system

Abstract

An object of the system according to the embodiment is to allow the NPO to efficiently perform the procedure for acquiring the certification.SOLUTION: A system according to an embodiment includes an information acquisition unit, a document generation unit, a check unit, and a submission support unit. The information acquisition unit acquires information provided from the NPO organization. The document generation unit generates a document necessary for acquisition of authorization based on the information acquired by the information acquisition unit. The check unit checks the document generated by the document generation unit and makes a correction proposal. The submission support unit supports submission of the document checked by the check unit to the administration.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, it was difficult for NPOs to efficiently complete the procedures for obtaining certification, making it difficult to balance this with support activities.

[0005] The system according to the embodiment aims to enable NPO organizations to efficiently complete the procedures for obtaining certification. [Means for solving the problem]

[0006] The system according to the embodiment includes an information acquisition unit, a document generation unit, a check unit, and a submission support unit. The information acquisition unit acquires information provided by an NPO organization. The document generation unit generates documents necessary for obtaining certification based on the information acquired by the information acquisition unit. The check unit checks the documents generated by the document generation unit and suggests corrections. The submission support unit provides support for submitting the documents checked by the check unit to the government. [Effects of the Invention]

[0007] The system according to the embodiment allows NPO organizations to efficiently complete the procedures for obtaining certification. [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) The certification acquisition support system according to an embodiment of the present invention is a system in which a generation AI automatically generates documents based on information provided by NPO organizations, checks them, and supports their submission. As a result, the certification acquisition support system reduces the effort required for NPO organizations to obtain certification, as well as the burden on the administrative side.

[0029] The certification support system according to the embodiment includes an information acquisition unit, a document generation unit, a check unit, and a submission support unit. The information acquisition unit acquires information provided by an NPO, such as activity reports, financial data, and member information. The information acquisition unit can also collect information from the NPO's website or social media, such as activity updates and event records. The information acquisition unit can also acquire information from the NPO's internal database, such as project results and financial data. The document generation unit generates documents necessary for certification based on the information acquired by the information acquisition unit. For example, the generation AI analyzes the NPO's activities and financial status and automatically generates application forms and activity reports. The generation AI can also generate optimal document creation templates based on past success stories, such as reflecting the effectiveness of specific expressions and structures. The generation AI can also monitor activities in real time and automatically suggest necessary document updates. For example, it suggests updates when a new project starts or ends. The check unit checks the documents generated by the document generation unit and suggests revisions. For example, the generation AI checks grammar and content accuracy and suggests corrections if there are any deficiencies. It can also refer to a database of past certified documents to suggest corrections with the highest success rate. For example, it can extract commonalities between successful documents and suggest corrections based on those. Furthermore, the generation AI can analyze the degree of correspondence between the NPO's activities and the document content and automatically suggest corrections if there are any discrepancies. For example, it can suggest corrections if the activities are not reflected in the document. The submission support department supports the submission of documents checked by the checking department to the government. For example, the generation AI automates the process of electronically transmitting submitted documents. The generation AI can also monitor the progress of submitted documents in real time and automatically notify users of necessary actions. For example, it can send reminders when the submission deadline is approaching. Furthermore, the generation AI can learn the administrative procedures of different countries and provide support for international submissions. For example, it can automatically generate documents that correspond to each country's submission format.As a result, the certification support system according to the embodiment can reduce the effort required for NPO organizations to obtain certification, as well as the burden on the administrative side. For example, the output unit displays the scoring results to students and teachers via a web application or mobile application. If students and teachers wish to receive feedback in paper form, the results can be printed using a printer. Sending the results via email provides quick feedback by sending the results directly to students and parents.

[0030] The document generation unit can analyze an NPO's past activity data and automatically generate the most effective document creation templates. For example, the document generation unit uses a generation AI to analyze an NPO's past activity reports and financial reports, extract common elements, and automatically generate templates. For example, it standardizes the way activities are described and the format of financial data. The document generation unit also uses the generation AI to study documents from previously certified NPOs and generate optimal document creation templates based on those success stories. For example, it reflects that certain expressions and structures are effective. Furthermore, the document generation unit uses the generation AI to analyze an NPO's activity data in real time and automatically generate templates that reflect the latest information. For example, it includes recent activities and new projects. This makes it possible to create optimal documents based on an NPO's past activity data.

[0031] The document generation unit can monitor an NPO's activities in real time and automatically suggest necessary document updates. For example, the document generation unit's generation AI monitors an NPO's activities in real time and automatically notifies them when activity reports and financial reports need to be updated. For example, it suggests updates when a new project starts or ends. The document generation unit also analyzes updates on an NPO's website and social media, detects changes in activities, and suggests document updates. For example, it reflects information about new events and campaigns. Furthermore, the document generation unit's generation AI periodically scans an NPO's internal database and automatically updates documents based on the latest data. For example, it reflects the latest financial data and member information. This makes it possible to create up-to-date documents tailored to the NPO's activities.

[0032] The document generation unit can learn from the success stories of other NPOs and, based on that, propose the optimal document creation method. For example, the document generation unit's generation AI collects success stories of other NPOs from a database and proposes the optimal document creation method based on that data. For example, it takes into account the structure and presentation methods of successful NPO documents. The document generation unit also analyzes the certification documents of other NPOs and extracts and proposes common success factors. For example, it shows that specific keywords and phrases are effective. Furthermore, the document generation unit's generation AI proposes best practices for document creation based on the success stories of other NPOs. For example, it shows specific ways to describe activities and present financial data. This makes it possible to create optimal documents based on the success stories of other NPOs.

[0033] The document generation unit supports the creation of documents in different languages, thereby supporting the acquisition of international certification. For example, the document generation unit uses generation AI to support the creation of documents in different languages, thereby supporting the acquisition of international certification. For example, it automatically generates documents in multiple languages, such as English, French, and Chinese. The document generation unit also supports the creation of documents in different languages, thereby supporting the acquisition of international certification. For example, it generates documents that take into account the terminology and expressions of each language. Furthermore, the document generation unit uses generation AI to support the creation of documents in different languages, thereby supporting the acquisition of international certification. For example, it provides a translation function necessary for creating documents in different languages. This supports the creation of documents in different languages, thereby making it possible to obtain international certification.

[0034] The checking unit can refer to a database of past certified documents and make revision suggestions with the highest success rate. For example, the generation AI in the checking unit refers to a database of past certified documents and makes revision suggestions with the highest success rate. For example, it extracts common points between successful documents and presents revision suggestions based on those. The generation AI in the checking unit also refers to a database of past certified documents and makes revision suggestions with the highest success rate. For example, it indicates that a particular expression or structure is effective. The generation AI in the checking unit also refers to a database of past certified documents and makes revision suggestions with the highest success rate. For example, it refers to the format and content of successful documents. This makes it possible to make optimal revision suggestions based on past successful cases.

[0035] The checking unit can analyze the degree of agreement between the NPO's activities and the document content, and automatically suggest corrections if there are any discrepancies. For example, the generation AI in the checking unit analyzes the degree of agreement between the NPO's activities and the document content, and automatically suggests corrections if there are any discrepancies. For example, it proposes a correction if the activity content is not reflected in the document. The checking unit also analyzes the degree of agreement between the NPO's activities and the document content, and automatically suggests corrections if there are any discrepancies. For example, it points out inconsistencies between the activity content and the document content and proposes a correction. The checking unit also analyzes the degree of agreement between the NPO's activities and the document content, and automatically suggests corrections if there are any discrepancies. For example, it proposes adding more details about the activity content. This can increase the degree of agreement between the activity content and the document content.

[0036] The checking department can refer to documents from other NPO organizations and make revision suggestions based on best practices. For example, the generation AI in the checking department refers to documents from other NPO organizations and makes revision suggestions based on best practices. For example, it refers to the structure and expression methods of documents from successful NPO organizations. The generation AI in the checking department also refers to documents from other NPO organizations and makes revision suggestions based on best practices. For example, it shows that specific keywords and phrases are effective. Furthermore, the generation AI in the checking department refers to documents from other NPO organizations and makes revision suggestions based on best practices. For example, it shows specific ways to describe activities and present financial data. This makes it possible to make optimal revision suggestions based on best practices from other NPO organizations.

[0037] The checking unit can learn document checking methods in different industries and make optimal correction suggestions based on that. For example, the generating AI in the checking unit can learn document checking methods in different industries and make optimal correction suggestions based on that. For example, it can refer to document checking methods in technical and medical fields. The generating AI can also learn document checking methods in different industries and make optimal correction suggestions based on that. For example, it can incorporate best practices from different industries. The checking unit can also learn document checking methods in different industries and make optimal correction suggestions based on that. For example, it can refer to successful cases from different industries. This makes it possible to make correction suggestions that incorporate best practices from different industries.

[0038] The submission support unit can learn the format of government submitted documents and automatically suggest the optimal submission method. For example, the submission support unit uses a generation AI to learn the format of government submitted documents and automatically suggest the optimal submission method. For example, it provides an automatic generation function that matches the format of the submitted documents. Furthermore, the submission support unit uses a generation AI to learn the format of government submitted documents and automatically suggest the optimal submission method. For example, it suggests a method of filling out documents that is suitable for the format of the submitted documents. Furthermore, the submission support unit uses a generation AI to learn the format of government submitted documents and automatically suggest the optimal submission method. For example, it provides an automatic check function that matches the format of the submitted documents. This makes it possible to submit documents in a way that is suitable for the format of the submitted documents.

[0039] The submission support unit can monitor the progress of submitted documents in real time and automatically notify the user of any necessary actions. For example, the generation AI in the submission support unit monitors the progress of submitted documents in real time and automatically notifies the user of any necessary actions. For example, it sends a reminder when the submission deadline approaches. The submission support unit also monitors the progress of submitted documents in real time and automatically notifies the user of any necessary actions. For example, it presents suggested corrections when deficiencies in documents are discovered. The submission support unit also monitors the progress of submitted documents in real time and automatically notifies the user of any necessary actions. For example, it sends a notification when submitted documents are accepted. This allows the user to grasp the progress of submitted documents in real time and take any necessary actions promptly.

[0040] The submission support unit can learn from the submission examples of other NPO organizations and suggest the optimal submission method based on that. For example, the generation AI of the submission support unit learns from the submission examples of other NPO organizations and suggests the optimal submission method based on that. For example, it refers to the submission methods of successful NPOs. The submission support unit can also learn from the submission examples of other NPO organizations and suggest the optimal submission method based on that. For example, it can show that a particular submission method is effective. The submission support unit can also learn from the submission examples of other NPO organizations and suggest the optimal submission method based on that. For example, it refers to the format and content of the submitted documents. This makes it possible to use the optimal submission method based on the success stories of other NPO organizations.

[0041] The submission support unit can learn the administrative procedures of different countries and provide international submission support. For example, the generation AI of the submission support unit learns the administrative procedures of different countries and provides international submission support. For example, it automatically generates documents that correspond to the submission document formats of each country. The submission support unit also learns the administrative procedures of different countries and provides international submission support. For example, it provides the information necessary for the submission procedures of each country. The submission support unit also learns the administrative procedures of different countries and provides international submission support. For example, it suggests a filling-out method that is suitable for the submission procedures of each country. This makes it possible to provide submission support that corresponds to the administrative procedures of different countries.

[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 certification acquisition support system can further include a data analysis unit. The data analysis unit can analyze the activity data of NPO organizations and quantitatively evaluate the effectiveness of the activities. For example, it can analyze trends in the number of activity participants and donation amounts to evaluate the success of the activities. The data analysis unit can also automatically generate reports to visualize the effectiveness of the activities. For example, it can visually show the results of the activities using graphs and charts. Furthermore, the data analysis unit can make suggestions to maximize the effectiveness of the activities. For example, it can propose an optimal activity plan based on past data. This allows NPO organizations to objectively evaluate the effectiveness of their activities and create effective activity plans.

[0044] The certification acquisition support system can further include a communication support unit. The communication support unit helps facilitate communication between NPOs and government agencies. For example, a generation AI can automatically create email templates and send them at the appropriate time. The communication support unit can also support communication between NPO members. For example, it can provide a chat function for sharing project progress. The communication support unit can also collect feedback on NPO activities and make improvement suggestions. For example, it can automatically collect feedback from activity participants and suggest improvements based on the analysis results. This allows NPOs to achieve effective communication and improve the quality of their activities.

[0045] The certification support system can further include a risk management department. The risk management department assesses risks associated with the NPO's activities and proposes risk mitigation measures. For example, generative AI analyzes activity plans and identifies potential risks. The risk management department can also automatically propose response measures in the event of a risk. For example, it can provide an emergency contact list and response procedures. Furthermore, the risk management department can provide training programs for risk management. For example, it can provide online courses on risk management to improve members' risk response capabilities. This enables NPOs to effectively manage risks and ensure safe activities.

[0046] The certification acquisition support system can further include a fundraising support section. The fundraising support section supports NPO organizations' fundraising activities. For example, the generation AI automatically generates presentation materials for fundraising. The fundraising support section can also support communication with donors. For example, it can automatically create and send thank-you emails to donors. The fundraising support section can also evaluate the effectiveness of fundraising and make suggestions for improvement. For example, it can analyze past fundraising data and suggest optimal fundraising methods. This allows NPO organizations to carry out effective fundraising activities and secure funds for their activities.

[0047] The certification acquisition support system can further include a volunteer management unit. The volunteer management unit efficiently manages the volunteer activities of NPO organizations. For example, the generative AI automatically adjusts volunteer schedules and suggests optimal placement. The volunteer management unit can also monitor the volunteer activity status in real time and provide necessary support. For example, it provides support to quickly resolve problems that occur during activities. Furthermore, the volunteer management unit can make suggestions to improve volunteer satisfaction. For example, it can improve the content of activities based on volunteer feedback. This allows NPO organizations to effectively manage volunteer activities and improve volunteer satisfaction.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The information acquisition unit acquires information provided by the NPO. For example, it acquires activity reports, financial data, member information, etc. The information acquisition unit can also collect information from the NPO's website or social media. For example, it acquires activity updates and event records. The information acquisition unit can also acquire information from the NPO's internal database. For example, it acquires project results and financial data. Step 2: The document generation unit generates the documents necessary to obtain certification based on the information acquired by the information acquisition unit. For example, the generation AI analyzes the activities and financial status of an NPO organization and automatically generates application forms and activity reports. The generation AI can also generate optimal document creation templates based on past success stories. For example, it can reflect that certain expressions and structures are effective. Furthermore, the generation AI can monitor activities in real time and automatically suggest necessary document updates. For example, it can suggest updates when a new project starts or ends. Step 3: The checking unit checks the documents generated by the document generation unit and makes correction suggestions. For example, the generation AI checks grammar and content accuracy, and suggests corrections if there are any deficiencies. The generation AI can also refer to a database of past certified documents to suggest corrections with the highest success rate. For example, it can extract common points between successful documents and suggest corrections based on those. Furthermore, the generation AI can analyze the degree of agreement between the NPO's activities and the document content, and automatically suggest corrections if there are any discrepancies. For example, it can suggest corrections if the activities are not reflected in the document. Step 4: The submission support department provides support for submitting the documents checked by the checking department to the government. For example, the generation AI automates the process of electronically sending documents to be submitted. The generation AI can also monitor the progress of submitted documents in real time and automatically notify users of necessary actions. For example, it can send reminders when the submission deadline approaches. Furthermore, the generation AI can learn the administrative procedures of different countries and provide international submission support. For example, it can automatically generate documents that correspond to the submission document formats of each country.

[0050] (Example 2) The certification acquisition support system according to an embodiment of the present invention is a system in which a generation AI automatically generates documents based on information provided by NPO organizations, checks them, and supports their submission. As a result, the certification acquisition support system reduces the effort required for NPO organizations to obtain certification, as well as the burden on the administrative side.

[0051] The certification support system according to the embodiment includes an information acquisition unit, a document generation unit, a check unit, and a submission support unit. The information acquisition unit acquires information provided by an NPO, such as activity reports, financial data, and member information. The information acquisition unit can also collect information from the NPO's website or social media, such as activity updates and event records. The information acquisition unit can also acquire information from the NPO's internal database, such as project results and financial data. The document generation unit generates documents necessary for certification based on the information acquired by the information acquisition unit. For example, the generation AI analyzes the NPO's activities and financial status and automatically generates application forms and activity reports. The generation AI can also generate optimal document creation templates based on past success stories, such as reflecting the effectiveness of specific expressions and structures. The generation AI can also monitor activities in real time and automatically suggest necessary document updates. For example, it suggests updates when a new project starts or ends. The check unit checks the documents generated by the document generation unit and suggests revisions. For example, the generation AI checks grammar and content accuracy and suggests corrections if there are any deficiencies. It can also refer to a database of past certified documents to suggest corrections with the highest success rate. For example, it can extract commonalities between successful documents and suggest corrections based on those. Furthermore, the generation AI can analyze the degree of correspondence between the NPO's activities and the document content and automatically suggest corrections if there are any discrepancies. For example, it can suggest corrections if the activities are not reflected in the document. The submission support department supports the submission of documents checked by the checking department to the government. For example, the generation AI automates the process of electronically transmitting submitted documents. The generation AI can also monitor the progress of submitted documents in real time and automatically notify users of necessary actions. For example, it can send reminders when the submission deadline is approaching. Furthermore, the generation AI can learn the administrative procedures of different countries and provide support for international submissions. For example, it can automatically generate documents that correspond to each country's submission format.As a result, the certification support system according to the embodiment can reduce the effort required for NPO organizations to obtain certification, as well as the burden on the administrative side. For example, the output unit displays the scoring results to students and teachers via a web application or mobile application. If students and teachers wish to receive feedback in paper form, the results can be printed using a printer. Sending the results via email provides quick feedback by sending the results directly to students and parents.

[0052] The document generation unit can analyze an NPO's past activity data and automatically generate the most effective document creation templates. For example, the document generation unit uses a generation AI to analyze an NPO's past activity reports and financial reports, extract common elements, and automatically generate templates. For example, it standardizes the way activities are described and the format of financial data. The document generation unit also uses the generation AI to study documents from previously certified NPOs and generate optimal document creation templates based on those success stories. For example, it reflects that certain expressions and structures are effective. Furthermore, the document generation unit uses the generation AI to analyze an NPO's activity data in real time and automatically generate templates that reflect the latest information. For example, it includes recent activities and new projects. This makes it possible to create optimal documents based on an NPO's past activity data.

[0053] The document generation unit can monitor an NPO's activities in real time and automatically suggest necessary document updates. For example, the document generation unit's generation AI monitors an NPO's activities in real time and automatically notifies them when activity reports and financial reports need to be updated. For example, it suggests updates when a new project starts or ends. The document generation unit also analyzes updates on an NPO's website and social media, detects changes in activities, and suggests document updates. For example, it reflects information about new events and campaigns. Furthermore, the document generation unit's generation AI periodically scans an NPO's internal database and automatically updates documents based on the latest data. For example, it reflects the latest financial data and member information. This makes it possible to create up-to-date documents tailored to the NPO's activities.

[0054] The document generation unit can use the emotion estimation function to analyze the emotions of NPO members and provide document creation support to reduce stress. For example, the document generation unit can use the emotion estimation function to analyze in real time the stress felt by NPO members while creating documents and suggest breaks at appropriate times. For example, it can suggest relaxation methods when stress levels are high. The document generation unit can also use the emotion estimation function to provide document creation advice based on the member's emotional state. For example, it can display words of praise when positive emotions are strong to maintain motivation. Furthermore, the document generation unit can use the emotion estimation function to identify parts of document creation that are likely to cause stress to members and suggest automating those parts. For example, automating repetitive tasks can reduce stress. This reduces stress for NPO members and enables efficient document creation.

[0055] The document generation unit can learn from the success stories of other NPOs and, based on that, propose the optimal document creation method. For example, the document generation unit's generation AI collects success stories of other NPOs from a database and proposes the optimal document creation method based on that data. For example, it takes into account the structure and presentation methods of successful NPO documents. The document generation unit also analyzes the certification documents of other NPOs and extracts and proposes common success factors. For example, it shows that specific keywords and phrases are effective. Furthermore, the document generation unit's generation AI proposes best practices for document creation based on the success stories of other NPOs. For example, it shows specific ways to describe activities and present financial data. This makes it possible to create optimal documents based on the success stories of other NPOs.

[0056] The document generation unit supports the creation of documents in different languages, thereby supporting the acquisition of international certification. For example, the document generation unit uses generation AI to support the creation of documents in different languages, thereby supporting the acquisition of international certification. For example, it automatically generates documents in multiple languages, such as English, French, and Chinese. The document generation unit also supports the creation of documents in different languages, thereby supporting the acquisition of international certification. For example, it generates documents that take into account the terminology and expressions of each language. Furthermore, the document generation unit uses generation AI to support the creation of documents in different languages, thereby supporting the acquisition of international certification. For example, it provides a translation function necessary for creating documents in different languages. This supports the creation of documents in different languages, thereby making it possible to obtain international certification.

[0057] The document generation unit can use the emotion estimation function to suggest a document creation method that will make the NPO members feel the most positive emotions. For example, the document generation unit uses the emotion estimation function to suggest a document creation method that will make the NPO members feel the most positive emotions. For example, it suggests the optimal work order and timing based on the members' emotion data. The document generation unit also uses the emotion estimation function to suggest a document creation method that will make the NPO members feel the most positive emotions. For example, it displays an encouraging message according to the members' emotional state. Furthermore, the document generation unit uses the emotion estimation function to suggest a document creation method that will make the NPO members feel the most positive emotions. For example, it suggests the optimal work environment and tools based on the members' emotion data. This makes it possible to create documents that will make the NPO members feel positive emotions.

[0058] The checking unit can refer to a database of past certified documents and make revision suggestions with the highest success rate. For example, the generation AI in the checking unit refers to a database of past certified documents and makes revision suggestions with the highest success rate. For example, it extracts common points between successful documents and presents revision suggestions based on those. The generation AI in the checking unit also refers to a database of past certified documents and makes revision suggestions with the highest success rate. For example, it indicates that a particular expression or structure is effective. The generation AI in the checking unit also refers to a database of past certified documents and makes revision suggestions with the highest success rate. For example, it refers to the format and content of successful documents. This makes it possible to make optimal revision suggestions based on past successful cases.

[0059] The checking unit can analyze the degree of agreement between the NPO's activities and the document content, and automatically suggest corrections if there are any discrepancies. For example, the generation AI in the checking unit analyzes the degree of agreement between the NPO's activities and the document content, and automatically suggests corrections if there are any discrepancies. For example, it proposes a correction if the activity content is not reflected in the document. The checking unit also analyzes the degree of agreement between the NPO's activities and the document content, and automatically suggests corrections if there are any discrepancies. For example, it points out inconsistencies between the activity content and the document content and proposes a correction. The checking unit also analyzes the degree of agreement between the NPO's activities and the document content, and automatically suggests corrections if there are any discrepancies. For example, it proposes adding more details about the activity content. This can increase the degree of agreement between the activity content and the document content.

[0060] The checking unit can use the emotion estimation function to analyze the emotions of the NPO members and make suggestions for revision to reduce stress. The checking unit, for example, uses the emotion estimation function to analyze the emotions of the NPO members and make suggestions for revision to reduce stress. For example, it automatically corrects parts with high stress levels. The checking unit also uses the emotion estimation function to analyze the emotions of the NPO members and make suggestions for revision to reduce stress. For example, it presents suggestions for revision based on the emotional state of the members. The checking unit also uses the emotion estimation function to analyze the emotions of the NPO members and make suggestions for revision to reduce stress. For example, it identifies parts that are likely to cause stress and makes suggestions to automate those parts. This reduces stress for the NPO members and enables efficient revision.

[0061] The checking department can refer to documents from other NPO organizations and make revision suggestions based on best practices. For example, the generation AI in the checking department refers to documents from other NPO organizations and makes revision suggestions based on best practices. For example, it refers to the structure and expression methods of documents from successful NPO organizations. The generation AI in the checking department also refers to documents from other NPO organizations and makes revision suggestions based on best practices. For example, it shows that specific keywords and phrases are effective. Furthermore, the generation AI in the checking department refers to documents from other NPO organizations and makes revision suggestions based on best practices. For example, it shows specific ways to describe activities and present financial data. This makes it possible to make optimal revision suggestions based on best practices from other NPO organizations.

[0062] The checking unit can learn document checking methods in different industries and make optimal correction suggestions based on that. For example, the generating AI in the checking unit can learn document checking methods in different industries and make optimal correction suggestions based on that. For example, it can refer to document checking methods in technical and medical fields. The generating AI can also learn document checking methods in different industries and make optimal correction suggestions based on that. For example, it can incorporate best practices from different industries. The checking unit can also learn document checking methods in different industries and make optimal correction suggestions based on that. For example, it can refer to successful cases from different industries. This makes it possible to make correction suggestions that incorporate best practices from different industries.

[0063] The checking unit can use the emotion estimation function to make revision suggestions that will evoke the most positive emotions from the members of the NPO organization. For example, the checking unit uses the emotion estimation function to make revision suggestions that will evoke the most positive emotions from the members of the NPO organization. For example, it presents optimal revision suggestions based on the emotional data of the members. The checking unit also uses the emotion estimation function to make revision suggestions that will evoke the most positive emotions from the members of the NPO organization. For example, it displays encouraging messages according to the emotional state of the members. Furthermore, the checking unit uses the emotion estimation function to make revision suggestions that will evoke the most positive emotions from the members of the NPO organization. For example, it suggests optimal work environments and tools based on the emotional data of the members. This makes it possible to make revision suggestions that will evoke positive emotions from the members of the NPO organization.

[0064] The submission support unit can learn the format of government submitted documents and automatically suggest the optimal submission method. For example, the submission support unit uses a generation AI to learn the format of government submitted documents and automatically suggest the optimal submission method. For example, it provides an automatic generation function that matches the format of the submitted documents. Furthermore, the submission support unit uses a generation AI to learn the format of government submitted documents and automatically suggest the optimal submission method. For example, it suggests a method of filling out documents that is suitable for the format of the submitted documents. Furthermore, the submission support unit uses a generation AI to learn the format of government submitted documents and automatically suggest the optimal submission method. For example, it provides an automatic check function that matches the format of the submitted documents. This makes it possible to submit documents in a way that is suitable for the format of the submitted documents.

[0065] The submission support unit can monitor the progress of submitted documents in real time and automatically notify the user of any necessary actions. For example, the generation AI in the submission support unit monitors the progress of submitted documents in real time and automatically notifies the user of any necessary actions. For example, it sends a reminder when the submission deadline approaches. The submission support unit also monitors the progress of submitted documents in real time and automatically notifies the user of any necessary actions. For example, it presents suggested corrections when deficiencies in documents are discovered. The submission support unit also monitors the progress of submitted documents in real time and automatically notifies the user of any necessary actions. For example, it sends a notification when submitted documents are accepted. This allows the user to grasp the progress of submitted documents in real time and take any necessary actions promptly.

[0066] The submission support unit can use the emotion estimation function to analyze the emotions of NPO members and provide submission support to reduce stress. The submission support unit, for example, uses the emotion estimation function to analyze the emotions of NPO members and provide submission support to reduce stress. For example, it suggests relaxation methods when the stress level is high. The submission support unit also uses the emotion estimation function to analyze the emotions of NPO members and provide submission support to reduce stress. For example, it displays encouraging messages according to the member's emotional state. Furthermore, the submission support unit uses the emotion estimation function to analyze the emotions of NPO members and provide submission support to reduce stress. For example, it identifies areas that are prone to stress and suggests automating those areas. This reduces stress for NPO members and enables efficient submission support.

[0067] The submission support unit can learn from the submission examples of other NPO organizations and suggest the optimal submission method based on that. For example, the generation AI of the submission support unit learns from the submission examples of other NPO organizations and suggests the optimal submission method based on that. For example, it refers to the submission methods of successful NPOs. The submission support unit can also learn from the submission examples of other NPO organizations and suggest the optimal submission method based on that. For example, it can show that a particular submission method is effective. The submission support unit can also learn from the submission examples of other NPO organizations and suggest the optimal submission method based on that. For example, it refers to the format and content of the submitted documents. This makes it possible to use the optimal submission method based on the success stories of other NPO organizations.

[0068] The submission support unit can learn the administrative procedures of different countries and provide international submission support. For example, the generation AI of the submission support unit learns the administrative procedures of different countries and provides international submission support. For example, it automatically generates documents that correspond to the submission document formats of each country. The submission support unit also learns the administrative procedures of different countries and provides international submission support. For example, it provides the information necessary for the submission procedures of each country. The submission support unit also learns the administrative procedures of different countries and provides international submission support. For example, it suggests a filling-out method that is suitable for the submission procedures of each country. This makes it possible to provide submission support that corresponds to the administrative procedures of different countries.

[0069] The submission support unit can use the emotion estimation function to suggest a submission method that will make the NPO members feel the most positive emotions. For example, the submission support unit uses the emotion estimation function to suggest a submission method that will make the NPO members feel the most positive emotions. For example, it presents the optimal submission method based on the members' emotion data. The submission support unit also uses the emotion estimation function to suggest a submission method that will make the NPO members feel the most positive emotions. For example, it displays an encouraging message according to the member's emotional state. Furthermore, the submission support unit uses the emotion estimation function to suggest a submission method that will make the NPO members feel the most positive emotions. For example, it suggests the optimal work environment and tools based on the members' emotion data. This makes it possible to suggest a submission method that will make the NPO members feel positive emotions.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The certification acquisition support system can further include a data analysis unit. The data analysis unit can analyze the activity data of NPO organizations and quantitatively evaluate the effectiveness of the activities. For example, it can analyze trends in the number of activity participants and donation amounts to evaluate the success of the activities. The data analysis unit can also automatically generate reports to visualize the effectiveness of the activities. For example, it can visually show the results of the activities using graphs and charts. Furthermore, the data analysis unit can make suggestions to maximize the effectiveness of the activities. For example, it can propose an optimal activity plan based on past data. This allows NPO organizations to objectively evaluate the effectiveness of their activities and create effective activity plans.

[0072] The certification acquisition support system can further include a communication support unit. The communication support unit helps facilitate communication between NPOs and government agencies. For example, a generation AI can automatically create email templates and send them at the appropriate time. The communication support unit can also support communication between NPO members. For example, it can provide a chat function for sharing project progress. The communication support unit can also collect feedback on NPO activities and make improvement suggestions. For example, it can automatically collect feedback from activity participants and suggest improvements based on the analysis results. This allows NPOs to achieve effective communication and improve the quality of their activities.

[0073] The certification support system can further include a risk management department. The risk management department assesses risks associated with the NPO's activities and proposes risk mitigation measures. For example, generative AI analyzes activity plans and identifies potential risks. The risk management department can also automatically propose response measures in the event of a risk. For example, it can provide an emergency contact list and response procedures. Furthermore, the risk management department can provide training programs for risk management. For example, it can provide online courses on risk management to improve members' risk response capabilities. This enables NPOs to effectively manage risks and ensure safe activities.

[0074] The certification acquisition support system can further include a fundraising support section. The fundraising support section supports NPO organizations' fundraising activities. For example, the generation AI automatically generates presentation materials for fundraising. The fundraising support section can also support communication with donors. For example, it can automatically create and send thank-you emails to donors. The fundraising support section can also evaluate the effectiveness of fundraising and make suggestions for improvement. For example, it can analyze past fundraising data and suggest optimal fundraising methods. This allows NPO organizations to carry out effective fundraising activities and secure funds for their activities.

[0075] The certification acquisition support system can further include a volunteer management unit. The volunteer management unit efficiently manages the volunteer activities of NPO organizations. For example, the generative AI automatically adjusts volunteer schedules and suggests optimal placement. The volunteer management unit can also monitor the volunteer activity status in real time and provide necessary support. For example, it provides support to quickly resolve problems that occur during activities. Furthermore, the volunteer management unit can make suggestions to improve volunteer satisfaction. For example, it can improve the content of activities based on volunteer feedback. This allows NPO organizations to effectively manage volunteer activities and improve volunteer satisfaction.

[0076] The certification support system can also use the emotion estimation function to analyze the emotions of NPO members and support team building. For example, the emotion estimation function can be used to grasp the emotional state of members in real time and make suggestions to facilitate communication within the team. The emotion estimation function can also be used to analyze members' stress levels and suggest activities to reduce stress. Furthermore, the emotion estimation function can be used to suggest optimal team building methods based on members' emotional data. For example, it can suggest team building activities that correspond to the members' emotional state. This allows NPOs to achieve effective team building that takes members' emotions into consideration.

[0077] The certification support system can also use the emotion estimation function to analyze the emotions of NPO members and support their mental health care. For example, the emotion estimation function can be used to understand the emotional state of members in real time and make suggestions for mental health care. The emotion estimation function can also be used to analyze members' stress levels and suggest relaxation methods to reduce stress. Furthermore, the emotion estimation function can be used to suggest optimal mental health care methods based on members' emotional data. For example, counseling and support can be provided according to the member's emotional state. This allows NPOs to effectively care for their members' mental health and provide a healthy working environment.

[0078] The certification support system can also use the emotion estimation function to analyze the emotions of NPO members and support performance improvement. For example, the emotion estimation function can be used to understand the emotional state of members in real time and make suggestions for improving performance. The emotion estimation function can also be used to analyze members' stress levels and suggest activities to reduce stress. Furthermore, the emotion estimation function can be used to suggest optimal performance improvement methods based on members' emotional data. For example, a training program can be provided that matches the members' emotional state. This allows NPOs to effectively improve their performance by taking their members' emotions into consideration.

[0079] The certification support system can also use the emotion estimation function to analyze the emotions of NPO members and help improve their motivation. For example, the emotion estimation function can be used to understand members' emotional state in real time and make suggestions for improving their motivation. The emotion estimation function can also be used to analyze members' stress levels and suggest activities to reduce stress. Furthermore, the emotion estimation function can be used to suggest optimal methods for improving motivation based on members' emotional data. For example, incentives and rewards can be provided according to the members' emotional state. This allows NPOs to effectively improve their motivation by taking their members' emotions into consideration.

[0080] The certification support system can also use an emotion estimation function to analyze the emotions of NPO members and improve the quality of communication. For example, the emotion estimation function can be used to grasp members' emotional states in real time and make suggestions to improve the quality of communication. The emotion estimation function can also be used to analyze members' stress levels and suggest communication methods to reduce stress. Furthermore, the emotion estimation function can be used to suggest optimal communication methods based on members' emotional data. For example, feedback and support can be provided according to the members' emotional states. This allows NPOs to achieve effective communication that takes members' emotions into consideration.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The information acquisition unit acquires information provided by the NPO. For example, it acquires activity reports, financial data, member information, etc. The information acquisition unit can also collect information from the NPO's website or social media. For example, it acquires activity updates and event records. The information acquisition unit can also acquire information from the NPO's internal database. For example, it acquires project results and financial data. Step 2: The document generation unit generates the documents necessary to obtain certification based on the information acquired by the information acquisition unit. For example, the generation AI analyzes the activities and financial status of an NPO organization and automatically generates application forms and activity reports. The generation AI can also generate optimal document creation templates based on past success stories. For example, it can reflect that certain expressions and structures are effective. Furthermore, the generation AI can monitor activities in real time and automatically suggest necessary document updates. For example, it can suggest updates when a new project starts or ends. Step 3: The checking unit checks the documents generated by the document generation unit and makes correction suggestions. For example, the generation AI checks grammar and content accuracy, and suggests corrections if there are any deficiencies. The generation AI can also refer to a database of past certified documents to suggest corrections with the highest success rate. For example, it can extract common points between successful documents and suggest corrections based on those. Furthermore, the generation AI can analyze the degree of agreement between the NPO's activities and the document content, and automatically suggest corrections if there are any discrepancies. For example, it can suggest corrections if the activities are not reflected in the document. Step 4: The submission support department provides support for submitting the documents checked by the checking department to the government. For example, the generation AI automates the process of electronically sending documents to be submitted. The generation AI can also monitor the progress of submitted documents in real time and automatically notify users of necessary actions. For example, it can send reminders when the submission deadline approaches. Furthermore, the generation AI can learn the administrative procedures of different countries and provide international submission support. For example, it can automatically generate documents that correspond to the submission document formats of each country.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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 AI 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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 AI 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, a 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] 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 AI 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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. an information acquisition unit that acquires information provided by NPO organizations; a document generation unit that generates documents necessary for obtaining certification based on the information acquired by the information acquisition unit; a checking unit that checks the document generated by the document generating unit and makes correction suggestions; a submission support unit that supports the submission of the documents checked by the check unit to the government. A system characterized by:

2. The document generation unit Analyze the NPO's past activity data and automatically generate the most effective document creation template.

2. The system of claim 1.

3. The document generation unit Monitor the NPO's activities in real time and automatically suggest necessary document updates.

2. The system of claim 1.

4. The document generation unit Analyze the emotions of the NPO members and provide support in creating documents to reduce stress.

2. The system of claim 1.

5. The document generation unit Learn from the success stories of other NPOs and propose optimal document preparation methods based on that knowledge 2. The system of claim 1.

6. The document generation unit Support for documentation in different languages ​​and assistance with obtaining international certification 2. The system of claim 1.

7. The document generation unit Suggest a document creation method that will evoke the most positive feelings from the members of the NPO organization.

2. The system of claim 1.

8. The checking unit Refer to a database of past certification documents to suggest revisions with the highest success rate 2. The system of claim 1.

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