system

Abstract

The system according to this embodiment aims to enable local governments to efficiently search for appropriate subsidy and grant information to solve problems and to create proposals. [Solution] The system according to the embodiment comprises a reception unit, a collection unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives input of social issues and proposal summaries. The collection unit searches for subsidy and grant information based on the information entered by the reception unit. The analysis unit analyzes the information collected by the collection unit and matches policies with subsidies. The generation unit generates proposal materials based on the matching results obtained by the analysis unit. The provision unit provides the proposal materials generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult for local governments to efficiently search for appropriate subsidy and grant information for problem-solving and create proposal documents.

[0005] The system according to the embodiment aims to enable local governments to efficiently search for appropriate subsidy and grant information for problem-solving and create proposal documents.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a collection unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives input of social issues and proposal summaries. The collection unit searches for subsidy and grant information based on the information entered by the reception unit. The analysis unit analyzes the information collected by the collection unit and matches policies with subsidies. The generation unit generates proposal materials based on the matching results obtained by the analysis unit. The provision unit provides the proposal materials generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment allows local governments to efficiently search for appropriate subsidy and grant information to solve problems and to create proposals. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The proposal generation system according to an embodiment of the present invention is a system that searches for subsidy and grant information provided by the national government and ministries and agencies to solve problems faced by local governments and generates proposal materials. This proposal generation system utilizes generation AI to efficiently collect subsidy and grant information that local governments can use as budget allocations and supports the creation of proposals. First, the user inputs the social problem they want to solve and the measures or proposal outline they want to propose. Next, the proposal generation system searches for relevant subsidy and grant information from external websites and performs matching. At this time, the generation AI accumulates information on measures and examples of subsidy acquisition to improve the accuracy of matching. Specifically, it consists of the following steps: 1. Input the social problem and the measures or proposal outline you want to propose. 2. Search for subsidy and grant information from external websites and perform matching. 3. Display subsidy information and public offering information along with the source. 4. Generate examples of how to include subsidy information in the proposal and sample entries for subsidy application items to support the creation of application materials. 5. Generate proposal slides. This proposal generation system enables local governments to efficiently utilize subsidies and grants and quickly make proposals to solve social issues. Furthermore, by utilizing AI generation, the collection of subsidy information and proposal creation are semi-automated, improving operational efficiency. For example, if a local government is planning the construction of a new public facility, it can use this proposal generation system to search for relevant subsidy information and generate proposal materials. This allows for a smoother subsidy application process and faster project implementation. Moreover, the generation AI accumulates past subsidy acquisition cases and proposal performance as learning data, enabling more sophisticated matching and proposals in the future. This allows local governments to always utilize the latest subsidy information and make optimal proposals. In summary, this proposal generation system enables local governments to efficiently utilize subsidies and grants and quickly make proposals to solve social issues.

[0029] The proposal generation system according to the embodiment comprises a reception unit, a collection unit, an analysis unit, a generation unit, and a provision unit. The reception unit inputs social issues and proposal summaries. The reception unit provides, for example, an interface for users to input social issues they want to solve and policies or proposal summaries they want to propose. The reception unit can accept input in the form of, for example, text input, voice input, or selection from a list of options. The collection unit searches for subsidy and grant information based on the information entered by the reception unit. The collection unit searches for subsidy and grant information from, for example, external websites and collects relevant information. The collection unit can collect information from, for example, the official website of the government, the website of a local government, or a private grant information website. The analysis unit analyzes the information collected by the collection unit and matches policies with subsidies. The analysis unit compares the collected subsidy information with the policy information and evaluates the suitability. The analysis unit accumulates, for example, policy information and examples of subsidy acquisition to improve the accuracy of the matching. The generation unit generates proposal materials based on the matching results obtained by the analysis unit. The generation unit generates, for example, examples of how to include subsidy information in a proposal and sample entries for subsidy application items. The generation unit can also generate, for example, proposal slides. The provision unit provides the proposal materials generated by the generation unit. The provision unit provides, for example, an interface for providing the generated proposal materials to the user. The provision unit can display the proposal materials through, for example, a web application or a mobile application. As a result, the proposal generation system according to this embodiment enables local governments to efficiently utilize subsidies and grants and quickly make proposals for solving social issues.

[0030] The reception desk receives input for social issues and proposal summaries. For example, the reception desk provides an interface for users to input social issues they wish to solve and proposed measures or proposal summaries. Specifically, users can input social issues and proposal summaries into text input fields via a web browser or mobile application. For voice input, speech recognition technology is used to convert the user's speech into text, which is then accepted as input. Alternatively, users can select from a list of pre-configured social issues and proposal summaries. This allows users to intuitively and quickly input the necessary information. Furthermore, the reception desk displays a confirmation screen, allowing users to review and correct their input. This prevents input errors and ensures accurate information collection. The reception desk is linked to a database that temporarily stores the entered information and passes it on to the next processing step. This ensures that user-entered information is securely stored and used for subsequent processing.

[0031] The data collection unit searches for subsidy and grant information based on the information entered by the reception unit. For example, the data collection unit searches for subsidy and grant information from external websites and collects relevant information. Specifically, the data collection unit uses web crawlers to collect data from reliable sources such as government websites, local government websites, and private grant information websites. The web crawlers regularly visit these sites and automatically collect new subsidy and grant information. The collected information is stored in a database and made accessible to the analysis unit. Furthermore, the data collection unit evaluates the reliability of the collected information and has filtering functions to eliminate inaccurate or outdated information. This allows the data collection unit to provide the latest and most accurate subsidy and grant information. The data collection unit also generates search queries to allow users to search for information based on specific criteria and efficiently collect relevant information. For example, it can search for subsidy information limited to specific regions or fields. This allows the data collection unit to quickly provide information that meets user needs.

[0032] The analysis unit analyzes the information collected by the data collection unit and matches policies with subsidies. For example, the analysis unit compares collected subsidy information with policy information and evaluates their suitability. Specifically, the analysis unit uses natural language processing technology to analyze the content of the collected subsidy information and the policy information entered by the user and evaluates the relationship between the two. For example, it extracts keywords such as the purpose of the policy, target area, and target audience, and evaluates suitability by matching them with the subsidy information. The analysis unit also stores past subsidy acquisition cases in a database and uses this to improve the accuracy of the matching. Specifically, it analyzes the content of past successful proposals and the conditions for acquiring subsidies, and uses this information to match new proposals. Furthermore, the analysis unit uses machine learning algorithms to continuously improve the accuracy of the matching. For example, it adjusts the matching algorithm based on user feedback to achieve more accurate matching. As a result, the analysis unit can provide the user with the most suitable subsidy information for their policies and increase the success rate of proposals.

[0033] The generation unit generates proposal materials based on the matching results obtained by the analysis unit. For example, the generation unit generates examples of how to include subsidy information in proposals and sample subsidy application items. Specifically, the generation unit automatically generates appropriate content for each section of the proposal based on the policy information entered by the user and the subsidy information provided by the analysis unit. For example, it inserts appropriate wording and data for items such as the proposal overview, objectives, target audience, budget, and schedule. The generation unit also has a function to automatically format and lay out the proposal. This allows users to quickly create visually appealing, professional proposals. Furthermore, the generation unit also provides a function to generate proposal slides, making it easy for users to create presentation materials. The generation unit can continuously improve the quality of the generated proposal materials based on user feedback. For example, it collects the results after users submit proposals and analyzes the characteristics of successful proposals to reflect these findings in future proposal generation. This allows the generation unit to consistently provide high-quality proposal materials and improve the user's proposal success rate.

[0034] The service provider provides the proposal materials generated by the generation service provider. For example, the service provider provides an interface for providing the generated proposal materials to users. Specifically, the service provider displays the generated proposal materials to users through web and mobile applications. Users can download or print the generated proposal materials through the service provider's interface. The service provider also provides a function that allows users to edit the proposal materials, enabling them to modify the content as needed. Furthermore, the service provider saves the generated proposal materials to cloud storage, allowing users to access them at any time. This ensures that users can securely store their proposal materials and access them quickly when needed. The service provider can collect user feedback and use it to improve the interface and features. For example, it can collect user results and impressions of using the proposal materials and use this to improve the usability and functionality of the interface. This allows the service provider to consistently provide the best possible service to users and enhance the value of the proposal generation system.

[0035] The analysis unit can improve the accuracy of matching by accumulating policy information and examples of successful subsidy acquisitions. For example, the analysis unit can accumulate policy information in a database and learn from past success stories and application points. For example, the analysis unit can analyze examples of successful subsidy acquisitions and improve the accuracy of matching based on the degree of agreement of evaluation criteria and conditions. For example, the analysis unit can perform more sophisticated matching by combining policy information and examples of successful subsidy acquisitions. As a result, the accuracy of matching improves by accumulating policy information and examples of successful subsidy acquisitions. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can improve the accuracy of matching by using an AI model that takes policy information and examples of successful subsidy acquisitions as input and outputs matching results.

[0036] The generation unit can generate examples of how to include subsidy information in a proposal and sample entries for subsidy application items. For example, the generation unit generates examples of how to include subsidy information based on a proposal template. For example, the generation unit generates sample entries for subsidy application items and provides them to the user. For example, the generation unit automatically generates examples of how to include subsidy information and sample entries for subsidy application items to support the creation of proposals. This makes it easier to create proposals by generating examples of how to include subsidy information and sample entries for subsidy application items in proposals. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can support the creation of proposals using an AI model that takes a proposal template and subsidy information as input and outputs examples of how to include subsidy information and sample entries for subsidy application items.

[0037] The generation unit can generate proposal slides. For example, the generation unit can automatically generate proposal slides based on the content of the proposal document. For example, the generation unit can create slides based on a proposal slide template, following necessary items and design guidelines. For example, the generation unit generates proposal slides and provides them to the user. This makes it easier to present the proposal by generating proposal slides. Some or all of the above processes in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can generate proposal slides using an AI model that takes the content of the proposal document and a slide template as input and outputs proposal slides.

[0038] The data collection unit can search for subsidy and grant information from external websites. For example, the data collection unit can search for subsidy information from the official website of the government. For example, the data collection unit can also search for grant information from the website of a local government. For example, the data collection unit can also search for subsidy information from private grant information website. In this way, the latest information can be collected by searching for subsidy and grant information from external websites. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can search for information using an AI model that takes the URL of an external website as input and outputs subsidy and grant information.

[0039] The service provider can provide the generated proposal materials to the user. For example, the service provider can provide the generated proposal materials to the user through a web application. The service provider can also provide the generated proposal materials to the user through a mobile application. The service provider can also send the generated proposal materials to the user via email. This streamlines the proposal creation process by providing the generated proposal materials to the user. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can select the optimal method of provision using an AI model that takes the generated proposal materials as input and outputs a method for providing them to the user.

[0040] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display as candidates the social issues and proposal summaries that the user has frequently entered in the past. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest social issues and proposal summaries to be used during a specific time period based on the user's past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method for the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input data into a generating AI and have the generating AI suggest the optimal input method.

[0041] The reception unit can simplify input by automatically acquiring the user's current location information when they input social issues and proposal summaries. For example, when a user opens the app, the reception unit automatically acquires their current location and suggests relevant social issues. For example, when a user inputs a proposal summary, the reception unit suggests the most suitable candidate location considering the distance from their current location. For example, if a user uses the app while on the move, the reception unit updates their current location in real time and suggests relevant social issues. This simplifies the input process by automatically acquiring the current location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's current location information into a generating AI and have the generating AI generate suggestions for relevant social issues.

[0042] The reception desk can automatically suggest candidate locations by referring to the user's past travel history when the user inputs a social issue and a proposal summary. For example, the reception desk can automatically display places the user has frequently visited in the past as candidate locations. For example, the reception desk can predict places the user visits on specific days of the week or times of day and suggest them as candidate locations. For example, the reception desk can analyze the user's past travel patterns and suggest the most suitable candidate locations. In this way, the reception desk can suggest the most suitable candidate locations by referring to past travel history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past travel data into a generating AI and have the generating AI perform the candidate location suggestion.

[0043] The reception desk can make proposals based on the user's schedule by referring to the user's calendar information when inputting social issues and proposal summaries. For example, the reception desk can refer to the schedule registered in the user's calendar and automatically set the social issues and proposal summaries. For example, the reception desk can suggest locations related to a specific event as candidate locations based on the user's calendar information. For example, the reception desk can make optimal proposals tailored to the schedule based on the user's calendar information. In this way, optimal proposals can be made based on the schedule by referring to the calendar information. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's calendar information into a generating AI and have the generating AI execute proposals based on the schedule.

[0044] The data collection unit can analyze past data collection history and select the optimal data collection method. For example, the data collection unit can select the most efficient data collection method from past data collection history. For example, the data collection unit can adjust the data collection frequency based on past data collection history. For example, the data collection unit can analyze past data collection history and optimize the data collection method. This allows the optimal data collection method to be selected by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection data into a generating AI and have the generating AI select the optimal data collection method.

[0045] The data collection unit can filter data based on the user's current projects and areas of interest during collection. For example, the data collection unit prioritizes collecting information related to the user's current projects. For example, the data collection unit filters highly relevant information based on the user's areas of interest. For example, the data collection unit collects necessary information according to the user's project progress. This allows for the collection of highly relevant information by filtering based on the current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's project information into a generating AI and have the generating AI perform the filtering of highly relevant information.

[0046] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location information during the collection process. For example, the data collection unit prioritizes the collection of subsidy information related to the user's current location. For example, the data collection unit collects region-specific subsidy information based on the user's geographical location information. For example, the data collection unit collects optimal subsidy information by considering the user's location information. This allows for the priority collection of highly relevant information by considering geographical location information. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0047] The data collection unit can analyze the user's social media activity and collect relevant information during the collection process. For example, the data collection unit can collect relevant subsidy information from the user's social media activity. For example, the data collection unit can collect relevant subsidy information based on the user's social media posts. For example, the data collection unit can collect relevant subsidy information by referring to the activities of the user's social media followers and friends. This allows for the collection of highly relevant information by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI collect relevant information.

[0048] The analysis unit can improve the accuracy of its analysis by referring to policy information and examples of subsidy acquisition during the analysis process. For example, the analysis unit improves the accuracy of its analysis based on policy information. For example, the analysis unit improves the accuracy of its analysis by referring to examples of subsidy acquisition. For example, the analysis unit improves the accuracy of its analysis by combining policy information and examples of subsidy acquisition. As a result, the accuracy of the analysis is improved by referring to policy information and examples of subsidy acquisition. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input policy information and examples of subsidy acquisition into a generating AI and have the generating AI perform an analysis to improve the accuracy of the analysis.

[0049] The analysis unit can perform analysis while considering user attribute information. For example, the analysis unit can improve the accuracy of the analysis based on user attribute information. For example, the analysis unit can select the optimal analysis method while considering user attribute information. For example, the analysis unit can prioritize the analysis of highly relevant information based on user attribute information. This improves the accuracy of the analysis by considering user attribute information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user attribute information into a generating AI and have the generating AI perform analysis to improve the accuracy of the analysis.

[0050] The analysis unit can perform analysis while considering the geographical distribution of information. For example, the analysis unit can improve the accuracy of the analysis based on the geographical distribution of information. For example, the analysis unit can select the optimal analysis method while considering the geographical distribution. For example, the analysis unit can prioritize the analysis of highly relevant information based on the geographical distribution. This improves the accuracy of the analysis by considering the geographical distribution of information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input geographical distribution data of information into a generating AI and have the generating AI perform an analysis to improve the accuracy of the analysis.

[0051] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis unit improves the accuracy of its analysis based on relevant literature. For example, the analysis unit selects the optimal analysis method by referring to relevant literature. For example, the analysis unit prioritizes the analysis of highly relevant information based on relevant literature. As a result, the accuracy of the analysis is improved by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform an analysis to improve the accuracy of the analysis.

[0052] The generation unit can improve the accuracy of its generation by referring to policy information and examples of subsidy acquisition during the generation process. For example, the generation unit improves the accuracy of its generation based on policy information. For example, the generation unit improves the accuracy of its generation by referring to examples of subsidy acquisition. For example, the generation unit improves the accuracy of its generation by combining policy information and examples of subsidy acquisition. As a result, the accuracy of the generation is improved by referring to policy information and examples of subsidy acquisition. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input policy information and examples of subsidy acquisition into a generation AI and have the generation AI perform generation to improve the accuracy of the generation.

[0053] The generation unit can perform generation while considering user attribute information. For example, the generation unit can improve the accuracy of generation based on user attribute information. For example, the generation unit can select the optimal generation method while considering user attribute information. For example, the generation unit can generate highly relevant proposal materials based on user attribute information. This improves the accuracy of generation by considering user attribute information. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user attribute information into a generation AI and have the generation AI perform generation to improve the accuracy of generation.

[0054] The generation unit can perform generation while considering the geographical distribution of information. For example, the generation unit can improve the accuracy of generation based on the geographical distribution of information. For example, the generation unit can select the optimal generation method while considering the geographical distribution. For example, the generation unit can generate highly relevant proposal materials based on the geographical distribution. This improves the accuracy of generation by considering the geographical distribution of information. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input geographical distribution data of information into a generation AI and have the generation AI perform generation to improve the accuracy of generation.

[0055] The generation unit can improve the accuracy of generation by referring to relevant literature during generation. For example, the generation unit improves the accuracy of generation based on relevant literature. For example, the generation unit selects the optimal generation method by referring to relevant literature. For example, the generation unit generates highly relevant proposal materials based on relevant literature. In this way, the accuracy of generation is improved by referring to relevant literature. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input relevant literature data into a generation AI and have the generation AI perform generation to improve the accuracy of generation.

[0056] The service provider can select the optimal service delivery method by referring to the user's past usage history at the time of delivery. For example, the service provider selects the optimal service delivery method based on the user's past usage history. For example, the service provider adjusts the delivery frequency by referring to the user's past usage history. For example, the service provider analyzes the user's past usage history and optimizes the service delivery method. This allows the service provider to select the optimal service delivery method by referring to past usage history. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past usage data into a generating AI and have the generating AI select the optimal service delivery method.

[0057] The delivery unit can select the optimal delivery method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the delivery unit will select a delivery method that matches the screen size. For example, if the user is using a tablet, the delivery unit will select a delivery method optimized for a large screen. For example, if the user is using a smartwatch, the delivery unit will select a concise and highly visible delivery method. In this way, the optimal delivery method can be selected by taking device information into consideration. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's device information into a generating AI and have the generating AI select the optimal delivery method.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The proposal generation system can further analyze the user's past proposal creation history and suggest the most suitable proposal template. For example, it can increase the success rate by prioritizing the suggestion of templates that have been successful in the user's past proposals. It can also automatically select and suggest templates that have received particularly high ratings from the user's past use. Furthermore, based on the user's past proposal creation history, it can suggest templates suitable for specific fields or themes. This allows users to create proposals efficiently and improve their success rate.

[0060] The proposal generation system can also provide optimal proposal materials by considering the user's current project progress. For example, in the initial stages of a project, it can provide proposal materials focusing on basic information and an overview. In the middle stages of the project, it can provide proposal materials including detailed data and specific plans. Furthermore, in the final stages of the project, it can provide proposal materials that emphasize results and achievements. This allows for the provision of optimal proposal materials according to the project's progress, thereby improving the success rate of proposals.

[0061] The proposal generation system can further analyze the user's past proposal evaluation data and suggest optimal proposal content. For example, it can automatically generate new proposal content by referencing the content of proposals that received high ratings in the past. It can also improve the quality of proposals by avoiding the content of proposals that received low ratings in the past. Furthermore, it can increase the success rate by prioritizing proposals that have received high ratings in specific fields or themes. This allows users to efficiently create high-quality proposals.

[0062] The proposal generation system can also prioritize providing region-specific subsidy information by considering the user's current location. For example, if a user is in a specific region, it can prioritize displaying subsidy information relevant to that region. Furthermore, if a user is on the move, it can provide the most relevant subsidy information in real time based on their current location. Additionally, if a user is interested in a particular region, it can automatically suggest subsidy information relevant to that region. This allows users to efficiently gather region-specific subsidy information and improve the quality of their proposals.

[0063] The proposal generation system can further analyze the user's past proposal creation history and suggest the most suitable proposal template. For example, it can increase the success rate by prioritizing the suggestion of templates that have been successful in the user's past proposals. It can also automatically select and suggest templates that have received particularly high ratings from the user's past use. Furthermore, based on the user's past proposal creation history, it can suggest templates suitable for specific fields or themes. This allows users to create proposals efficiently and improve their success rate.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The reception desk receives input on social issues and proposal summaries. The reception desk provides an interface for users to input, for example, the social issues they want to solve and the measures or proposal summaries they want to suggest. The reception desk can accept input in the form of text input, voice input, or selection from multiple-choice options. Step 2: The collection department searches for subsidy and grant information based on the information entered by the reception department. The collection department searches for subsidy and grant information from external websites, for example, and collects relevant information. The collection department can collect information from sources such as the official government website, local government websites, and private grant information websites. Step 3: The analysis unit analyzes the information collected by the collection unit and matches policies with subsidies. For example, the analysis unit compares the collected subsidy information with policy information and evaluates their suitability. For example, the analysis unit accumulates policy information and examples of subsidy acquisition to improve the accuracy of the matching. Step 4: The generation unit generates proposal materials based on the matching results obtained by the analysis unit. The generation unit generates, for example, examples of how to include subsidy information in the proposal and sample entries for subsidy application items. The generation unit can also generate, for example, proposal slides. Step 5: The provider unit provides the proposal materials generated by the generator unit. The provider unit provides, for example, an interface for providing the generated proposal materials to the user. The provider unit can display the proposal materials, for example, through a web application or a mobile application.

[0066] (Example of form 2) The proposal generation system according to an embodiment of the present invention is a system that searches for subsidy and grant information provided by the national government and ministries and agencies to solve problems faced by local governments and generates proposal materials. This proposal generation system utilizes generation AI to efficiently collect subsidy and grant information that local governments can use as budget allocations and supports the creation of proposals. First, the user inputs the social problem they want to solve and the measures or proposal outline they want to propose. Next, the proposal generation system searches for relevant subsidy and grant information from external websites and performs matching. At this time, the generation AI accumulates information on measures and examples of subsidy acquisition to improve the accuracy of matching. Specifically, it consists of the following steps: 1. Input the social problem and the measures or proposal outline you want to propose. 2. Search for subsidy and grant information from external websites and perform matching. 3. Display subsidy information and public offering information along with the source. 4. Generate examples of how to include subsidy information in the proposal and sample entries for subsidy application items to support the creation of application materials. 5. Generate proposal slides. This proposal generation system enables local governments to efficiently utilize subsidies and grants and quickly make proposals to solve social issues. Furthermore, by utilizing AI generation, the collection of subsidy information and proposal creation are semi-automated, improving operational efficiency. For example, if a local government is planning the construction of a new public facility, it can use this proposal generation system to search for relevant subsidy information and generate proposal materials. This allows for a smoother subsidy application process and faster project implementation. Moreover, the generation AI accumulates past subsidy acquisition cases and proposal performance as learning data, enabling more sophisticated matching and proposals in the future. This allows local governments to always utilize the latest subsidy information and make optimal proposals. In summary, this proposal generation system enables local governments to efficiently utilize subsidies and grants and quickly make proposals to solve social issues.

[0067] The proposal generation system according to the embodiment comprises a reception unit, a collection unit, an analysis unit, a generation unit, and a provision unit. The reception unit inputs social issues and proposal summaries. The reception unit provides, for example, an interface for users to input social issues they want to solve and policies or proposal summaries they want to propose. The reception unit can accept input in the form of, for example, text input, voice input, or selection from a list of options. The collection unit searches for subsidy and grant information based on the information entered by the reception unit. The collection unit searches for subsidy and grant information from, for example, external websites and collects relevant information. The collection unit can collect information from, for example, the official website of the government, the website of a local government, or a private grant information website. The analysis unit analyzes the information collected by the collection unit and matches policies with subsidies. The analysis unit compares the collected subsidy information with the policy information and evaluates the suitability. The analysis unit accumulates, for example, policy information and examples of subsidy acquisition to improve the accuracy of the matching. The generation unit generates proposal materials based on the matching results obtained by the analysis unit. The generation unit generates, for example, examples of how to include subsidy information in a proposal and sample entries for subsidy application items. The generation unit can also generate, for example, proposal slides. The provision unit provides the proposal materials generated by the generation unit. The provision unit provides, for example, an interface for providing the generated proposal materials to the user. The provision unit can display the proposal materials through, for example, a web application or a mobile application. As a result, the proposal generation system according to this embodiment enables local governments to efficiently utilize subsidies and grants and quickly make proposals for solving social issues.

[0068] The reception desk receives input for social issues and proposal summaries. For example, the reception desk provides an interface for users to input social issues they wish to solve and proposed measures or proposal summaries. Specifically, users can input social issues and proposal summaries into text input fields via a web browser or mobile application. For voice input, speech recognition technology is used to convert the user's speech into text, which is then accepted as input. Alternatively, users can select from a list of pre-configured social issues and proposal summaries. This allows users to intuitively and quickly input the necessary information. Furthermore, the reception desk displays a confirmation screen, allowing users to review and correct their input. This prevents input errors and ensures accurate information collection. The reception desk is linked to a database that temporarily stores the entered information and passes it on to the next processing step. This ensures that user-entered information is securely stored and used for subsequent processing.

[0069] The data collection unit searches for subsidy and grant information based on the information entered by the reception unit. For example, the data collection unit searches for subsidy and grant information from external websites and collects relevant information. Specifically, the data collection unit uses web crawlers to collect data from reliable sources such as government websites, local government websites, and private grant information websites. The web crawlers regularly visit these sites and automatically collect new subsidy and grant information. The collected information is stored in a database and made accessible to the analysis unit. Furthermore, the data collection unit evaluates the reliability of the collected information and has filtering functions to eliminate inaccurate or outdated information. This allows the data collection unit to provide the latest and most accurate subsidy and grant information. The data collection unit also generates search queries to allow users to search for information based on specific criteria and efficiently collect relevant information. For example, it can search for subsidy information limited to specific regions or fields. This allows the data collection unit to quickly provide information that meets user needs.

[0070] The analysis unit analyzes the information collected by the data collection unit and matches policies with subsidies. For example, the analysis unit compares collected subsidy information with policy information and evaluates their suitability. Specifically, the analysis unit uses natural language processing technology to analyze the content of the collected subsidy information and the policy information entered by the user and evaluates the relationship between the two. For example, it extracts keywords such as the purpose of the policy, target area, and target audience, and evaluates suitability by matching them with the subsidy information. The analysis unit also stores past subsidy acquisition cases in a database and uses this to improve the accuracy of the matching. Specifically, it analyzes the content of past successful proposals and the conditions for acquiring subsidies, and uses this information to match new proposals. Furthermore, the analysis unit uses machine learning algorithms to continuously improve the accuracy of the matching. For example, it adjusts the matching algorithm based on user feedback to achieve more accurate matching. As a result, the analysis unit can provide the user with the most suitable subsidy information for their policies and increase the success rate of proposals.

[0071] The generation unit generates proposal materials based on the matching results obtained by the analysis unit. For example, the generation unit generates examples of how to include subsidy information in proposals and sample subsidy application items. Specifically, the generation unit automatically generates appropriate content for each section of the proposal based on the policy information entered by the user and the subsidy information provided by the analysis unit. For example, it inserts appropriate wording and data for items such as the proposal overview, objectives, target audience, budget, and schedule. The generation unit also has a function to automatically format and lay out the proposal. This allows users to quickly create visually appealing, professional proposals. Furthermore, the generation unit also provides a function to generate proposal slides, making it easy for users to create presentation materials. The generation unit can continuously improve the quality of the generated proposal materials based on user feedback. For example, it collects the results after users submit proposals and analyzes the characteristics of successful proposals to reflect these findings in future proposal generation. This allows the generation unit to consistently provide high-quality proposal materials and improve the user's proposal success rate.

[0072] The service provider provides the proposal materials generated by the generation service provider. For example, the service provider provides an interface for providing the generated proposal materials to users. Specifically, the service provider displays the generated proposal materials to users through web and mobile applications. Users can download or print the generated proposal materials through the service provider's interface. The service provider also provides a function that allows users to edit the proposal materials, enabling them to modify the content as needed. Furthermore, the service provider saves the generated proposal materials to cloud storage, allowing users to access them at any time. This ensures that users can securely store their proposal materials and access them quickly when needed. The service provider can collect user feedback and use it to improve the interface and features. For example, it can collect user results and impressions of using the proposal materials and use this to improve the usability and functionality of the interface. This allows the service provider to consistently provide the best possible service to users and enhance the value of the proposal generation system.

[0073] The analysis unit can improve the accuracy of matching by accumulating policy information and examples of successful subsidy acquisitions. For example, the analysis unit can accumulate policy information in a database and learn from past success stories and application points. For example, the analysis unit can analyze examples of successful subsidy acquisitions and improve the accuracy of matching based on the degree of agreement of evaluation criteria and conditions. For example, the analysis unit can perform more sophisticated matching by combining policy information and examples of successful subsidy acquisitions. As a result, the accuracy of matching improves by accumulating policy information and examples of successful subsidy acquisitions. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can improve the accuracy of matching by using an AI model that takes policy information and examples of successful subsidy acquisitions as input and outputs matching results.

[0074] The generation unit can generate examples of how to include subsidy information in a proposal and sample entries for subsidy application items. For example, the generation unit generates examples of how to include subsidy information based on a proposal template. For example, the generation unit generates sample entries for subsidy application items and provides them to the user. For example, the generation unit automatically generates examples of how to include subsidy information and sample entries for subsidy application items to support the creation of proposals. This makes it easier to create proposals by generating examples of how to include subsidy information and sample entries for subsidy application items in proposals. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can support the creation of proposals using an AI model that takes a proposal template and subsidy information as input and outputs examples of how to include subsidy information and sample entries for subsidy application items.

[0075] The generation unit can generate proposal slides. For example, the generation unit can automatically generate proposal slides based on the content of the proposal document. For example, the generation unit can create slides based on a proposal slide template, following necessary items and design guidelines. For example, the generation unit generates proposal slides and provides them to the user. This makes it easier to present the proposal by generating proposal slides. Some or all of the above processes in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can generate proposal slides using an AI model that takes the content of the proposal document and a slide template as input and outputs proposal slides.

[0076] The data collection unit can search for subsidy and grant information from external websites. For example, the data collection unit can search for subsidy information from the official website of the government. For example, the data collection unit can also search for grant information from the website of a local government. For example, the data collection unit can also search for subsidy information from private grant information website. In this way, the latest information can be collected by searching for subsidy and grant information from external websites. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can search for information using an AI model that takes the URL of an external website as input and outputs subsidy and grant information.

[0077] The service provider can provide the generated proposal materials to the user. For example, the service provider can provide the generated proposal materials to the user through a web application. The service provider can also provide the generated proposal materials to the user through a mobile application. The service provider can also send the generated proposal materials to the user via email. This streamlines the proposal creation process by providing the generated proposal materials to the user. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can select the optimal method of provision using an AI model that takes the generated proposal materials as input and outputs a method for providing them to the user.

[0078] The reception desk can estimate the user's emotions and adjust the input method for social issues and proposal summaries based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. For example, if the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. For example, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of social issues and proposal summaries. This reduces the burden on the user by adjusting the input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0079] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display as candidates the social issues and proposal summaries that the user has frequently entered in the past. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest social issues and proposal summaries to be used during a specific time period based on the user's past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method for the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input data into a generating AI and have the generating AI suggest the optimal input method.

[0080] The reception unit can simplify input by automatically acquiring the user's current location information when they input social issues and proposal summaries. For example, when a user opens the app, the reception unit automatically acquires their current location and suggests relevant social issues. For example, when a user inputs a proposal summary, the reception unit suggests the most suitable candidate location considering the distance from their current location. For example, if a user uses the app while on the move, the reception unit updates their current location in real time and suggests relevant social issues. This simplifies the input process by automatically acquiring the current location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's current location information into a generating AI and have the generating AI generate suggestions for relevant social issues.

[0081] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated emotions. For example, if the user is tense, the reception unit can provide an interface with calming colors to reduce visual stress. For example, if the user is having fun, the reception unit can provide an interface with bright colors to make the input process enjoyable. For example, if the user is tired, the reception unit can provide a simple and highly visible interface to facilitate the input process. In this way, the burden on the user is reduced by adjusting the interface design according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0082] The reception desk can automatically suggest candidate locations by referring to the user's past travel history when the user inputs a social issue and a proposal summary. For example, the reception desk can automatically display places the user has frequently visited in the past as candidate locations. For example, the reception desk can predict places the user visits on specific days of the week or times of day and suggest them as candidate locations. For example, the reception desk can analyze the user's past travel patterns and suggest the most suitable candidate locations. In this way, the reception desk can suggest the most suitable candidate locations by referring to past travel history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past travel data into a generating AI and have the generating AI perform the candidate location suggestion.

[0083] The reception desk can make proposals based on the user's schedule by referring to the user's calendar information when inputting social issues and proposal summaries. For example, the reception desk can refer to the schedule registered in the user's calendar and automatically set the social issues and proposal summaries. For example, the reception desk can suggest locations related to a specific event as candidate locations based on the user's calendar information. For example, the reception desk can make optimal proposals tailored to the schedule based on the user's calendar information. In this way, optimal proposals can be made based on the schedule by referring to the calendar information. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's calendar information into a generating AI and have the generating AI execute proposals based on the schedule.

[0084] The data collection unit can estimate the user's emotions and adjust the data collection timing based on the estimated emotions. For example, if the user is relaxed, the data collection unit collects information at the normal timing. If the user is in a hurry, for example, the data collection unit shortens the data collection timing and collects information quickly. If the user is excited, for example, the data collection unit adjusts the data collection timing to collect information at the appropriate time. This allows for efficient information collection by adjusting the data collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0085] The data collection unit can analyze past data collection history and select the optimal data collection method. For example, the data collection unit can select the most efficient data collection method from past data collection history. For example, the data collection unit can adjust the data collection frequency based on past data collection history. For example, the data collection unit can analyze past data collection history and optimize the data collection method. This allows the optimal data collection method to be selected by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection data into a generating AI and have the generating AI select the optimal data collection method.

[0086] The data collection unit can filter data based on the user's current projects and areas of interest during collection. For example, the data collection unit prioritizes collecting information related to the user's current projects. For example, the data collection unit filters highly relevant information based on the user's areas of interest. For example, the data collection unit collects necessary information according to the user's project progress. This allows for the collection of highly relevant information by filtering based on the current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's project information into a generating AI and have the generating AI perform the filtering of highly relevant information.

[0087] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit collects information with normal priority. For example, if the user is in a hurry, the data collection unit prioritizes collecting important information. For example, if the user is excited, the data collection unit prioritizes collecting highly relevant information. This allows for the priority collection of important information by determining the priority of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0088] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location information during the collection process. For example, the data collection unit prioritizes the collection of subsidy information related to the user's current location. For example, the data collection unit collects region-specific subsidy information based on the user's geographical location information. For example, the data collection unit collects optimal subsidy information by considering the user's location information. This allows for the priority collection of highly relevant information by considering geographical location information. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0089] The data collection unit can analyze the user's social media activity and collect relevant information during the collection process. For example, the data collection unit can collect relevant subsidy information from the user's social media activity. For example, the data collection unit can collect relevant subsidy information based on the user's social media posts. For example, the data collection unit can collect relevant subsidy information by referring to the activities of the user's social media followers and friends. This allows for the collection of highly relevant information by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI collect relevant information.

[0090] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the user is relaxed, the analysis unit performs the analysis using normal analysis criteria. If the user is in a hurry, the analysis unit prioritizes analyzing important information. If the user is excited, the analysis unit prioritizes analyzing highly relevant information. This allows for efficient analysis by adjusting the analysis criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0091] The analysis unit can improve the accuracy of its analysis by referring to policy information and examples of subsidy acquisition during the analysis process. For example, the analysis unit improves the accuracy of its analysis based on policy information. For example, the analysis unit improves the accuracy of its analysis by referring to examples of subsidy acquisition. For example, the analysis unit improves the accuracy of its analysis by combining policy information and examples of subsidy acquisition. As a result, the accuracy of the analysis is improved by referring to policy information and examples of subsidy acquisition. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input policy information and examples of subsidy acquisition into a generating AI and have the generating AI perform an analysis to improve the accuracy of the analysis.

[0092] The analysis unit can perform analysis while considering user attribute information. For example, the analysis unit can improve the accuracy of the analysis based on user attribute information. For example, the analysis unit can select the optimal analysis method while considering user attribute information. For example, the analysis unit can prioritize the analysis of highly relevant information based on user attribute information. This improves the accuracy of the analysis by considering user attribute information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user attribute information into a generating AI and have the generating AI perform analysis to improve the accuracy of the analysis.

[0093] The analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated user emotions. For example, if the user is relaxed, the analysis unit will display the analysis results in the normal display order. For example, if the user is in a hurry, the analysis unit will prioritize displaying important analysis results. For example, if the user is excited, the analysis unit will prioritize displaying highly relevant analysis results. In this way, by adjusting the display order of the analysis results according to the user's emotions, important information can be displayed preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0094] The analysis unit can perform analysis while considering the geographical distribution of information. For example, the analysis unit can improve the accuracy of the analysis based on the geographical distribution of information. For example, the analysis unit can select the optimal analysis method while considering the geographical distribution. For example, the analysis unit can prioritize the analysis of highly relevant information based on the geographical distribution. This improves the accuracy of the analysis by considering the geographical distribution of information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input geographical distribution data of information into a generating AI and have the generating AI perform an analysis to improve the accuracy of the analysis.

[0095] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis unit improves the accuracy of its analysis based on relevant literature. For example, the analysis unit selects the optimal analysis method by referring to relevant literature. For example, the analysis unit prioritizes the analysis of highly relevant information based on relevant literature. As a result, the accuracy of the analysis is improved by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform an analysis to improve the accuracy of the analysis.

[0096] The generation unit can estimate the user's emotions and adjust the presentation style of the generated proposal materials based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates proposal materials using a normal presentation style. If the user is in a hurry, for example, the generation unit generates proposal materials using a concise and to-the-point presentation style. If the user is excited, for example, the generation unit generates proposal materials using a visually stimulating presentation style. By adjusting the presentation style of the proposal materials according to the user's emotions, a more effective proposal can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0097] The generation unit can improve the accuracy of its generation by referring to policy information and examples of subsidy acquisition during the generation process. For example, the generation unit improves the accuracy of its generation based on policy information. For example, the generation unit improves the accuracy of its generation by referring to examples of subsidy acquisition. For example, the generation unit improves the accuracy of its generation by combining policy information and examples of subsidy acquisition. As a result, the accuracy of the generation is improved by referring to policy information and examples of subsidy acquisition. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input policy information and examples of subsidy acquisition into a generation AI and have the generation AI perform generation to improve the accuracy of the generation.

[0098] The generation unit can perform generation while considering user attribute information. For example, the generation unit can improve the accuracy of generation based on user attribute information. For example, the generation unit can select the optimal generation method while considering user attribute information. For example, the generation unit can generate highly relevant proposal materials based on user attribute information. This improves the accuracy of generation by considering user attribute information. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user attribute information into a generation AI and have the generation AI perform generation to improve the accuracy of generation.

[0099] The generation unit can estimate the user's emotions and determine the priority of proposal materials to generate based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates proposal materials with normal priority. If the user is in a hurry, for example, the generation unit prioritizes generating important proposal materials. If the user is excited, for example, the generation unit prioritizes generating highly relevant proposal materials. In this way, by determining the priority of proposal materials according to the user's emotions, important proposal materials can be generated preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0100] The generation unit can perform generation while considering the geographical distribution of information. For example, the generation unit can improve the accuracy of generation based on the geographical distribution of information. For example, the generation unit can select the optimal generation method while considering the geographical distribution. For example, the generation unit can generate highly relevant proposal materials based on the geographical distribution. This improves the accuracy of generation by considering the geographical distribution of information. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input geographical distribution data of information into a generation AI and have the generation AI perform generation to improve the accuracy of generation.

[0101] The generation unit can improve the accuracy of generation by referring to relevant literature during generation. For example, the generation unit improves the accuracy of generation based on relevant literature. For example, the generation unit selects the optimal generation method by referring to relevant literature. For example, the generation unit generates highly relevant proposal materials based on relevant literature. In this way, the accuracy of generation is improved by referring to relevant literature. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input relevant literature data into a generation AI and have the generation AI perform generation to improve the accuracy of generation.

[0102] The service provider can estimate the user's emotions and adjust the display method of the proposal materials based on the estimated emotions. For example, if the user is relaxed, the service provider will provide the proposal materials in a normal display method. If the user is in a hurry, the service provider will provide the proposal materials in a concise and to-the-point display method. If the user is excited, the service provider will provide the proposal materials in a visually stimulating display method. By adjusting the display method according to the user's emotions, more effective proposal materials can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0103] The service provider can select the optimal service delivery method by referring to the user's past usage history at the time of delivery. For example, the service provider selects the optimal service delivery method based on the user's past usage history. For example, the service provider adjusts the delivery frequency by referring to the user's past usage history. For example, the service provider analyzes the user's past usage history and optimizes the service delivery method. This allows the service provider to select the optimal service delivery method by referring to past usage history. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past usage data into a generating AI and have the generating AI select the optimal service delivery method.

[0104] The service provider can estimate the user's emotions and determine the priority of proposal materials to provide based on the estimated emotions. For example, if the user is relaxed, the service provider will provide proposal materials with normal priority. If the user is in a hurry, the service provider will prioritize providing important proposal materials. If the user is excited, the service provider will prioritize providing highly relevant proposal materials. This allows for the priority provision of important proposal materials by determining the priority of proposal materials according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0105] The delivery unit can select the optimal delivery method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the delivery unit will select a delivery method that matches the screen size. For example, if the user is using a tablet, the delivery unit will select a delivery method optimized for a large screen. For example, if the user is using a smartwatch, the delivery unit will select a concise and highly visible delivery method. In this way, the optimal delivery method can be selected by taking device information into consideration. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's device information into a generating AI and have the generating AI select the optimal delivery method.

[0106] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0107] The proposal generation system can further estimate the user's emotions and adjust the proposal content based on those emotions. For example, if the user is stressed, the proposal can be made concise and key points emphasized to reduce the user's burden. If the user is relaxed, the system can generate a proposal with detailed information to allow for deeper understanding. Furthermore, if the user is excited, the system can employ a visually appealing design to enhance the impact of the proposal. This allows for proposal content adjustments tailored to the user's emotions, resulting in more effective proposals.

[0108] The proposal generation system can further analyze the user's past proposal creation history and suggest the most suitable proposal template. For example, it can increase the success rate by prioritizing the suggestion of templates that have been successful in the user's past proposals. It can also automatically select and suggest templates that have received particularly high ratings from the user's past use. Furthermore, based on the user's past proposal creation history, it can suggest templates suitable for specific fields or themes. This allows users to create proposals efficiently and improve their success rate.

[0109] The proposal generation system can further estimate the user's emotions and adjust the proposal layout based on those emotions. For example, if the user is nervous, it can provide a simple and highly visible layout to allow the user to easily understand the information. If the user is relaxed, it can provide a layout that includes detailed information to allow the user to understand it more deeply. Furthermore, if the user is excited, it can provide a visually appealing layout to enhance the impact of the proposal. This allows for layout adjustments that respond to the user's emotions, resulting in the creation of more effective proposals.

[0110] The proposal generation system can also provide optimal proposal materials by considering the user's current project progress. For example, in the initial stages of a project, it can provide proposal materials focusing on basic information and an overview. In the middle stages of the project, it can provide proposal materials including detailed data and specific plans. Furthermore, in the final stages of the project, it can provide proposal materials that emphasize results and achievements. This allows for the provision of optimal proposal materials according to the project's progress, thereby improving the success rate of proposals.

[0111] The proposal generation system can further estimate the user's emotions and adjust the proposal design based on those emotions. For example, if the user is stressed, a design with calming colors can be adopted to reduce visual stress. If the user is relaxed, a design with bright colors can be adopted to enhance the proposal's appeal. Furthermore, if the user is excited, a visually stimulating design can be adopted to increase the proposal's impact. This allows for design adjustments that respond to the user's emotions, resulting in the creation of more effective proposals.

[0112] The proposal generation system can further analyze the user's past proposal evaluation data and suggest optimal proposal content. For example, it can automatically generate new proposal content by referencing the content of proposals that received high ratings in the past. It can also improve the quality of proposals by avoiding the content of proposals that received low ratings in the past. Furthermore, it can increase the success rate by prioritizing proposals that have received high ratings in specific fields or themes. This allows users to efficiently create high-quality proposals.

[0113] The proposal generation system can further estimate the user's emotions and adjust the proposal's language style based on those emotions. For example, if the user is stressed, a concise and clear language style can be adopted to allow the user to easily understand the information. If the user is relaxed, a detailed and polite language style can be adopted to allow the user to understand more deeply. Furthermore, if the user is excited, a visually appealing language style can be adopted to enhance the impact of the proposal. This allows for language style adjustments tailored to the user's emotions, resulting in the creation of more effective proposals.

[0114] The proposal generation system can also prioritize providing region-specific subsidy information by considering the user's current location. For example, if a user is in a specific region, it can prioritize displaying subsidy information relevant to that region. Furthermore, if a user is on the move, it can provide the most relevant subsidy information in real time based on their current location. Additionally, if a user is interested in a particular region, it can automatically suggest subsidy information relevant to that region. This allows users to efficiently gather region-specific subsidy information and improve the quality of their proposals.

[0115] The proposal generation system can further estimate the user's emotions and customize the proposal content based on those emotions. For example, if the user is stressed, the proposal can be made concise and key points emphasized to reduce the user's burden. If the user is relaxed, a proposal with detailed information can be generated to allow the user to understand it more deeply. Furthermore, if the user is excited, a visually appealing design can be adopted to enhance the impact of the proposal. This allows for the customization of proposal content according to the user's emotions, resulting in more effective proposals.

[0116] The proposal generation system can further analyze the user's past proposal creation history and suggest the most suitable proposal template. For example, it can increase the success rate by prioritizing the suggestion of templates that have been successful in the user's past proposals. It can also automatically select and suggest templates that have received particularly high ratings from the user's past use. Furthermore, based on the user's past proposal creation history, it can suggest templates suitable for specific fields or themes. This allows users to create proposals efficiently and improve their success rate.

[0117] The following briefly describes the processing flow for example form 2.

[0118] Step 1: The reception desk receives input on social issues and proposal summaries. The reception desk provides an interface for users to input, for example, the social issues they want to solve and the measures or proposal summaries they want to suggest. The reception desk can accept input in the form of text input, voice input, or selection from multiple-choice options. Step 2: The collection department searches for subsidy and grant information based on the information entered by the reception department. The collection department searches for subsidy and grant information from external websites, for example, and collects relevant information. The collection department can collect information from sources such as the official government website, local government websites, and private grant information websites. Step 3: The analysis unit analyzes the information collected by the collection unit and matches policies with subsidies. For example, the analysis unit compares the collected subsidy information with policy information and evaluates their suitability. For example, the analysis unit accumulates policy information and examples of subsidy acquisition to improve the accuracy of the matching. Step 4: The generation unit generates proposal materials based on the matching results obtained by the analysis unit. The generation unit generates, for example, examples of how to include subsidy information in the proposal and sample entries for subsidy application items. The generation unit can also generate, for example, proposal slides. Step 5: The provider unit provides the proposal materials generated by the generator unit. The provider unit provides, for example, an interface for providing the generated proposal materials to the user. The provider unit can display the proposal materials, for example, through a web application or a mobile application.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0121] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] Each of the multiple elements described above, including the reception unit, collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and provides an interface for the user to input the social issues they want to solve and the measures or proposal outlines they want to propose. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and searches for subsidy and grant information from external websites and collects relevant information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and compares the collected subsidy information and policy information to evaluate their suitability. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates examples of how to include subsidy information in a proposal and sample entries for subsidy application items. The provision unit is implemented by the control unit 46A of the smart device 14 and provides an interface for providing the generated proposal materials to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0123] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0124] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements described above, including the reception unit, collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface for the user to input the social issues they want to solve and the measures or proposal outlines they want to propose. The collection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and searches for subsidy and grant information from external websites and collects relevant information. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and compares the collected subsidy information and policy information to evaluate their suitability. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates examples of how to include subsidy information in a proposal and sample entries for subsidy application items. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides an interface for providing the generated proposal materials to the user. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0139] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0140] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0147] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements described above, including the reception unit, collection unit, analysis unit, generation unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for the user to input the social issues they want to solve and the measures or proposal outlines they want to propose. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and searches for subsidy and grant information from external websites and collects relevant information. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and compares the collected subsidy information and policy information to evaluate their suitability. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates examples of how to include subsidy information in a proposal and sample entries for subsidy application items. The provision unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides an interface for providing the generated proposal materials to the user. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0155] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0156] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0159] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0161] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0162] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0163] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0164] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0165] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0166] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0167] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0169] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0171] Each of the multiple elements described above, including the reception unit, collection unit, analysis unit, generation unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and provides an interface for the user to input the social issues they want to solve and the measures or proposal outlines they want to propose. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and searches for subsidy and grant information from external websites and collects relevant information. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and compares the collected subsidy information and policy information to evaluate their suitability. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates examples of how to include subsidy information in a proposal and sample entries for subsidy application items. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides an interface for providing the generated proposal materials to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0172] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0174] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0175] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0176] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0180] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0181] 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.

[0182] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0183] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0184] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0185] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0187] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0188] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0189] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0190] (Note 1) A reception area where you input social issues and proposal summaries, A collection unit searches for subsidy and grant information based on the information entered by the reception unit, The analysis unit analyzes the information collected by the aforementioned collection unit and matches policies with subsidies, A generation unit generates proposal materials based on the matching results obtained by the analysis unit, The system includes a providing unit that provides proposal materials generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, We will accumulate policy information and examples of how to obtain subsidies to improve the accuracy of matching. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generate examples of how to include subsidy information in proposals and sample entries for subsidy application items. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate proposal slides The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Search for subsidy and grant information from external websites. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Provide the generated proposal materials to the user. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the input method for social issues and proposal summaries based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users input social issues and proposal summaries, the system automatically acquires their current location information to simplify the input process. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users input social issues and proposal summaries, the system automatically suggests potential locations based on their past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When inputting social issues and proposal summaries, the system references the user's calendar information to generate proposals based on their schedule. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is We estimate the user's emotions and adjust the data collection timing based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is Analyze past collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is During data collection, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned collection unit is During data collection, the user's social media activity is analyzed to gather relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, we improve the accuracy of the analysis by referring to policy information and examples of subsidy acquisition. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, During analysis, user attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, It estimates the user's emotions and adjusts the display order of the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, During the analysis, the geographical distribution of the information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, During analysis, we refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is It estimates the user's emotions and adjusts the presentation style of the proposal materials generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is During generation, we improve the accuracy of the data by referencing policy information and examples of subsidy acquisition. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is During generation, user attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is It estimates the user's emotions and determines the priority of proposal materials to generate based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is During generation, the geographical distribution of the information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 30) The generating unit is During generation, we refer to relevant literature to improve the accuracy of the generation. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the proposal materials are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing the service, the optimal delivery method is selected by referring to the user's past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of proposal materials to provide based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where you input social issues and proposal summaries, A collection unit searches for subsidy and grant information based on the information entered by the reception unit, The analysis unit analyzes the information collected by the aforementioned collection unit and matches policies with subsidies, A generation unit generates proposal materials based on the matching results obtained by the analysis unit, The system includes a providing unit that provides proposal materials generated by the generation unit. A system characterized by the following features.

2. The aforementioned analysis unit, We will accumulate policy information and examples of how to obtain subsidies to improve the accuracy of matching. The system according to feature 1.

3. The generating unit is Generate examples of how to include subsidy information in proposals and sample entries for subsidy application items. The system according to feature 1.

4. The generating unit is Generate proposal slides The system according to feature 1.

5. The aforementioned collection unit is Search for subsidy and grant information from external websites. The system according to feature 1.

6. The aforementioned supply unit is, Provide the generated proposal materials to the user. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the input method for social issues and proposal summaries based on the estimated user emotions. The system according to feature 1.

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

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