze the data and adjust the level of detail of the guidelines.

[0099] The guideline unit can estimate the user's emotions and adjust the length of the guideline based on the estimated user's emotions. For example, if the user is feeling stressed, the guideline unit can display short, to-the-point guidelines. Furthermore, if the user is relaxed, the guideline unit can display longer guidelines including detailed information. Furthermore, if the user is in a hurry, the guideline unit can display short guidelines including only the most important information. This improves the user's visibility by adjusting the length of the guideline according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the guideline unit can be performed using, for example, AI, or without AI. For example, the guideline unit can input the user's emotion data into the generation AI, which can then analyze the data and adjust the length of the guideline.

[0100] When providing guidelines, the guideline unit can determine the priority of the guidelines based on the application deadline for the subsidy or grant. For example, the guideline unit can prioritize displaying guidelines for subsidies or grants with an upcoming application deadline. The guideline unit can also postpone guidelines for subsidies or grants with a more distant application deadline. Furthermore, the guideline unit can dynamically adjust the display order of the guidelines according to the application deadline. This makes it easier for users to understand application deadlines by determining the priority of guidelines based on the application deadline for the subsidy or grant. Some or all of the above-mentioned processing in the guideline unit may be performed using, for example, AI, or may be performed without AI. For example, the guideline unit can input application deadline data for subsidies and grants into a generation AI, which can analyze the data to determine the priority of the guidelines.

[0101] The document unit can estimate the user's emotions and adjust the display method of the document list based on the estimated user emotions. For example, if the user is feeling stressed, the document unit can display a simple, highly visible document list. Furthermore, if the user is relaxed, the document unit can display a document list containing detailed information. Furthermore, if the user is in a hurry, the document unit can display a document list that focuses on the main points. This improves the user's visibility by adjusting the display method of the document list according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the document unit can be performed using, for example, AI, or without AI. For example, the document unit can input the user's emotion data into a generation AI, which can then analyze the data and adjust the display method of the document list.

[0102] When providing a document list, the document unit can adjust the level of detail of the document list based on the importance of the subsidy or grant. For example, the document unit can display a detailed document list for subsidies or grants with high importance. The document unit can also display a concise document list for subsidies or grants with low importance. Furthermore, the document unit can adjust the display order of the document list according to importance. By adjusting the level of detail of the document list based on the importance of the subsidy or grant, the user can prioritize checking important information. Some or all of the above-mentioned processing in the document unit may be performed using, for example, AI, or may be performed without AI. For example, the document unit can input subsidy or grant importance data into a generation AI, which can then analyze the data and adjust the level of detail of the document list.

[0103] The document unit can estimate the user's emotions and adjust the length of the document list based on the estimated user emotions. For example, if the user is stressed, the document unit can display a short, concise document list. Alternatively, if the user is relaxed, the document unit can display a longer document list with detailed information. Furthermore, if the user is in a hurry, the document unit can display a short document list with only the most important information. This improves the user's visibility by adjusting the length of the document list according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the document unit can be performed using, for example, AI, or without AI. For example, the document unit can input the user's emotion data into a generation AI, which can then analyze the data and adjust the length of the document list.

[0104] When providing a document list, the document unit can determine the priority of the document list based on the application deadline for the subsidy or grant. For example, the document unit can prioritize the display of document lists for subsidies or grants with upcoming application deadlines. The document unit can also postpone the display of document lists for subsidies or grants with distant application deadlines. Furthermore, the document unit can dynamically adjust the display order of the document list according to the application deadline. This makes it easier for users to understand application deadlines by determining the priority of the document list based on the application deadline for the subsidy or grant. Some or all of the above-mentioned processing in the document unit may be performed using, for example, AI, or may be performed without AI. For example, the document unit can input application deadline data for subsidies and grants into a generation AI, which can analyze the data to determine the priority of the document list.

[0105] The display unit can estimate the user's emotions and adjust the display method based on the estimated user emotions. For example, when the user is stressed, the display unit provides a simple, highly visible display method. Furthermore, when the user is relaxed, the display unit can provide a display method including detailed information. Furthermore, when the user is in a hurry, the display unit can provide a display method that focuses on the main points. This improves the user's visibility by adjusting the display method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the display unit can input the user's emotion data into the generation AI, which then analyzes the data and adjusts the display method.

[0106] The display unit can adjust the level of detail of the display based on the importance of the subsidy or grant. For example, the display unit can display detailed information for subsidies or grants with high importance. The display unit can also display concise information for subsidies or grants with low importance. Furthermore, the display unit can adjust the display order according to importance. By adjusting the level of detail of the display based on the importance of the subsidy or grant, the user can prioritize checking important information. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without AI. For example, the display unit can input subsidy or grant importance data into a generation AI, which can analyze the data and adjust the level of detail of the display.

[0107] The display unit can estimate the user's emotions and adjust the length of the display based on the estimated user emotions. For example, if the user is stressed, the display unit can display a short, to-the-point message. If the user is relaxed, the display unit can also display a longer message containing detailed information. If the user is in a hurry, the display unit can also display a short message containing only the most important information. This improves the user's visibility by adjusting the display length according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit can be performed using, for example, AI, or without AI. For example, the display unit can input the user's emotion data into the generation AI, which can then analyze the data and adjust the display length.

[0108] The display unit can determine the display priority based on the application deadline for subsidies and grants when displaying the information. For example, the display unit can prioritize displaying information about subsidies and grants with upcoming application deadlines. The display unit can also postpone displaying information about subsidies and grants with more distant application deadlines. Furthermore, the display unit can dynamically adjust the display order according to the application deadline. This makes it easier for users to understand application deadlines by determining the display priority based on the application deadline for subsidies and grants. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without AI. For example, the display unit can input application deadline data for subsidies and grants into a generation AI, which can analyze the data and determine the display priority. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, listing unit, and recommendation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives input from the user regarding the type of business, the item to be purchased, and the location. The listing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and lists available subsidies and grants by referring to national and local government databases. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recommends the most appropriate subsidies and grants based on the information input by the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, listing unit, and recommendation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives input from the user regarding the type of business, the item to be purchased, and the location. The listing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and lists available subsidies and grants by referring to national and local government databases. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recommends optimal subsidies and grants based on the user's input information. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, listing unit, and recommendation unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives input from the user regarding the type of business, the item to be purchased, and the location. The listing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and lists available subsidies and grants by referring to national and local government databases. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recommends optimal subsidies and grants based on the information input by the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, listing unit, and recommendation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives input from the user regarding the type of business, the item to be purchased, and the location. The listing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and lists available subsidies and grants by referring to national and local government databases. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recommends the most appropriate subsidies and grants based on the information input by the user.

[0109] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0110] The reception unit can display related past success stories based on the user's input. For example, by displaying success stories of companies in the same industry that received similar subsidies, the user can get a concrete image. The reception unit can also provide information based on past success stories, such as what documents were required and how smoothly the procedures went. Furthermore, the reception unit can display detailed information, such as the strategies used by the successful companies and the challenges they overcame. This allows users to refer to specific success stories and increase their motivation to apply for subsidies and grants.

[0111] The listing unit can provide region-specific advice based on the user's industry or location for the list of subsidies and grants. For example, it can display points to note about application procedures in a particular region and advice on how to use region-specific subsidies and grants. The listing unit can also indicate which subsidies and grants are particularly effective based on the region's economic situation and industry trends. Furthermore, the listing unit can provide contact information for local experts and consultants so that the user can consult them directly. This allows the user to obtain region-specific information and make more effective use of subsidies and grants.

[0112] In addition to detailed information about grants and subsidies, the provider can also provide information on related laws, regulations, and guidelines. For example, it can display information about legal requirements and restrictions regarding the use of grants and subsidies. The provider can also explain guidelines and best practices that must be observed when using grants and subsidies. Furthermore, the provider can update information in real time and notify users if there are any changes in laws, regulations, or guidelines. This allows users to avoid legal risks and use grants and subsidies appropriately.

[0113] The Documents section can provide templates for documents required for applying for subsidies and grants. For example, it can make templates for application forms and plans available for download. The Documents section can also display examples of how to fill out each template and important points to note, helping users to create documents accurately. Furthermore, the Documents section has an automatic template generation function that can automatically generate the necessary documents based on the information entered by the user. This saves users the trouble of creating documents and allows them to proceed with the application process efficiently.

[0114] The display section can display basic information about the subsidy / grants-eligible companies, as well as ratings and reviews of the companies. For example, it can display what other users have rated the company and what reviews it has. The display section can also provide information about the company's past performance and success stories. Furthermore, the display section can display the company's contact information and website links so that users can contact them directly. This allows users to obtain detailed information about the target companies and select reliable companies.

[0115] The reception unit can estimate the user's emotions and provide feedback on the input content based on the estimated user emotions. For example, if the user is feeling anxious, an encouraging message or a success story can be displayed. If the user is confident, a message encouraging further challenges can be displayed. Furthermore, if the user is unsure, advice can be provided to narrow down the options. In this way, the user's input experience can be improved by providing feedback according to the user's emotions.

[0116] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display as candidates the type of business, items to purchase, and location that the user has frequently input in the past. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the type of business, items to purchase, and location that the user will use during a specific time period based on the user's past input history. This allows the reception unit to suggest the optimal input method by analyzing the user's past input history, thereby improving input efficiency. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's past input history data into a generation AI, which can then analyze the data and suggest the optimal input method.

[0117] The reception unit can provide an auto-completion function based on the user's industry and location when inputting information. For example, when the user starts inputting their industry, it automatically displays related industry candidates. The reception unit can also automatically display prefecture and city / ward / town candidates when the user starts inputting their location. Furthermore, the reception unit can automatically display related product candidates when the user starts inputting what they want to purchase. This reduces the effort required for input by providing an auto-completion function based on the user's industry and location. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data into a generation AI, which then analyzes the data and provides the auto-completion function.

[0118] The reception unit can estimate the user's emotions and adjust the priority of inputs based on the estimated user emotions. For example, if the user is stressed, the reception unit can prioritize displaying the most important input items and deferring other items. Alternatively, if the user is relaxed, the reception unit can display all input items at once to allow the user to input freely. Furthermore, if the user is in a hurry, the reception unit can prompt the user to input the most important input items first and allow the user to complete the details later. This improves the user's input experience by adjusting the priority of inputs according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input the user's emotion data into a generation AI, which can then analyze the data and adjust the priority of inputs.

[0119] The reception unit can prioritize displaying highly relevant options by taking into account the user's geographical location information when inputting information. For example, if the user is in a specific area, it can prioritize displaying candidates for industries or items related to that area. The reception unit can also prioritize displaying information on subsidies and grants in that area. Furthermore, the reception unit can prioritize displaying information on companies in that area. This prioritizes displaying highly relevant options by taking the user's geographical location information into account, improving input efficiency. Some or all of the above-described processing in the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can input the user's geographical location data into a generation AI, which can analyze the data and prioritize displaying highly relevant options.

[0120] The processing flow of the second embodiment will be briefly explained below.

[0121] Step 1: The reception unit accepts input from the user about the type of business, the item they wish to purchase, and their location. For example, if a user is in the manufacturing industry and wants to purchase a new machine, they would enter "manufacturing" as the type of business, "machinery" as the item they wish to purchase, and "Chiyoda-ku, Tokyo" as the location. This information is entered into the reception unit. Step 2: The listing unit creates a list of available subsidies and grants based on the information received by the reception unit. The listing unit references national and local government databases to search for subsidies and grants that match the input industry, desired purchase, and location. For example, subsidies and grants that match "manufacturing," "machinery," and "Chiyoda Ward, Tokyo" are displayed in the list. The list includes detailed information about each subsidy and grant, such as the name of the subsidy, provider, application deadline, subsidy amount, and eligibility conditions. This allows users to see at a glance which subsidies and grants are available. Step 3: The recommendation unit recommends the most suitable subsidy or grant from among those listed by the listing unit. The recommendation unit selects and recommends the most suitable subsidy or grant based on the information input by the user. For example, the recommendation unit recommends the subsidy or grant that is most suitable for the user's industry, desired purchase, and location. The recommendation unit can also recommend the most suitable subsidy or grant based on the user's past usage history and evaluations.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0124] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0127] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0140] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0143] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0150] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0154] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0156] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0161] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0162] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0164] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0165] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0166] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0167] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0168] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0169] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0170] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0171] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0172] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0173] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0176] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0177] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0178] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0179] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0180] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0181] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0182] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0183] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0184] 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.

[0185] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0186] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0187] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0188] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0189] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0190] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0191] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0192] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0193] [Explanation of symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input from a user about the type of business, the item to be purchased, and the location; a listing unit that lists available subsidies and grants based on the information received by the receiving unit; a recommendation unit that recommends appropriate subsidies and grants from among those listed by the listing unit. A system characterized by:

2. The listing unit Equipped with a section that provides detailed information on subsidies and grants The system of claim 1 .

3. The providing unit Establish a guidelines department that provides guidelines for application procedures for subsidies and grants 3. The system of claim 2.

4. The providing unit Have a documentation department that provides a list of documents required for applying for grants and subsidies.

3. The system of claim 2.

5. The listing unit Equipped with a display that shows basic information about companies that are eligible for subsidies and grants The system of claim 1 .

6. The reception unit Estimate user emotions and dynamically change the design of the input interface based on the estimated user emotions. The system of claim 1 .

7. The reception unit Analyzes the user's past input history and suggests appropriate input methods The system of claim 1 .

8. The reception unit As you type, provide auto-complete based on the user's industry and location The system of claim 1 .

9. The reception unit Estimate the user's emotions and adjust the priority of inputs based on the estimated user emotions. The system of claim 1 .

Images