system

Abstract

An object of a system according to an embodiment is to support appropriate fund raising and selection of a donation destination between an NPO and a company.SOLUTION: A system includes an input unit, an analysis unit, and a proposal generation unit. The input device receives information from an NPO or a company. The analysis unit analyzes the information received by the input unit. The proposal generation unit generates an appropriate proposal content based on the information analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult for NPOs and companies to raise funds appropriately and select donation recipients, and there are issues such as a lack of resources and the dispersion of information.

[0005] The system according to the embodiment aims to support appropriate fundraising and the selection of donation recipients between NPOs and companies. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, and a proposal generation unit. The input unit receives information from an NPO or a company. The analysis unit analyzes the information received by the input unit. The proposal generation unit generates appropriate proposal content based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can support appropriate fundraising and selection of donation recipients between NPO corporations and companies. [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 fundraising and donation support system according to an embodiment of the present invention is a system that uses a generation AI to enable both NPO corporations and companies to efficiently carry out fundraising and donation activities. As a result, the fundraising and donation support system enables both NPO corporations and companies to efficiently carry out fundraising and donation activities.

[0029] A fundraising and donation support system according to an embodiment includes an input unit, an analysis unit, and a proposal generation unit. The input unit receives information from an NPO or a company. For example, when an NPO wishes to raise funds, it inputs the amount needed and its purpose. Furthermore, when a company wishes to make a donation, it can also input a message it wishes to convey to society. The analysis unit analyzes the information received by the input unit. For example, it analyzes the purpose and amount of fundraising input by the NPO to identify appropriate candidate companies for fundraising requests. It can also analyze messages input by companies to identify appropriate candidate donation recipients. The proposal generation unit generates appropriate proposal content based on the information analyzed by the analysis unit. For example, for an NPO, it can generate a donation request message for a specific company. It can also generate a donation proposal message for a specific NPO. This allows both NPOs and companies to efficiently carry out fundraising and donation activities.

[0030] The proposal generation unit can analyze an NPO's past fundraising history and automatically generate proposals with a high success rate. For example, the proposal generation unit uses a generation AI to analyze an NPO's past fundraising history and extract commonalities among successful proposals. For example, if specific wording or structure contributes to success, the proposal generation unit automatically generates new proposals based on that. The proposal generation unit also generates proposals with a high success rate for specific companies or industries based on past fundraising history. For example, if there is a history of many successful fundraising efforts related to education, proposals for education-related companies can be strengthened. The proposal generation unit also uses a generation AI to analyze past fundraising history and automatically generate proposals with a high success rate according to the season or event. For example, proposals can be generated to coincide with the year-end donation season. This can improve the NPO's fundraising success rate.

[0031] The proposal generation unit can make proposals targeting companies specializing in a specific region or community based on the activities of the NPO. For example, the generation AI in the proposal generation unit analyzes the activities of the NPO and generates proposals targeting companies related to a specific region or community. For example, for an NPO that is engaged in local educational support activities, the unit can target local education-related companies. The proposal generation unit also generates proposals that take into account the characteristics and needs of the region based on the activities of the NPO. For example, for an NPO that is engaged in local environmental protection activities, the unit can target local environment-related companies. The generation AI in the proposal generation unit also analyzes the local economic situation and corporate CSR activities to generate proposals that are specialized for the region. For example, for an NPO that is engaged in activities that contribute to local economic development, the unit can target local economy-related companies. This makes it easier to receive support from companies specializing in the region or community.

[0032] The proposal generation unit can automatically generate proposal content in multiple languages ​​when an NPO seeks fundraising, making it easier to receive support from international companies. For example, the proposal generation unit uses a generation AI to automatically translate the NPO's fundraising proposal content into multiple languages, making it easier to receive support from international companies. For example, translation into English, French, Chinese, etc. is possible. The proposal generation unit also strengthens its approach to international companies by generating proposal content in multiple languages. For example, it can generate proposal content that takes into account the cultural background and expressions corresponding to each language. The proposal generation unit also uses a generation AI to generate proposal content in multiple languages, automatically creating presentation materials and email copy for international companies. For example, it can approach international companies in a consistent manner. This makes it easier to receive support from international companies.

[0033] The proposal generation unit can visualize the activities of an NPO and make a visually appealing proposal. For example, the proposal generation unit uses a generation AI to visualize the activities of an NPO and generate a visually appealing proposal. For example, it can create presentation materials using photos and graphs of the activities. The proposal generation unit can also visualize the activities of an NPO, thereby making proposals to companies more persuasive. For example, it can generate infographics that show the results of the activities. The proposal generation unit can also visualize the activities of an NPO and generate visually appealing videos and animations. For example, it can create a video introducing the activities and use it in proposals to companies. This makes it possible to make visually appealing proposals.

[0034] The proposal generation unit can analyze a company's past donation history and suggest NPOs that are most suitable for the company's CSR activities. For example, the generation AI in the proposal generation unit analyzes a company's past donation history and suggests NPOs that are most suitable for CSR activities. For example, for a company that has made many education-related donations in the past, it can suggest NPOs that engage in educational support activities. The proposal generation unit can also suggest NPOs related to specific fields or themes based on the company's donation history. For example, for a company that has donated to environmental protection activities, it can suggest NPOs that engage in environmental protection activities. The proposal generation unit can also analyze a company's donation history and suggest NPOs that have a high donation success rate. For example, it can generate proposal content based on the results of NPOs that have donated in the past. This makes it possible to suggest NPOs that are most suitable for a company's CSR activities.

[0035] The proposal generation unit can analyze a company's industry and market trends and propose an NPO that best matches the company's brand image. For example, the generation AI in the proposal generation unit can analyze a company's industry and market trends and propose an NPO that best matches the brand image. For example, for a technology company, it can propose an NPO that provides technical education. The proposal generation unit can also propose an NPO that matches the brand image based on the company's industry characteristics and market needs. For example, for a company in the fashion industry, it can propose an NPO that provides fashion education. The proposal generation unit can also analyze a company's market trends and propose an NPO that best matches the brand image. For example, for a market where environmental protection is trending, it can propose an NPO that engages in environmental protection activities. This makes it possible to propose an NPO that best matches the company's brand image.

[0036] When selecting a donation recipient for a company, the proposal generation unit can compare NPOs from different industries and regions and suggest the most appropriate donation recipient. For example, the generation AI in the proposal generation unit compares NPOs from different industries and regions and suggests the most appropriate donation recipient. For example, it can compare NPOs in different fields such as education, environmental protection, and healthcare. The proposal generation unit also compares NPOs from different regions to support a company in selecting a donation recipient. For example, it can compare NPOs from both Japan and overseas and suggest the most appropriate donation recipient. The proposal generation unit also compares NPOs from different industries and regions and suggests the most appropriate donation recipient for a company's CSR activities. For example, it can suggest NPOs related to the company's area of ​​activity. This makes it possible to compare NPOs from different industries and regions and suggest the most appropriate donation recipient.

[0037] The proposal generation unit can visualize a company's donation activities and propose visually appealing potential donation recipients. For example, the proposal generation unit uses a generation AI to visualize a company's donation activities and propose visually appealing potential donation recipients. For example, the activities of the donation recipients can be shown using photos and graphs. The proposal generation unit also increases the persuasiveness of potential donation recipients by visualizing a company's donation activities. For example, it can generate infographics showing the results of the donation recipients. The proposal generation unit also uses a generation AI to visualize a company's donation activities and generate visually appealing videos and animations. For example, an introductory video for the donation recipient can be created and used in proposals to the company. This makes it possible to propose visually appealing potential donation recipients.

[0038] The analysis unit automatically analyzes the activities of NPOs and companies, and constantly updates the database with the latest information. For example, the generation AI automatically analyzes the activities of NPOs and companies, and constantly updates the database with the latest information. For example, it can automatically add new NPO projects and the latest CSR activities of companies. In addition, to regularly update the information in the database, the generation AI automatically scans websites and news articles to collect new information. For example, it can add the latest activity reports of NPOs and corporate donation records. In addition, the generation AI analyzes the activities of NPOs and companies, and constantly updates the information in the database in real time. For example, it can instantly reflect NPO event information and new corporate donation destinations. This allows the database to always be constantly updated with the latest information.

[0039] The analysis unit analyzes the information contained in the database from multiple angles, improving the accuracy of matching between NPOs and companies. For example, the generation AI analyzes the information contained in the database from multiple angles to improve the accuracy of matching between NPOs and companies. For example, matching can be performed taking into account the activities, region, fundraising purpose, etc. The analysis unit also analyzes the commonalities and mutual benefits between NPOs and companies based on the information in the database to propose optimal matches. For example, it can match NPOs and companies with common goals and values. The analysis unit also develops algorithms for the generation AI to analyze the information in the database and improve the accuracy of matching between NPOs and companies. For example, it can improve matching accuracy based on past success stories and feedback. This improves the accuracy of matching between NPOs and companies.

[0040] The analysis unit links the database with different platforms, allowing information about NPOs and companies to be shared widely. For example, the generation AI in the analysis unit links the database with different platforms, allowing information about NPOs and companies to be shared widely. For example, it can link with social media and corporate CSR portal sites. The analysis unit also automatically distributes information from the database to different platforms, allowing information about NPOs and companies to be shared widely. For example, it can provide information to news sites and industry magazines. The generation AI in the analysis unit also links the database with different platforms, allowing information about NPOs and companies to be shared in real time. For example, it can link data with other systems through an API. This allows information about NPOs and companies to be shared widely.

[0041] The analysis unit can visualize the information in the database and provide it in a visually easy-to-understand format. For example, the generation AI in the analysis unit can visualize the information in the database and provide it in a visually easy-to-understand format. For example, the analysis unit can display the details of activities and fundraising purposes in graphs and charts. The analysis unit can also visualize the information in the database to make information about NPOs and companies easier to understand intuitively. For example, it can generate infographics and dashboards. The analysis unit can also visualize the information in the database and generate visually appealing presentation materials and reports. For example, it can create a visual report showing the results of activities. This makes it possible to provide information in a visually easy-to-understand format.

[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 proposal generation unit can make proposals targeting companies specializing in specific regions or communities based on the activities of the NPO. For example, for an NPO that engages in local educational support activities, it can target local education-related companies. The proposal generation unit also generates proposal content that takes into account the characteristics and needs of the region based on the activities of the NPO. For example, for an NPO that engages in local environmental protection activities, it can target local environment-related companies. The proposal generation unit also uses the generation AI to analyze the local economic situation and companies' CSR activities to generate proposal content that is specialized for the region. For example, for an NPO that engages in activities that contribute to the economic development of the region, it can target local economy-related companies. This makes it easier to receive support from companies specializing in the region or community.

[0044] The proposal generation unit can visualize the activities of an NPO and make a visually appealing proposal. For example, the generation AI can visualize the activities of an NPO and generate a visually appealing proposal. For example, it can create presentation materials using photos and graphs of the activities. The proposal generation unit can also visualize the activities of an NPO, thereby making proposals more persuasive to companies. For example, it can generate infographics that show the results of the activities. The proposal generation unit can also visualize the activities of an NPO and generate visually appealing videos and animations. For example, it can create a video introducing the activities and use it in proposals to companies. This makes it possible to make visually appealing proposals.

[0045] The proposal generation unit can analyze a company's past donation history and suggest NPOs that are most suitable for the company's CSR activities. For example, the generation AI can analyze a company's past donation history and suggest NPOs that are most suitable for CSR activities. For example, for a company that has made many education-related donations in the past, it can suggest NPOs that engage in educational support activities. The proposal generation unit can also suggest NPOs related to specific fields or themes based on the company's donation history. For example, for a company that has donated to environmental protection activities, it can suggest NPOs that engage in environmental protection activities. The proposal generation unit can also analyze a company's donation history and suggest NPOs that have a high donation success rate. For example, it can generate proposal content based on the results of NPOs that have donated in the past. This makes it possible to suggest NPOs that are most suitable for a company's CSR activities.

[0046] The proposal generation unit can analyze a company's industry and market trends and suggest an NPO that best matches the company's brand image. For example, the generation AI can analyze a company's industry and market trends and suggest an NPO that best matches the brand image. For example, for a technology company, it can suggest an NPO that provides technical education. The proposal generation unit also suggests an NPO that matches the brand image based on the company's industry characteristics and market needs. For example, for a company in the fashion industry, it can suggest an NPO that provides fashion education. The proposal generation unit can also analyze a company's market trends and suggest an NPO that best matches the brand image. For example, for a market where environmental protection is trending, it can suggest an NPO that engages in environmental protection activities. This makes it possible to suggest an NPO that best matches the company's brand image.

[0047] When selecting a donation recipient for a company, the proposal generation unit can compare NPOs from different industries and regions and suggest the most appropriate donation recipient. For example, the generation AI can compare NPOs from different industries and regions and suggest the most appropriate donation recipient. For example, it can compare NPOs in different fields such as education, environmental protection, and healthcare. The proposal generation unit also compares NPOs from different regions to support a company in selecting a donation recipient. For example, it can compare NPOs from both Japan and overseas and suggest the most appropriate donation recipient. The proposal generation unit also compares NPOs from different industries and regions and suggests the most appropriate donation recipient for a company's CSR activities. For example, it can suggest NPOs related to the company's area of ​​activity. This makes it possible to compare NPOs from different industries and regions and suggest the most appropriate donation recipient.

[0048] The proposal generation unit can visualize a company's donation activities and propose visually appealing potential donation recipients. For example, the generation AI can visualize a company's donation activities and propose visually appealing potential donation recipients. For example, the details of the donation recipient's activities can be shown using photos and graphs. The proposal generation unit also increases the persuasiveness of potential donation recipients by visualizing a company's donation activities. For example, it can generate infographics showing the results of the donation recipient. The proposal generation unit can also visualize a company's donation activities and generate visually appealing videos and animations. For example, an introductory video for the donation recipient can be created and used in proposals to the company. This makes it possible to propose visually appealing potential donation recipients.

[0049] The analysis unit automatically analyzes the activities of NPOs and companies, constantly updating the database with the latest information. For example, the generation AI automatically analyzes the activities of NPOs and companies, constantly updating the database with the latest information. For example, it can automatically add new NPO projects and the latest CSR activities of companies. In addition, to regularly update the information in the database, the generation AI automatically scans websites and news articles to collect new information. For example, it can add the latest activity reports of NPOs and corporate donation records. In addition, the analysis unit analyzes the activities of NPOs and companies, constantly updating the information in the database in real time. For example, it can instantly reflect NPO event information and new corporate donation recipients. This allows the database to always be updated with the latest information.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The input section accepts information from NPOs or companies. For example, when an NPO wishes to raise funds, it inputs the amount needed and the purpose for which it will be used. Similarly, when a company wishes to make a donation, it can also input the message it wants to convey to society. Step 2: The analysis unit analyzes the information received by the input unit. For example, it analyzes the fundraising purpose and amount entered by the NPO and identifies suitable candidate companies for procurement requests. It can also analyze messages entered by companies and identify suitable candidate donation recipients. Step 3: The proposal generation unit generates appropriate proposal content based on the information analyzed by the analysis unit. For example, for an NPO, it generates a donation request for a specific company. For a company, it can also generate a donation proposal for a specific NPO.

[0052] (Example 2) The fundraising and donation support system according to an embodiment of the present invention is a system that uses a generation AI to enable both NPO corporations and companies to efficiently carry out fundraising and donation activities. As a result, the fundraising and donation support system enables both NPO corporations and companies to efficiently carry out fundraising and donation activities.

[0053] A fundraising and donation support system according to an embodiment includes an input unit, an analysis unit, and a proposal generation unit. The input unit receives information from an NPO or a company. For example, when an NPO wishes to raise funds, it inputs the amount needed and its purpose. Furthermore, when a company wishes to make a donation, it can also input a message it wishes to convey to society. The analysis unit analyzes the information received by the input unit. For example, it analyzes the purpose and amount of fundraising input by the NPO to identify appropriate candidate companies for fundraising requests. It can also analyze messages input by companies to identify appropriate candidate donation recipients. The proposal generation unit generates appropriate proposal content based on the information analyzed by the analysis unit. For example, for an NPO, it can generate a donation request message for a specific company. It can also generate a donation proposal message for a specific NPO. This allows both NPOs and companies to efficiently carry out fundraising and donation activities.

[0054] The proposal generation unit can analyze an NPO's past fundraising history and automatically generate proposals with a high success rate. For example, the proposal generation unit uses a generation AI to analyze an NPO's past fundraising history and extract commonalities among successful proposals. For example, if specific wording or structure contributes to success, the proposal generation unit automatically generates new proposals based on that. The proposal generation unit also generates proposals with a high success rate for specific companies or industries based on past fundraising history. For example, if there is a history of many successful fundraising efforts related to education, proposals for education-related companies can be strengthened. The proposal generation unit also uses a generation AI to analyze past fundraising history and automatically generate proposals with a high success rate according to the season or event. For example, proposals can be generated to coincide with the year-end donation season. This can improve the NPO's fundraising success rate.

[0055] The proposal generation unit can make proposals targeting companies specializing in a specific region or community based on the activities of the NPO. For example, the generation AI in the proposal generation unit analyzes the activities of the NPO and generates proposals targeting companies related to a specific region or community. For example, for an NPO that is engaged in local educational support activities, the unit can target local education-related companies. The proposal generation unit also generates proposals that take into account the characteristics and needs of the region based on the activities of the NPO. For example, for an NPO that is engaged in local environmental protection activities, the unit can target local environment-related companies. The generation AI in the proposal generation unit also analyzes the local economic situation and corporate CSR activities to generate proposals that are specialized for the region. For example, for an NPO that is engaged in activities that contribute to local economic development, the unit can target local economy-related companies. This makes it easier to receive support from companies specializing in the region or community.

[0056] The proposal generation unit can use the emotion estimation function to analyze social sentiment toward the NPO's activities and generate proposal content that elicits positive sentiment. For example, the proposal generation unit uses the emotion estimation function to analyze social sentiment toward the NPO's activities and generate proposal content that elicits positive sentiment. For example, the proposal generation unit can incorporate inspiring stories and success stories into the proposal content. The proposal generation unit also uses the emotion estimation function to monitor social sentiment toward the NPO's activities in real time and continuously update proposal content that elicits positive sentiment. For example, the proposal content can be adjusted based on the latest emotion data. The proposal generation unit also uses the emotion estimation function to analyze social sentiment toward the NPO's activities and generate proposal content that eliminates negative sentiment. For example, negative elements can be avoided and positive elements can be emphasized. This makes it possible to maintain positive social sentiment toward the NPO's activities.

[0057] The proposal generation unit can automatically generate proposal content in multiple languages ​​when an NPO seeks fundraising, making it easier to receive support from international companies. For example, the proposal generation unit uses a generation AI to automatically translate the NPO's fundraising proposal content into multiple languages, making it easier to receive support from international companies. For example, translation into English, French, Chinese, etc. is possible. The proposal generation unit also strengthens its approach to international companies by generating proposal content in multiple languages. For example, it can generate proposal content that takes into account the cultural background and expressions corresponding to each language. The proposal generation unit also uses a generation AI to generate proposal content in multiple languages, automatically creating presentation materials and email copy for international companies. For example, it can approach international companies in a consistent manner. This makes it easier to receive support from international companies.

[0058] The proposal generation unit can visualize the activities of an NPO and make a visually appealing proposal. For example, the proposal generation unit uses a generation AI to visualize the activities of an NPO and generate a visually appealing proposal. For example, it can create presentation materials using photos and graphs of the activities. The proposal generation unit can also visualize the activities of an NPO, thereby making proposals to companies more persuasive. For example, it can generate infographics that show the results of the activities. The proposal generation unit can also visualize the activities of an NPO and generate visually appealing videos and animations. For example, it can create a video introducing the activities and use it in proposals to companies. This makes it possible to make visually appealing proposals.

[0059] The proposal generation unit can use the emotion estimation function to monitor the emotional reactions of companies to the NPO's activities in real time and continuously update the optimal proposal content. The proposal generation unit, for example, uses the emotion estimation function to monitor the emotional reactions of companies to the NPO's activities in real time and continuously update the optimal proposal content. For example, the proposal content can be adjusted based on the company's emotional data. Furthermore, the proposal generation unit uses the emotion estimation function to analyze the company's emotional reactions and generate proposal content that elicits positive emotions. For example, the proposal content can be strengthened based on the company's emotional data. Furthermore, the proposal generation unit uses the emotion estimation function to monitor the company's emotional reactions in real time and generate proposal content that eliminates negative emotions. For example, it can avoid negative elements and emphasize positive elements. This makes it possible to continuously update the optimal proposal content based on the company's emotional reactions.

[0060] The proposal generation unit can analyze a company's past donation history and suggest NPOs that are most suitable for the company's CSR activities. For example, the generation AI in the proposal generation unit analyzes a company's past donation history and suggests NPOs that are most suitable for CSR activities. For example, for a company that has made many education-related donations in the past, it can suggest NPOs that engage in educational support activities. The proposal generation unit can also suggest NPOs related to specific fields or themes based on the company's donation history. For example, for a company that has donated to environmental protection activities, it can suggest NPOs that engage in environmental protection activities. The proposal generation unit can also analyze a company's donation history and suggest NPOs that have a high donation success rate. For example, it can generate proposal content based on the results of NPOs that have donated in the past. This makes it possible to suggest NPOs that are most suitable for a company's CSR activities.

[0061] The proposal generation unit can analyze a company's industry and market trends and propose an NPO that best matches the company's brand image. For example, the generation AI in the proposal generation unit can analyze a company's industry and market trends and propose an NPO that best matches the brand image. For example, for a technology company, it can propose an NPO that provides technical education. The proposal generation unit can also propose an NPO that matches the brand image based on the company's industry characteristics and market needs. For example, for a company in the fashion industry, it can propose an NPO that provides fashion education. The proposal generation unit can also analyze a company's market trends and propose an NPO that best matches the brand image. For example, for a market where environmental protection is trending, it can propose an NPO that engages in environmental protection activities. This makes it possible to propose an NPO that best matches the company's brand image.

[0062] The proposal generation unit can use the emotion estimation function to analyze social sentiment toward the company's donation activities and propose donation destination candidates that elicit positive emotions. For example, the proposal generation unit can use the emotion estimation function to analyze social sentiment toward the company's donation activities and propose donation destination candidates that elicit positive emotions. For example, it can propose nonprofit organizations with inspiring stories. The proposal generation unit also uses the generation AI emotion estimation function to monitor social sentiment toward the company's donation activities in real time and continuously update donation destination candidates that elicit positive emotions. For example, it can adjust the donation destination candidates based on the latest emotion data. The proposal generation unit also uses the emotion estimation function to analyze social sentiment toward the company's donation activities and propose donation destination candidates that eliminate negative emotions. For example, it can avoid negative elements and emphasize positive elements. This makes it possible to maintain positive social sentiment toward the company's donation activities.

[0063] When selecting a donation recipient for a company, the proposal generation unit can compare NPOs from different industries and regions and suggest the most appropriate donation recipient. For example, the generation AI in the proposal generation unit compares NPOs from different industries and regions and suggests the most appropriate donation recipient. For example, it can compare NPOs in different fields such as education, environmental protection, and healthcare. The proposal generation unit also compares NPOs from different regions to support a company in selecting a donation recipient. For example, it can compare NPOs from both Japan and overseas and suggest the most appropriate donation recipient. The proposal generation unit also compares NPOs from different industries and regions and suggests the most appropriate donation recipient for a company's CSR activities. For example, it can suggest NPOs related to the company's area of ​​activity. This makes it possible to compare NPOs from different industries and regions and suggest the most appropriate donation recipient.

[0064] The proposal generation unit can visualize a company's donation activities and propose visually appealing potential donation recipients. For example, the proposal generation unit uses a generation AI to visualize a company's donation activities and propose visually appealing potential donation recipients. For example, the activities of the donation recipients can be shown using photos and graphs. The proposal generation unit also increases the persuasiveness of potential donation recipients by visualizing a company's donation activities. For example, it can generate infographics showing the results of the donation recipients. The proposal generation unit also uses a generation AI to visualize a company's donation activities and generate visually appealing videos and animations. For example, an introductory video for the donation recipient can be created and used in proposals to the company. This makes it possible to propose visually appealing potential donation recipients.

[0065] The proposal generation unit can use the emotion estimation function to monitor the emotional reactions of employees and customers to the company's donation activities in real time and continuously update the optimal donation destination candidates. For example, the proposal generation unit can use the emotion estimation function to monitor the emotional reactions of employees and customers to the company's donation activities in real time and continuously update the optimal donation destination candidates. For example, the proposal generation unit can adjust the donation destination candidates based on the emotional data of employees and customers. The proposal generation unit also uses the generation AI emotion estimation function to analyze the emotional reactions of employees and customers and propose donation destination candidates that elicit positive emotions. For example, it can propose nonprofit organizations with inspiring stories. The proposal generation unit also uses the emotion estimation function to monitor the emotional reactions of employees and customers in real time and generate donation destination candidates that eliminate negative emotions. For example, it can avoid negative elements and emphasize positive elements. This makes it possible to continuously update the optimal donation destination candidates based on the emotional reactions of employees and customers.

[0066] The analysis unit automatically analyzes the activities of NPOs and companies, and constantly updates the database with the latest information. For example, the generation AI automatically analyzes the activities of NPOs and companies, and constantly updates the database with the latest information. For example, it can automatically add new NPO projects and the latest CSR activities of companies. In addition, to regularly update the information in the database, the generation AI automatically scans websites and news articles to collect new information. For example, it can add the latest activity reports of NPOs and corporate donation records. In addition, the generation AI analyzes the activities of NPOs and companies, and constantly updates the information in the database in real time. For example, it can instantly reflect NPO event information and new corporate donation destinations. This allows the database to always be constantly updated with the latest information.

[0067] The analysis unit analyzes the information contained in the database from multiple angles, improving the accuracy of matching between NPOs and companies. For example, the generation AI analyzes the information contained in the database from multiple angles to improve the accuracy of matching between NPOs and companies. For example, matching can be performed taking into account the activities, region, fundraising purpose, etc. The analysis unit also analyzes the commonalities and mutual benefits between NPOs and companies based on the information in the database to propose optimal matches. For example, it can match NPOs and companies with common goals and values. The analysis unit also develops algorithms for the generation AI to analyze the information in the database and improve the accuracy of matching between NPOs and companies. For example, it can improve matching accuracy based on past success stories and feedback. This improves the accuracy of matching between NPOs and companies.

[0068] The analysis unit can use the emotion estimation function to analyze social sentiment toward the information in the database and prioritize displaying information that elicits positive emotions. For example, the analysis unit can use the emotion estimation function to analyze social sentiment toward the information in the database and prioritize displaying information that elicits positive emotions. For example, it can highlight inspiring stories and success stories. The analysis unit also uses the emotion estimation function to monitor social sentiment toward the information in the database in real time and continuously update information that elicits positive emotions. For example, it can adjust the information based on the latest emotion data. The analysis unit also uses the emotion estimation function to analyze social sentiment toward the information in the database and prioritize displaying information that eliminates negative emotions. For example, it can avoid negative elements and emphasize positive elements. This makes it possible to prioritize displaying information that elicits positive emotions.

[0069] The analysis unit links the database with different platforms, allowing information about NPOs and companies to be shared widely. For example, the generation AI in the analysis unit links the database with different platforms, allowing information about NPOs and companies to be shared widely. For example, it can link with social media and corporate CSR portal sites. The analysis unit also automatically distributes information from the database to different platforms, allowing information about NPOs and companies to be shared widely. For example, it can provide information to news sites and industry magazines. The generation AI in the analysis unit also links the database with different platforms, allowing information about NPOs and companies to be shared in real time. For example, it can link data with other systems through an API. This allows information about NPOs and companies to be shared widely.

[0070] The analysis unit can visualize the information in the database and provide it in a visually easy-to-understand format. For example, the generation AI in the analysis unit can visualize the information in the database and provide it in a visually easy-to-understand format. For example, the analysis unit can display the details of activities and fundraising purposes in graphs and charts. The analysis unit can also visualize the information in the database to make information about NPOs and companies easier to understand intuitively. For example, it can generate infographics and dashboards. The analysis unit can also visualize the information in the database and generate visually appealing presentation materials and reports. For example, it can create a visual report showing the results of activities. This makes it possible to provide information in a visually easy-to-understand format.

[0071] The analysis unit uses the emotion estimation function to monitor the user's emotional reactions to the information in the database in real time and continuously update the optimal information. For example, the analysis unit uses the emotion estimation function to monitor the user's emotional reactions to the information in the database in real time and continuously update the optimal information. For example, the analysis unit can adjust the information based on the user's emotional data. Furthermore, the analysis unit uses the emotion estimation function to have the generation AI analyze the user's emotional reactions and prioritize displaying information that elicits positive emotions. For example, it can highlight inspiring stories and success stories. Furthermore, the analysis unit uses the emotion estimation function to monitor the user's emotional reactions in real time and prioritize displaying information that eliminates negative emotions. For example, it can avoid negative elements and emphasize positive elements. This allows the optimal information to be continuously updated based on the user's emotional reactions.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The proposal generation unit can make proposals targeting companies specializing in specific regions or communities based on the activities of the NPO. For example, for an NPO that engages in local educational support activities, it can target local education-related companies. The proposal generation unit also generates proposal content that takes into account the characteristics and needs of the region based on the activities of the NPO. For example, for an NPO that engages in local environmental protection activities, it can target local environment-related companies. The proposal generation unit also uses the generation AI to analyze the local economic situation and companies' CSR activities to generate proposal content that is specialized for the region. For example, for an NPO that engages in activities that contribute to the economic development of the region, it can target local economy-related companies. This makes it easier to receive support from companies specializing in the region or community.

[0074] The proposal generation unit can visualize the activities of an NPO and make a visually appealing proposal. For example, the generation AI can visualize the activities of an NPO and generate a visually appealing proposal. For example, it can create presentation materials using photos and graphs of the activities. The proposal generation unit can also visualize the activities of an NPO, thereby making proposals more persuasive to companies. For example, it can generate infographics that show the results of the activities. The proposal generation unit can also visualize the activities of an NPO and generate visually appealing videos and animations. For example, it can create a video introducing the activities and use it in proposals to companies. This makes it possible to make visually appealing proposals.

[0075] The proposal generation unit can use the emotion estimation function to analyze social sentiment toward the NPO's activities and generate proposal content that elicits positive sentiment. For example, the generation AI can use the emotion estimation function to analyze social sentiment toward the NPO's activities and generate proposal content that elicits positive sentiment. For example, inspiring stories and success stories can be incorporated into the proposal content. The proposal generation unit can also use the emotion estimation function to monitor social sentiment toward the NPO's activities in real time and continuously update proposal content that elicits positive sentiment. For example, the proposal content can be adjusted based on the latest emotion data. The proposal generation unit can also use the emotion estimation function to analyze social sentiment toward the NPO's activities and generate proposal content that eliminates negative sentiment. For example, negative elements can be avoided and positive elements can be emphasized. This makes it possible to maintain positive social sentiment toward the NPO's activities.

[0076] The proposal generation unit can analyze a company's past donation history and suggest NPOs that are most suitable for the company's CSR activities. For example, the generation AI can analyze a company's past donation history and suggest NPOs that are most suitable for CSR activities. For example, for a company that has made many education-related donations in the past, it can suggest NPOs that engage in educational support activities. The proposal generation unit can also suggest NPOs related to specific fields or themes based on the company's donation history. For example, for a company that has donated to environmental protection activities, it can suggest NPOs that engage in environmental protection activities. The proposal generation unit can also analyze a company's donation history and suggest NPOs that have a high donation success rate. For example, it can generate proposal content based on the results of NPOs that have donated in the past. This makes it possible to suggest NPOs that are most suitable for a company's CSR activities.

[0077] The proposal generation unit can analyze a company's industry and market trends and suggest an NPO that best matches the company's brand image. For example, the generation AI can analyze a company's industry and market trends and suggest an NPO that best matches the brand image. For example, for a technology company, it can suggest an NPO that provides technical education. The proposal generation unit also suggests an NPO that matches the brand image based on the company's industry characteristics and market needs. For example, for a company in the fashion industry, it can suggest an NPO that provides fashion education. The proposal generation unit can also analyze a company's market trends and suggest an NPO that best matches the brand image. For example, for a market where environmental protection is trending, it can suggest an NPO that engages in environmental protection activities. This makes it possible to suggest an NPO that best matches the company's brand image.

[0078] The proposal generation unit can use the emotion estimation function to analyze social sentiment toward a company's donation activities and propose donation candidate candidates that elicit positive emotions. For example, the emotion estimation function can be used to analyze social sentiment toward a company's donation activities and propose donation candidate candidates that elicit positive emotions. For example, it can propose nonprofit organizations with inspiring stories. The proposal generation unit also uses the generation AI emotion estimation function to monitor social sentiment toward a company's donation activities in real time and continuously update donation candidate candidates that elicit positive emotions. For example, it can adjust donation candidate candidates based on the latest emotion data. The proposal generation unit also uses the emotion estimation function to analyze social sentiment toward a company's donation activities and propose donation candidate candidates that eliminate negative emotions. For example, it can avoid negative elements and emphasize positive elements. This makes it possible to maintain positive social sentiment toward a company's donation activities.

[0079] When selecting a donation recipient for a company, the proposal generation unit can compare NPOs from different industries and regions and suggest the most appropriate donation recipient. For example, the generation AI can compare NPOs from different industries and regions and suggest the most appropriate donation recipient. For example, it can compare NPOs in different fields such as education, environmental protection, and healthcare. The proposal generation unit also compares NPOs from different regions to support a company in selecting a donation recipient. For example, it can compare NPOs from both Japan and overseas and suggest the most appropriate donation recipient. The proposal generation unit also compares NPOs from different industries and regions and suggests the most appropriate donation recipient for a company's CSR activities. For example, it can suggest NPOs related to the company's area of ​​activity. This makes it possible to compare NPOs from different industries and regions and suggest the most appropriate donation recipient.

[0080] The proposal generation unit can visualize a company's donation activities and propose visually appealing potential donation recipients. For example, the generation AI can visualize a company's donation activities and propose visually appealing potential donation recipients. For example, the details of the donation recipient's activities can be shown using photos and graphs. The proposal generation unit also increases the persuasiveness of potential donation recipients by visualizing a company's donation activities. For example, it can generate infographics showing the results of the donation recipient. The proposal generation unit can also visualize a company's donation activities and generate visually appealing videos and animations. For example, an introductory video for the donation recipient can be created and used in proposals to the company. This makes it possible to propose visually appealing potential donation recipients.

[0081] The proposal generation unit can use the emotion estimation function to monitor the emotional reactions of employees and customers to a company's donation activities in real time and continuously update the optimal donation destination candidates. For example, the emotion estimation function can be used to monitor the emotional reactions of employees and customers to a company's donation activities in real time and continuously update the optimal donation destination candidates. For example, the donation destination candidates can be adjusted based on the emotional data of employees and customers. The proposal generation unit also uses the emotion estimation function to analyze the emotional reactions of employees and customers and propose donation destination candidates that elicit positive emotions. For example, it can propose non-profit organizations with inspiring stories. The proposal generation unit also uses the emotion estimation function to monitor the emotional reactions of employees and customers in real time and generate donation destination candidates that eliminate negative emotions. For example, it can avoid negative elements and emphasize positive elements. This makes it possible to continuously update the optimal donation destination candidates based on the emotional reactions of employees and customers.

[0082] The analysis unit automatically analyzes the activities of NPOs and companies, constantly updating the database with the latest information. For example, the generation AI automatically analyzes the activities of NPOs and companies, constantly updating the database with the latest information. For example, it can automatically add new NPO projects and the latest CSR activities of companies. In addition, to regularly update the information in the database, the generation AI automatically scans websites and news articles to collect new information. For example, it can add the latest activity reports of NPOs and corporate donation records. In addition, the analysis unit analyzes the activities of NPOs and companies, constantly updating the information in the database in real time. For example, it can instantly reflect NPO event information and new corporate donation recipients. This allows the database to always be updated with the latest information.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The input section accepts information from NPOs or companies. For example, when an NPO wishes to raise funds, it inputs the amount needed and the purpose for which it will be used. Similarly, when a company wishes to make a donation, it can also input the message it wants to convey to society. Step 2: The analysis unit analyzes the information received by the input unit. For example, it analyzes the fundraising purpose and amount entered by the NPO and identifies suitable candidate companies for procurement requests. It can also analyze messages entered by companies and identify suitable candidate donation recipients. Step 3: The proposal generation unit generates appropriate proposal content based on the information analyzed by the analysis unit. For example, for an NPO, it generates a donation request for a specific company. For a company, it can also generate a donation proposal for a specific NPO.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0151] 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]

[0152] 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 input section for receiving information from NPOs or companies; an analysis unit that analyzes the information received by the input unit; a proposal generation unit that generates appropriate proposal content based on the information analyzed by the analysis unit. A system characterized by:

2. The proposal generation unit Analyze the NPO's past fundraising history and automatically generate proposals with a high success rate.

2. The system of claim 1.

3. The proposal generation unit Based on the activities of the NPO, make proposals targeting companies specializing in specific regions or communities.

2. The system of claim 1.

4. The proposal generation unit Analyze social sentiment toward the NPO's activities and generate proposals that elicit positive sentiment.

2. The system of claim 1.

5. The proposal generation unit When the NPO seeks funding, the proposal is automatically generated in multiple languages, making it easier for the NPO to receive support from international companies.

2. The system of claim 1.

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