System

Abstract

An object of the system according to the embodiment is to propose an appropriate subsidy based on company information and to support creation of an application document.SOLUTION: A system includes a company information analysis part, a subsidy proposal part, and an application document preparation support part. The company information analyzing unit analyzes the company information by using the generated AI. The subsidy proposing section proposes an appropriate subsidy based on the company information analyzed by the company information analyzing section. The application document preparation support unit supports preparation of an application document for the subsidy proposed by the subsidy proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the process of finding appropriate subsidies based on company information and preparing application documents was complicated, time-consuming, and labor-intensive.

[0005] The system according to the embodiment aims to propose appropriate subsidies based on company information and to support the preparation of application documents. [Means for solving the problem]

[0006] The system according to the embodiment includes a company information analysis unit, a subsidy proposal unit, and an application document preparation support unit. The company information analysis unit analyzes company information using a generation AI. The subsidy proposal unit proposes appropriate subsidies based on the company information analyzed by the company information analysis unit. The application document preparation support unit supports the preparation of application documents for the subsidies proposed by the subsidy proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose appropriate subsidies based on company information and assist in the preparation of application documents. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A subsidy proposal system according to an embodiment of the present invention is a system that uses a generation AI to input company information, proposes appropriate subsidies, and supports the creation of application documents for the selected subsidy. This allows the subsidy proposal system to analyze company information, propose optimal subsidies, and support the creation of application documents.

[0029] A subsidy proposal system according to an embodiment includes a company information analysis unit, a subsidy proposal unit, and an application document creation support unit. The company information analysis unit analyzes company information using a generation AI. For example, the company information analysis unit analyzes the company's industry, size, location, business activities, financial status, etc. The company information analysis unit can also analyze this information in detail so that the generation AI can understand the company's characteristics and needs. The subsidy proposal unit proposes appropriate subsidies based on the company information analyzed by the company information analysis unit. For example, the subsidy proposal unit uses the generation AI to list subsidies that meet the company's needs, such as "subsidies for new product development" or "subsidies for environmental measures." The subsidy proposal unit can also propose the optimal subsidy based on the company's characteristics. The application document creation support unit supports the creation of application documents for the subsidies proposed by the subsidy proposal unit. For example, the application document creation support unit uses the generation AI to collect information required for the application documents from a user and automatically generate application documents based on that information. In addition, the application document preparation support unit allows the generation AI to make final adjustments to the application documents by reflecting the user's input. As a result, the subsidy proposal system according to the embodiment can analyze company information, propose appropriate subsidies, and support the preparation of application documents.

[0030] The company information analysis department can refer to past subsidy application data to identify application patterns with a high success rate and reflect them in the analysis. For example, the generation AI in the company information analysis department analyzes past subsidy application data to identify application patterns with a high success rate. For example, it analyzes what types of subsidies companies of a particular industry and size have successfully received and reflects these patterns in new analyses. The generation AI in the company information analysis department also extracts characteristics of application documents with a high success rate based on past subsidy application data and uses them in analyzing company information. For example, it identifies that application documents containing specific keywords or phrases are more likely to be successful and reflects this information in the analysis. The generation AI in the company information analysis department also uses data mining technology to refer to past subsidy application data and identify application patterns with a high success rate. For example, it uses clustering and association rules to extract successful patterns and reflect them in the analysis. Reflecting application patterns with a high success rate in the analysis improves the accuracy of subsidy proposals.

[0031] The Company Information Analysis Department can propose optimal subsidies from a risk management perspective, taking into account industry-specific risk factors and market trends. For example, the Company Information Analysis Department's generation AI analyzes industry-specific risk factors and proposes optimal subsidies based on those analysis results. For example, it considers supply chain risks and technological innovation risks in the manufacturing industry and proposes subsidies that will help mitigate those risks. The Company Information Analysis Department's generation AI also analyzes market trends and compares them with company information to propose optimal subsidies. For example, it analyzes trends in growing and shrinking markets and proposes subsidies that are suitable for growing markets. The Company Information Analysis Department's generation AI also proposes subsidies from a risk management perspective, taking into account industry-specific risk factors. For example, it considers environmental risks and legal and regulatory risks and proposes subsidies to mitigate those risks. In this way, optimal subsidies are proposed from a risk management perspective, thereby reducing corporate risk.

[0032] The company information analysis department can perform a comparative analysis with other companies and propose subsidies to enhance competitive advantage. For example, the generation AI in the company information analysis department collects information on other companies and performs a comparative analysis to propose subsidies to enhance competitive advantage. For example, it analyzes the subsidy receipt status of competitors in the same industry and proposes subsidies to strengthen competitiveness. In addition, the generation AI in the company information analysis department performs a comparative analysis with other companies and proposes subsidies to enhance competitive advantage. For example, it analyzes the technology adoption status of competitors in the same industry and proposes subsidies to support technological innovation. In addition, the generation AI in the company information analysis department performs a comparative analysis with other companies and proposes subsidies to enhance competitive advantage. For example, it analyzes the market share and growth rate of competitors in the same industry and proposes subsidies to support growth. In this way, proposing subsidies to enhance competitive advantage strengthens the company's competitiveness.

[0033] The Company Information Analysis Department can propose subsidies that support sustainable management, taking into consideration corporate social responsibility (CSR) activities and environmental initiatives. For example, the Company Information Analysis Department uses a generation AI to analyze a company's CSR activities and environmental initiatives and propose subsidies that support sustainable management. For example, it proposes subsidies for environmental measures to a company that is engaged in environmental protection activities. The Company Information Analysis Department also uses a generation AI to analyze a company's CSR activities and propose subsidies that support sustainable management. For example, it proposes subsidies for regional development to a company that is engaged in activities that contribute to the local community. The Company Information Analysis Department also uses a generation AI to analyze a company's environmental initiatives and propose subsidies that support sustainable management. For example, it proposes subsidies for energy efficiency to a company that is promoting the introduction of renewable energy. In this way, the company fulfills its social responsibility by proposing subsidies that support sustainable management.

[0034] The subsidy proposal unit can refer to past subsidy receipt history and prioritize proposing subsidies that have a proven track record. For example, the subsidy proposal unit uses the generation AI to analyze past subsidy receipt history and prioritize proposing subsidies that have a proven track record. For example, it can refer to subsidies received by other companies in the same industry and propose subsidies with a high success rate. The subsidy proposal unit also uses the generation AI to prioritize proposing subsidies that have a proven track record based on past subsidy receipt history. For example, it can prioritize listing subsidies received by companies of a specific industry or size. The subsidy proposal unit also uses the generation AI to refer to past subsidy receipt history and prioritize proposing subsidies that have a proven track record. For example, it can analyze the success factors of subsidies that have been received in the past and make proposals based on that information. In this way, by prioritizing proposing subsidies that have a proven track record, it is possible to propose subsidies with a high success rate.

[0035] The subsidy proposal unit can propose subsidies with a high probability of success, taking into account the application conditions and competition rate for the subsidy. For example, the generation AI in the subsidy proposal unit analyzes the application conditions for the subsidy and proposes subsidies with a high probability of success. For example, it prioritizes listing subsidies with lenient application conditions and low competition rate. The subsidy proposal unit also analyzes the competition rate for the subsidy and proposes subsidies with a high probability of success. For example, it predicts the competition rate based on past application data and proposes subsidies with a high probability of success. The subsidy proposal unit also considers the application conditions and competition rate for the subsidy and proposes subsidies with a high probability of success. For example, it prioritizes proposing subsidies that are easy for companies that meet certain conditions to receive. In this way, by proposing subsidies with a high probability of success, the success rate of companies receiving subsidies is improved.

[0036] The subsidy proposal unit can also refer to subsidy information from different industries and regions to provide a wide range of options. For example, the generation AI can refer to subsidy information from different industries to provide a wide range of options. For example, it can list subsidies not only for the manufacturing industry but also for the service and IT industries. The subsidy proposal unit can also refer to subsidy information from different regions to provide a wide range of options. For example, it can propose not only nationwide subsidies but also subsidies from local governments. The subsidy proposal unit can also refer to subsidy information from different industries and regions to provide a wide range of options. For example, it can list subsidies that are specialized for specific regions or industries. This allows for a wide range of options to be provided, meeting the diverse needs of companies.

[0037] The subsidy proposal unit can propose subsidies according to a company's growth stage (startup, growth period, mature period). For example, the generation AI in the subsidy proposal unit analyzes a company's growth stage and proposes subsidies for startups. For example, it lists subsidies to support new business launches. The generation AI in the subsidy proposal unit also analyzes a company's growth stage and proposes subsidies for the growth period. For example, it lists subsidies to support business expansion and capital investment. The generation AI in the subsidy proposal unit also analyzes a company's growth stage and proposes subsidies for the mature period. For example, it lists subsidies to support business continuity and technological innovation. In this way, the growth of companies can be supported by proposing subsidies according to their growth stage.

[0038] The application document creation support unit can refer to past success cases and provide application document templates with a high success rate. For example, the generation AI in the application document creation support unit analyzes past success cases and provides application document templates with a high success rate. For example, a template is created based on the application document format that has been successful for companies of a particular industry and size. The application document creation support unit also provides application document templates with a high success rate based on past success cases. For example, it provides application document templates that include specific keywords or phrases. The application document creation support unit also provides application document templates with a high success rate by referring to past success cases. For example, it analyzes the structure and content of successful application documents and creates templates based on that information. In this way, by providing application document templates with a high success rate, the quality of application documents is improved.

[0039] The application document creation support unit can automatically check legal requirements and regulations to ensure compliance. For example, when the generation AI creates application documents, the application document creation support unit automatically checks legal requirements and regulations to ensure compliance. For example, it lists the legal requirements necessary for application documents and provides a checklist. The application document creation support unit also automatically checks legal requirements and regulations to ensure compliance when the generation AI creates application documents. For example, it automatically verifies whether the contents of the application documents meet the legal requirements. The application document creation support unit also automatically checks legal requirements and regulations to ensure compliance when the generation AI creates application documents. For example, it adjusts the content of the application documents based on the latest legal and regulatory information. In this way, compliance can be ensured by automatically checking legal requirements and regulations.

[0040] The application document creation support department can refer to the success stories of other companies and incorporate best practices. For example, the generation AI in the application document creation support department analyzes the success stories of other companies and incorporates best practices when creating application documents. For example, it refers to the format and content of successful application documents. The application document creation support department also incorporates best practices when creating application documents based on the success stories of other companies. For example, it reflects the characteristics of application documents that have been successful for companies of a particular industry or size. The application document creation support department also incorporates best practices when creating application documents based on the success stories of other companies. For example, it analyzes the structure and content of successful application documents and creates application documents based on that information. In this way, incorporating best practices improves the quality of application documents.

[0041] The application document creation support unit can reflect user feedback in real time to improve the quality of application documents. For example, when the generation AI creates application documents, the application document creation support unit collects user feedback in real time and improves the quality of the application documents based on the results. For example, the application document creation support unit modifies the application documents by reflecting the user's opinions. The application document creation support unit also reflects user feedback in real time when the generation AI creates application documents to improve the quality of the application documents. For example, it adjusts the content of the application documents based on the user's evaluation. The application document creation support unit also reflects user feedback in real time when the generation AI creates application documents to improve the quality of the application documents. For example, it identifies areas for improvement in the application documents based on the user's emotional reactions. In this way, the quality of the application documents is improved by reflecting user feedback in real time.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The subsidy proposal system can also analyze the user's past subsidy application history, extract characteristics of successful applications, and reflect them in new proposals. For example, it can analyze the structure and content of past successful application documents and use that information to support the creation of new application documents. It can also predict the success rate for specific subsidies based on past application history and prioritize proposals for subsidies with a high success rate. Furthermore, it can refer to past application history to create a list of successful subsidies under similar conditions and propose them to the user. This allows for more accurate subsidy proposals to be made by utilizing past success cases.

[0044] The subsidy proposal system can also propose subsidies according to a company's stage of growth. For example, it can propose subsidies to support new business launches to startup companies, and subsidies to support business expansion and capital investment to companies in the growth stage. It can also propose subsidies to support business continuity and technological innovation to mature companies. Furthermore, by proposing subsidies according to a company's stage of growth, it is possible to support corporate growth and help raise funds at the appropriate time. This makes it possible to propose optimal subsidies according to a company's stage of growth, thereby effectively supporting corporate growth.

[0045] The subsidy proposal system can also refer to subsidy information for different industries and regions to provide a wide range of options. For example, it can list and propose subsidies not only for the manufacturing industry but also for the service and IT industries. It can also propose subsidies from local governments in addition to nationwide subsidies. Furthermore, it can respond to the diverse needs of companies by listing subsidies specialized for specific regions or industries and providing them to users. This allows it to provide a wide range of options to meet the diverse needs of companies and make optimal subsidy proposals.

[0046] The subsidy proposal system also takes into consideration corporate social responsibility (CSR) activities and environmental initiatives, and can propose subsidies that support sustainable management. For example, it can propose environmental subsidies to companies engaged in environmental protection activities. It can also propose regional development subsidies to companies that contribute to the local community. It can also propose energy efficiency subsidies to companies that are promoting the introduction of renewable energy. In this way, companies can fulfill their social responsibility by proposing subsidies that support sustainable management.

[0047] The subsidy proposal system can also perform comparative analysis with other companies and propose subsidies to enhance competitive advantage. For example, it can analyze the subsidy status of competitors and propose subsidies to strengthen competitiveness. It can also analyze the technology adoption status of competitors and propose subsidies to support technological innovation. It can also analyze the market share and growth rate of competitors and propose subsidies to support growth. In this way, it is possible to strengthen a company's competitiveness by proposing subsidies to enhance competitive advantage.

[0048] The subsidy proposal system can also automatically check legal requirements and regulations in the application document preparation support section to ensure compliance. For example, it can list the legal requirements necessary for application documents and provide a checklist. It can also automatically verify whether the contents of application documents meet legal requirements. Furthermore, by adjusting the contents of application documents based on the latest legal and regulatory information, it can automatically check legal requirements and regulations to ensure compliance. This makes it possible to ensure compliance by automatically checking legal requirements and regulations.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The Company Information Analysis Department uses the Generative AI to analyze company information. For example, the Company Information Analysis Department analyzes the company's industry, size, location, business activities, financial status, etc. The Generative AI can then analyze this information in detail to understand the company's characteristics and needs. Step 2: The Subsidy Proposal Department proposes appropriate subsidies based on the company information analyzed by the Company Information Analysis Department. For example, the Generation AI will list subsidies that meet the company's needs, such as "subsidies for new product development" or "subsidies for environmental measures." The Generation AI can also propose optimal subsidies based on the company's characteristics. Step 3: The application document creation support unit supports the creation of application documents for the subsidy proposed by the subsidy proposal unit. For example, the generation AI collects information required for the application documents from the user and automatically generates the application documents based on that information. The generation AI can also make final adjustments to the application documents by reflecting the user's input.

[0051] (Example 2) A subsidy proposal system according to an embodiment of the present invention is a system that uses a generation AI to input company information, proposes appropriate subsidies, and supports the creation of application documents for the selected subsidy. This allows the subsidy proposal system to analyze company information, propose optimal subsidies, and support the creation of application documents.

[0052] A subsidy proposal system according to an embodiment includes a company information analysis unit, a subsidy proposal unit, and an application document creation support unit. The company information analysis unit analyzes company information using a generation AI. For example, the company information analysis unit analyzes the company's industry, size, location, business activities, financial status, etc. The company information analysis unit can also analyze this information in detail so that the generation AI can understand the company's characteristics and needs. The subsidy proposal unit proposes appropriate subsidies based on the company information analyzed by the company information analysis unit. For example, the subsidy proposal unit uses the generation AI to list subsidies that meet the company's needs, such as "subsidies for new product development" or "subsidies for environmental measures." The subsidy proposal unit can also propose the optimal subsidy based on the company's characteristics. The application document creation support unit supports the creation of application documents for the subsidies proposed by the subsidy proposal unit. For example, the application document creation support unit uses the generation AI to collect information required for the application documents from a user and automatically generate application documents based on that information. In addition, the application document preparation support unit allows the generation AI to make final adjustments to the application documents by reflecting the user's input. As a result, the subsidy proposal system according to the embodiment can analyze company information, propose appropriate subsidies, and support the preparation of application documents.

[0053] The company information analysis department can refer to past subsidy application data to identify application patterns with a high success rate and reflect them in the analysis. For example, the generation AI in the company information analysis department analyzes past subsidy application data to identify application patterns with a high success rate. For example, it analyzes what types of subsidies companies of a particular industry and size have successfully received and reflects these patterns in new analyses. The generation AI in the company information analysis department also extracts characteristics of application documents with a high success rate based on past subsidy application data and uses them in analyzing company information. For example, it identifies that application documents containing specific keywords or phrases are more likely to be successful and reflects this information in the analysis. The generation AI in the company information analysis department also uses data mining technology to refer to past subsidy application data and identify application patterns with a high success rate. For example, it uses clustering and association rules to extract successful patterns and reflect them in the analysis. Reflecting application patterns with a high success rate in the analysis improves the accuracy of subsidy proposals.

[0054] The Company Information Analysis Department can propose optimal subsidies from a risk management perspective, taking into account industry-specific risk factors and market trends. For example, the Company Information Analysis Department's generation AI analyzes industry-specific risk factors and proposes optimal subsidies based on those analysis results. For example, it considers supply chain risks and technological innovation risks in the manufacturing industry and proposes subsidies that will help mitigate those risks. The Company Information Analysis Department's generation AI also analyzes market trends and compares them with company information to propose optimal subsidies. For example, it analyzes trends in growing and shrinking markets and proposes subsidies that are suitable for growing markets. The Company Information Analysis Department's generation AI also proposes subsidies from a risk management perspective, taking into account industry-specific risk factors. For example, it considers environmental risks and legal and regulatory risks and proposes subsidies to mitigate those risks. In this way, optimal subsidies are proposed from a risk management perspective, thereby reducing corporate risk.

[0055] The company information analysis unit can use the emotion estimation function to analyze emotions regarding the company information entered by the user and provide information input support to elicit positive emotions. The company information analysis unit, for example, uses the emotion estimation function to analyze emotions regarding the company information entered by the user in real time and provides feedback to elicit positive emotions. For example, it displays an encouraging message in response to the input content. The company information analysis unit also uses the emotion estimation function to analyze emotions regarding the company information entered by the user and provides advice to reduce negative emotions. For example, it suggests improvements to the input content. The company information analysis unit also uses the emotion estimation function to analyze emotions regarding the company information entered by the user and provides an interface to elicit positive emotions. For example, it displays positive feedback in real time according to the input content. This improves the user's input experience by providing information input support to elicit positive emotions.

[0056] The company information analysis department can perform a comparative analysis with other companies and propose subsidies to enhance competitive advantage. For example, the generation AI in the company information analysis department collects information on other companies and performs a comparative analysis to propose subsidies to enhance competitive advantage. For example, it analyzes the subsidy receipt status of competitors in the same industry and proposes subsidies to strengthen competitiveness. In addition, the generation AI in the company information analysis department performs a comparative analysis with other companies and proposes subsidies to enhance competitive advantage. For example, it analyzes the technology adoption status of competitors in the same industry and proposes subsidies to support technological innovation. In addition, the generation AI in the company information analysis department performs a comparative analysis with other companies and proposes subsidies to enhance competitive advantage. For example, it analyzes the market share and growth rate of competitors in the same industry and proposes subsidies to support growth. In this way, proposing subsidies to enhance competitive advantage strengthens the company's competitiveness.

[0057] The Company Information Analysis Department can propose subsidies that support sustainable management, taking into consideration corporate social responsibility (CSR) activities and environmental initiatives. For example, the Company Information Analysis Department uses a generation AI to analyze a company's CSR activities and environmental initiatives and propose subsidies that support sustainable management. For example, it proposes subsidies for environmental measures to a company that is engaged in environmental protection activities. The Company Information Analysis Department also uses a generation AI to analyze a company's CSR activities and propose subsidies that support sustainable management. For example, it proposes subsidies for regional development to a company that is engaged in activities that contribute to the local community. The Company Information Analysis Department also uses a generation AI to analyze a company's environmental initiatives and propose subsidies that support sustainable management. For example, it proposes subsidies for energy efficiency to a company that is promoting the introduction of renewable energy. In this way, the company fulfills its social responsibility by proposing subsidies that support sustainable management.

[0058] The company information analysis unit can use the emotion estimation function to provide feedback on emotions regarding the company information entered by the user in real time, thereby encouraging improvement of the input content. For example, the company information analysis unit uses the emotion estimation function to analyze emotions regarding the company information entered by the user in real time and provide feedback. For example, if negative emotions are detected, the company information analysis unit suggests areas for improvement. The company information analysis unit also uses the emotion estimation function to provide feedback on emotions regarding the company information entered by the user in real time, thereby encouraging improvement of the input content. For example, advice on how to elicit positive emotions is provided. The company information analysis unit also uses the emotion estimation function to analyze emotions regarding the company information entered by the user in real time, thereby encouraging improvement of the input content. For example, specific improvement suggestions for the input content are displayed in real time. This encourages improvement of the input content, thereby improving the user's input experience.

[0059] The subsidy proposal unit can refer to past subsidy receipt history and prioritize proposing subsidies that have a proven track record. For example, the subsidy proposal unit uses the generation AI to analyze past subsidy receipt history and prioritize proposing subsidies that have a proven track record. For example, it can refer to subsidies received by other companies in the same industry and propose subsidies with a high success rate. The subsidy proposal unit also uses the generation AI to prioritize proposing subsidies that have a proven track record based on past subsidy receipt history. For example, it can prioritize listing subsidies received by companies of a specific industry or size. The subsidy proposal unit also uses the generation AI to refer to past subsidy receipt history and prioritize proposing subsidies that have a proven track record. For example, it can analyze the success factors of subsidies that have been received in the past and make proposals based on that information. In this way, by prioritizing proposing subsidies that have a proven track record, it is possible to propose subsidies with a high success rate.

[0060] The subsidy proposal unit can propose subsidies with a high probability of success, taking into account the application conditions and competition rate for the subsidy. For example, the generation AI in the subsidy proposal unit analyzes the application conditions for the subsidy and proposes subsidies with a high probability of success. For example, it prioritizes listing subsidies with lenient application conditions and low competition rate. The subsidy proposal unit also analyzes the competition rate for the subsidy and proposes subsidies with a high probability of success. For example, it predicts the competition rate based on past application data and proposes subsidies with a high probability of success. The subsidy proposal unit also considers the application conditions and competition rate for the subsidy and proposes subsidies with a high probability of success. For example, it prioritizes proposing subsidies that are easy for companies that meet certain conditions to receive. In this way, by proposing subsidies with a high probability of success, the success rate of companies receiving subsidies is improved.

[0061] The subsidy proposal unit can use the emotion estimation function to analyze the emotion the user has toward the subsidy proposal and preferentially propose subsidies that elicit positive emotions. The subsidy proposal unit, for example, uses the emotion estimation function to analyze the emotion the user has toward the subsidy proposal and preferentially propose subsidies that elicit positive emotions. For example, it proposes subsidies that are likely to interest the user. The subsidy proposal unit also uses the emotion estimation function to analyze the emotion the user has toward the subsidy proposal and preferentially propose subsidies that elicit positive emotions. For example, it lists subsidies that the user is highly interested in. The subsidy proposal unit also uses the emotion estimation function to analyze the emotion the user has toward the subsidy proposal and preferentially propose subsidies that elicit positive emotions. For example, it preferentially proposes subsidies with a high user emotion score. In this way, user satisfaction is improved by proposing subsidies that elicit positive emotions.

[0062] The subsidy proposal unit can also refer to subsidy information from different industries and regions to provide a wide range of options. For example, the generation AI can refer to subsidy information from different industries to provide a wide range of options. For example, it can list subsidies not only for the manufacturing industry but also for the service and IT industries. The subsidy proposal unit can also refer to subsidy information from different regions to provide a wide range of options. For example, it can propose not only nationwide subsidies but also subsidies from local governments. The subsidy proposal unit can also refer to subsidy information from different industries and regions to provide a wide range of options. For example, it can list subsidies that are specialized for specific regions or industries. This allows for a wide range of options to be provided, meeting the diverse needs of companies.

[0063] The subsidy proposal unit can propose subsidies according to a company's growth stage (startup, growth period, mature period). For example, the generation AI in the subsidy proposal unit analyzes a company's growth stage and proposes subsidies for startups. For example, it lists subsidies to support new business launches. The generation AI in the subsidy proposal unit also analyzes a company's growth stage and proposes subsidies for the growth period. For example, it lists subsidies to support business expansion and capital investment. The generation AI in the subsidy proposal unit also analyzes a company's growth stage and proposes subsidies for the mature period. For example, it lists subsidies to support business continuity and technological innovation. In this way, the growth of companies can be supported by proposing subsidies according to their growth stage.

[0064] The subsidy proposal unit can use the emotion estimation function to provide feedback on the emotions the user has toward the subsidy proposal in real time, and encourage improvement of the proposal content. For example, the subsidy proposal unit uses the emotion estimation function to analyze the emotions the user has toward the subsidy proposal in real time and provide feedback. For example, if negative emotions are detected, the proposal content is improved. The subsidy proposal unit also uses the emotion estimation function to provide feedback on the emotions the user has toward the subsidy proposal in real time, and encourage improvement of the proposal content. For example, it makes suggestions to elicit positive emotions. The subsidy proposal unit also uses the emotion estimation function to analyze the emotions the user has toward the subsidy proposal in real time, and encourage improvement of the proposal content. For example, it dynamically adjusts the proposal content based on the user's emotion score. This encourages improvement of the proposal content, thereby improving user satisfaction.

[0065] The application document creation support unit can refer to past success cases and provide application document templates with a high success rate. For example, the generation AI in the application document creation support unit analyzes past success cases and provides application document templates with a high success rate. For example, a template is created based on the application document format that has been successful for companies of a particular industry and size. The application document creation support unit also provides application document templates with a high success rate based on past success cases. For example, it provides application document templates that include specific keywords or phrases. The application document creation support unit also provides application document templates with a high success rate by referring to past success cases. For example, it analyzes the structure and content of successful application documents and creates templates based on that information. In this way, by providing application document templates with a high success rate, the quality of application documents is improved.

[0066] The application document creation support unit can automatically check legal requirements and regulations to ensure compliance. For example, when the generation AI creates application documents, the application document creation support unit automatically checks legal requirements and regulations to ensure compliance. For example, it lists the legal requirements necessary for application documents and provides a checklist. The application document creation support unit also automatically checks legal requirements and regulations to ensure compliance when the generation AI creates application documents. For example, it automatically verifies whether the contents of the application documents meet the legal requirements. The application document creation support unit also automatically checks legal requirements and regulations to ensure compliance when the generation AI creates application documents. For example, it adjusts the content of the application documents based on the latest legal and regulatory information. In this way, compliance can be ensured by automatically checking legal requirements and regulations.

[0067] The application document creation support unit can use the emotion estimation function to analyze the emotions felt by the user while creating the application documents and provide support for reducing stress. For example, the application document creation support unit can use the emotion estimation function to analyze the emotions felt by the user while creating the application documents in real time and provide feedback to reduce stress. For example, it can display an encouraging message. The application document creation support unit can also use the emotion estimation function to analyze the emotions felt by the user while creating the application documents and provide advice to reduce stress. For example, it can make suggestions for relaxation. The application document creation support unit can also use the emotion estimation function to analyze the emotions felt by the user while creating the application documents in real time and provide support for reducing stress. For example, it can provide an interface to elicit positive emotions. This can improve the user's application document creation experience by providing support for reducing stress.

[0068] The application document creation support department can refer to the success stories of other companies and incorporate best practices. For example, the generation AI in the application document creation support department analyzes the success stories of other companies and incorporates best practices when creating application documents. For example, it refers to the format and content of successful application documents. The application document creation support department also incorporates best practices when creating application documents based on the success stories of other companies. For example, it reflects the characteristics of application documents that have been successful for companies of a particular industry or size. The application document creation support department also incorporates best practices when creating application documents based on the success stories of other companies. For example, it analyzes the structure and content of successful application documents and creates application documents based on that information. In this way, incorporating best practices improves the quality of application documents.

[0069] The application document creation support unit can reflect user feedback in real time to improve the quality of application documents. For example, when the generation AI creates application documents, the application document creation support unit collects user feedback in real time and improves the quality of the application documents based on the results. For example, the application document creation support unit modifies the application documents by reflecting the user's opinions. The application document creation support unit also reflects user feedback in real time when the generation AI creates application documents to improve the quality of the application documents. For example, it adjusts the content of the application documents based on the user's evaluation. The application document creation support unit also reflects user feedback in real time when the generation AI creates application documents to improve the quality of the application documents. For example, it identifies areas for improvement in the application documents based on the user's emotional reactions. In this way, the quality of the application documents is improved by reflecting user feedback in real time.

[0070] The application document creation support unit can use the emotion estimation function to provide real-time feedback on the emotions felt by the user while creating application documents, thereby encouraging improvements to the creation process. For example, the application document creation support unit uses the emotion estimation function to analyze the emotions felt by the user while creating application documents in real time and provide feedback. For example, if negative emotions are detected, the application document creation support unit suggests improvements to the creation process. The application document creation support unit also uses the emotion estimation function to provide real-time feedback on the emotions felt by the user while creating application documents, thereby encouraging improvements to the creation process. For example, the application document creation support unit provides advice on how to elicit positive emotions. The application document creation support unit also uses the emotion estimation function to analyze the emotions felt by the user while creating application documents in real time and encourages improvements to the creation process. For example, the application document creation support unit dynamically adjusts the creation process based on the user's emotion score. This encourages improvements to the creation process, thereby improving the user's application document creation experience.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The subsidy proposal system can also analyze the user's past subsidy application history, extract characteristics of successful applications, and reflect them in new proposals. For example, it can analyze the structure and content of past successful application documents and use that information to support the creation of new application documents. It can also predict the success rate for specific subsidies based on past application history and prioritize proposals for subsidies with a high success rate. Furthermore, it can refer to past application history to create a list of successful subsidies under similar conditions and propose them to the user. This allows for more accurate subsidy proposals to be made by utilizing past success cases.

[0073] The subsidy proposal system can also estimate the user's emotions and dynamically adjust the content of proposals. For example, if the user expresses positive emotions toward a subsidy proposal, the system will prioritize proposing that subsidy. On the other hand, if negative emotions are detected, the system can suggest a different subsidy, thereby improving user satisfaction. Furthermore, the system can adjust the content of proposals in real time based on the user's emotion score and suggest the optimal subsidy. This enables flexible proposals according to the user's emotions, improving the accuracy of subsidy proposals and user satisfaction.

[0074] The subsidy proposal system can also propose subsidies according to a company's stage of growth. For example, it can propose subsidies to support new business launches to startup companies, and subsidies to support business expansion and capital investment to companies in the growth stage. It can also propose subsidies to support business continuity and technological innovation to mature companies. Furthermore, by proposing subsidies according to a company's stage of growth, it is possible to support corporate growth and help raise funds at the appropriate time. This makes it possible to propose optimal subsidies according to a company's stage of growth, thereby effectively supporting corporate growth.

[0075] The subsidy proposal system can also refer to subsidy information for different industries and regions to provide a wide range of options. For example, it can list and propose subsidies not only for the manufacturing industry but also for the service and IT industries. It can also propose subsidies from local governments in addition to nationwide subsidies. Furthermore, it can respond to the diverse needs of companies by listing subsidies specialized for specific regions or industries and providing them to users. This allows it to provide a wide range of options to meet the diverse needs of companies and make optimal subsidy proposals.

[0076] The subsidy proposal system can also use an emotion estimation function to provide real-time feedback on the user's emotions regarding the subsidy proposal, encouraging them to improve the proposal. For example, if negative emotions are detected, the proposal can be improved. It can also make suggestions to elicit positive emotions. Furthermore, based on the user's emotion score, the proposal can be dynamically adjusted to suggest the optimal subsidy. This can improve the proposal and increase user satisfaction.

[0077] The subsidy proposal system also takes into consideration corporate social responsibility (CSR) activities and environmental initiatives, and can propose subsidies that support sustainable management. For example, it can propose environmental subsidies to companies engaged in environmental protection activities. It can also propose regional development subsidies to companies that contribute to the local community. It can also propose energy efficiency subsidies to companies that are promoting the introduction of renewable energy. In this way, companies can fulfill their social responsibility by proposing subsidies that support sustainable management.

[0078] The subsidy proposal system also uses an emotion estimation function to provide real-time feedback on the emotions users have regarding the company information they enter, encouraging them to improve their input. For example, if negative emotions are detected, the system can suggest areas for improvement. It can also provide advice on how to elicit positive emotions. Furthermore, by displaying specific suggestions for improvement regarding the input content in real time, the system can improve the user's input experience. This can improve the user's input experience by encouraging them to improve their input content.

[0079] The subsidy proposal system can also perform comparative analysis with other companies and propose subsidies to enhance competitive advantage. For example, it can analyze the subsidy status of competitors and propose subsidies to strengthen competitiveness. It can also analyze the technology adoption status of competitors and propose subsidies to support technological innovation. It can also analyze the market share and growth rate of competitors and propose subsidies to support growth. In this way, it is possible to strengthen a company's competitiveness by proposing subsidies to enhance competitive advantage.

[0080] The grant proposal system can also use the emotion estimation function to provide real-time feedback on the emotions felt by the user while creating the application document, encouraging them to improve the creation process. For example, if negative emotions are detected, the system can suggest improvements to the creation process. It can also provide advice on how to elicit positive emotions. Furthermore, the system can dynamically adjust the creation process based on the user's emotion score, improving the user's application document creation experience. This can improve the user's application document creation experience by encouraging them to improve the creation process.

[0081] The subsidy proposal system can also automatically check legal requirements and regulations in the application document preparation support section to ensure compliance. For example, it can list the legal requirements necessary for application documents and provide a checklist. It can also automatically verify whether the contents of application documents meet legal requirements. Furthermore, by adjusting the contents of application documents based on the latest legal and regulatory information, it can automatically check legal requirements and regulations to ensure compliance. This makes it possible to ensure compliance by automatically checking legal requirements and regulations.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The Company Information Analysis Department uses the Generative AI to analyze company information. For example, the Company Information Analysis Department analyzes the company's industry, size, location, business activities, financial status, etc. The Generative AI can then analyze this information in detail to understand the company's characteristics and needs. Step 2: The Subsidy Proposal Department proposes appropriate subsidies based on the company information analyzed by the Company Information Analysis Department. For example, the Generation AI will list subsidies that meet the company's needs, such as "subsidies for new product development" or "subsidies for environmental measures." The Generation AI can also propose optimal subsidies based on the company's characteristics. Step 3: The application document creation support unit supports the creation of application documents for the subsidy proposed by the subsidy proposal unit. For example, the generation AI collects information required for the application documents from the user and automatically generates the application documents based on that information. The generation AI can also make final adjustments to the application documents by reflecting the user's input.

[0084] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0090] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0094] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0095] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0098] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0125] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0134] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0135] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0136] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0139] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0140] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0141] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0142] 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.

[0143] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0144] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0145] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0146] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0147] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0148] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A company information analysis department that uses generative AI to analyze company information; a subsidy proposal unit that proposes appropriate subsidies based on the company information analyzed by the company information analysis unit; an application document preparation support unit that supports the preparation of application documents for the subsidy proposed by the subsidy proposal unit. A system characterized by:

2. The company information analysis unit Referencing past grant application data, identifying application patterns with high success rates and incorporating them into analysis 2. The system of claim 1.

3. The company information analysis unit We consider industry-specific risk factors and market trends to propose optimal subsidies from a risk management perspective.

2. The system of claim 1.

4. The company information analysis unit Analyzes the emotions users have about the company information they input, and provides support for inputting information to elicit positive emotions.

2. The system of claim 1.

5. The company information analysis unit Conduct comparative analysis with other companies and propose the above subsidies to enhance your competitive advantage 2. The system of claim 1.

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