system

Abstract

An object of a system according to an embodiment is to analyze information on an NPO and a company and provide an appropriate donation destination and suggestion content.SOLUTION: A system includes an NPO information collection part, a company information collection part, an information analysis part, a proposal generation part, a proposal provision part, and a candidate selection part. The NPO information collecting unit collects information on a NPO. The company information collection unit collects information on a company. The information analyzing unit analyzes the information collected by the NPO information collecting unit and the company information collecting unit. The proposal generation unit generates a proposal content based on the information analyzed by the information analysis unit. The proposal providing unit provides the proposal content generated by the proposal generation unit to the company. The candidate selection section selects a donation destination candidate based on the information analyzed by the information analysis section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult for NPOs and companies to find appropriate donation recipients and proposals, resulting in low efficiency in fundraising and donations.

[0005] The system according to the embodiment aims to analyze information on NPOs and companies and provide appropriate donation destinations and proposals. [Means for solving the problem]

[0006] The system according to the embodiment includes an NPO information collection unit, a company information collection unit, an information analysis unit, a proposal generation unit, a proposal provision unit, and a candidate selection unit. The NPO information collection unit collects information on NPO corporations. The company information collection unit collects information on companies. The information analysis unit analyzes the information collected by the NPO information collection unit and the company information collection unit. The proposal generation unit generates proposal content based on the information analyzed by the information analysis unit. The proposal provision unit provides the proposal content generated by the proposal generation unit to companies. The candidate selection unit selects potential donation recipients based on the information analyzed by the information analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze information on NPOs and companies and provide appropriate donation destinations and proposals. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention uses a generation AI to enable both NPOs and corporations to efficiently raise funds and make donations. When an NPO seeks fundraising, it inputs the amount needed and its intended purpose. The generation AI analyzes the input information and generates appropriate candidate companies and proposals. When a corporation seeks donations, it inputs the message it wishes to convey to society. The generation AI analyzes this information and suggests appropriate potential recipients. For example, when an NPO seeks fundraising, it is required to provide a detailed description of the specific project and activities. For example, if 1 million yen is needed for environmental protection activities, the corporation inputs the detailed purpose and expected benefits. This information is input into the generation AI. The generation AI then analyzes the input information and generates appropriate candidate companies and proposals. The generation AI selects the most suitable company based on a database of the company's past donation history and corporate social responsibility (CSR) activities. For example, the system prioritizes companies that have donated to environmental protection activities in the past and generates specific proposals for those companies. Meanwhile, when a corporation seeks donations, it inputs the message it wishes to convey to society. For example, the corporation inputs a message such as "I want to contribute to environmental protection." This information is also input into the generation AI. The generation AI analyzes the information input by the company and suggests appropriate donation candidates. Based on a database of NPOs, the generation AI selects NPOs that match the company's message. For example, it may prioritize NPOs engaged in environmental protection activities and suggest donations to those NPOs. This allows NPOs to raise funds efficiently and companies to effectively communicate their social messages. The system allows both NPOs and companies to raise funds and make donations efficiently. For example, it simplifies the fundraising process for NPOs and allows companies to effectively communicate their social messages. In addition, the SB, which acts as a bridge between the two parties, can increase profits and improve their corporate image by receiving matching rewards.

[0029] The fundraising and donation support system according to the embodiment includes an NPO information collection unit, a company information collection unit, an information analysis unit, a proposal generation unit, a proposal provision unit, and a candidate selection unit. The NPO information collection unit collects information about NPOs. The NPO information includes, for example, but is not limited to, the desired amount of fundraising, its intended purpose, and specific projects and activities. For example, when an NPO requests fundraising, the NPO information collection unit inputs the amount needed and its intended purpose. The NPO information collection unit can also collect the NPO's past fundraising history and current project information. The company information collection unit collects company information. The company information includes, for example, but is not limited to, the desired donation amount, a message the company wishes to convey to society, and past donation history. For example, when a company requests a donation, the company information collection unit inputs the message the company wishes to convey to society. The company information collection unit can also collect information based on a database of companies' CSR activities. The information analysis unit analyzes the information collected by the NPO information collection unit and the company information collection unit. The analysis may involve, but is not limited to, statistical analysis, machine learning algorithms, text mining, or other methods. The information analysis unit selects the most suitable company based on, for example, a database of the company's past donation history and CSR activities. The proposal generation unit generates proposal content based on the information analyzed by the information analysis unit. The proposal content may include, for example, a fundraising proposal, a collaborative project proposal, or the like, but is not limited to these examples. The proposal generation unit generates specific proposal content for the company, for example. The proposal providing unit provides the proposal content generated by the proposal generation unit to the company. Methods of providing the proposal include, for example, notification by email, display on a dashboard, or the like, but are not limited to these examples. The proposal providing unit proposes, for example, a donation to the company. The candidate selection unit selects candidate donation recipients based on the information analyzed by the information analysis unit. Selection criteria may include, for example, a scoring system, filtering conditions, or the like, but are not limited to these examples. The candidate selection unit selects NPOs that match the company's message based on, for example, a database of NPOs.As a result, the fundraising and donation support system according to the embodiment allows both NPO corporations and companies to efficiently raise funds and make donations.

[0030] The NPO information collection unit can input the amount needed and its purpose when an NPO seeks to raise funds. For example, when an NPO seeks to raise funds, the NPO information collection unit inputs the amount needed and its purpose. The amount needed includes, but is not limited to, a minimum amount, a maximum amount, and a unit of amount. The purpose includes, but is not limited to, the type of project and the purpose of the funds. This allows the NPO to input its specific fundraising needs. Some or all of the above-described processing in the NPO information collection unit may be performed using, or without, AI. For example, the NPO information collection unit can input the information input by the NPO into AI, which then analyzes and converts the information into an appropriate format.

[0031] The company information collection unit can input a message that a company wishes to send to society when it wishes to make a donation. For example, when a company wishes to make a donation, the company information collection unit inputs a message that the company wishes to send to society. The message that the company wishes to send to society includes, but is not limited to, details of CSR activities, the company's vision and mission, etc. This allows the company to input a social message. Some or all of the above-mentioned processing in the company information collection unit may be performed, for example, using AI or may be performed without using AI. For example, the company information collection unit can input the message input by the company into AI, which can analyze the message and convert it into an appropriate format.

[0032] The information analysis unit can select appropriate companies based on a database of companies' past donation histories and corporate social responsibility (CSR) activities. The information analysis unit selects appropriate companies based on, for example, a database of companies' past donation histories and CSR activities. Criteria for selecting appropriate companies include, for example, evaluation criteria for donation history and evaluation criteria for CSR activities, but are not limited to these examples. This allows the optimal company to be selected taking into account the company's past donation history and CSR activities. Some or all of the above-described processing in the information analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the information analysis unit can input companies' donation history data into AI, which then analyzes the data to select appropriate companies.

[0033] The proposal generation unit can generate specific proposal content for a company. The proposal generation unit generates specific proposal content for a company, for example. The specific proposal content includes, for example, the donation amount, the donation method, and the recipient of the donation, but is not limited to these examples. This allows specific proposal content to be generated for a company. Some or all of the above-mentioned processing in the proposal generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal generation unit can input company information into AI, which analyzes the information and generates specific proposal content.

[0034] The candidate selection unit can select an NPO that matches the company's message based on a database of NPOs. The candidate selection unit selects an NPO that matches the company's message based on, for example, a database of NPOs. Criteria for matching a company's message include, but are not limited to, keyword matching, theme matching, etc. This allows an NPO that matches the company's message to be selected. Some or all of the above-described processing in the candidate selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the candidate selection unit can input the company's message into AI, which analyzes the message and selects an appropriate NPO.

[0035] The proposal providing unit can propose a donation to a company. The proposal providing unit, for example, proposes a donation to a company. Methods for proposing a donation include, but are not limited to, for example, notification by email or display on a dashboard. In this way, a donation can be proposed to a company. Some or all of the above-mentioned processing in the proposal providing unit may be performed, for example, using AI or may be performed without using AI. For example, the proposal providing unit can input the proposal content into AI, which analyzes the proposal content and provides it to the company in an appropriate format.

[0036] The NPO information collection unit can analyze the NPO's past fundraising history and select an appropriate information collection method. For example, the NPO information collection unit can analyze the NPO's past fundraising history and prioritize successful methods. The NPO information collection unit can also select an information collection method based on the NPO's past fundraising history to avoid unsuccessful methods. Furthermore, the most effective information collection means can be selected based on the NPO's past fundraising history. This allows the optimal information collection method to be selected based on the NPO's past fundraising history. Some or all of the above-described processing in the NPO information collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the NPO information collection unit can input the NPO's past fundraising data into AI, which can analyze the data and select an appropriate information collection method.

[0037] When collecting information about NPOs, the NPO information collection unit can filter the information based on the NPO's current projects and activities. For example, the NPO information collection unit prioritizes collecting information related to the NPO's ongoing projects. It can also filter less relevant information based on the NPO's activities. It can also focus on collecting information necessary for the NPO's current projects. This allows information to be filtered based on the NPO's current projects and activities. Some or all of the above-mentioned processing in the NPO information collection unit may be performed using, or without, AI, for example. For example, the NPO information collection unit can input NPO project information into AI, which analyzes the information and performs appropriate filtering.

[0038] When collecting information about an NPO, the NPO information collection unit can select an appropriate collection means depending on the input method. For example, if the NPO uses voice input, the NPO information collection unit can collect information using voice recognition technology. Also, if the NPO uses text input, the NPO information collection unit can collect information using text analysis technology. Furthermore, if the NPO uses image input, the NPO information collection unit can collect information using image recognition technology. This allows the optimal collection means to be selected depending on the input method of the NPO. Some or all of the above-mentioned processing in the NPO information collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the NPO information collection unit can input the NPO's input data into AI, which then analyzes the data and selects an appropriate collection means.

[0039] When collecting information on NPOs, the NPO information collection unit can prioritize collecting highly relevant information by taking geographical location information into consideration. The NPO information collection unit, for example, prioritizes collecting information related to the NPO's area of ​​activity. It can also filter highly relevant information based on the NPO's geographical location information. It can also collect optimal information depending on the NPO's area of ​​activity. This allows highly relevant information to be prioritized by taking the NPO's geographical location information into consideration. Some or all of the above-described processing in the NPO information collection unit may be performed using, or without, AI, for example. For example, the NPO information collection unit can input the NPO's geographical location data into AI, which can analyze the data and collect appropriate information.

[0040] When collecting information about an NPO, the NPO information collection unit can analyze social media activities and collect related information. For example, the NPO information collection unit analyzes the NPO's social media activities and collects related information. The NPO information collection unit can also collect related information based on the content of the NPO's social media posts. Furthermore, it can analyze the reactions of the NPO's followers on social media and collect related information. In this way, the NPO's social media activities can be analyzed and related information can be collected. Some or all of the above-mentioned processing in the NPO information collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the NPO information collection unit can input the NPO's social media data into AI, which can analyze the data and collect appropriate information.

[0041] When collecting information on NPOs, the NPO information collection unit can customize the collection method by reflecting past feedback. The NPO information collection unit, for example, improves the information collection method based on the NPO's past feedback. It can also adjust the type of information to be collected by reflecting the NPO's past feedback. Furthermore, it can select the optimal information collection means by referring to the NPO's past feedback. This allows the collection method to be customized by reflecting the NPO's past feedback. Some or all of the above-mentioned processing in the NPO information collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the NPO information collection unit can input the NPO's feedback data into AI, which analyzes the data and selects an appropriate collection method.

[0042] The corporate information collection unit can analyze a company's past donation history and select an appropriate information collection method. For example, the corporate information collection unit can analyze a company's past donation history and prioritize successful methods. The corporate information collection unit can also select an information collection method based on a company's past donation history to avoid unsuccessful methods. Furthermore, the most effective information collection means can be selected based on a company's past donation history. This makes it possible to select the optimal information collection method based on a company's past donation history. Some or all of the above-mentioned processing in the corporate information collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the corporate information collection unit can input a company's donation history data into AI, which can analyze the data and select an appropriate information collection method.

[0043] When collecting information about a company, the company information collection unit can filter the information based on the company's current CSR activities and social messages. For example, the company information collection unit prioritizes collecting information related to the company's current CSR activities. It can also filter out less relevant information based on the company's social messages. It can also focus on collecting information necessary for the company's current CSR activities. This allows information to be filtered based on the company's current CSR activities and social messages. Some or all of the above-mentioned processing in the company information collection unit may be performed using, or without, AI, for example. For example, the company information collection unit can input company CSR activity data into AI, which can analyze the data and collect appropriate information.

[0044] When collecting company information, the company information collection unit can select an appropriate collection means depending on the input method. For example, if a company uses voice input, the company information collection unit can collect information using voice recognition technology. Also, if a company uses text input, the company information collection unit can collect information using text analysis technology. Furthermore, if a company uses image input, the company information collection unit can collect information using image recognition technology. This makes it possible to select the optimal collection means depending on the company's input method. Some or all of the above-mentioned processing in the company information collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the company information collection unit can input the company's input data into AI, which then analyzes the data and selects the appropriate collection means.

[0045] When collecting information about a company, the company information collection unit can prioritize collecting highly relevant information by taking geographical location information into consideration. The company information collection unit, for example, prioritizes collecting information related to the company's area of ​​activity. The company information collection unit can also filter highly relevant information based on the company's geographical location information. Furthermore, optimal information can be collected depending on the company's area of ​​activity. This makes it possible to prioritize collecting highly relevant information by taking the company's geographical location information into consideration. Some or all of the above-mentioned processing in the company information collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the company information collection unit can input company geographical location data into AI, which can analyze the data and collect appropriate information.

[0046] When collecting information about a company, the company information collection unit can analyze social media activities and collect related information. The company information collection unit, for example, analyzes the company's social media activities and collects related information. The company information collection unit can also collect related information based on the content of the company's social media posts. Furthermore, the company information collection unit can analyze the reactions of the company's followers on social media and collect related information. In this way, the company's social media activities can be analyzed and related information can be collected. Some or all of the above-mentioned processing in the company information collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the company information collection unit can input the company's social media data into AI, which can analyze the data and collect appropriate information.

[0047] When collecting information on a company, the company information collection unit can customize the collection method by reflecting past feedback. The company information collection unit, for example, improves the information collection method based on the company's past feedback. The company information collection unit can also adjust the type of information to be collected by reflecting the company's past feedback. Furthermore, the company's past feedback can be used as a reference to select the optimal information collection means. This allows the collection method to be customized by reflecting the company's past feedback. Some or all of the above-mentioned processing in the company information collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the company information collection unit can input company feedback data into AI, which analyzes the data and selects an appropriate collection method.

[0048] During analysis, the information analysis unit can improve the accuracy of the analysis by taking into account the mutual relationships between NPOs and companies. For example, the information analysis unit performs analysis by taking into account past collaborative relationships between NPOs and companies. Furthermore, the information analysis unit can prioritize analysis of highly relevant information based on the mutual relationships between NPOs and companies. Furthermore, the accuracy of the analysis can also be improved by taking into account the mutual relationships between NPOs and companies. This allows the accuracy of the analysis to be improved by taking into account the mutual relationships between NPOs and companies. Some or all of the above-described processing in the information analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the information analysis unit can input relationship data between NPOs and companies into AI, which then analyzes the data and performs an appropriate analysis.

[0049] The information analysis unit can perform the analysis while taking into account the attribute information of NPOs and companies. The information analysis unit performs the analysis while taking into account, for example, the size and activities of the NPOs and companies. Furthermore, the analysis can prioritize highly relevant information based on the attribute information of the NPOs and companies. Furthermore, the accuracy of the analysis can be improved by taking into account the attribute information of the NPOs and companies. This allows the analysis to be performed while taking into account the attribute information of the NPOs and companies. Some or all of the above-described processing in the information analysis unit can be performed using, for example, AI, or without AI. For example, the information analysis unit can input attribute data of NPOs and companies into AI, which then analyzes the data and performs an appropriate analysis.

[0050] During analysis, the information analysis unit can weight the analysis based on the activity frequency of NPOs and companies. For example, if the activity frequency of NPOs and companies is high, the information analysis unit can increase the weighting of the analysis. Also, if the activity frequency of NPOs and companies is low, the information analysis unit can relax the weighting of the analysis. Furthermore, the analysis weighting can be adjusted based on the activity frequency of NPOs and companies. This allows the analysis to be weighted based on the activity frequency of NPOs and companies. Some or all of the above-mentioned processing in the information analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the information analysis unit can input activity data of NPOs and companies into AI, which can analyze the data and perform appropriate weighting.

[0051] The information analysis unit can perform analysis taking into account the geographic distribution of NPOs and companies. For example, the information analysis unit prioritizes analysis of highly relevant information based on the geographic distribution of NPOs and companies. The accuracy of the analysis can also be improved by taking into account the geographic distribution of NPOs and companies. Furthermore, the optimal analysis method can be selected based on the geographic distribution of NPOs and companies. This allows analysis to be performed taking into account the geographic distribution of NPOs and companies. Some or all of the above-described processing in the information analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the information analysis unit can input geographic data of NPOs and companies into AI, which can then analyze the data and perform an appropriate analysis.

[0052] The information analysis unit can improve the accuracy of the analysis by referring to related literature during analysis. The information analysis unit, for example, refers to related literature to improve the accuracy of the analysis. The analysis method can also be improved based on the related literature. Furthermore, the optimal analysis method can be selected by referring to the related literature. This makes it possible to improve the accuracy of the analysis by referring to the related literature. Some or all of the above-mentioned processing in the information analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the information analysis unit can input related literature data into AI, which can analyze the data and perform an appropriate analysis.

[0053] The information analysis unit can take into account the market value of NPOs and companies during analysis. For example, the information analysis unit prioritizes analysis of highly relevant information based on the market value of NPOs and companies. The accuracy of the analysis can also be improved by taking into account the market value of NPOs and companies. Furthermore, the optimal analysis method can be selected based on the market value of NPOs and companies. This allows analysis to be performed taking into account the market value of NPOs and companies. Some or all of the above-mentioned processing in the information analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the information analysis unit can input market value data of NPOs and companies into AI, which can analyze the data and perform an appropriate analysis.

[0054] The proposal generation unit can adjust the level of detail of the proposal based on the importance of the NPO and the company when generating the proposal. For example, if the importance of the NPO and the company is high, the proposal generation unit generates a detailed proposal. Also, if the importance of the NPO and the company is low, the proposal generation unit can generate a simple proposal. Furthermore, the level of detail of the proposal can be adjusted based on the importance of the NPO and the company. This allows the level of detail of the proposal to be adjusted based on the importance of the NPO and the company. Some or all of the above-mentioned processing in the proposal generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal generation unit can input importance data of the NPO and the company into AI, which analyzes the data and adjusts the level of detail of the proposal.

[0055] The proposal generation unit can apply different proposal algorithms depending on the NPO and company category when generating proposals. The proposal generation unit, for example, selects an optimal proposal algorithm depending on the NPO and company category. The proposal algorithm can also be customized based on the NPO and company category. Furthermore, different proposal algorithms can be applied depending on the NPO and company category. This allows different proposal algorithms to be applied depending on the NPO and company category. Some or all of the above-mentioned processing in the proposal generation unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal generation unit can input NPO and company category data into AI, which analyzes the data and applies an appropriate proposal algorithm.

[0056] When generating a proposal, the proposal generation unit can improve the accuracy of the proposal by referring to past proposal results of NPOs and companies. The proposal generation unit can improve the accuracy of the proposal based on, for example, past proposal results of NPOs and companies. The proposal content can also be improved by referring to past proposal results of NPOs and companies. Furthermore, the proposal generation unit can analyze past proposal results of NPOs and companies to generate optimal proposals. This allows the accuracy of proposals to be improved by referring to past proposal results of NPOs and companies. Some or all of the above-mentioned processing in the proposal generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the proposal generation unit can input past proposal data of NPOs and companies into AI, which can analyze the data and generate appropriate proposals.

[0057] When generating proposals, the proposal generation unit can determine the priority of proposals based on the submission dates of NPOs and companies. For example, if the submission dates of NPOs and companies are early, the proposal generation unit can generate proposals with priority. Also, if the submission dates of NPOs and companies are late, the proposal generation unit can postpone the generation of proposals. Furthermore, the priority of proposals can be determined based on the submission dates of NPOs and companies. This makes it possible to determine the priority of proposals based on the submission dates of NPOs and companies. Some or all of the above-mentioned processing in the proposal generation unit may be performed using, or without, AI, for example. For example, the proposal generation unit can input submission date data of NPOs and companies into AI, which can analyze the data and determine the priority of proposals.

[0058] The proposal generation unit can adjust the order of proposals based on the relevance between NPOs and companies when generating proposals. For example, if the relevance between NPOs and companies is high, the proposal generation unit can generate proposals as a priority. Also, if the relevance between NPOs and companies is low, the proposal generation unit can postpone the generation of proposals. Furthermore, the order of proposals can be adjusted based on the relevance between NPOs and companies. This allows the order of proposals to be adjusted based on the relevance between NPOs and companies. Some or all of the above-mentioned processing in the proposal generation unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal generation unit can input relevance data between NPOs and companies into AI, which analyzes the data and adjusts the order of proposals.

[0059] When generating a proposal, the proposal generation unit can adjust the use of technical terminology in the proposal depending on the expertise level of the NPO and the company. For example, if the expertise level of the NPO and the company is high, the proposal generation unit can generate a proposal that uses a lot of technical terminology. Also, if the expertise level of the NPO and the company is low, the proposal generation unit can generate a proposal that avoids technical terminology. Furthermore, the use of technical terminology in the proposal can be adjusted depending on the expertise level of the NPO and the company. This allows the use of technical terminology in the proposal to be adjusted depending on the expertise level of the NPO and the company. Some or all of the above-mentioned processing in the proposal generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal generation unit can input expertise level data of the NPO and the company into AI, which can analyze the data and adjust the use of technical terminology in the proposal.

[0060] When providing a proposal, the proposal providing unit can select an appropriate delivery method by referring to the company's past proposal history. The proposal providing unit, for example, selects the optimal delivery method based on the company's past proposal history. The proposal content can also be improved by referring to the company's past proposal history. Furthermore, the proposal providing unit can analyze the company's past proposal history and select the optimal delivery method. In this way, the optimal delivery method can be selected by referring to the company's past proposal history. Some or all of the above-mentioned processing in the proposal providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal providing unit can input the company's past proposal history data into AI, which can analyze the data and select an appropriate delivery method.

[0061] When providing a proposal, the proposal providing unit can customize the content to be provided according to the company's current CSR activities. The proposal providing unit, for example, provides proposal content related to the company's current CSR activities. The proposal content can also be customized based on the company's CSR activities. Furthermore, the proposal content can be provided that is optimal according to the company's current CSR activities. This allows the content to be customized according to the company's current CSR activities. Some or all of the above-mentioned processing in the proposal providing unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal providing unit can input the company's CSR activity data into AI, which analyzes the data and provides appropriate proposal content.

[0062] The proposal providing unit can improve the proposal providing method by reflecting the company's feedback when providing a proposal. The proposal providing unit can, for example, improve the proposal providing method based on the company's feedback. It can also adjust the content of the proposal by reflecting the company's feedback. It can also select the optimal proposal providing method by referring to the company's feedback. This allows the proposal providing method to be improved by reflecting the company's feedback. Some or all of the above-mentioned processing in the proposal providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the proposal providing unit can input the company's feedback data into AI, which can analyze the data and select an appropriate proposal providing method.

[0063] When providing a proposal, the proposal providing unit can select an appropriate delivery method by taking into consideration the geographic location information of the company. The proposal providing unit, for example, selects the optimal delivery method based on the geographic location information of the company. The proposal content can also be customized by taking into consideration the geographic location information of the company. Furthermore, highly relevant proposals can be provided based on the geographic location information of the company. This makes it possible to select the optimal delivery method by taking into consideration the geographic location information of the company. Some or all of the above-mentioned processing in the proposal providing unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal providing unit can input the geographic location data of the company into AI, which analyzes the data and selects an appropriate delivery method.

[0064] When providing proposals, the proposal providing unit can analyze the company's social media activity and propose proposal content. The proposal providing unit, for example, analyzes the company's social media activity and provides relevant proposal content. The proposal providing unit can also customize the proposal content based on the content posted on the company's social media. Furthermore, it can analyze the reactions of the company's followers on social media and provide relevant proposal content. In this way, the company's social media activity can be analyzed and proposal content can be proposed. Some or all of the above-mentioned processing in the proposal providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal providing unit can input the company's social media data into AI, which analyzes the data and provides appropriate proposal content.

[0065] When providing a proposal, the proposal providing unit can customize the proposal providing method by reflecting the company's past feedback. The proposal providing unit can, for example, improve the proposal providing method based on the company's past feedback. It can also adjust the content of the proposal by reflecting the company's past feedback. It can also select the optimal proposal method by referring to the company's past feedback. This allows the proposal providing method to be customized by reflecting the company's past feedback. Some or all of the above-mentioned processing in the proposal providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the proposal providing unit can input the company's feedback data into AI, which can analyze the data and select an appropriate proposal method.

[0066] When selecting candidates, the candidate selection unit can improve the accuracy of the selection by taking into account the mutual relationships between NPOs and companies. The candidate selection unit, for example, selects candidates by taking into account past collaborative relationships between NPOs and companies. It can also prioritize the selection of highly relevant candidates based on the mutual relationships between NPOs and companies. Furthermore, the accuracy of the selection can also be improved by taking into account the mutual relationships between NPOs and companies. This improves the accuracy of the selection by taking into account the mutual relationships between NPOs and companies. Some or all of the above-described processing in the candidate selection unit may be performed using, or without, AI, for example. For example, the candidate selection unit can input relationship data between NPOs and companies into AI, which then analyzes the data to select appropriate candidates.

[0067] When selecting candidates, the candidate selection unit can take into consideration the attribute information of the NPOs and companies. The candidate selection unit can select candidates, for example, taking into consideration the size and activities of the NPOs and companies. It can also prioritize the selection of highly relevant candidates based on the attribute information of the NPOs and companies. Furthermore, the accuracy of the selection can be improved by taking into consideration the attribute information of the NPOs and companies. This allows the selection to be made taking into consideration the attribute information of the NPOs and companies. Some or all of the above-described processing in the candidate selection unit can be performed using, for example, AI, or can be performed without using AI. For example, the candidate selection unit can input attribute data of the NPOs and companies into AI, which can then analyze the data and select appropriate candidates.

[0068] When selecting candidates, the candidate selection unit can weight the selection based on the frequency of NPO and company activities. For example, if the frequency of NPO and company activities is high, the candidate selection unit can increase the weighting of the selection. Also, if the frequency of NPO and company activities is low, the candidate selection unit can relax the weighting of the selection. Furthermore, the candidate selection unit can adjust the weighting of the selection based on the frequency of NPO and company activities. This allows the candidate selection to be weighted based on the frequency of NPO and company activities. Some or all of the above-described processing in the candidate selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the candidate selection unit can input activity data of NPOs and companies into AI, which analyzes the data and performs appropriate weighting.

[0069] When selecting candidates, the candidate selection unit can take into consideration the geographic distribution of NPOs and companies. For example, the candidate selection unit preferentially selects highly relevant candidates based on the geographic distribution of NPOs and companies. The accuracy of the selection can also be improved by taking into consideration the geographic distribution of NPOs and companies. Furthermore, the optimal candidate can be selected based on the geographic distribution of NPOs and companies. This allows the selection to be made taking into consideration the geographic distribution of NPOs and companies. Some or all of the above-described processing in the candidate selection unit can be performed using, for example, AI, or can be performed without using AI. For example, the candidate selection unit can input geographic data of NPOs and companies into AI, which can analyze the data to select appropriate candidates.

[0070] The candidate selection unit can improve the accuracy of selection by referring to related literature when selecting candidates. The candidate selection unit can, for example, refer to related literature to improve the accuracy of selection. The selection method can also be improved based on the related literature. Furthermore, the optimal selection method can be selected by referring to the related literature. This can improve the accuracy of selection by referring to the related literature. Some or all of the above-mentioned processing in the candidate selection unit can be performed, for example, using AI, or can be performed without using AI. For example, the candidate selection unit can input related literature data into AI, which analyzes the data and makes an appropriate selection.

[0071] When selecting candidates, the candidate selection unit can take into consideration the market value of the NPO and the company. For example, the candidate selection unit preferentially selects highly relevant candidates based on the market value of the NPO and the company. The accuracy of the selection can also be improved by taking into consideration the market value of the NPO and the company. Furthermore, the optimal candidate can be selected based on the market value of the NPO and the company. This allows the selection to be made taking into consideration the market value of the NPO and the company. Some or all of the above-mentioned processing in the candidate selection unit can be performed using, for example, AI, or can be performed without using AI. For example, the candidate selection unit can input market value data of the NPO and the company into AI, which can analyze the data and select appropriate candidates.

[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 NPO Information Collection Department analyzes information entered by NPO personnel in real time, automatically detecting and completing any incomplete or missing information. For example, if an NPO is unclear about the unit of measurement when entering the amount of funding it wishes to raise, the system will automatically suggest the appropriate unit. Also, if the project description is insufficient, the system can ask additional questions to complete the information. Furthermore, it can also optimize the information entered by comparing it with a past database and referring to successful examples of similar projects. This streamlines the data entry process for NPOs and collects more accurate information.

[0074] When a company wishes to make a donation, the Corporate Information Collection Department can provide a simulation function to visualize the impact of the donation. For example, if a company donates a specific amount, it can simulate what kind of social effect that donation will have. It can also predict the effect of the donation based on the past performance of the recipient NPO. It can also generate graphs and charts that show the relationship between a company's CSR activities and donations, making it easier for companies to understand the significance of donations. This allows companies to concretely understand the effects of their donations and become more motivated to donate.

[0075] The NPO Information Collection Department can propose the possibility of collaboration with other NPOs based on the information entered by NPO staff. For example, it can introduce NPOs that are engaged in similar activities in the same area and propose joint projects. It can also explain the benefits of collaboration based on past successful collaboration cases. It can also simulate the effect of fundraising through collaboration and make specific proposals. This can promote collaboration between NPOs and improve fundraising efficiency.

[0076] When a company wishes to make a donation, the Corporate Information Collection Department can provide a simulation function to visualize the impact of the donation. For example, if a company donates a specific amount, it can simulate what kind of social effect that donation will have. It can also predict the effect of the donation based on the past performance of the recipient NPO. It can also generate graphs and charts that show the relationship between a company's CSR activities and donations, making it easier for companies to understand the significance of donations. This allows companies to concretely understand the effects of their donations and become more motivated to donate.

[0077] The processing flow of the first embodiment will be briefly explained below.

[0078] Step 1: The NPO Information Collection Department collects information about NPOs. This information includes the amount of funding they wish to raise, how it will be used, and specific projects and activities. For example, when an NPO wishes to raise funds, they enter the amount they need and how it will be used. It can also collect information about the NPO's past fundraising history and current projects. Step 2: The Corporate Information Collection Department collects corporate information. Corporate information includes the desired donation amount, the message the company wishes to convey to society, and past donation history. For example, when a company wishes to make a donation, it inputs the message it wishes to convey to society. Information can also be collected from a database of corporate CSR activities. Step 3: The Information Analysis Department analyzes the information collected by the NPO Information Collection Department and the Corporate Information Collection Department. This analysis uses methods such as statistical analysis, machine learning algorithms, and text mining. For example, the department selects the most suitable companies based on a database of companies' past donation histories and CSR activities. Step 4: The proposal generation unit generates proposal content based on the information analyzed by the information analysis unit. The proposal content includes fundraising proposals, cooperation project proposals, etc. For example, specific proposal content is generated for a company. Step 5: The proposal providing unit provides the proposal content generated by the proposal generating unit to the company. The method of providing the proposal includes notifying the company by email, displaying the proposal on a dashboard, etc. For example, the proposal providing unit may suggest a donation to the company. Step 6: The candidate selection unit selects potential donation recipients based on the information analyzed by the information analysis unit. Selection criteria include a scoring system and filtering conditions. For example, based on a database of NPOs, it selects NPOs that match the company's message.

[0079] (Example 2) A system according to an embodiment of the present invention uses a generation AI to enable both NPOs and corporations to efficiently raise funds and make donations. When an NPO seeks fundraising, it inputs the amount needed and its intended purpose. The generation AI analyzes the input information and generates appropriate candidate companies and proposals. When a corporation seeks donations, it inputs the message it wishes to convey to society. The generation AI analyzes this information and suggests appropriate potential recipients. For example, when an NPO seeks fundraising, it is required to provide a detailed description of the specific project and activities. For example, if 1 million yen is needed for environmental protection activities, the corporation inputs the detailed purpose and expected benefits. This information is input into the generation AI. The generation AI then analyzes the input information and generates appropriate candidate companies and proposals. The generation AI selects the most suitable company based on a database of the company's past donation history and corporate social responsibility (CSR) activities. For example, the system prioritizes companies that have donated to environmental protection activities in the past and generates specific proposals for those companies. Meanwhile, when a corporation seeks donations, it inputs the message it wishes to convey to society. For example, the corporation inputs a message such as "I want to contribute to environmental protection." This information is also input into the generation AI. The generation AI analyzes the information input by the company and suggests appropriate donation candidates. Based on a database of NPOs, the generation AI selects NPOs that match the company's message. For example, it may prioritize NPOs engaged in environmental protection activities and suggest donations to those NPOs. This allows NPOs to raise funds efficiently and companies to effectively communicate their social messages. The system allows both NPOs and companies to raise funds and make donations efficiently. For example, it simplifies the fundraising process for NPOs and allows companies to effectively communicate their social messages. In addition, the SB, which acts as a bridge between the two parties, can increase profits and improve their corporate image by receiving matching rewards.

[0080] The fundraising and donation support system according to the embodiment includes an NPO information collection unit, a company information collection unit, an information analysis unit, a proposal generation unit, a proposal provision unit, and a candidate selection unit. The NPO information collection unit collects information about NPOs. The NPO information includes, for example, but is not limited to, the desired amount of fundraising, its intended purpose, and specific projects and activities. For example, when an NPO requests fundraising, the NPO information collection unit inputs the amount needed and its intended purpose. The NPO information collection unit can also collect the NPO's past fundraising history and current project information. The company information collection unit collects company information. The company information includes, for example, but is not limited to, the desired donation amount, a message the company wishes to convey to society, and past donation history. For example, when a company requests a donation, the company information collection unit inputs the message the company wishes to convey to society. The company information collection unit can also collect information based on a database of companies' CSR activities. The information analysis unit analyzes the information collected by the NPO information collection unit and the company information collection unit. The analysis may involve, but is not limited to, statistical analysis, machine learning algorithms, text mining, or other methods. The information analysis unit selects the most suitable company based on, for example, a database of the company's past donation history and CSR activities. The proposal generation unit generates proposal content based on the information analyzed by the information analysis unit. The proposal content may include, for example, a fundraising proposal, a collaborative project proposal, or the like, but is not limited to these examples. The proposal generation unit generates specific proposal content for the company, for example. The proposal providing unit provides the proposal content generated by the proposal generation unit to the company. Methods of providing the proposal include, for example, notification by email, display on a dashboard, or the like, but are not limited to these examples. The proposal providing unit proposes, for example, a donation to the company. The candidate selection unit selects candidate donation recipients based on the information analyzed by the information analysis unit. Selection criteria may include, for example, a scoring system, filtering conditions, or the like, but are not limited to these examples. The candidate selection unit selects NPOs that match the company's message based on, for example, a database of NPOs.As a result, the fundraising and donation support system according to the embodiment allows both NPO corporations and companies to efficiently raise funds and make donations.

[0081] The NPO information collection unit can input the amount needed and its purpose when an NPO seeks to raise funds. For example, when an NPO seeks to raise funds, the NPO information collection unit inputs the amount needed and its purpose. The amount needed includes, but is not limited to, a minimum amount, a maximum amount, and a unit of amount. The purpose includes, but is not limited to, the type of project and the purpose of the funds. This allows the NPO to input its specific fundraising needs. Some or all of the above-described processing in the NPO information collection unit may be performed using, or without, AI. For example, the NPO information collection unit can input the information input by the NPO into AI, which then analyzes and converts the information into an appropriate format.

[0082] The company information collection unit can input a message that a company wishes to send to society when it wishes to make a donation. For example, when a company wishes to make a donation, the company information collection unit inputs a message that the company wishes to send to society. The message that the company wishes to send to society includes, but is not limited to, details of CSR activities, the company's vision and mission, etc. This allows the company to input a social message. Some or all of the above-mentioned processing in the company information collection unit may be performed, for example, using AI or may be performed without using AI. For example, the company information collection unit can input the message input by the company into AI, which can analyze the message and convert it into an appropriate format.

[0083] The information analysis unit can select appropriate companies based on a database of companies' past donation histories and corporate social responsibility (CSR) activities. The information analysis unit selects appropriate companies based on, for example, a database of companies' past donation histories and CSR activities. Criteria for selecting appropriate companies include, for example, evaluation criteria for donation history and evaluation criteria for CSR activities, but are not limited to these examples. This allows the optimal company to be selected taking into account the company's past donation history and CSR activities. Some or all of the above-described processing in the information analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the information analysis unit can input companies' donation history data into AI, which then analyzes the data to select appropriate companies.

[0084] The proposal generation unit can generate specific proposal content for a company. The proposal generation unit generates specific proposal content for a company, for example. The specific proposal content includes, for example, the donation amount, the donation method, and the recipient of the donation, but is not limited to these examples. This allows specific proposal content to be generated for a company. Some or all of the above-mentioned processing in the proposal generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal generation unit can input company information into AI, which analyzes the information and generates specific proposal content.

[0085] The candidate selection unit can select an NPO that matches the company's message based on a database of NPOs. The candidate selection unit selects an NPO that matches the company's message based on, for example, a database of NPOs. Criteria for matching a company's message include, but are not limited to, keyword matching, theme matching, etc. This allows an NPO that matches the company's message to be selected. Some or all of the above-described processing in the candidate selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the candidate selection unit can input the company's message into AI, which analyzes the message and selects an appropriate NPO.

[0086] The proposal providing unit can propose a donation to a company. The proposal providing unit, for example, proposes a donation to a company. Methods for proposing a donation include, but are not limited to, for example, notification by email or display on a dashboard. In this way, a donation can be proposed to a company. Some or all of the above-mentioned processing in the proposal providing unit may be performed, for example, using AI or may be performed without using AI. For example, the proposal providing unit can input the proposal content into AI, which analyzes the proposal content and provides it to the company in an appropriate format.

[0087] The NPO information collection unit can estimate the emotions of NPO staff and adjust the timing of information collection based on the estimated emotions. For example, if an NPO staff member is stressed, the NPO information collection unit can delay the timing of information collection so that the staff member can provide information in a relaxed state. Furthermore, if an NPO staff member is busy, the NPO information collection unit can adjust the timing of information collection to suit the staff member's schedule. Furthermore, if an NPO staff member is relaxed, information can be collected immediately and data can be acquired quickly. This allows the timing of information collection to be adjusted according to the emotions of the NPO staff member. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the NPO information collection unit can be performed using, for example, AI, or without AI. For example, the NPO information collection unit can input emotional data of an NPO staff member into AI, which can analyze the emotions and adjust the timing of information collection.

[0088] The NPO information collection unit can analyze the NPO's past fundraising history and select an appropriate information collection method. For example, the NPO information collection unit can analyze the NPO's past fundraising history and prioritize successful methods. The NPO information collection unit can also select an information collection method based on the NPO's past fundraising history to avoid unsuccessful methods. Furthermore, the most effective information collection means can be selected based on the NPO's past fundraising history. This allows the optimal information collection method to be selected based on the NPO's past fundraising history. Some or all of the above-described processing in the NPO information collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the NPO information collection unit can input the NPO's past fundraising data into AI, which can analyze the data and select an appropriate information collection method.

[0089] When collecting information about NPOs, the NPO information collection unit can filter the information based on the NPO's current projects and activities. For example, the NPO information collection unit prioritizes collecting information related to the NPO's ongoing projects. It can also filter less relevant information based on the NPO's activities. It can also focus on collecting information necessary for the NPO's current projects. This allows information to be filtered based on the NPO's current projects and activities. Some or all of the above-mentioned processing in the NPO information collection unit may be performed using, or without, AI, for example. For example, the NPO information collection unit can input NPO project information into AI, which analyzes the information and performs appropriate filtering.

[0090] When collecting information about an NPO, the NPO information collection unit can select an appropriate collection means depending on the input method. For example, if the NPO uses voice input, the NPO information collection unit can collect information using voice recognition technology. Also, if the NPO uses text input, the NPO information collection unit can collect information using text analysis technology. Furthermore, if the NPO uses image input, the NPO information collection unit can collect information using image recognition technology. This allows the optimal collection means to be selected depending on the input method of the NPO. Some or all of the above-mentioned processing in the NPO information collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the NPO information collection unit can input the NPO's input data into AI, which then analyzes the data and selects an appropriate collection means.

[0091] The NPO information collection unit can estimate the emotions of NPO staff and determine the priority of information to be collected based on the estimated emotions. For example, if the NPO staff is stressed, the NPO information collection unit can prioritize collecting information of high importance. Also, if the NPO staff is relaxed, the NPO information collection unit can collect detailed information. Furthermore, if the NPO staff is busy, the NPO information collection unit can prioritize collecting the minimum necessary information. This allows the priority of information to be collected to be determined based on the emotions of the NPO staff. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the NPO information collection unit may be performed using, for example, AI, or without AI. For example, the NPO information collection unit can input emotional data of the NPO staff into AI, which can analyze the emotions and determine the priority of information.

[0092] When collecting information on NPOs, the NPO information collection unit can prioritize collecting highly relevant information by taking geographical location information into consideration. The NPO information collection unit, for example, prioritizes collecting information related to the NPO's area of ​​activity. It can also filter highly relevant information based on the NPO's geographical location information. It can also collect optimal information depending on the NPO's area of ​​activity. This allows highly relevant information to be prioritized by taking the NPO's geographical location information into consideration. Some or all of the above-described processing in the NPO information collection unit may be performed using, or without, AI, for example. For example, the NPO information collection unit can input the NPO's geographical location data into AI, which can analyze the data and collect appropriate information.

[0093] When collecting information about an NPO, the NPO information collection unit can analyze social media activities and collect related information. For example, the NPO information collection unit analyzes the NPO's social media activities and collects related information. The NPO information collection unit can also collect related information based on the content of the NPO's social media posts. Furthermore, it can analyze the reactions of the NPO's followers on social media and collect related information. In this way, the NPO's social media activities can be analyzed and related information can be collected. Some or all of the above-mentioned processing in the NPO information collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the NPO information collection unit can input the NPO's social media data into AI, which can analyze the data and collect appropriate information.

[0094] When collecting information on NPOs, the NPO information collection unit can customize the collection method by reflecting past feedback. The NPO information collection unit, for example, improves the information collection method based on the NPO's past feedback. It can also adjust the type of information to be collected by reflecting the NPO's past feedback. Furthermore, it can select the optimal information collection means by referring to the NPO's past feedback. This allows the collection method to be customized by reflecting the NPO's past feedback. Some or all of the above-mentioned processing in the NPO information collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the NPO information collection unit can input the NPO's feedback data into AI, which analyzes the data and selects an appropriate collection method.

[0095] The corporate information collection unit can estimate the emotions of the company representative and adjust the timing of information collection based on the estimated emotions. For example, if the company representative is stressed, the corporate information collection unit can delay the timing of information collection so that the representative can provide information in a relaxed state. Furthermore, if the company representative is busy, the corporate information collection unit can adjust the timing of information collection to suit the representative's schedule. Furthermore, if the company representative is relaxed, information can be collected immediately and data can be obtained quickly. This allows the timing of information collection to be adjusted according to the company representative's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the corporate information collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the corporate information collection unit can input emotional data of the company representative into an AI, which can analyze the emotions and adjust the timing of information collection.

[0096] The corporate information collection unit can analyze a company's past donation history and select an appropriate information collection method. For example, the corporate information collection unit can analyze a company's past donation history and prioritize successful methods. The corporate information collection unit can also select an information collection method based on a company's past donation history to avoid unsuccessful methods. Furthermore, the most effective information collection means can be selected based on a company's past donation history. This makes it possible to select the optimal information collection method based on a company's past donation history. Some or all of the above-mentioned processing in the corporate information collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the corporate information collection unit can input a company's donation history data into AI, which can analyze the data and select an appropriate information collection method.

[0097] When collecting information about a company, the company information collection unit can filter the information based on the company's current CSR activities and social messages. For example, the company information collection unit prioritizes collecting information related to the company's current CSR activities. It can also filter out less relevant information based on the company's social messages. It can also focus on collecting information necessary for the company's current CSR activities. This allows information to be filtered based on the company's current CSR activities and social messages. Some or all of the above-mentioned processing in the company information collection unit may be performed using, or without, AI, for example. For example, the company information collection unit can input company CSR activity data into AI, which can analyze the data and collect appropriate information.

[0098] When collecting company information, the company information collection unit can select an appropriate collection means depending on the input method. For example, if a company uses voice input, the company information collection unit can collect information using voice recognition technology. Also, if a company uses text input, the company information collection unit can collect information using text analysis technology. Furthermore, if a company uses image input, the company information collection unit can collect information using image recognition technology. This makes it possible to select the optimal collection means depending on the company's input method. Some or all of the above-mentioned processing in the company information collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the company information collection unit can input the company's input data into AI, which then analyzes the data and selects the appropriate collection means.

[0099] The corporate information collection unit can estimate the emotions of the company personnel and determine the priority of information to be collected based on the estimated emotions. For example, if the company personnel is stressed, the corporate information collection unit can prioritize collecting information of high importance. Furthermore, if the company personnel is relaxed, the corporate information collection unit can also prioritize collecting detailed information. Furthermore, if the company personnel is busy, the corporate information collection unit can prioritize collecting the minimum necessary information. This allows the priority of information to be collected to be determined based on the emotions of the company personnel. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the corporate information collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the corporate information collection unit can input emotional data of the company personnel into an AI, which then analyzes the emotions and determines the priority of information.

[0100] When collecting information about a company, the company information collection unit can prioritize collecting highly relevant information by taking geographical location information into consideration. The company information collection unit, for example, prioritizes collecting information related to the company's area of ​​activity. The company information collection unit can also filter highly relevant information based on the company's geographical location information. Furthermore, optimal information can be collected depending on the company's area of ​​activity. This makes it possible to prioritize collecting highly relevant information by taking the company's geographical location information into consideration. Some or all of the above-mentioned processing in the company information collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the company information collection unit can input company geographical location data into AI, which can analyze the data and collect appropriate information.

[0101] When collecting information about a company, the company information collection unit can analyze social media activities and collect related information. The company information collection unit, for example, analyzes the company's social media activities and collects related information. The company information collection unit can also collect related information based on the content of the company's social media posts. Furthermore, the company information collection unit can analyze the reactions of the company's followers on social media and collect related information. In this way, the company's social media activities can be analyzed and related information can be collected. Some or all of the above-mentioned processing in the company information collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the company information collection unit can input the company's social media data into AI, which can analyze the data and collect appropriate information.

[0102] When collecting information on a company, the company information collection unit can customize the collection method by reflecting past feedback. The company information collection unit, for example, improves the information collection method based on the company's past feedback. The company information collection unit can also adjust the type of information to be collected by reflecting the company's past feedback. Furthermore, the company's past feedback can be used as a reference to select the optimal information collection means. This allows the collection method to be customized by reflecting the company's past feedback. Some or all of the above-mentioned processing in the company information collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the company information collection unit can input company feedback data into AI, which analyzes the data and selects an appropriate collection method.

[0103] The information analysis unit can estimate the emotions of NPO and company personnel and adjust the analysis criteria based on the estimated emotions. For example, if the NPO and company personnel are stressed, the information analysis unit can relax the analysis criteria and perform a simple analysis. If the NPO and company personnel are relaxed, the information analysis unit can also perform a detailed analysis. Furthermore, if the NPO and company personnel are busy, the information analysis unit can also perform the minimum necessary analysis. This allows the analysis criteria to be adjusted according to the emotions of the NPO and company personnel. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the information analysis unit can be performed using, for example, an AI, or without an AI. For example, the information analysis unit can input emotion data of the NPO and company personnel into an AI, which can analyze the emotions and adjust the analysis criteria.

[0104] During analysis, the information analysis unit can improve the accuracy of the analysis by taking into account the mutual relationships between NPOs and companies. For example, the information analysis unit performs analysis by taking into account past collaborative relationships between NPOs and companies. Furthermore, the information analysis unit can prioritize analysis of highly relevant information based on the mutual relationships between NPOs and companies. Furthermore, the accuracy of the analysis can also be improved by taking into account the mutual relationships between NPOs and companies. This allows the accuracy of the analysis to be improved by taking into account the mutual relationships between NPOs and companies. Some or all of the above-described processing in the information analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the information analysis unit can input relationship data between NPOs and companies into AI, which then analyzes the data and performs an appropriate analysis.

[0105] The information analysis unit can perform the analysis while taking into account the attribute information of NPOs and companies. The information analysis unit performs the analysis while taking into account, for example, the size and activities of the NPOs and companies. Furthermore, the analysis can prioritize highly relevant information based on the attribute information of the NPOs and companies. Furthermore, the accuracy of the analysis can be improved by taking into account the attribute information of the NPOs and companies. This allows the analysis to be performed while taking into account the attribute information of the NPOs and companies. Some or all of the above-described processing in the information analysis unit can be performed using, for example, AI, or without AI. For example, the information analysis unit can input attribute data of NPOs and companies into AI, which then analyzes the data and performs an appropriate analysis.

[0106] During analysis, the information analysis unit can weight the analysis based on the activity frequency of NPOs and companies. For example, if the activity frequency of NPOs and companies is high, the information analysis unit can increase the weighting of the analysis. Also, if the activity frequency of NPOs and companies is low, the information analysis unit can relax the weighting of the analysis. Furthermore, the analysis weighting can be adjusted based on the activity frequency of NPOs and companies. This allows the analysis to be weighted based on the activity frequency of NPOs and companies. Some or all of the above-mentioned processing in the information analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the information analysis unit can input activity data of NPOs and companies into AI, which can analyze the data and perform appropriate weighting.

[0107] The information analysis unit can estimate the emotions of NPO and company personnel and adjust the display order of analysis results based on the estimated emotions. For example, if the NPO and company personnel are feeling stressed, the information analysis unit can prioritize displaying important analysis results. Furthermore, if the NPO and company personnel are relaxed, the information analysis unit can also display detailed analysis results. Furthermore, if the NPO and company personnel are busy, the information analysis unit can prioritize displaying the minimum necessary analysis results. This allows the display order of analysis results to be adjusted according to the emotions of the NPO and company personnel. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the information analysis unit can be performed using, for example, AI, or without AI. For example, the information analysis unit can input emotion data of the NPO and company personnel into AI, which can analyze the emotions and adjust the display order of the analysis results.

[0108] The information analysis unit can perform analysis taking into account the geographic distribution of NPOs and companies. For example, the information analysis unit prioritizes analysis of highly relevant information based on the geographic distribution of NPOs and companies. The accuracy of the analysis can also be improved by taking into account the geographic distribution of NPOs and companies. Furthermore, the optimal analysis method can be selected based on the geographic distribution of NPOs and companies. This allows analysis to be performed taking into account the geographic distribution of NPOs and companies. Some or all of the above-described processing in the information analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the information analysis unit can input geographic data of NPOs and companies into AI, which can then analyze the data and perform an appropriate analysis.

[0109] The information analysis unit can improve the accuracy of the analysis by referring to related literature during analysis. The information analysis unit, for example, refers to related literature to improve the accuracy of the analysis. The analysis method can also be improved based on the related literature. Furthermore, the optimal analysis method can be selected by referring to the related literature. This makes it possible to improve the accuracy of the analysis by referring to the related literature. Some or all of the above-mentioned processing in the information analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the information analysis unit can input related literature data into AI, which can analyze the data and perform an appropriate analysis.

[0110] The information analysis unit can take into account the market value of NPOs and companies during analysis. For example, the information analysis unit prioritizes analysis of highly relevant information based on the market value of NPOs and companies. The accuracy of the analysis can also be improved by taking into account the market value of NPOs and companies. Furthermore, the optimal analysis method can be selected based on the market value of NPOs and companies. This allows analysis to be performed taking into account the market value of NPOs and companies. Some or all of the above-mentioned processing in the information analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the information analysis unit can input market value data of NPOs and companies into AI, which can analyze the data and perform an appropriate analysis.

[0111] The proposal generation unit can estimate the emotions of the NPO and company personnel and adjust the way the proposal is presented based on the estimated emotions. For example, if the NPO and company personnel are stressed, the proposal generation unit can generate a simple, highly visible proposal. If the NPO and company personnel are relaxed, the proposal generation unit can also generate a detailed proposal. Furthermore, if the NPO and company personnel are busy, the proposal generation unit can also generate a proposal that focuses on the main points. This allows the way the proposal is presented to be adjusted based on the emotions of the NPO and company personnel. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the proposal generation unit can be performed using, for example, AI, or without AI. For example, the proposal generation unit can input emotional data of the NPO and company personnel into AI, which can analyze the emotions and adjust the way the proposal is presented.

[0112] The proposal generation unit can adjust the level of detail of the proposal based on the importance of the NPO and the company when generating the proposal. For example, if the importance of the NPO and the company is high, the proposal generation unit generates a detailed proposal. Also, if the importance of the NPO and the company is low, the proposal generation unit can generate a simple proposal. Furthermore, the level of detail of the proposal can be adjusted based on the importance of the NPO and the company. This allows the level of detail of the proposal to be adjusted based on the importance of the NPO and the company. Some or all of the above-mentioned processing in the proposal generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal generation unit can input importance data of the NPO and the company into AI, which analyzes the data and adjusts the level of detail of the proposal.

[0113] The proposal generation unit can apply different proposal algorithms depending on the NPO and company category when generating proposals. The proposal generation unit, for example, selects an optimal proposal algorithm depending on the NPO and company category. The proposal algorithm can also be customized based on the NPO and company category. Furthermore, different proposal algorithms can be applied depending on the NPO and company category. This allows different proposal algorithms to be applied depending on the NPO and company category. Some or all of the above-mentioned processing in the proposal generation unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal generation unit can input NPO and company category data into AI, which analyzes the data and applies an appropriate proposal algorithm.

[0114] When generating a proposal, the proposal generation unit can improve the accuracy of the proposal by referring to past proposal results of NPOs and companies. The proposal generation unit can improve the accuracy of the proposal based on, for example, past proposal results of NPOs and companies. The proposal content can also be improved by referring to past proposal results of NPOs and companies. Furthermore, the proposal generation unit can analyze past proposal results of NPOs and companies to generate optimal proposals. This allows the accuracy of proposals to be improved by referring to past proposal results of NPOs and companies. Some or all of the above-mentioned processing in the proposal generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the proposal generation unit can input past proposal data of NPOs and companies into AI, which can analyze the data and generate appropriate proposals.

[0115] The proposal generation unit can estimate the emotions of the NPO and company personnel and adjust the length of the proposal based on the estimated emotions. For example, if the NPO and company personnel are stressed, the proposal generation unit can generate a short, to-the-point proposal. Alternatively, if the NPO and company personnel are relaxed, the proposal generation unit can generate a longer proposal with detailed explanations. Furthermore, if the NPO and company personnel are busy, the proposal generation unit can generate a concise proposal. This allows the length of the proposal to be adjusted according to the emotions of the NPO and company personnel. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the proposal generation unit can be performed using, for example, AI, or without AI. For example, the proposal generation unit can input emotion data of the NPO and company personnel into AI, which can analyze the emotions and adjust the length of the proposal.

[0116] When generating proposals, the proposal generation unit can determine the priority of proposals based on the submission dates of NPOs and companies. For example, if the submission dates of NPOs and companies are early, the proposal generation unit can generate proposals with priority. Also, if the submission dates of NPOs and companies are late, the proposal generation unit can postpone the generation of proposals. Furthermore, the priority of proposals can be determined based on the submission dates of NPOs and companies. This makes it possible to determine the priority of proposals based on the submission dates of NPOs and companies. Some or all of the above-mentioned processing in the proposal generation unit may be performed using, or without, AI, for example. For example, the proposal generation unit can input submission date data of NPOs and companies into AI, which can analyze the data and determine the priority of proposals.

[0117] The proposal generation unit can adjust the order of proposals based on the relevance between NPOs and companies when generating proposals. For example, if the relevance between NPOs and companies is high, the proposal generation unit can generate proposals as a priority. Also, if the relevance between NPOs and companies is low, the proposal generation unit can postpone the generation of proposals. Furthermore, the order of proposals can be adjusted based on the relevance between NPOs and companies. This allows the order of proposals to be adjusted based on the relevance between NPOs and companies. Some or all of the above-mentioned processing in the proposal generation unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal generation unit can input relevance data between NPOs and companies into AI, which analyzes the data and adjusts the order of proposals.

[0118] When generating a proposal, the proposal generation unit can adjust the use of technical terminology in the proposal depending on the expertise level of the NPO and the company. For example, if the expertise level of the NPO and the company is high, the proposal generation unit can generate a proposal that uses a lot of technical terminology. Also, if the expertise level of the NPO and the company is low, the proposal generation unit can generate a proposal that avoids technical terminology. Furthermore, the use of technical terminology in the proposal can be adjusted depending on the expertise level of the NPO and the company. This allows the use of technical terminology in the proposal to be adjusted depending on the expertise level of the NPO and the company. Some or all of the above-mentioned processing in the proposal generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal generation unit can input expertise level data of the NPO and the company into AI, which can analyze the data and adjust the use of technical terminology in the proposal.

[0119] The proposal providing unit can estimate the emotions of the company representative and adjust the method of providing proposals based on the estimated emotions. For example, if the company representative is stressed, the proposal providing unit can provide proposals in a simple and highly visible manner. Furthermore, if the company representative is relaxed, the proposal providing unit can provide detailed proposals. Furthermore, if the company representative is busy, the proposal providing unit can provide proposals that focus on the main points. This allows the method of providing proposals to be adjusted according to the emotions of the company representative. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the proposal providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the proposal providing unit can input emotional data of the company representative into an AI, which can analyze the emotions and adjust the method of providing proposals.

[0120] When providing a proposal, the proposal providing unit can select an appropriate delivery method by referring to the company's past proposal history. The proposal providing unit, for example, selects the optimal delivery method based on the company's past proposal history. The proposal content can also be improved by referring to the company's past proposal history. Furthermore, the proposal providing unit can analyze the company's past proposal history and select the optimal delivery method. In this way, the optimal delivery method can be selected by referring to the company's past proposal history. Some or all of the above-mentioned processing in the proposal providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal providing unit can input the company's past proposal history data into AI, which can analyze the data and select an appropriate delivery method.

[0121] When providing a proposal, the proposal providing unit can customize the content to be provided according to the company's current CSR activities. The proposal providing unit, for example, provides proposal content related to the company's current CSR activities. The proposal content can also be customized based on the company's CSR activities. Furthermore, the proposal content can be provided that is optimal according to the company's current CSR activities. This allows the content to be customized according to the company's current CSR activities. Some or all of the above-mentioned processing in the proposal providing unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal providing unit can input the company's CSR activity data into AI, which analyzes the data and provides appropriate proposal content.

[0122] The proposal providing unit can improve the proposal providing method by reflecting the company's feedback when providing a proposal. The proposal providing unit can, for example, improve the proposal providing method based on the company's feedback. It can also adjust the content of the proposal by reflecting the company's feedback. It can also select the optimal proposal providing method by referring to the company's feedback. This allows the proposal providing method to be improved by reflecting the company's feedback. Some or all of the above-mentioned processing in the proposal providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the proposal providing unit can input the company's feedback data into AI, which can analyze the data and select an appropriate proposal providing method.

[0123] The proposal providing unit can estimate the emotions of the company representative and determine the priority of providing proposals based on the estimated emotions. For example, if the company representative is stressed, the proposal providing unit can prioritize providing important proposals. Also, if the company representative is relaxed, the proposal providing unit can provide detailed proposals. Furthermore, if the company representative is busy, the proposal providing unit can prioritize providing the minimum necessary proposals. This makes it possible to determine the priority of providing proposals according to the emotions of the company representative. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the proposal providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the proposal providing unit can input emotional data of the company representative into an AI, which can analyze the emotions and determine the priority of providing proposals.

[0124] When providing a proposal, the proposal providing unit can select an appropriate delivery method by taking into consideration the geographic location information of the company. The proposal providing unit, for example, selects the optimal delivery method based on the geographic location information of the company. The proposal content can also be customized by taking into consideration the geographic location information of the company. Furthermore, highly relevant proposals can be provided based on the geographic location information of the company. This makes it possible to select the optimal delivery method by taking into consideration the geographic location information of the company. Some or all of the above-mentioned processing in the proposal providing unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal providing unit can input the geographic location data of the company into AI, which analyzes the data and selects an appropriate delivery method.

[0125] When providing proposals, the proposal providing unit can analyze the company's social media activity and propose proposal content. The proposal providing unit, for example, analyzes the company's social media activity and provides relevant proposal content. The proposal providing unit can also customize the proposal content based on the content posted on the company's social media. Furthermore, it can analyze the reactions of the company's followers on social media and provide relevant proposal content. In this way, the company's social media activity can be analyzed and proposal content can be proposed. Some or all of the above-mentioned processing in the proposal providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal providing unit can input the company's social media data into AI, which analyzes the data and provides appropriate proposal content.

[0126] When providing a proposal, the proposal providing unit can customize the proposal providing method by reflecting the company's past feedback. The proposal providing unit can, for example, improve the proposal providing method based on the company's past feedback. It can also adjust the content of the proposal by reflecting the company's past feedback. It can also select the optimal proposal method by referring to the company's past feedback. This allows the proposal providing method to be customized by reflecting the company's past feedback. Some or all of the above-mentioned processing in the proposal providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the proposal providing unit can input the company's feedback data into AI, which can analyze the data and select an appropriate proposal method.

[0127] The candidate selection unit can estimate the emotions of NPO and company personnel and adjust the candidate selection criteria based on the estimated emotions. For example, if the NPO and company personnel are stressed, the candidate selection unit can relax the selection criteria and select simple candidates. Furthermore, if the NPO and company personnel are relaxed, the candidate selection unit can select detailed candidates. Furthermore, if the NPO and company personnel are busy, the candidate selection unit can select the minimum number of candidates necessary. This allows the candidate selection criteria to be adjusted according to the emotions of the NPO and company personnel. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the candidate selection unit can be performed using, for example, an AI, or without an AI. For example, the candidate selection unit can input emotion data of the NPO and company personnel into an AI, which can analyze the emotions and adjust the candidate selection criteria.

[0128] When selecting candidates, the candidate selection unit can improve the accuracy of the selection by taking into account the mutual relationships between NPOs and companies. The candidate selection unit, for example, selects candidates by taking into account past collaborative relationships between NPOs and companies. It can also prioritize the selection of highly relevant candidates based on the mutual relationships between NPOs and companies. Furthermore, the accuracy of the selection can also be improved by taking into account the mutual relationships between NPOs and companies. This improves the accuracy of the selection by taking into account the mutual relationships between NPOs and companies. Some or all of the above-described processing in the candidate selection unit may be performed using, or without, AI, for example. For example, the candidate selection unit can input relationship data between NPOs and companies into AI, which then analyzes the data to select appropriate candidates.

[0129] When selecting candidates, the candidate selection unit can take into consideration the attribute information of the NPOs and companies. The candidate selection unit can select candidates, for example, taking into consideration the size and activities of the NPOs and companies. It can also prioritize the selection of highly relevant candidates based on the attribute information of the NPOs and companies. Furthermore, the accuracy of the selection can be improved by taking into consideration the attribute information of the NPOs and companies. This allows the selection to be made taking into consideration the attribute information of the NPOs and companies. Some or all of the above-described processing in the candidate selection unit can be performed using, for example, AI, or can be performed without using AI. For example, the candidate selection unit can input attribute data of the NPOs and companies into AI, which can then analyze the data and select appropriate candidates.

[0130] When selecting candidates, the candidate selection unit can weight the selection based on the frequency of NPO and company activities. For example, if the frequency of NPO and company activities is high, the candidate selection unit can increase the weighting of the selection. Also, if the frequency of NPO and company activities is low, the candidate selection unit can relax the weighting of the selection. Furthermore, the candidate selection unit can adjust the weighting of the selection based on the frequency of NPO and company activities. This allows the candidate selection to be weighted based on the frequency of NPO and company activities. Some or all of the above-described processing in the candidate selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the candidate selection unit can input activity data of NPOs and companies into AI, which analyzes the data and performs appropriate weighting.

[0131] The candidate selection unit can estimate the emotions of the NPO and company personnel and adjust the order in which the candidate selection results are displayed based on the estimated emotions. For example, if the NPO and company personnel are stressed, the candidate selection unit can prioritize displaying important candidates. Furthermore, if the NPO and company personnel are relaxed, the candidate selection unit can also display detailed candidates. Furthermore, if the NPO and company personnel are busy, the candidate selection unit can prioritize displaying the minimum number of candidates necessary. This allows the order in which the candidate selection results are displayed to be adjusted according to the emotions of the NPO and company personnel. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the candidate selection unit can be performed using, for example, an AI, or without an AI. For example, the candidate selection unit can input emotion data of the NPO and company personnel into an AI, which can analyze the emotions and adjust the order in which the candidate selection results are displayed.

[0132] When selecting candidates, the candidate selection unit can take into consideration the geographic distribution of NPOs and companies. For example, the candidate selection unit preferentially selects highly relevant candidates based on the geographic distribution of NPOs and companies. The accuracy of the selection can also be improved by taking into consideration the geographic distribution of NPOs and companies. Furthermore, the optimal candidate can be selected based on the geographic distribution of NPOs and companies. This allows the selection to be made taking into consideration the geographic distribution of NPOs and companies. Some or all of the above-described processing in the candidate selection unit can be performed using, for example, AI, or can be performed without using AI. For example, the candidate selection unit can input geographic data of NPOs and companies into AI, which can analyze the data to select appropriate candidates.

[0133] The candidate selection unit can improve the accuracy of selection by referring to related literature when selecting candidates. The candidate selection unit can, for example, refer to related literature to improve the accuracy of selection. The selection method can also be improved based on the related literature. Furthermore, the optimal selection method can be selected by referring to the related literature. This can improve the accuracy of selection by referring to the related literature. Some or all of the above-mentioned processing in the candidate selection unit can be performed, for example, using AI, or can be performed without using AI. For example, the candidate selection unit can input related literature data into AI, which analyzes the data and makes an appropriate selection.

[0134] When selecting candidates, the candidate selection unit can take into consideration the market value of the NPO and the company. For example, the candidate selection unit preferentially selects highly relevant candidates based on the market value of the NPO and the company. The accuracy of the selection can also be improved by taking into consideration the market value of the NPO and the company. Furthermore, the optimal candidate can be selected based on the market value of the NPO and the company. This allows the selection to be made taking into consideration the market value of the NPO and the company. Some or all of the above-mentioned processing in the candidate selection unit can be performed using, for example, AI, or can be performed without using AI. For example, the candidate selection unit can input market value data of the NPO and the company into AI, which can analyze the data and select appropriate candidates. === Hard Collateral 1-1 === Each of the multiple elements, including the NPO information collection unit, company information collection unit, information analysis unit, proposal generation unit, proposal providing unit, and candidate selection unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the NPO information collection unit is realized by the control unit 46A of the smart device 14 and collects information on NPO corporations. The company information collection unit is realized by the control unit 46A of the smart device 14 and collects information on companies. The information analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The proposal generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates proposal content based on the analyzed information. The proposal providing unit is realized by the control unit 46A of the smart device 14 and provides the generated proposal content to companies. The candidate selection unit is realized by the specific processing unit 290 of the data processing device 12 and selects donation recipient candidates. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned NPO information collection unit, company information collection unit, information analysis unit, proposal generation unit, proposal providing unit, and candidate selection unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the NPO information collection unit is realized by the control unit 46A of the smart glasses 214 and collects information on NPO corporations. The company information collection unit is realized by the control unit 46A of the smart glasses 214 and collects information on companies. The information analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The proposal generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates proposal content based on the analyzed information. The proposal providing unit is realized by the control unit 46A of the smart glasses 214 and provides the generated proposal content to companies. The candidate selection unit is realized by the specific processing unit 290 of the data processing device 12 and selects donation destination candidates. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned NPO information collection unit, company information collection unit, information analysis unit, proposal generation unit, proposal providing unit, and candidate selection unit, is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the NPO information collection unit is realized by the control unit 46A of the headset type terminal 314 and collects information on NPO corporations. The company information collection unit is realized by the control unit 46A of the headset type terminal 314 and collects information on companies. The information analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The proposal generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates proposal content based on the analyzed information. The proposal providing unit is realized by the control unit 46A of the headset type terminal 314 and provides the generated proposal content to companies. The candidate selection unit is realized by the specific processing unit 290 of the data processing device 12 and selects donation recipient candidates. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned NPO information collection unit, company information collection unit, information analysis unit, proposal generation unit, proposal providing unit, and candidate selection unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the NPO information collection unit is realized by the control unit 46A of the robot 414 and collects information on NPO corporations. The company information collection unit is realized by the control unit 46A of the robot 414 and collects information on companies. The information analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The proposal generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates proposal content based on the analyzed information. The proposal providing unit is realized by the control unit 46A of the robot 414 and provides the generated proposal content to companies. The candidate selection unit is realized by the specific processing unit 290 of the data processing device 12 and selects donation recipient candidates.

[0135] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0136] The NPO Information Collection Department analyzes information entered by NPO personnel in real time, automatically detecting and completing any incomplete or missing information. For example, if an NPO is unclear about the unit of measurement when entering the amount of funding it wishes to raise, the system will automatically suggest the appropriate unit. Also, if the project description is insufficient, the system can ask additional questions to complete the information. Furthermore, it can also optimize the information entered by comparing it with a past database and referring to successful examples of similar projects. This streamlines the data entry process for NPOs and collects more accurate information.

[0137] When a company wishes to make a donation, the Corporate Information Collection Department can provide a simulation function to visualize the impact of the donation. For example, if a company donates a specific amount, it can simulate what kind of social effect that donation will have. It can also predict the effect of the donation based on the past performance of the recipient NPO. It can also generate graphs and charts that show the relationship between a company's CSR activities and donations, making it easier for companies to understand the significance of donations. This allows companies to concretely understand the effects of their donations and become more motivated to donate.

[0138] The information analysis unit can estimate the emotions of NPO and corporate staff and adjust the display method of the analysis results based on the estimated emotions. For example, if a staff member is feeling stressed, the analysis results can be displayed in a concise dashboard format. If the staff member is relaxed, detailed analysis results can be provided. Furthermore, if the staff member is busy, only the important points can be highlighted. This makes it possible to select the optimal display method according to the staff member's emotions and promote understanding of the analysis results.

[0139] The proposal generation unit can estimate the emotions of NPO and corporate staff and adjust the way proposals are presented based on the estimated emotions. For example, if the staff is feeling stressed, a simple, highly visible proposal can be generated. If the staff is relaxed, a detailed proposal can be generated. Furthermore, if the staff is busy, a proposal that focuses on the main points can be generated. This allows the way proposals are presented to be adjusted according to the staff's emotions, making it easier for the proposal to be accepted.

[0140] The proposal providing unit can estimate the emotions of company personnel and adjust the way in which proposals are provided based on the estimated emotions. For example, if the personnel is feeling stressed, the proposal can be provided in a simple, highly visible manner. If the personnel is relaxed, detailed proposals can be provided. Furthermore, if the personnel is busy, proposals that focus on the main points can be provided. In this way, the way in which proposals are provided can be adjusted according to the personnel's emotions, and the likelihood of proposals being accepted can be improved.

[0141] The NPO Information Collection Department can propose the possibility of collaboration with other NPOs based on the information entered by NPO staff. For example, it can introduce NPOs that are engaged in similar activities in the same area and propose joint projects. It can also explain the benefits of collaboration based on past successful collaboration cases. It can also simulate the effect of fundraising through collaboration and make specific proposals. This can promote collaboration between NPOs and improve fundraising efficiency.

[0142] When a company wishes to make a donation, the Corporate Information Collection Department can provide a simulation function to visualize the impact of the donation. For example, if a company donates a specific amount, it can simulate what kind of social effect that donation will have. It can also predict the effect of the donation based on the past performance of the recipient NPO. It can also generate graphs and charts that show the relationship between a company's CSR activities and donations, making it easier for companies to understand the significance of donations. This allows companies to concretely understand the effects of their donations and become more motivated to donate.

[0143] The information analysis unit can estimate the emotions of NPO and corporate staff and adjust the display method of the analysis results based on the estimated emotions. For example, if a staff member is feeling stressed, the analysis results can be displayed in a concise dashboard format. If the staff member is relaxed, detailed analysis results can be provided. Furthermore, if the staff member is busy, only the important points can be highlighted. This makes it possible to select the optimal display method according to the staff member's emotions and promote understanding of the analysis results.

[0144] The proposal generation unit can estimate the emotions of NPO and corporate staff and adjust the way proposals are presented based on the estimated emotions. For example, if the staff is feeling stressed, a simple, highly visible proposal can be generated. If the staff is relaxed, a detailed proposal can be generated. Furthermore, if the staff is busy, a proposal that focuses on the main points can be generated. This allows the way proposals are presented to be adjusted according to the staff's emotions, making it easier for the proposal to be accepted.

[0145] The proposal providing unit can estimate the emotions of company personnel and adjust the way in which proposals are provided based on the estimated emotions. For example, if the personnel is feeling stressed, the proposal can be provided in a simple, highly visible manner. If the personnel is relaxed, detailed proposals can be provided. Furthermore, if the personnel is busy, proposals that focus on the main points can be provided. In this way, the way in which proposals are provided can be adjusted according to the personnel's emotions, and the likelihood of proposals being accepted can be improved.

[0146] The processing flow of the second embodiment will be briefly explained below.

[0147] Step 1: The NPO Information Collection Department collects information about NPOs. This information includes the amount of funding they wish to raise, how it will be used, and specific projects and activities. For example, when an NPO wishes to raise funds, they enter the amount they need and how it will be used. It can also collect information about the NPO's past fundraising history and current projects. Step 2: The Corporate Information Collection Department collects corporate information. Corporate information includes the desired donation amount, the message the company wishes to convey to society, and past donation history. For example, when a company wishes to make a donation, it inputs the message it wishes to convey to society. Information can also be collected from a database of corporate CSR activities. Step 3: The Information Analysis Department analyzes the information collected by the NPO Information Collection Department and the Corporate Information Collection Department. This analysis uses methods such as statistical analysis, machine learning algorithms, and text mining. For example, the department selects the most suitable companies based on a database of companies' past donation histories and CSR activities. Step 4: The proposal generation unit generates proposal content based on the information analyzed by the information analysis unit. The proposal content includes fundraising proposals, cooperation project proposals, etc. For example, specific proposal content is generated for a company. Step 5: The proposal providing unit provides the proposal content generated by the proposal generating unit to the company. The method of providing the proposal includes notifying the company by email, displaying the proposal on a dashboard, etc. For example, the proposal providing unit may suggest a donation to the company. Step 6: The candidate selection unit selects potential donation recipients based on the information analyzed by the information analysis unit. Selection criteria include a scoring system and filtering conditions. For example, based on a database of NPOs, it selects NPOs that match the company's message.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0153] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0169] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0176] 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.

[0177] 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.

[0178] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0179] 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.

[0180] 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.

[0181] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0182] 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.

[0183] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0184] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0196] 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.

[0197] 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.

[0198] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0199] 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.

[0200] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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).

[0205] 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.

[0206] 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."

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0218] 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.

[0219] [Explanation of symbols]

[0220] 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. NPO Information Collection Department, which collects information on NPO corporations; A corporate information collection department that collects corporate information; an information analysis unit that analyzes the information collected by the NPO information collection unit and the company information collection unit; a proposal generation unit that generates proposal content based on the information analyzed by the information analysis unit; a proposal providing unit that provides the proposal content generated by the proposal generating unit to a company; a candidate selection unit that selects candidate donation recipients based on the information analyzed by the information analysis unit. A system characterized by:

2. The NPO Information Collection Department: When an NPO wishes to raise funds, it inputs the amount needed and its purpose.

2. The system of claim 1.

3. The company information collection unit When a company wishes to make a donation, they can enter the message they want to convey to society.

2. The system of claim 1.

4. The information analysis unit Select appropriate companies based on a database of their past donation history and corporate social responsibility (CSR) activities 2. The system of claim 1.

5. The proposal generation unit Generate specific proposals for companies 2. The system of claim 1.

6. The candidate selection unit Using a database of NPOs, select NPOs that match the company's message.

2. The system of claim 1.

7. The proposal providing unit Propose a donation to a company 2. The system of claim 1.

8. The NPO Information Collection Department: Estimate the emotions of NPO staff and adjust the timing of information gathering based on the estimated emotions.

2. The system of claim 1.

9. The NPO Information Collection Department: Analyze the NPO's past fundraising history and select the appropriate information gathering method 2. The system of claim 1.

10. The NPO Information Collection Department: When gathering information about NPOs, filter based on their current projects and activities.

2. The system of claim 1.

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